{ "version": "2026-09-29", "counts": { "point": 283, "unresolved": 28, "range": 88 }, "y_note": "Existing Y coordinates retained. Y is a work in progress and was not recalculated with X. Missing Y values remain unassigned.", "records": [ { "id": "3", "stable_id": "IRM_OP_3", "software": "Ableton Live", "operation": "Trim and arrange generated video", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Live trims movie playback boundaries and places the retained interval in Arrangement View at normal speed.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional source-time mapping retains no learned decision from within this editing operation. Earlier movie generation lies outside its scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Live 12 manual, Working with Video", "url": "https://www.ableton.com/en/manual/working-with-video/" }, { "label": "Ableton video support", "url": "https://help.ableton.com/hc/en-us/articles/209773125-Using-Video" }, { "label": "Live 12 release notes", "url": "https://www.ableton.com/en/release-notes/live-12/" }, { "label": "Ableton · Arrangement", "url": "https://www.ableton.com/en/live/" } ] }, { "id": "7", "stable_id": "IRM_OP_7", "software": "Adobe After Effects", "operation": "Content-Aware Fill Object mode on generated video", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "After Effects Content-Aware Fill Object mode combines motion-based temporal reuse with synthesis of missing background.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The sources distinguish it from Firefly but do not resolve the active learned contribution. Determine that contribution before selecting the learned or conventional equation branch; background visibility supplies no score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The sources distinguish it from Firefly but do not resolve the active learned contribution. Determine that contribution before selecting the learned or conventional equation branch; background visibility supplies no score." }, "sources": [ { "label": "https://helpx.adobe.com/after-effects/desktop/remove-objects-from-your-videos/content-aware-fill.html", "url": "https://helpx.adobe.com/after-effects/desktop/remove-objects-from-your-videos/content-aware-fill.html" }, { "label": "https://research.adobe.com/news/cloak-remove-unwanted-objects-in-video/", "url": "https://research.adobe.com/news/cloak-remove-unwanted-objects-in-video/" }, { "label": "https://blog.adobe.com/en/publish/2019/05/08/content-aware-fill-for-video", "url": "https://blog.adobe.com/en/publish/2019/05/08/content-aware-fill-for-video" }, { "label": "https://www.adobe.com/learn/after-effects/web/remove-unwanted-element", "url": "https://www.adobe.com/learn/after-effects/web/remove-unwanted-element" }, { "label": "https://blog.adobe.com/en/publish/2019/04/03/java-post-adds-to-its-invisible-effects-toolbox-with-content-aware-fill-in-adobe-after-effects", "url": "https://blog.adobe.com/en/publish/2019/04/03/java-post-adds-to-its-invisible-effects-toolbox-with-content-aware-fill-in-adobe-after-effects" }, { "label": "Adobe · Content-Aware Fill (video)", "url": "https://helpx.adobe.com/after-effects/using/content-aware-fill.html" } ] }, { "id": "8", "stable_id": "IRM_OP_8", "software": "Adobe After Effects", "operation": "Keyframes and linked expressions on generated artwork", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "After Effects evaluates manually authored keyframes and linked expressions to animate artwork.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Interpolation and expression evaluation implement prescribed functions without a trained predictor. Source artwork provenance does not change this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "https://helpx.adobe.com/after-effects/desktop/animate-in-after-effects/animation-keyframes/keyframe-interpolation.html", "url": "https://helpx.adobe.com/after-effects/desktop/animate-in-after-effects/animation-keyframes/keyframe-interpolation.html" }, { "label": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/expression-basics/expression-basics.html", "url": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/expression-basics/expression-basics.html" }, { "label": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/edit-expressions/edit-expressions.html", "url": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/edit-expressions/edit-expressions.html" }, { "label": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/expression-language-reference/expression-language-reference.html", "url": "https://helpx.adobe.com/after-effects/desktop/work-with-expressions/expression-language-reference/expression-language-reference.html" }, { "label": "Wikipedia · Adobe After Effects", "url": "https://en.wikipedia.org/wiki/Adobe_After_Effects" } ] }, { "id": "4", "stable_id": "IRM_OP_4", "software": "Adobe After Effects", "operation": "MCP LLM Composition Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "An LLM selects composition objects and their properties; the After Effects MCP bridge executes native construction commands.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned composition decisions survive conventional command execution. Semantic and spatial construction crosses the synthesis and region-determination references; available evidence supports an interval rather than a particular t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "https://github.com/Dakkshin/after-effects-mcp", "url": "https://github.com/Dakkshin/after-effects-mcp" }, { "label": "https://github.com/Dakkshin/after-effects-mcp/blob/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202/src/index.ts", "url": "https://github.com/Dakkshin/after-effects-mcp/blob/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202/src/index.ts" }, { "label": "https://github.com/Dakkshin/after-effects-mcp/blob/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202/src/scripts/mcp-bridge-auto.jsx", "url": "https://github.com/Dakkshin/after-effects-mcp/blob/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202/src/scripts/mcp-bridge-auto.jsx" }, { "label": "https://github.com/Dakkshin/after-effects-mcp/commit/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202", "url": "https://github.com/Dakkshin/after-effects-mcp/commit/88d5fbf08b7ae9f015ee98e5f8c4904095cf8202" } ] }, { "id": "9", "stable_id": "IRM_OP_9", "software": "Adobe After Effects", "operation": "Roto Brush 3 segmentation of generated video", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Roto Brush 3 predicts and propagates foreground segmentation through video.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Temporal mask determination matches the region reference at the present resolution. Repeated frames and corrective strokes do not automatically change the coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "https://helpx.adobe.com/after-effects/desktop/roto-brush-and-refine-matte/roto-brush/roto-brush-refine-matte.html", "url": "https://helpx.adobe.com/after-effects/desktop/roto-brush-and-refine-matte/roto-brush/roto-brush-refine-matte.html" }, { "label": "https://research.adobe.com/news/project-fast-mask/", "url": "https://research.adobe.com/news/project-fast-mask/" }, { "label": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Oh_Video_Object_Segmentation_Using_Space-Time_Memory_Networks_ICCV_2019_paper.pdf", "url": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Oh_Video_Object_Segmentation_Using_Space-Time_Memory_Networks_ICCV_2019_paper.pdf" }, { "label": "https://www.adobe.com/learn/after-effects/web/roto-brush-isolate-elements", "url": "https://www.adobe.com/learn/after-effects/web/roto-brush-isolate-elements" }, { "label": "https://helpx.adobe.com/after-effects/desktop/roto-brush-and-refine-matte/roto-brush/object-matte.html", "url": "https://helpx.adobe.com/after-effects/desktop/roto-brush-and-refine-matte/roto-brush/object-matte.html" }, { "label": "Adobe · Roto Brush (Next-Gen)", "url": "https://helpx.adobe.com/after-effects/using/roto-brush-refine-matte.html" } ] }, { "id": "5", "stable_id": "IRM_OP_5", "software": "Adobe Express", "operation": "Express AI Assistant", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Express AI Assistant interprets a design request and orchestrates models, assets and editing tools to construct the design.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "The retained design includes learned planning and possibly image synthesis. The controller and selected tool path are insufficiently specified for one point within the synthesis-to-region comparison.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Express AI Assistant launch explanation", "url": "https://news.adobe.com/news/2025/10/adobe-max-2025-express-ai-assistant" }, { "label": "Express AI Assistant overview", "url": "https://helpx.adobe.com/express/web/ai-assistant/adobe-express-ai-assistant-overview.html" }, { "label": "FLUX.1 Kontext technical paper", "url": "https://arxiv.org/html/2506.15742v1" }, { "label": "Flash Image card", "url": "https://deepmind.google/models/model-cards/gemini-3-1-flash-image/" }, { "label": "Flash base card", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Flash-Model-Card.pdf" }, { "label": "Pro family card", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Model-Card.pdf" }, { "label": "Express generation guide", "url": "https://helpx.adobe.com/express/web/ai-assistant/get-started-with-the-adobe-express-ai-assistant.html" }, { "label": "Express assistant FAQ", "url": "https://helpx.adobe.com/express/web/ai-assistant/adobe-express-ai-assistant-faq.html" }, { "label": "eWeek · Express AI Assistant", "url": "https://www.eweek.com/news/adobe-max-express-ai-assistant-firefly-foundry/" } ] }, { "id": "93", "stable_id": "IRM_OP_93", "software": "Adobe Firefly Boards", "operation": "Generative Fill on Board Images", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Firefly Boards invokes Generative Fill on one board image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The selected regional image-generation operation matches synthesis. Board arrangement and collaboration functions are outside the assessment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe: Edit images in Boards", "url": "https://helpx.adobe.com/firefly/web/create-mood-boards/firefly-boards/edit-images.html" }, { "label": "Adobe: Use generative fill", "url": "https://helpx.adobe.com/firefly/web/work-with-images/edit-images/generative-fill.html" }, { "label": "Adobe Developer: Fill Image API tutorial and source code", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/how-tos/firefly-fill-image-api-tutorial" }, { "label": "Adobe: How generative AI works", "url": "https://www.adobe.com/products/firefly/discover/how-generative-ai-work.html" }, { "label": "Adobe Research, September 15, 2025", "url": "https://research.adobe.com/news/photoshop-turns-35/" }, { "label": "TurboFill, §§3–5", "url": "https://arxiv.org/html/2504.00996v1" }, { "label": "designboom · Firefly Boards", "url": "https://www.designboom.com/design/adobe-firefly-boards-reimagines-moodboarding-creative-professionals-09-29-2025/" }, { "label": "Adobe · Generative Fill image editing", "url": "https://helpx.adobe.com/ca/firefly/web/work-with-images/edit-images/generative-fill.html" } ] }, { "id": "91", "stable_id": "IRM_OP_91", "software": "Adobe Firefly", "operation": "Image 5 Text to Image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Firefly Image 5 generates the image through the explicitly selected Adobe model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image realization matches extensive synthesis. This coordinate does not carry over to partner models or other Firefly functions by name alone.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe · Text to Image", "url": "https://news.adobe.com/news/2025/10/adobe-max-2025-firefly" }, { "label": "Adobe · Image generation models and controls", "url": "https://helpx.adobe.com/ca/firefly/web/work-with-images/generate-images/generate-images-from-text-descriptions.html" }, { "label": "Adobe Firefly Help: Generate images from text descriptions, 07092026", "url": "https://helpx.adobe.com/firefly/web/work-with-images/generate-images/generate-images-from-text-descriptions.html" }, { "label": "Adobe Firefly Image Model 5 prompting and controls guide; consulted 09222026", "url": "https://community.adobe.com/p/firefly-image-guide" }, { "label": "Adobe developer documentation: Using Generate Image with Image 5", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/how-tos/cm-generate-image/feature-guide" }, { "label": "Adobe developer documentation: Migrating to Image 5; schema conflicts retained", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/how-tos/cm-generate-image/breaking-changes" }, { "label": "Adobe Firefly API overview: Image 5 native 4 MP output", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/" } ] }, { "id": "90", "stable_id": "IRM_OP_90", "software": "Adobe Firefly (web app)", "operation": "AI Assistant Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Firefly AI Assistant invokes an image generator through its model-routing and tool orchestration process.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "For this generation-only endpoint, learned image synthesis is retained. The selected model remains a configuration field; assistant routing is not an extra point weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Announcement and lineage", "url": "https://blog.adobe.com/en/publish/2026/04/15/introducing-firefly-ai-assistant-new-way-create-with-our-creative-agent" }, { "label": "operational overview", "url": "https://helpx.adobe.com/firefly/web/firefly-ai-assistant/firefly-ai-assistant-overview.html" }, { "label": "current FAQ", "url": "https://helpx.adobe.com/firefly/web/firefly-ai-assistant/ai-assistant-faq.html" }, { "label": "Adobe’s Firefly mechanism explanation", "url": "https://www.adobe.com/products/firefly/discover/how-generative-ai-work.html" }, { "label": "Training contributor’s project account", "url": "https://thekevinli.github.io/" }, { "label": "Structure-reference documentation", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/concepts/structure-image-reference/" }, { "label": "style-reference documentation", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/concepts/style-image-reference/" }, { "label": "Adobe · Image variations and subsequent editing", "url": "https://helpx.adobe.com/firefly/web/work-with-images/generate-images/modify-generated-images.html" } ] }, { "id": "92", "stable_id": "IRM_OP_92", "software": "Adobe Firefly (web app)", "operation": "Text to Video with Firefly Video", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Firefly Video generates a new clip through the explicitly selected Adobe text-to-video route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned scene and motion synthesis is compared with the generation reference. The image-to-video transfer is a responsibility analogy, not equal compute.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe · Text to Video / Generate Video", "url": "https://blog.adobe.com/en/publish/2025/02/12/meet-firefly-video-model-ai-powered-creation-with-unparalleled-creative-control" }, { "label": "Adobe · Firefly Video generation and output", "url": "https://helpx.adobe.com/firefly/web/work-with-audio-and-video/work-with-video/generate-videos-using-text-prompts.html" }, { "label": "Adobe Research: Firefly Video Model and GenExtend technology, 03122025", "url": "https://research.adobe.com/news/adobe-research-and-firefly-team-pioneer-new-video-generation-tech/" }, { "label": "Adobe Firefly Video prompting and controls guide; consulted 09222026", "url": "https://community.adobe.com/p/firefly-video-guide" }, { "label": "Adobe Firefly: Use video as composition reference, 06232026", "url": "https://helpx.adobe.com/ph_fil/firefly/web/work-with-audio-and-video/work-with-video/use-video-as-composition-reference.html" }, { "label": "Adobe Firefly: Match camera motion to reference video, 06092026", "url": "https://helpx.adobe.com/ca/firefly/web/work-with-audio-and-video/work-with-video/match-camera-motion-to-reference-video.html" }, { "label": "Adobe Firefly: Generate Video FAQ, 09042025", "url": "https://helpx.adobe.com/firefly/web/work-with-audio-and-video/work-with-video/about-generate-video.html" }, { "label": "Adobe Firefly: Generate video within timeline editor, 06162026", "url": "https://helpx.adobe.com/firefly/web/firefly-video-editor/generate-videos/generate-video-using-firefly-models.html" } ] }, { "id": "171", "stable_id": "IRM_OP_171", "software": "Adobe Illustrator", "operation": "Generative Recolor", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Illustrator Generative Recolor predicts a palette and its assignment to existing vector artwork.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned appearance allocation crosses fixed mapping and perceptual reconstruction, supporting 5–12. Existing path geometry does not numerically dilute that responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/recolor-artwork-with-generative-recolor.html", "url": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/recolor-artwork-with-generative-recolor.html" }, { "label": "https://www.adobe.com/learn/illustrator/web/generative-recolor-overview", "url": "https://www.adobe.com/learn/illustrator/web/generative-recolor-overview" }, { "label": "https://blog.adobe.com/en/publish/2023/06/13/future-of-illustrator-is-here-hue-will-never-be-same", "url": "https://blog.adobe.com/en/publish/2023/06/13/future-of-illustrator-is-here-hue-will-never-be-same" }, { "label": "Forbes · Generative Recolor", "url": "https://www.forbes.com/sites/rashishrivastava/2023/06/13/adobe-announces-generative-recolor-ai-feature-for-adobe-illustrator/" } ] }, { "id": "172", "stable_id": "IRM_OP_172", "software": "Adobe Illustrator", "operation": "Manual Path and Anchor Editing Comparison", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Illustrator directly modifies selected paths and anchor points.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Bezier geometry edits and specified appearance changes are conventional; the nearby Firefly operations are separately scoped.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe: Direct Selection", "url": "https://helpx.adobe.com/illustrator/using/tool-techniques/direct-selection-tool.html" }, { "label": "Adobe: Adjust path segments", "url": "https://helpx.adobe.com/illustrator/using/adjust-path-segments.html" }, { "label": "Wikipedia · Adobe Illustrator", "url": "https://en.wikipedia.org/wiki/Adobe_Illustrator" }, { "label": "I2 Adobe Illustrator Help — Adjust path segments; consulted 09272026", "url": "https://helpx.adobe.com/in/illustrator/using/adjust-path-segments.html" }, { "label": "I3 Adobe Illustrator Help — Set app preferences; consulted 09272026", "url": "https://helpx.adobe.com/illustrator/desktop/get-started/preferences-and-settings/set-app-preferences.html" } ] }, { "id": "173", "stable_id": "IRM_OP_173", "software": "Adobe Illustrator", "operation": "Text to Vector Graphic with Firefly", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Firefly Text to Vector Graphic synthesizes vector shapes and their visual relationships.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated graphic supports extensive synthesis. Editable vectors do not receive a higher X merely because they support later direct editing.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/generate-scenes-subjects-and-icons.html", "url": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/generate-scenes-subjects-and-icons.html" }, { "label": "https://research.adobe.com/news/adobe-research-at-max-2023/", "url": "https://research.adobe.com/news/adobe-research-at-max-2023/" }, { "label": "https://www.adobe.com/learn/illustrator/web/text-to-vector-graphic-tips", "url": "https://www.adobe.com/learn/illustrator/web/text-to-vector-graphic-tips" }, { "label": "https://blog.adobe.com/jp/publish/2026/03/03/cc-firefly-practical-tips-17-illustrator-partner-model", "url": "https://blog.adobe.com/jp/publish/2026/03/03/cc-firefly-practical-tips-17-illustrator-partner-model" }, { "label": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/partner-models-overview.html", "url": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/partner-models-overview.html" }, { "label": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/generative-ai-faq-illustrator.html", "url": "https://helpx.adobe.com/illustrator/desktop/use-generative-ai/generative-ai-faq-illustrator.html" }, { "label": "Adobe · Text to Vector Graphic", "url": "https://community.adobe.com/announcements-651/illustrator-2024-v28-0-text-to-vector-mockup-retype-smooth-slider-and-improved-share-for-review-805905" } ] }, { "id": "386", "stable_id": "IRM_OP_386", "software": "Adobe Lightroom / Adobe Lightroom Classic", "operation": "Super Resolution", "y": null, "y_status": "not_assigned", "x": 7.25, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Lightroom or Lightroom Classic Super Resolution applies its learned reconstruction to a raw photograph; Denoise and later editing are excluded.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0.75", "t_interval": [ "0.5", "1" ] }, "rating_rationale": "Paired low- and high-resolution training and the fidelity-oriented task place this comparison closer to DnCNN-S than ESRGAN: t = 0.75 between 5 and 8. The retained sensitivity interval t = 0.5–1 yields 6.5–8; this is expert calibration, not an Adobe-reported measurement.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "6.5", "8" ], "t_interval": [ "0.5", "1" ], "status": "point", "raw_x": "7.25", "display": "7.3", "t": "0.75", "calculation": "X = 5 + 0.75 × (8 − 5) = 7.25." }, "sources": [ { "label": "Adobe Super Resolution development account", "url": "https://blog.adobe.com/en/publish/2021/03/10/from-the-acr-team-super-resolution", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Adobe Enhance documentation", "url": "https://helpx.adobe.com/lightroom-classic/help/enhance-details.html", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "198", "stable_id": "IRM_OP_198", "software": "Adobe Lightroom Classic 15.5", "operation": "Denoise in the active Detail panel", "y": 8.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Lightroom Denoise uses its documented learned RAW-restoration operation in the active Detail panel.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "Learned photographic signal recovery matches fidelity restoration. Amount controls are recorded without being converted into t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "https://blog.adobe.com/en/publish/2023/04/18/denoise-demystified", "url": "https://blog.adobe.com/en/publish/2023/04/18/denoise-demystified" }, { "label": "https://helpx.adobe.com/lightroom-classic/desktop/process-and-develop-photos/enhance-details.html", "url": "https://helpx.adobe.com/lightroom-classic/desktop/process-and-develop-photos/enhance-details.html" }, { "label": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/release-notes.html", "url": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/release-notes.html" }, { "label": "https://helpx.adobe.com/lightroom-classic/desktop/troubleshooting/known-issues.html", "url": "https://helpx.adobe.com/lightroom-classic/desktop/troubleshooting/known-issues.html" }, { "label": "Adobe · Lightroom 2023 updates", "url": "https://helpx.adobe.com/ca/lightroom-cc/using/whats-new/2023-3.html" }, { "label": "Adobe · Lightroom June 2025", "url": "https://helpx.adobe.com/lightroom-classic/desktop/help/whats-new/2025-4.html" } ] }, { "id": "196", "stable_id": "IRM_OP_196", "software": "Adobe Lightroom Classic 15.5", "operation": "Generative Remove", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Lightroom Generative Remove uses Firefly to synthesize replacement content in a brushed removal region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The replacement-content task matches synthesis. Removal area and brush precision are not rating inputs.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe: Classic release notes", "url": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/release-notes.html" }, { "label": "Adobe: Generative Remove procedure", "url": "https://helpx.adobe.com/ca/lightroom-classic/desktop/process-and-develop-photos/remove-tool.html" }, { "label": "Adobe’s original Lightroom announcement", "url": "https://blog.adobe.com/en/publish/2024/05/21/lightroom-introduces-power-adobe-firefly-with-generative-remove" }, { "label": "Zeng et al., §§3.1–3.3", "url": "https://arxiv.org/html/2005.11742v2" }, { "label": "Adobe · Generative Remove", "url": "https://news.adobe.com/news/news-details/2024/adobe-unveils-firefly-powered-generative-remove-in-lightroom-for-fast-and-easy-ai-editing-across-surfaces" }, { "label": "Adobe · Lightroom AI edit updates", "url": "https://www.adobe.com/learn/lightroom-cc/web/lightroom-ai-order-of-operations" }, { "label": "Adobe — current Classic release", "url": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/whats-new.html" } ] }, { "id": "197", "stable_id": "IRM_OP_197", "software": "Adobe Lightroom Classic 15.5", "operation": "Select Subject and manual mask refinement", "y": 5.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Lightroom Select Subject predicts a mask followed by conventional manual brush refinement with inferred coverage retained.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The retained learned membership supports a region-reference match. Brush coverage does not create a justified proportional reduction in X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Adobe ACR team: Masking Reimagined", "url": "https://blog.adobe.com/en/publish/2021/09/28/from-the-acr-team-masking-reimagined" }, { "label": "Adobe: Lightroom Classic masking", "url": "https://helpx.adobe.com/lightroom-classic/desktop/process-and-develop-photos/masking.html" }, { "label": "Adobe: Classic release notes, updated August 4, 2026", "url": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/release-notes.html" }, { "label": "Xu et al., §§3–4", "url": "https://arxiv.org/pdf/1703.03872v3" }, { "label": "PetaPixel · AI Masking (Select Subject / Sky)", "url": "https://petapixel.com/2021/09/28/adobe-has-completely-redesigned-masking-in-lightroom-and-acr/" }, { "label": "Adobe — Classic 15.5 controls", "url": "https://helpx.adobe.com/lightroom-classic/desktop/introduction-to-lightroom-classic/whats-new.html" }, { "label": "B32-L2 Adobe — MAX 2021 Photography Releases, 10262021", "url": "https://blog.adobe.com/en/publish/2021/10/26/max-2021-photography-releases" } ] }, { "id": "252", "stable_id": "IRM_OP_252", "software": "Adobe Photoshop", "operation": "Current Beta AI Assistant Whole Image Variation", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop's recorded AI Assistant image-generation route produces a whole-image variation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image synthesis matches the generation reference. This covers the generation route, not every native editing command available through the assistant; the deployed model is undisclosed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe: AI Assistant overview", "url": "https://helpx.adobe.com/photoshop/desktop/whats-new/ai-assistant-overview.html" }, { "label": "Adobe: editing workflow", "url": "https://helpx.adobe.com/photoshop/desktop/get-started/set-up-toolbars-panels/edit-images-with-ai-assistant.html" }, { "label": "Adobe Research: the work behind Photoshop’s AI Assistant", "url": "https://research.adobe.com/news/do-it-for-me-or-show-me-how-the-research-behind-photoshops-ai-assistant/" }, { "label": "ImageEdit-R1, §§2–3", "url": "https://arxiv.org/html/2603.08059v1" }, { "label": "linked repository", "url": "https://github.com/zhaoyiran924/ImageEdit-R1" }, { "label": "Adobe Learn: watercolor with Generative Fill", "url": "https://www.adobe.com/learn/photoshop/web/create-art-styles-generative-fill" }, { "label": "DPReview · AI Assistant", "url": "https://www.dpreview.com/news/1952544048/adobe-photoshop-ai-assistant-desktop-beta/" }, { "label": "Adobe Photoshop Beta version 27.9", "url": "https://helpx.adobe.com/photoshop/desktop/whats-new/whats-new-in-photoshop-beta-on-desktop.html" }, { "label": "Adobe Assistant versus Assisted Editor", "url": "https://helpx.adobe.com/photoshop/desktop/generative-ai/ai-assisted-editor-faq.html" } ] }, { "id": "397", "stable_id": "IRM_OP_397", "software": "Adobe Photoshop", "operation": "Generative Expand with Firefly", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Generative Expand with Firefly synthesizes image content outside the original canvas.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned scene completion matches generation at 2, as does Fill. An empty or populated prompt and the crop rectangle do not create numerical inference adjustments.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe Photoshop Generative Expand guide, updated February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/create-open-import-images/create-images/explore-beyond-the-canvas-with-generative-expand.html" }, { "label": "Adobe Research, September 15, 2025", "url": "https://research.adobe.com/news/photoshop-turns-35/" }, { "label": "Adobe product update, January 27, 2026", "url": "https://blog.adobe.com/en/publish/2026/01/27/new-photoshop-innovations-provide-creative-pros-more-control-realism-precision" }, { "label": "Adobe developer tutorial and request code", "url": "https://developer.adobe.com/firefly-services/docs/firefly-api/guides/how-tos/firefly-expand-image-api-tutorial" } ] }, { "id": "253", "stable_id": "IRM_OP_253", "software": "Adobe Photoshop", "operation": "Generative Fill with Firefly", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Generative Fill with Firefly Fill and Expand synthesizes replacement image content within the selected region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The model determines image appearance through learned synthesis, matching the generation reference at 2. A selection or prompt does not subtract inferential responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe Fill guide, updated September 15, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/create-open-import-images/create-images/edit-images-with-generative-fill.html" }, { "label": "Adobe Research, September 15, 2025", "url": "https://research.adobe.com/news/photoshop-turns-35/" }, { "label": "Adobe product update, January 27, 2026", "url": "https://blog.adobe.com/en/publish/2026/01/27/new-photoshop-innovations-provide-creative-pros-more-control-realism-precision" }, { "label": "Adobe Expand guide, updated February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/create-open-import-images/create-images/explore-beyond-the-canvas-with-generative-expand.html" } ] }, { "id": "254", "stable_id": "IRM_OP_254", "software": "Adobe Photoshop", "operation": "Harmonize Subject Generation", "y": 6.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Harmonize uses a trained unified process for foreground relighting, boundary blending and generated shadows or reflections.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t_interval": [ "0", "1" ] }, "rating_rationale": "The product-linked research establishes more than a fixed color transform. Appearance reconstruction and generated scene effects support a 2–5 synthesis-to-perceptual comparison; the evidence does not select an exact t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "conditional_range": [ "2", "5" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–5.0", "calculation": "X = 2 + t × (5 − 2); t = 0–1; X = 2–5. No midpoint assigned." }, "sources": [ { "label": "Adobe Harmonize development account", "url": "https://research.adobe.com/news/the-research-behind-harmonize/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "https://helpx.adobe.com/photoshop/desktop/repair-retouch/remove-objects-fill-space/blend-subjects-with-harmonize.html", "url": "https://helpx.adobe.com/photoshop/desktop/repair-retouch/remove-objects-fill-space/blend-subjects-with-harmonize.html" }, { "label": "https://arxiv.org/html/2312.06886v2", "url": "https://arxiv.org/html/2312.06886v2" }, { "label": "https://www.adobe.com/learn/photoshop/web/blend-subjects-with-harmonize", "url": "https://www.adobe.com/learn/photoshop/web/blend-subjects-with-harmonize" }, { "label": "https://www.adobe.com/learn/photoshop/web/harmonize-photoshop-web", "url": "https://www.adobe.com/learn/photoshop/web/harmonize-photoshop-web" }, { "label": "Photoshop Roadmap · Harmonize", "url": "https://www.photoshoproadmap.com/photoshop-2025-new-features-harmonize-improved-remove-tool-and-model-picker/" }, { "label": "Adobe · Harmonize generative AI", "url": "https://www.adobe.com/products/photoshop/harmonize-image-blender.html" } ] }, { "id": "390", "stable_id": "IRM_OP_390", "software": "Adobe Photoshop", "operation": "Magic Wand — color-based selection", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Magic Wand selects colors using the supplied sample and documented tolerance and connectivity rules.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional color comparison determines the region without a documented trained predictor. Automatic selection is therefore insufficient evidence for the learned branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Magic Wand guide, February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/make-selections/automatic-color-based-selections/select-areas-by-color-with-the-magic-wand-tool.html" } ] }, { "id": "389", "stable_id": "IRM_OP_389", "software": "Adobe Photoshop", "operation": "Manual Brush editing of an existing layer mask", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop's ordinary Brush tool changes a supplied layer mask with a manually specified stroke.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed brush application contains no in-scope learned determination. Any earlier automatic selection is outside this separate edit boundary.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Edit layer masks, July 18, 2024", "url": "https://helpx.adobe.com/photoshop/using/editing-layer-masks.html" } ] }, { "id": "255", "stable_id": "IRM_OP_255", "software": "Adobe Photoshop", "operation": "Manual Layer and Mask Compositing Comparison", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop applies manually arranged layers, masks and blend settings in a conventional composite.", "retained_learning": false, "rating_input": {}, "rating_rationale": "No trained determination occurs within the scoped composition. An imported generated layer retains its provenance, but that earlier generation is outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe: editing layer masks", "url": "https://helpx.adobe.com/photoshop/using/editing-layer-masks.html" }, { "label": "Adobe UXP: Imaging API", "url": "https://developer.adobe.com/photoshop/uxp/2022/ps-reference/media/imaging" }, { "label": "Adobe UXP: Layer", "url": "https://developer.adobe.com/photoshop/uxp/2022/ps-reference/classes/layer" }, { "label": "Adobe UXP: interpolation methods", "url": "https://developer.adobe.com/photoshop/uxp/2022/ps-reference/modules/constants#interpolationmethod" }, { "label": "W3C: source-over compositing and Normal blending", "url": "https://www.w3.org/TR/compositing-1/#porterduffcompositingoperators_srcover" }, { "label": "Wikipedia · Adobe Photoshop", "url": "https://en.wikipedia.org/wiki/Adobe_Photoshop" }, { "label": "Adobe · Nondestructive editing", "url": "https://helpx.adobe.com/ca/photoshop/using/nondestructive-editing.html" }, { "label": "Adobe · Smart Filters", "url": "https://helpx.adobe.com/photoshop/using/applying-smart-filters.html" }, { "label": "Adobe · Layer structure", "url": "https://helpx.adobe.com/photoshop/desktop/create-manage-layers/get-started-layers/layers-overview.html" }, { "label": "B32-M3 Adobe — Nondestructive editing", "url": "https://helpx.adobe.com/photoshop/using/nondestructive-editing.html" } ] }, { "id": "256", "stable_id": "IRM_OP_256", "software": "Adobe Photoshop", "operation": "Neural Filters Colorize with Smart Filter Output", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Colorize predicts image color and returns it through the selected Smart Filter output route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned color estimation changes appearance beyond a fixed prescribed transform. The fixed-function-to-perceptual comparison supports 5–12 without a precise t; colorization is not equated with unrestricted scene synthesis.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "https://helpx.adobe.com/photoshop/using/colorize.html", "url": "https://helpx.adobe.com/photoshop/using/colorize.html" }, { "label": "https://helpx.adobe.com/photoshop/desktop/effects-filters/neural-filters/use-neural-filters-to-enhance-images.html", "url": "https://helpx.adobe.com/photoshop/desktop/effects-filters/neural-filters/use-neural-filters-to-enhance-images.html" }, { "label": "https://helpx.adobe.com/ca/photoshop/using/neural-filters-list-and-faq.html", "url": "https://helpx.adobe.com/ca/photoshop/using/neural-filters-list-and-faq.html" }, { "label": "https://helpx.adobe.com/photoshop/using/neural-filters-feedback.html", "url": "https://helpx.adobe.com/photoshop/using/neural-filters-feedback.html" }, { "label": "https://blog.adobe.com/en/publish/2023/09/12/adobe-research-scientist-named-top-innovator-under-35-mit-technology-review", "url": "https://blog.adobe.com/en/publish/2023/09/12/adobe-research-scientist-named-top-innovator-under-35-mit-technology-review" }, { "label": "https://arxiv.org/abs/1705.02999", "url": "https://arxiv.org/abs/1705.02999" }, { "label": "https://www.adobe.com/products/photoshop/colorize.html", "url": "https://www.adobe.com/products/photoshop/colorize.html" }, { "label": "Adobe · Neural Filters", "url": "https://helpx.adobe.com/photoshop/desktop/effects-filters/neural-filters/overview-of-neural-filters.html" }, { "label": "Adobe · Filter output modes", "url": "https://helpx.adobe.com/nz/photoshop/desktop/effects-filters/neural-filters/neural-filter-categories-and-output-options.html" } ] }, { "id": "392", "stable_id": "IRM_OP_392", "software": "Adobe Photoshop", "operation": "Path to selection — Make Selection", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Make Selection rasterizes a supplied closed path with specified feathering and anti-aliasing.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The path conversion is prescribed computation; construction of the path and any preceding learned boundary prediction are outside this stage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe path/selection conversion guide, May 24, 2023", "url": "https://helpx.adobe.com/photoshop/using/converting-paths-selection-borders.html" } ] }, { "id": "391", "stable_id": "IRM_OP_391", "software": "Adobe Photoshop", "operation": "Pen tool — manually specified path", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop's standard Pen tool constructs a path from supplied anchors and direction handles.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional curve evaluation determines the path. Content-Aware Tracing and automatic boundary detection are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Pen tool guide, February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/draw-shapes-paths/draw-lines-curves/draw-paths-with-the-pen-tool.html" } ] }, { "id": "257", "stable_id": "IRM_OP_257", "software": "Adobe Photoshop", "operation": "Remove Tool Brushed Region with Generative AI On", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Remove Tool with Generative AI On synthesizes replacement content for the brushed region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned completion of image appearance matches the generation reference. Brush extent is a scope description, not a numerical weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe · Remove Tool / Distraction Removal", "url": "https://helpx.adobe.com/photoshop/desktop/repair-retouch/remove-objects-fill-space/remove-unwanted-objects-and-distractions.html" }, { "label": "Adobe · Remove Tool modes", "url": "https://helpx.adobe.com/photoshop/using/tool-techniques/remove-tool.html" }, { "label": "See Adobe Research · Selection and generation mechanisms", "url": "https://research.adobe.com/news/photoshop-turns-35/" }, { "label": "Meredith Stotzner, Photoshop product manager: Generative AI in Remove Tool beta, 07222024", "url": "https://community.adobe.com/questions-700/generative-ai-in-the-remove-tool-for-photoshop-beta-669378" }, { "label": "Adobe announcement: Remove Tool on-device generative AI beta, 04232026", "url": "https://community.adobe.com/announcements-698/remove-tool-on-device-generative-ai-model-beta-1558892" }, { "label": "Adobe Photoshop Help: Remove tool hardware requirements, 06102026", "url": "https://helpx.adobe.com/ie/photoshop/desktop/repair-retouch/remove-objects-fill-space/remove-tool-minimum-and-recommended-hardware-requirements.html" } ] }, { "id": "388", "stable_id": "IRM_OP_388", "software": "Adobe Photoshop", "operation": "Select Subject — automatic selection (Cloud)", "y": null, "y_status": "not_assigned", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Select Subject in Cloud mode predicts initial selection coverage through the documented learned selection lineage.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Region determination matches the SAM task reference at 10. This does not claim that Adobe deploys SAM, and later painting or edge refinement is excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Adobe Select Subject guide, February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/make-selections/automatic-color-based-selections/detect-subject-using-select-subject.html" }, { "label": "Adobe processing-mode guide, February 23, 2026", "url": "https://helpx.adobe.com/photoshop/desktop/make-selections/automatic-color-based-selections/improved-select-subject-and-remove-background-results.html" }, { "label": "Adobe Research, September 15, 2025", "url": "https://research.adobe.com/news/photoshop-turns-35/" } ] }, { "id": "259", "stable_id": "IRM_OP_259", "software": "Adobe Photoshop", "operation": "Sky Replacement with Editable Layer Output", "y": 9.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Photoshop Sky Replacement predicts the sky boundary, then combines a supplied sky with the image.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The documented learned region decision survives subsequent compositing, supporting the region reference at 10. This does not assign a generation score to the separately supplied sky asset.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "https://helpx.adobe.com/photoshop/desktop/effects-filters/artistic-stylize-filters/replace-the-sky-in-images.html", "url": "https://helpx.adobe.com/photoshop/desktop/effects-filters/artistic-stylize-filters/replace-the-sky-in-images.html" }, { "label": "https://www.adobe.com/learn/photoshop/web/sky-replacement", "url": "https://www.adobe.com/learn/photoshop/web/sky-replacement" }, { "label": "https://blog.adobe.com/en/publish/2020/10/20/photoshop-the-worlds-most-advanced-ai-application-for-creatives", "url": "https://blog.adobe.com/en/publish/2020/10/20/photoshop-the-worlds-most-advanced-ai-application-for-creatives" }, { "label": "https://www.adobe.com/products/photoshop/sky-replacement.html", "url": "https://www.adobe.com/products/photoshop/sky-replacement.html" }, { "label": "Adobe · Photoshop 2021 updates", "url": "https://helpx.adobe.com/hk_en/photoshop/using/whats-new/2021.html" }, { "label": "Adobe Research · Deep learning masks", "url": "https://research.adobe.com/news/jianming-zhang-explores-how-humans-see-images-to-create-new-editing-tools/" } ] }, { "id": "273", "stable_id": "IRM_OP_273", "software": "Adobe Premiere / adb-mcp", "operation": "LLM Timeline Construction", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere's recorded adb-mcp workflow uses an LLM to construct a timeline through native commands.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned structural choices survive conventional command execution. The synthesis-to-region comparison is an explicit cross-domain envelope; evidence does not support one exact t for timeline authorship.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Project developer · Adobe MCP supported apps and operations", "url": "https://github.com/mikechambers/adb-mcp" }, { "label": "B34-P2 Developer source: Premiere MCP tool definitions", "url": "https://github.com/mikechambers/adb-mcp/blob/main/mcp/pr-mcp.py" }, { "label": "B34-P3 Developer source: Premiere command implementation", "url": "https://github.com/mikechambers/adb-mcp/blob/main/uxp/pr/commands/core.js" } ] }, { "id": "269", "stable_id": "IRM_OP_269", "software": "Adobe Premiere", "operation": "Auto Reframe", "y": 6.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere Auto Reframe uses learned subject and action analysis to determine a moving crop.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The retained responsibility is identifying and following the relevant region, supporting the region-determination reference at 10. Conventional crop execution adds no second score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Adobe · Subject saliency and crop-path computation", "url": "https://blog.adobe.com/en/publish/2018/11/14/projectsmoothoperator-brings-intelligent-automated-cropping-to-video-publishing" }, { "label": "Adobe · Released Auto Reframe workflow", "url": "https://blog.adobe.com/en/publish/2019/11/04/streamline-video-editing-and-deliver-better-stories-faster" }, { "label": "Adobe · Auto Reframe mechanism overview", "url": "https://helpx.adobe.com/premiere/desktop/add-video-effects/commonly-used-effects/auto-reframe-overview.html" }, { "label": "Adobe · Reframing keyframes and preservation conditions", "url": "https://helpx.adobe.com/premiere/desktop/add-video-effects/commonly-used-effects/add-auto-reframe-effect-to-a-sequence.html" }, { "label": "Adobe Premiere Help: Auto Reframe clip controls, 01072026", "url": "https://helpx.adobe.com/premiere/desktop/add-video-effects/commonly-used-effects/add-auto-reframe-effect-to-a-clip.html" } ] }, { "id": "396", "stable_id": "IRM_OP_396", "software": "Adobe Premiere", "operation": "Close Gap — Sequence command", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere Sequence Close Gap closes eligible empty timeline intervals after prior edits.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The documented command applies conventional interval handling. Exact multitrack eligibility requires configuration checks, but the available operation account provides no trained content decision.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe staff explanation", "url": "https://community.adobe.com/questions-729/transfering-edits-to-different-tracks-1416345" }, { "label": "Adobe editor interview", "url": "https://blog.adobe.com/en/publish/2025/01/26/sundance-2025-behind-the-scenes-with-the-editor-of-bunnylovr" } ] }, { "id": "270", "stable_id": "IRM_OP_270", "software": "Adobe Premiere", "operation": "Generative Extend", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere Generative Extend uses Firefly Video to synthesize additional frames.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned temporal image synthesis matches generation at 2. The supplied preceding footage does not reduce X by an input-based adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "https://research.adobe.com/news/adobe-research-and-firefly-team-pioneer-new-video-generation-tech/", "url": "https://research.adobe.com/news/adobe-research-and-firefly-team-pioneer-new-video-generation-tech/" }, { "label": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/generative-extend-overview.html", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/generative-extend-overview.html" }, { "label": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/add-frames-using-generative-extend.html", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/add-frames-using-generative-extend.html" }, { "label": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/generative-extend-faq.html", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-with-generative-ai/generative-extend-faq.html" }, { "label": "https://helpx.adobe.com/premiere/desktop/troubleshooting/limitations-and-known-issues/generative-extend-known-issues.html", "url": "https://helpx.adobe.com/premiere/desktop/troubleshooting/limitations-and-known-issues/generative-extend-known-issues.html" } ] }, { "id": "395", "stable_id": "IRM_OP_395", "software": "Adobe Premiere", "operation": "Manual clip rearrangement — insert move", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere performs an insert move of an existing clip to a specified timeline destination.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Documented track and interval rules determine the rearrangement. No learned choice of destination is included.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Rearrange clips, January 7, 2026", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/change-clip-sequence/rearrange-clips-on-the-timeline.html" } ] }, { "id": "394", "stable_id": "IRM_OP_394", "software": "Adobe Premiere", "operation": "Manual trim of a clip edge", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere trims one clip edge to a manually specified frame through ordinary edge dragging.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The supplied endpoint changes a source interval conventionally. Generative Extend and other trim modes are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Trim a clip, August 22, 2025", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/trim-clips/trim-a-clip.html" } ] }, { "id": "393", "stable_id": "IRM_OP_393", "software": "Adobe Premiere", "operation": "Razor cut at a manually specified position", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere Razor splits a supplied timeline clip at the editor's specified position while retaining both pieces.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The split changes source-interval bookkeeping conventionally. No trained prediction determines the cut position in this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe Cut clips, August 22, 2025", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/trim-clips/cut-clips.html" } ] }, { "id": "271", "stable_id": "IRM_OP_271", "software": "Adobe Premiere", "operation": "Text-Based Editing", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Premiere Text Based Editing includes learned transcription and time alignment, with those alignments retained in edit boundaries.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "The fidelity-to-region envelope 8–10 records constrained recognition and localization. It differs from a separately scoped edit of an already aligned transcript, which would exclude the learned stage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Speechmatics · Neural transcription engine supplied to Premiere", "url": "https://www.speechmatics.com/company/articles-and-news/speechmatics-versus-whisper-how-adobe-premieres-on-device-speech-engine-got-rebuilt" }, { "label": "Adobe · Transcript edits and editable video timeline", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-video-using-text-based-editing/edit-sequences-using-text-based-editing.html" }, { "label": "B33-T1 Adobe — Text-Based Editing overview, 01072026", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-video-using-text-based-editing/overview-of-text-based-editing.html" }, { "label": "B33-T3 Adobe — Transcribe video, 01072026", "url": "https://helpx.adobe.com/premiere/desktop/edit-projects/edit-video-using-text-based-editing/transcribe-video.html" } ] }, { "id": "328", "stable_id": "IRM_OP_328", "software": "Adobe Substance 3D Painter", "operation": "Procedural Smart Material Layer Construction", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Substance Painter constructs Smart Materials through conventional layers, generators and baked mesh maps.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The documented procedural route contains no trained predictor. The word Smart does not establish learning.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Adobe · Smart Materials", "url": "https://helpx.adobe.com/substance-3d-painter/using/materials-smart-materials.html" }, { "label": "Adobe · Procedural smart materials and masks", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-painter/using/getting-started/glossary" }, { "label": "Adobe Painter: Smart Materials and Masks, 07032026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-painter/using/features/smart-materials-and-masks" }, { "label": "Adobe Painter: Generators, 08112026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-painter/using/effects/generator" }, { "label": "Adobe Painter: Mask Builder, 08112026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-painter/using/effects/generators/mask-builder" }, { "label": "Adobe Painter: Masking and effects, 07032026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-painter/using/interface/layer-stack/masking-and-effects" }, { "label": "Adobe Learn: Creating a flannel Smart Material, written technical tutorial", "url": "https://www.adobe.com/learn/substance-3d-painter/web/creating-a-flannel-smart-material-in-substance-painter" }, { "label": "Adobe developer API: Smart mask presets and inserted effect nodes, 09172026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-dev/painter-python/api/substancepainter-package/layerstack-module/layers-and-effects/smartmask" } ] }, { "id": "329", "stable_id": "IRM_OP_329", "software": "Adobe Substance 3D Sampler", "operation": "Image to Material AI Filter", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Substance Sampler's Image to Material AI filter estimates material maps from a photograph.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned decomposition of appearance into material properties crosses fixed-function mapping and perceptual reconstruction, supporting 5–12. The map count is not an inference weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Adobe · Image to Material", "url": "https://research.adobe.com/news/behind-the-tech-image-to-material-in-adobe-substance-3d-sampler-and-adobe-capture" }, { "label": "Adobe Research · Image to material neural models", "url": "https://research.adobe.com/news/how-adobe-researchers-simplified-capturing-and-applying-materials-in-3d/" }, { "label": "Adobe · Active Image to Material filter parameters", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/filters/tools/atlas-creator" }, { "label": "Adobe Sampler: Image to Material, 04072026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/filters/tools/image-to-material" }, { "label": "Adobe Learn: Create complex 3D materials with Sampler 6.0, 04302026; transcript", "url": "https://www.adobe.com/learn/substance-3d-sampler/web/openpbr-new-features-6-0" }, { "label": "Adobe Sampler: release history; 4.2, 4.2.1, 5.1.2 and 6.0.3 entries, updated 08262026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/release-notes/all-changes" }, { "label": "Adobe Research: MaterIA, EUROGRAPHICS 2022; related research abstract", "url": "https://research.adobe.com/publication/materia/" }, { "label": "Adobe Research: ControlMat, ACM TOG 2024; related research abstract", "url": "https://research.adobe.com/publication/controlmat-controlled-generative-approach-to-material-capture/" }, { "label": "Adobe Sampler: Layer Resolution", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/interface/preferences/layer-resolution" } ] }, { "id": "330", "stable_id": "IRM_OP_330", "software": "Adobe Substance 3D Sampler", "operation": "Text to Texture", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Substance Sampler Text to Texture uses Firefly to synthesize a texture image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned appearance construction matches generation at 2. Material conversion and later layers are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Adobe · Text to Texture", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/features-and-workflows/generative-workflows" }, { "label": "Adobe Sampler: release history; 4.2, 4.2.1, 5.1.2 and 6.0.3 entries, updated 08262026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/release-notes/all-changes" }, { "label": "Adobe Creative Cloud: Generative AI features list; still lists Sampler features", "url": "https://helpx.adobe.com/creative-cloud/apps/generative-ai/creative-cloud-generative-ai-features.html" }, { "label": "Adobe Learn: Create complex 3D materials with Sampler 6.0, 04302026; transcript", "url": "https://www.adobe.com/learn/substance-3d-sampler/web/openpbr-new-features-6-0" }, { "label": "B34-S3 Adobe historical Sampler beta record, updated 04162026", "url": "https://experienceleague.adobe.com/en/docs/substance-3d-sampler/using/release-notes/beta-version" }, { "label": "B34-S4 Adobe launch explanation: texture generation and material conversion, 03182024", "url": "https://blog.adobe.com/en/publish/2024/03/18/adobe-announces-firefly-powered-features-substance-3d-apps" } ] }, { "id": "6", "stable_id": "IRM_OP_6", "software": "Affinity by Canva", "operation": "Object Selection in the Unified App", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Object Selection in unified Affinity determines object membership in an image; the trained segmentation lineage is documented for its predecessor.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "A region-determination match follows the operation's task. Current model continuity is inferred and does not establish identical weights or mask quality.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "TidBITS · Affinity object selection", "url": "https://tidbits.com/2025/10/31/canvas-affinity-combines-photo-designer-and-publisher-into-one-free-app/" }, { "label": "Affinity Object Selection model and controls", "url": "https://affinity.help/photo2/English.lproj/pages/Tools/tools_objectSelection.html" }, { "label": "Qualcomm Affinity neural selection implementation", "url": "https://www.qualcomm.com/news/onq/2024/10/coming-to-snapdragon-x-series-new-npu-powered-creator-tools-and-daw-apps" }, { "label": "Affinity March 2026 update", "url": "https://www.affinity.studio/blog/affinity-update-march-2026" }, { "label": "Affinity September 2026 update", "url": "https://www.affinity.studio/blog/blend-tool-mask-tool-3d-model-support" }, { "label": "B34-A2 Affinity current help: detected objects and regions; distinct selection route", "url": "https://www.affinity.studio/help/selections-selecting-detected-objects-regions/" }, { "label": "B34-A4 Affinity Photo 2.6 machine learning implementation statement", "url": "https://support.serif.com/hc/en-us/articles/12017241295119-Affinity-Photo-v2-6-includes-Machine-Learning-Features" } ] }, { "id": "10", "stable_id": "IRM_OP_10", "software": "Agisoft Metashape", "operation": "2.3.2 Depth Maps to Editable Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Metashape depth-map reconstruction produces a mesh using the documented conventional reconstruction route; optional AI masks are excluded.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Multiview matching and geometric reconstruction do not establish a retained trained predictor within this defined route.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Agisoft · Metashape Photogrammetry", "url": "https://agisoft.freshdesk.com/support/solutions/articles/31000173952-new-features-in-agisoft-metashape-2-2-x" }, { "label": "Agisoft · Metashape 1.6 manual", "url": "https://www.agisoft.com/pdf/metashape-pro_1_6_en.pdf" }, { "label": "Agisoft current installers", "url": "https://www.agisoft.com/downloads/installer/" }, { "label": "Metashape changes", "url": "https://www.agisoft.com/pdf/metashape_changelog.pdf" }, { "label": "Agisoft current Professional 2.3 manual", "url": "https://www.agisoft.com/pdf/metashape-pro_2_3_en.pdf" }, { "label": "B35-M2 Agisoft 3D model reconstruction, updated 8 April 2026", "url": "https://agisoft.freshdesk.com/support/solutions/articles/31000152092-3d-model-reconstruction" }, { "label": "B35-M4 Agisoft BuildModel API, reconstruction parameters", "url": "https://download.agisoft.com/metashape-java-api/latest/com/agisoft/metashape/tasks/BuildModel.html" } ] }, { "id": "11", "stable_id": "IRM_OP_11", "software": "Alpha3D", "operation": "Single Image to Mesh", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Alpha3D predicts a three-dimensional asset from an image, ending at the generated mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned completion of object geometry is compared with extensive synthesis. This is a cross-modal responsibility match, not an assertion of a published network architecture.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Alpha3D · Image-to-3D", "url": "https://www.alpha3d.io" }, { "label": "Alpha3D · Image to mesh and export", "url": "https://www.alpha3d.io/image-to-3d" }, { "label": "B35-A2 Alpha3D FAQ, Alpha 5 and operation boundaries", "url": "https://www.alpha3d.io/faq" }, { "label": "B35-A3 Alpha3D knowledge base, single image to 3D", "url": "https://www.alpha3d.io/knowledge-base/what-is-image-to-3d" }, { "label": "B35-A4 Alpha3D photo to model guide, export and subsequent editing", "url": "https://www.alpha3d.io/kb/generate-3d/how-to-create-3d-model-from-photos/" } ] }, { "id": "12", "stable_id": "IRM_OP_12", "software": "Apple Keynote / Pages / Freeform", "operation": "Apple Image Playground (Keynote/Pages/Freeform)", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Image Playground synthesizes an image through the selected Apple generative image mode.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned realization of forms, appearance and detail matches synthesis. Supplied concepts and styles do not subtract inference points.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Apple · Apple Image Playground (Keynote/Pages/Freeform)", "url": "https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/" }, { "label": "Apple · Image Playground revision", "url": "https://support.apple.com/en-sg/guide/mac-help/mchld5412d00/mac" } ] }, { "id": "13", "stable_id": "IRM_OP_13", "software": "Archicad", "operation": "AI Visualizer", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Archicad AI Visualizer uses the documented Stable Diffusion route to synthesize a visualization from a model view.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated visualization retains extensive learned image determination. Conventional upstream CAD geometry supplies no arithmetic discount.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Graphisoft · AI Visualizer", "url": "https://www.graphisoft.com/solutions/innovation/archicad-ai-visualizer/" }, { "label": "Graphisoft · AI Visualizer workflow", "url": "https://help.graphisoft.com/AC/28/INT/_AC28_Help/100_Visualization/100_Visualization-10.htm" }, { "label": "B36-V1 Graphisoft current AI Visualizer 2.0 capabilities", "url": "https://www.graphisoft.com/en-us/plans-and-products/ai-solutions/" }, { "label": "B36-V3 Graphisoft AI Visualizer 2.0 Guide, indexed composition and creativity guidance; full PDF access unavailable", "url": "https://community.graphisoft.com/gowhw84577/attachments/gowhw84577/hidden-forum/16/2/AI%20Visualizer%202.1.pdf" } ] }, { "id": "14", "stable_id": "IRM_OP_14", "software": "Archicad", "operation": "Parametric Building Element Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Archicad evaluates manually specified building elements and parametric relationships.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Native parametric construction supplies no trained predictor in this scope; AI Visualizer is a separate operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Graphisoft · BIM Modeling", "url": "https://www.graphisoft.com/solutions/archicad" }, { "label": "Graphisoft · Parametric building elements", "url": "https://help.graphisoft.com/AC/28/INT/_AC28_Help/040_ElementsVB/040_ElementsVB-1.htm" }, { "label": "B36-P1 Graphisoft Archicad 29, top links and changed story elevations", "url": "https://help.graphisoft.com/AC/29/INT/_AC29_Help/040_ElementsVB/040_ElementsVB-5.htm" }, { "label": "B36-P2 Graphisoft Archicad 29, Wall Tool Settings", "url": "https://help.graphisoft.com/AC/29/INT/_AC29_Help/150_UserInterfaceToolSettings/150_UserInterfaceToolSettings-3.htm" }, { "label": "B36-P3 Graphisoft Archicad 29, Create a Straight Wall", "url": "https://help.graphisoft.com/AC/29/INT/_AC29_Help/040_ElementsVB/040_ElementsVB-8.htm" } ] }, { "id": "15", "stable_id": "IRM_OP_15", "software": "ArkoAI (in Rhino, SketchUp, Autodesk Revit)", "operation": "Viewport to Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ArkoAI produces a generated visualization through its hosted viewport-to-image operation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The documented synthesis task supports a coarse match to generation. Exact backend architecture remains undisclosed and is not borrowed from another vendor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "ArkoAI · Viewport Rendering", "url": "https://arko.ai/" }, { "label": "B36-A1 ArkoAI current SketchUp integration and plugin versions", "url": "https://arko.ai/sketchup" }, { "label": "B36-A2 ArkoAI Basic and Pro model distinction and settings", "url": "https://arko.ai/pricing" }, { "label": "B36-A3 ArkoAI public rendering interface, controls and displayed values", "url": "https://app.arko.ai/" }, { "label": "B36-A4 ArkoAI modern luxury home case study, 10 March 2023", "url": "https://forum.arko.ai/t/case-study-modern-luxury-home-sample-model-included/406" } ] }, { "id": "398", "stable_id": "IRM_OP_398", "software": "Autodesk 3ds Max", "operation": "Manual move of one Editable Poly vertex", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "3ds Max moves one specified Editable Poly vertex by a supplied displacement, excluding Soft Selection and downstream modifiers.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The coordinate update and connected geometry evaluation are conventional. This is a direct edit, separate from automatic retopology.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Autodesk 3ds Max 2026 Editable Poly Vertex guide", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/3DSMax-Modifiers/files/GUID-047B00EC-D4E3-4D05-8FB7-220795D13874.htm" }, { "label": "Autodesk 2026 vertex-method reference", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/MAXScript-Help/files/3ds-Max-Objects-and-Interfaces/Editable-Meshes-Splines-Patches/Editable_Poly/Editable_Poly-Methods/GUID-07318A19-8091-4B3E-BB46-463942B0F39B.html" } ] }, { "id": "1", "stable_id": "IRM_OP_1", "software": "Autodesk 3ds Max", "operation": "Modifier Stack Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Bend modifier evaluated within a specified 3ds Max modifier stack; assessment ends at the deformed mesh.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed geometric transformation contains no trained predictor. Stack order affects geometry but supplies no inference points.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "M1 Autodesk — Modifier Stack", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/3DSMax-Reference/files/GUID-2C58DCD5-6126-4443-9110-29E47A42E7E7.htm" }, { "label": "M2 Autodesk — Bend Modifier", "url": "https://help.autodesk.com/cloudhelp/2022/ENU/3DSMax-Modifiers/files/GUID-C83CBEE2-19FE-4D19-A1F8-52EC04D89A2A.htm" }, { "label": "Wikipedia · Autodesk 3ds Max", "url": "https://en.wikipedia.org/wiki/Autodesk_3ds_Max" }, { "label": "Autodesk · Editable modifier stack", "url": "https://help.autodesk.com/cloudhelp/2022/ENU/3DSMax-Modifiers/files/GUID-911C535D-9003-4925-A064-52835C581DAA.htm" } ] }, { "id": "2", "stable_id": "IRM_OP_2", "software": "Autodesk 3ds Max", "operation": "ReForm Retopology", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ReForm reconstructs quad topology through the documented global optimization solver.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Search and optimization alone do not establish a trained mechanism. The chosen solver is compared with conventional computation, irrespective of how surprising its topology is.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Autodesk 3ds Max 2026 Retopology Tools Interface", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/3DSMax-Retopology/files/GUID-5A960813-FBCC-4A5D-A423-3FCD60825B10.html" }, { "label": "Autodesk 2026 release account", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/3DSMax-What-s-New/files/GUID-DCF4F5ED-BD3B-4273-88B3-9B2C687752FE.htm" }, { "label": "Autodesk · Retopology", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/3DSMax-Retopology/files/Retopology-Tools-for-3ds-Max-ReleaseNotes/GUID-618B8C1A-6FA5-4EFE-AE9D-72B84C603636.html" }, { "label": "Autodesk · ReForm retopology algorithm and controls", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/3DSMax-Retopology/files/GUID-5A960813-FBCC-4A5D-A423-3FCD60825B10.html" }, { "label": "R2 Autodesk — Retopology Workflow and Best Practices", "url": "https://help.autodesk.com/view/3DSMAX/2026/ENU/?guid=GUID-57B77A00-5300-47CB-99E1-22B9C536B060" }, { "label": "B37-R3 Autodesk 3ds Max 2026 Retopology workflow and best practices", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/3DSMax-Retopology/files/GUID-57B77A00-5300-47CB-99E1-22B9C536B060.html" } ] }, { "id": "16", "stable_id": "IRM_OP_16", "software": "Autodesk Arnold", "operation": "Open Image Denoise Pass", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Arnold's Open Image Denoise imager predicts a clean render from noisy render data.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "Trained noise removal matches fidelity restoration. Albedo and normal guidance describe execution and add no score adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "D1 Autodesk — Open Image Denoise Imager", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/ac-denoising/arnold_user_guide_ac_denoising_ac_denoiser_oidn_html.html" }, { "label": "D2 Intel — Open Image Denoise Documentation", "url": "https://www.openimagedenoise.org/documentation.html" }, { "label": "D3 Autodesk — Arnold Denoising", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/arnold_user_guide_ac_denoising_html.html" }, { "label": "D4 Autodesk — Arnold Imagers", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/arnold_user_guide_ac_post_processing_html.html" }, { "label": "Autodesk · ML Denoiser", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/ac-whats-new-cards/arnold_user_guide_ac_wn_731_default_denoiser_html.html" }, { "label": "Intel · Open Image Denoise deep learning filters", "url": "https://www.openimagedenoise.org/" } ] }, { "id": "17", "stable_id": "IRM_OP_17", "software": "Autodesk Arnold", "operation": "Path Tracing Final Image", "y": 3.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Arnold path tracing evaluates light transport for a defined scene, with learned denoising excluded.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Monte Carlo estimation is computational inference in a broad sense but contains no trained predictor in this scope. The current X convention places this conventional operation at 15.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Wikipedia · Arnold (software)", "url": "https://en.wikipedia.org/wiki/Arnold_(software)" }, { "label": "Autodesk · Arnold Monte Carlo path tracing", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/AR-Maya/files/am-Arnold_for_Maya_User_Guide/getting-started/arnold_for_maya_getting_started_am_Arnold_html.html" }, { "label": "B37-A1 Autodesk Arnold general FAQ, path tracing", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/ac-faq/arnold_user_guide_ac_faq_ac_general_faq_html.html" }, { "label": "B37-A2 Autodesk Arnold Core sampling controls", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/ac-render-settings/ac-sampling/arnold_user_guide_ac_render_settings_ac_samples_html.html" }, { "label": "B37-A3 Autodesk Arnold denoising methods", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/arnold_user_guide_ac_denoising_html.html" }, { "label": "B37-A4 Autodesk Arnold feature overrides, Ignore Imagers", "url": "https://help.autodesk.com/cloudhelp/ENU/AR-Core/files/ac-diagnostics/arnold_user_guide_ac_diagnostics_ac_feature_overrides_html.html" } ] }, { "id": "20", "stable_id": "IRM_OP_20", "software": "Autodesk AutoCAD", "operation": "2027 Smart Blocks Detect and Convert", "y": 8.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Smart Blocks Detect and Convert identifies similar object groups before creating native block definitions.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Learned grouping is the retained determination; native conversion transmits it. Match region determination without awarding points for editable blocks.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Autodesk · Smart Blocks", "url": "https://help.autodesk.com/view/ACD/2025/ENU/?guid=GUID-8D87AF53-BD0C-4E01-A015-42E92E6AA715" }, { "label": "Autodesk · Smart Blocks placement and replacement", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/AutoCAD-LT/files/GUID-8D87AF53-BD0C-4E01-A015-42E92E6AA715.htm" }, { "label": "AutoCAD 2025 · Object Detection", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/AutoCAD-WhatsNew/files/GUID-25BD5FB7-119A-42A8-B1C1-62BB812A3F4F.htm" }, { "label": "AutoCAD 2027 Detect and Convert", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/AutoCAD-MAC-WhatsNew/files/GUID-0876F8BC-9ED5-4EBB-A19C-FF019BAEFA9A.htm" }, { "label": "B38-B1 Autodesk AutoCAD for Mac 2027 Smart Blocks Detect and Convert", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/AutoCAD-MAC-WhatsNew/files/GUID-0876F8BC-9ED5-4EBB-A19C-FF019BAA3510.htm" }, { "label": "B38-B2 Autodesk AutoCAD for Mac 2027 BDETECT command", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/AutoCAD-MAC-Core/files/GUID-B3E5C654-612C-4395-B3A8-7316DB678465.htm" } ] }, { "id": "18", "stable_id": "IRM_OP_18", "software": "Autodesk AutoCAD", "operation": "3D Solid Face Editing", "y": 6.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "AutoCAD SOLIDEDIT offsets selected solid faces according to a specified distance or point.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Geometric face modification is prescribed computation; a complex boundary does not imply learned determination.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Autodesk · AutoCAD overview", "url": "https://www.autodesk.com/eu/products/autocad/features" }, { "label": "Autodesk · SOLIDEDIT 3D face editing", "url": "https://help.autodesk.com/view/ACD/2026/ENU/?caas=caas%2Fdocumentation%2FCIV3D%2F2014%2FENU%2FfilesACD%2FGUID-D54C266B-2B68-4660-ACA5-0579432F149C-htm.html" }, { "label": "B37-C1 Autodesk AutoCAD 2026 SOLIDEDIT command reference", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/AutoCAD-Core/files/GUID-D54C266B-2B68-4660-ACA5-0579432F149C.htm" }, { "label": "B37-C2 Autodesk AutoCAD 2026 Solid3d.OffsetFaces API", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/OARX-ManagedRefGuide/files/OARX-ManagedRefGuide-Autodesk_AutoCAD_DatabaseServices_Solid3d_OffsetFaces_SubentityId___double.html" } ] }, { "id": "19", "stable_id": "IRM_OP_19", "software": "Autodesk AutoCAD", "operation": "Markup Assist to Native Drawing Objects", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Markup Assist recognizes written annotations and converts them into native drawing text or leaders.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Recognition estimates structured content, between region determination and fidelity reconstruction. Public task evidence does not support a more precise position within 8–10.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Autodesk · Markup Assist", "url": "https://help.autodesk.com/view/ACD/2023/ENU/?guid=GUID-64CFD65E-ABD0-49A8-9218-D4E2D22BC070" }, { "label": "Autodesk · Markup Assist recognition and CAD conversion", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/AutoCAD-Subscription/files/GUID-75DC6F68-0CCA-4968-AB81-74B38B335B1E.htm" }, { "label": "AutoCAD 2025 · Markup Assist", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/AutoCAD-Platform/files/GUID-0CD6B2CE-EAF0-478B-B39B-F4821F9DC91D.htm" }, { "label": "B38-M1 Autodesk Working With Markup Import and Markup Assist", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/AutoCAD-Platform/files/GUID-3F94ED5A-EAB3-46F9-89F1-6605248EEA56.htm" }, { "label": "B38-M2 Autodesk Cross Platform Features and machine learning attribution", "url": "https://help.autodesk.com/cloudhelp/2023/ENU/AutoCAD-Platform/files/GUID-C46065E4-DC1B-430F-944A-CB4F20E08F88.htm" }, { "label": "B38-M3 Autodesk Markup Import and Markup Assist enhancements", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/AutoCAD-WhatsNew/files/GUID-4811E96E-4CF9-4A02-B3A0-2C9080BBB57C.htm" } ] }, { "id": "94", "stable_id": "IRM_OP_94", "software": "Autodesk Flame", "operation": "Inference Node LaMa Masked Inpainting", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Flame's documented LaMa Inference-node configuration synthesizes missing image content inside a mask.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "LaMa's learned completion of missing content supports the synthesis comparison. The Inference host is not itself a universal score for arbitrary ONNX models.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Autodesk · Inference Node", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/Flame-WhatsNew/files/What-s-New-in-Flame-Family-2025/What-s-New-in-2025-2/wn-2025-2-inference.html" }, { "label": "Flame 2026 · Inference models and masks", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Flame-WhatsNew/files/What-s-New-in-Flame-Family-2026/What-s-New-in-2026/wn-2026-machine-learning.html" }, { "label": "B38-L1 Autodesk Flame 2026 Inference node reference", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Flame-EffectsToolsReference/files/inference_node.html" }, { "label": "B38-L3 Suvorov et al Resolution Robust Large Mask Inpainting With Fourier Convolutions WACV 2022", "url": "https://openaccess.thecvf.com/content/WACV2022/html/Suvorov_Resolution-Robust_Large_Mask_Inpainting_With_Fourier_Convolutions_WACV_2022_paper.html" }, { "label": "B38-L4 LaMa authors implementation and image mask inputs", "url": "https://github.com/advimman/lama" } ] }, { "id": "95", "stable_id": "IRM_OP_95", "software": "Autodesk Flame", "operation": "ML Depth Map Active Node", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Flame's active ML Depth Map route predicts relative scene depth.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Spatial inference crosses region relations and signal reconstruction, supporting 8–10. MiDaS or DPT is not attributed to Flame without product evidence.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Digital Production · ML Depth Map", "url": "https://digitalproduction.com/2026/04/20/autodesk-flame-2027-metadata-and-depth-maps/" }, { "label": "Flame · Machine learning maps", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/Flame-WhatsNew/files/Previous-Releases/2020/GUID-FE433726-ACE9-4610-B427-A89D79177C14.html" }, { "label": "B39-D1 Autodesk Flame 2027 Machine Learning depth model changes", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Flame-WhatsNew/files/What-s-New-in-Flame-Family-2027/What-s-New-in-2027/wn-2027-ml.html" }, { "label": "B39-D2 Autodesk Flame 2024 Z-Depth Mapping historical workflow", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/Flame-Compositingin3d/files/GUID-AB6C3880-0CA2-4629-9BFE-1AD4AAAFBDDC.htm" } ] }, { "id": "96", "stable_id": "IRM_OP_96", "software": "Autodesk Flame", "operation": "ML Face Normal Map Active Node", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Flame Face Analysis predicts facial surface normals for the active normal map.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned geometric estimation supports an 8–10 comparison. The region of interest is an execution control, not a fraction of inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "befores & afters · Face Normal Map", "url": "https://beforesandafters.com/2020/04/17/inside-flames-new-machine-learning-powered-human-face-segmentation/" }, { "label": "Flame · Face normal map controls", "url": "https://help.autodesk.com/cloudhelp/2020/ENU/Flame-Compositingin3d/files/GUID-D7D7C9B7-FE85-497A-BBC2-455BEB112FC4.htm" }, { "label": "B39-N1 Autodesk Flame 2026 Normal Mapping and Face Analysis", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Flame-Action/files/Action-Shading-and-Textures/GUID-1A2F6A6D-F2A1-4BE7-8B36-1D2F1773DB2F.html" }, { "label": "B39-N2 Autodesk Flame 2022 Normal Menu Settings control reference", "url": "https://help.autodesk.com/cloudhelp/2022/ENU/Flame-Compositingin3d/files/GUID-D7D7C9B7-FE85-497A-BBC2-455BEB112FC4.htm" } ] }, { "id": "97", "stable_id": "IRM_OP_97", "software": "Autodesk Flame", "operation": "ML Sky Extraction Active Selective", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Flame ML Sky Extraction predicts sky membership for the active selective.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Neural recognition and boundary estimation match region determination. The current model is not assumed identical to its documented predecessor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "CG Channel · Sky Extraction", "url": "https://www.cgchannel.com/2019/07/autodesk-ships-flame-2020-1/" }, { "label": "Flame · Neural sky recognition", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/Flame-WhatsNew/files/GUID-EB3FAC1F-1F55-4A5D-A977-F2408729234E.htm" }, { "label": "Flame · Machine learning selectives", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Flame-Action/files/Action-Selectives/GUID-A4A91A5E-A2F2-48CE-8405-0E4D6CF436CC.html" } ] }, { "id": "98", "stable_id": "IRM_OP_98", "software": "Autodesk Flame", "operation": "ML Timewarp 2026 Active Effect", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Flame Machine Learning 2026 Timewarp predicts intermediate video frames.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Temporal signal reconstruction crosses fidelity restoration and perceptual completion, supporting 5–8. Speed and frame counts do not determine t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "CG Channel · ML Timewarp", "url": "https://www.cgchannel.com/2024/10/autodesk-releases-flame-2025/" }, { "label": "Flame · ML Timewarp models", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Flame-EffectsToolsReference/files/GUID-09DE74B2-7668-41FD-BB58-6885800F5BAE.html" } ] }, { "id": "21", "stable_id": "IRM_OP_21", "software": "Autodesk Forma", "operation": "Detailed Noise Analysis Visual Map", "y": 3.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Forma Detailed Noise Analysis computes a visual noise map through the documented detailed propagation simulation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The selected detailed solver is conventional. A predicted environmental quantity and a complex visualization do not by themselves establish trained inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Autodesk · Forma Detailed Analyses", "url": "https://www.autodesk.com/products/forma-site-design/environmental-impact-analysis" }, { "label": "Autodesk · Forma Site Design massing workspace", "url": "https://help.autodesk.com/cloudhelp/ENU/Forma-SD-Get-Started/files/Getting_Started_SD.html" }, { "label": "Forma · Detailed noise analysis", "url": "https://blogs.autodesk.com/forma/2023/09/18/detailed-noise-analysis-helps-architects-do-groundwork-for-noise-assessments/" }, { "label": "Forma · Fall 2023 release", "url": "https://blogs.autodesk.com/forma/2023/10/19/whats-new-in-autodesk-forma-fall-2023/" }, { "label": "B40-D2 Autodesk Forma Site Design feature overview August 2026", "url": "https://blogs.autodesk.com/forma/2026/08/20/what-is-forma-site-design/" }, { "label": "B40-D3 European Commission JRC CNOSSOS-EU method record", "url": "https://publications.jrc.ec.europa.eu/repository/handle/JRC72550" }, { "label": "B40-D4 Autodesk detailed noise help sign-in required content not inspected", "url": "https://help.autodeskforma.com/en/articles/7793576-detailed-noise-analysis" } ] }, { "id": "22", "stable_id": "IRM_OP_22", "software": "Autodesk Forma", "operation": "Rapid Noise Analysis Visual Map", "y": 3.0, "y_status": "work_in_progress", "x": 12.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Forma Rapid Noise Analysis uses a trained predictor to approximate detailed noise analysis as a visual site map.", "retained_learning": true, "rating_input": { "lower": "REF12", "higher": "REF12", "t": "0" }, "rating_rationale": "A learned surrogate for a defined analysis function matches fixed-operator emulation. Its scientific subject matter does not make it generative image synthesis.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF12", "REF12" ], "status": "point", "raw_x": "12", "display": "12.0", "t": "0", "calculation": "X = 12 + 0 × (12 − 12) = 12." }, "sources": [ { "label": "Autodesk · Forma Rapid Analyses", "url": "https://blogs.autodesk.com/forma/2023/06/06/formas-rapid-wind-analysis-offers-instant-insights-into-wind-conditions/" }, { "label": "Autodesk · Forma neural rapid noise analysis", "url": "https://blogs.autodesk.com/forma/2023/07/03/game-changing-rapid-noise-analysis-comes-to-forma-2/" }, { "label": "B40-D2 Autodesk Forma Site Design feature overview August 2026", "url": "https://blogs.autodesk.com/forma/2026/08/20/what-is-forma-site-design/" }, { "label": "B40-R2 Autodesk rapid noise help sign-in required content not inspected", "url": "https://help.autodeskforma.com/en/articles/7851788-rapid-noise-analysis" } ] }, { "id": "23", "stable_id": "IRM_OP_23", "software": "Autodesk Forma", "operation": "Site Massing Object Editing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Forma direct massing edits create and modify cuboids, faces and edges with specified dimensions.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional geometric modeling contains no trained predictor within this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "illustrarch · Forma Core", "url": "https://illustrarch.com/articles/design-softwares/73363-autodesk-forma-review.html" }, { "label": "B41-M1 Autodesk Forma 3D Sketch building tutorial April 2026", "url": "https://blogs.autodesk.com/forma/2026/04/21/how-to-create-a-3d-sketch-building-in-forma-site-design/" }, { "label": "B41-M2 Autodesk Forma massing dimensions grouping and instances July 2023", "url": "https://blogs.autodesk.com/forma/2023/07/11/designing-cantilevered-buildings-with-formas-3d-sketch-tool/" } ] }, { "id": "111", "stable_id": "IRM_OP_111", "software": "Autodesk Fusion", "operation": "Automated Modeling with parametric solid output", "y": 9.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Fusion Automated Modeling constructs connecting shapes using its documented geometric synthesis technology.", "retained_learning": null, "rating_input": {}, "rating_rationale": "AI and generative-design labels do not establish a trained predictor. The current learned contribution remains unresolved, preventing an honest numerical branch choice.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "AI and generative-design labels do not establish a trained predictor. The current learned contribution remains unresolved, preventing an honest numerical branch choice." }, "sources": [ { "label": "Autodesk Fusion help — Automated Modeling, output and feature structure; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Model/files/SLD-AUTOMATED-MODELING.htm" }, { "label": "Autodesk Fusion help — Generate design alternatives, workflow and retention limits; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Model/files/SLD-GENERATE-DESIGN-ALTERNATIVES.htm" }, { "label": "Autodesk Fusion help — Automated Modeling reference, input selection and symmetry; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Model/files/SLD-AM-BEST-PRACTICES.htm" }, { "label": "Mike Smell / Autodesk — Automated Modeling tutorial, 07282022; historical mechanism account; consulted 09212026", "url": "https://www.autodesk.com/products/fusion-360/blog/automated-modeling-fusion-360-tutorial/" }, { "label": "Peter Champneys / Autodesk — Three ways to use Automated Modeling, 01082024; consulted 09212026", "url": "https://www.autodesk.com/products/fusion-360/blog/3-ways-to-use-automated-modeling-in-autodesk-fusion/" }, { "label": "Autodesk — January 2026 Fusion update, Edit in Place support; consulted 09212026", "url": "https://www.autodesk.com/products/fusion-360/blog/january-2026-product-update-whats-new/" }, { "label": "Autodesk — AI in manufacturing; branding does not establish machine learning; consulted 09212026", "url": "https://www.autodesk.com/solutions/autodesk-ai/ai-in-manufacturing" }, { "label": "Engineering.com · Automated Modeling", "url": "https://www.engineering.com/generative-design-for-everyone-meet-automated-modeling-from-autodesk/" }, { "label": "Autodesk · Automated Modeling output types", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=SLD-AUTOMATED-MODELING" } ] }, { "id": "112", "stable_id": "IRM_OP_112", "software": "Autodesk Fusion", "operation": "Drawing Automation with AI Planning and Native Views", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Fusion Drawing Automation uses trained AI planning to choose drawing views, arrangement and dimensions before native drawing construction.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned structural planning remains in conventional drawing objects. The synthesis-to-relational 2–10 envelope preserves the unresolved division of responsibility among internal steps.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Documentation", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Drawing/files/DWG-REF-AUTO-PREFERENCES.htm" }, { "label": "Autodesk · Drawing Automation", "url": "https://www.autodesk.com/products/fusion-360/blog/drawing-automation-autodesk-fusion/" }, { "label": "Fusion · Automatic drawings and dimensions", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Drawing/files/DWG-AUTO-DRAWING.htm" }, { "label": "Autodesk · Trained models in Drawing Automation", "url": "https://adsknews.autodesk.com/en/views/autodesk-ai-strategy/" }, { "label": "B41-D3 Autodesk Fusion manage automation preferences", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Drawing/files/DWG-AUTO-SETTINGS.htm" }, { "label": "B41-D4 Autodesk Fusion create and relocate auto dimensions", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Drawing/files/DWG-AUTO-DIMENSION.htm" }, { "label": "B41-D7 Autodesk Fusion projected view dependencies", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Drawing/files/DWG-PROJECTED-VIEW.htm" } ] }, { "id": "113", "stable_id": "IRM_OP_113", "software": "Autodesk Fusion", "operation": "Generative Design Outcome to Editable Solid", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Fusion Generative Design produces optimized design outcomes and converts a selected result to an editable solid.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The active solver and any learned geometry contribution are not established by the broad workflow record. Neither the word generative nor later editability supplies X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The active solver and any learned geometry contribution are not established by the broad workflow record. Neither the word generative nor later editability supplies X." }, "sources": [ { "label": "Autodesk · Generative Design", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-OVERVIEW.htm" }, { "label": "Autodesk · Parametric and direct modeling modes", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=DESIGN_HISTORY" }, { "label": "Autodesk University · Fusion solver technical presentation", "url": "https://static.au-uw2-prd.autodesk.com/Class_Presentation_IM501270_Enhance_a_Generative_Design_Model_with_Event_Simulation_in_Autodesk_Fusion_360-IM501270.pdf" }, { "label": "Fusion · Generative Design outcomes", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=GD-F360-GENERATIVE-DESIGN" }, { "label": "Fusion current outcome generation", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-OUTCOME-GEN.htm" }, { "label": "Fusion design from outcome", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-EXPLORE-DESIGN-FROM-OUTCOME.htm" }, { "label": "B41-G1 Autodesk GE Bracket Quick Start sample and units", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-TUT-GE-QS-OPEN.htm" }, { "label": "B41-G2 Autodesk GE Bracket preserve and obstacle assignments", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-TUT-GE-QS-ASSIGN-GEOM-TYPE.htm" }, { "label": "B41-G3 Autodesk Fusion design space roles and optional starting shape", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-DESIGN-SPACE.htm" }, { "label": "B41-G4 Autodesk GE Bracket constraints and four load cases", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-TUT-GE-QS-APPLY-LOADS.htm" }, { "label": "B41-G5 Autodesk GE Bracket objectives manufacturing methods and materials", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-TUT-GE-QS-SPEC-OBJECTIVES.htm" }, { "label": "B41-G6 Autodesk Fusion generative study resolution settings", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-STUDY-SETTINGS.htm" }, { "label": "B41-G9 Autodesk Fusion outcome preview scope", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-GenerativeDesign/files/GD-GENERATE-PREVIEW-CONCEPT.htm" } ] }, { "id": "114", "stable_id": "IRM_OP_114", "software": "Autodesk Fusion", "operation": "Parametric Feature Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Fusion evaluates a specified extrusion and its parametric dependencies in the native feature timeline.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Geometric construction and recomputation implement prescribed functions without a trained predictor in this scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Autodesk · Fusion overview", "url": "https://www.autodesk.com/products/fusion-360/overview" }, { "label": "B42-P1 Autodesk Fusion Extrude a solid body", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=SLD-EXTRUDE" }, { "label": "B42-P2 Autodesk Fusion Modeling modes", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=DESIGN_HISTORY" }, { "label": "B42-P3 Autodesk Fusion Parameters", "url": "https://help.autodesk.com/view/fusion360/ENU/?contextId=SLD-MODIFY-PARAMETERS" } ] }, { "id": "115", "stable_id": "IRM_OP_115", "software": "Autodesk Fusion", "operation": "Sketch AutoConstrain", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Fusion AutoConstrain predicts constraints and dimensions from sketch geometry through the documented Transformer lineage.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned relational completion crosses region determination and fidelity estimation, supporting 8–10. The conventional constraint solver retains the predicted relationships.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Autodesk · Sketch AutoConstrain", "url": "https://www.research.autodesk.com/blog/using-ai-to-power-autoconstrain-in-fusion-automated-sketching/" }, { "label": "Autodesk · AutoConstrain editing", "url": "https://help.autodesk.com/cloudhelp/ENU/Fusion-Sketch/files/SKT-AUTO-CONSTRAIN.htm" }, { "label": "B42-A3 Autodesk Research constraint alignment October 2025", "url": "https://www.research.autodesk.com/blog/ai-alignment-in-cad-design-teaching-machines-to-understand-design-intent-in-autoconstrain/" } ] }, { "id": "218", "stable_id": "IRM_OP_218", "software": "Autodesk Maya", "operation": "Conventional Dependency Graph Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Maya evaluates a NURBS revolve node from a profile and its geometric parameters.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The resulting surface follows a prescribed construction and dependency graph, without a trained predictor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "M1 Autodesk Maya 2027 Dependency graph", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Maya-Basics/files/GUID-51096BC4-32B7-4391-BE39-21641B374745.htm" }, { "label": "M2 Autodesk Maya 2027 Revolve Options", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Maya-Modeling/files/GUID-DEDBFFEC-0AE6-4392-B5FD-F153B1D59FCF.htm" }, { "label": "M3 Autodesk Maya 2026 revolve node reference", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-Tech-Docs/Nodes/revolve.html" }, { "label": "M4 Autodesk Maya and 3ds Max release announcement 03252026", "url": "https://blogs.autodesk.com/media-and-entertainment/2026/03/25/whats-new-in-maya-and-3ds-max/" }, { "label": "Autodesk · Maya overview", "url": "https://help.autodesk.com/cloudhelp/2022/ENU/Maya-SDK/Dependency-graph-plug-ins/About-the-dependency-graph.html" } ] }, { "id": "24", "stable_id": "IRM_OP_24", "software": "Autodesk Maya", "operation": "MayaMCP LLM Scene Object Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "A MayaMCP LLM selects native objects and transform attributes; Maya executes the construction.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned scene decisions remain in native geometry. The source supports the controller-to-tool dependency but not a precise cross-domain position within 2–10.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "GitHub · MayaMCP", "url": "https://github.com/PatrickPalmer/MayaMCP" }, { "label": "B42-M1 MayaMCP primitive creation pinned source", "url": "https://github.com/PatrickPalmer/MayaMCP/blob/5aa3d7b575facf699f88d5334aafa785516bb7e5/src/mayatools/object/create_object.py" }, { "label": "B42-M2 MayaMCP transform editing pinned source", "url": "https://github.com/PatrickPalmer/MayaMCP/blob/5aa3d7b575facf699f88d5334aafa785516bb7e5/src/mayatools/object/set_object_transform_attributes.py" }, { "label": "B42-M3 MayaMCP server pinned source", "url": "https://github.com/PatrickPalmer/MayaMCP/blob/5aa3d7b575facf699f88d5334aafa785516bb7e5/src/maya_mcp_server.py" }, { "label": "B42-M4 Autodesk Maya 2026 polyCylinder command", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-Tech-Docs/CommandsPython/polyCylinder.html" } ] }, { "id": "219", "stable_id": "IRM_OP_219", "software": "Autodesk Maya", "operation": "ML Deformer Delta Prediction", "y": 7.0, "y_status": "work_in_progress", "x": 12.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Maya ML Deformer Delta mode predicts corrective deformation learned from a rig's training examples.", "retained_learning": true, "rating_input": { "lower": "REF12", "higher": "REF12", "t": "0" }, "rating_rationale": "The learned surrogate approximates a defined deformation relationship, matching fixed-function emulation at 12. Correction size and vertex count do not supply weights.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF12", "REF12" ], "status": "point", "raw_x": "12", "display": "12.0", "t": "0", "calculation": "X = 12 + 0 × (12 − 12) = 12." }, "sources": [ { "label": "Autodesk Maya 2026 help — ML Deformer, purpose and limitations; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-CharacterAnimation/files/GUID-F386DC20-6C66-40D7-AD40-2C1B66937A71.htm" }, { "label": "Autodesk Maya 2026 help — ML Deformer Training settings, modes and architecture controls; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-CharacterAnimation/files/GUID-2D7AF32F-814C-43FB-A93B-A8FBD95265A6.htm" }, { "label": "Autodesk Maya 2026 help — ML Deformer metadata, measurable training record; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-CharacterAnimation/files/GUID-4DBE63E0-F08F-4A3A-AD63-A7466EAD1564.htm" }, { "label": "Autodesk Maya 2026 help — Export Training Data, delta representation; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-CharacterAnimation/files/GUID-DB744421-DE81-42EA-910D-AD7F32C915DC.htm" }, { "label": "Autodesk Maya 2026 help — Create an ML Deformer, stack and driver setup; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-CharacterAnimation/files/GUID-13051D83-FF77-4A8C-BB4B-D6CA4D9CC82C.htm" }, { "label": "CG Channel · ML Deformer", "url": "https://www.cgchannel.com/2024/07/maya-2025-2-adds-a-new-ml-deformer/" } ] }, { "id": "220", "stable_id": "IRM_OP_220", "software": "Autodesk Maya", "operation": "MotionMaker Guided Motion Generation", "y": 6.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Maya MotionMaker generates guided character motion through its learned motion model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned pose-sequence realization supports a synthesis analogy. Guide-path detail does not reduce inference numerically, and downstream manual curves remain a separate operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Evan Atherton / Autodesk — Meet MotionMaker, 06042025; developer interview; consulted 09212026", "url": "https://blogs.autodesk.com/media-and-entertainment/2025/06/04/meet-motionmaker/" }, { "label": "Autodesk — MotionMaker questions answered, 10152025; initial-release facts; consulted 09212026", "url": "https://blogs.autodesk.com/media-and-entertainment/2025/10/15/ai-powered-motionmaker-for-animation-your-top-questions-answered/" }, { "label": "Autodesk Maya 2026 help — MotionMaker QuickStart, controls and 3D retargeting; consulted 09212026", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Maya-MotionMaker/files/GUID-473B9D09-124C-4A5C-94F8-F3B4B89FAB18.html" }, { "label": "Autodesk — What is next for Maya animation, 07272026; newer training capability; consulted 09212026", "url": "https://blogs.autodesk.com/media-and-entertainment/2026/07/27/whats-next-for-animation-in-autodesk-maya/" }, { "label": "CG Channel · MotionMaker", "url": "https://www.cgchannel.com/2025/06/autodesk-adds-ai-animation-tool-motionmaker-to-maya-2026-1/" } ] }, { "id": "289", "stable_id": "IRM_OP_289", "software": "Autodesk Revit", "operation": "Generative Design Dynamo Study", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Revit's Three Box Massing study uses NSGA-II to optimize a specified Dynamo definition.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Evolutionary search evaluates candidate parameter combinations without a trained predictor in this example. Optimization and unexpected alternatives do not themselves satisfy the learned branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "G1 Autodesk Revit 2026 Three Box Massing", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Revit-GDiR/files/GUID-3B0AA77C-27BA-4967-8026-690D42611FC4.htm" }, { "label": "G2 Autodesk Revit 2026 Generation Settings", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Revit-GDiR/files/GUID-710D71BE-3376-4647-822A-DA4D793DCBFA.htm" }, { "label": "G3 Autodesk Revit 2026 About Generation Methods", "url": "https://help.autodesk.com/cloudhelp/2026/ENU/Revit-GDiR/files/GUID-887BCC49-06E9-4C52-AECD-BE92646DF2F5.htm" }, { "label": "G4 Morykin and Jaffee Autodesk University 2022 A Case Study for Generative Design in Horizontal Infrastructure", "url": "https://static.au-uw2-stg.autodesk.com/Class_Handout_CES501766_ClassHandout-CES501766-Jaffee-AU2022.pdf" }, { "label": "G5 Generative Design Primer What is a Genetic Algorithm", "url": "https://www.generativedesign.org/02-deeper-dive/02-04_genetic-algorithms/02-04-01_what-is-a-genetic-algorithm" }, { "label": "G6 Autodesk Revit 2021 availability announcement 04092020", "url": "https://blogs.autodesk.com/bimblog/revit-2021-ab-sofort-verfugbar-mit-geneigten-wanden-generative-design-neuen-realistischen-echtzeit-ansichten-und-vielem-mehr/" }, { "label": "G7 Autodesk Revit 2026 release announcement 04022025", "url": "https://www.autodesk.com/blogs/aec/2025/04/02/whats-new-in-revit-2026/" }, { "label": "G8 Autodesk Revit 2027 release announcement 04072026", "url": "https://www.autodesk.com/blogs/aec/2026/04/07/whats-new-in-revit-2027/" }, { "label": "Generative Design · Generative Design", "url": "https://www.generativedesign.org/01-introduction/01-05_gd-for-revit" }, { "label": "Autodesk · Genetic optimization method", "url": "https://help.autodesk.com/cloudhelp/2022/ENU/Revit-Model/files/GUID-710D71BE-3376-4647-822A-DA4D793DCBFA.htm" }, { "label": "Autodesk · Editable Dynamo study definitions", "url": "https://help.autodesk.com/cloudhelp/2025/ENU/Revit-GDiR/files/GUID-8D3E5A9B-F94C-494E-B445-25B1092E328F.htm" } ] }, { "id": "290", "stable_id": "IRM_OP_290", "software": "Autodesk Revit", "operation": "Parametric Building Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Revit updates a native Basic Wall when its constrained top level changes.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The supplied level, offsets and wall relationships determine the parametric update through conventional computation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "R1 Autodesk Revit 2027 Height and Depth of Walls", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Revit-ArchDesign/files/GUID-D69E6B7D-8D8D-40E4-A6D4-7CAF1A927894.htm" }, { "label": "R2 Autodesk Revit 2027 Wall Instance Properties", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Revit-ArchDesign/files/GUID-ED2045E5-90BF-418F-AAD6-3BB1544F34F5.htm" }, { "label": "R3 Autodesk Revit 2027 Place a Wall", "url": "https://help.autodesk.com/cloudhelp/2027/ENU/Revit-ArchDesign/files/GUID-05BAFEAA-5186-484E-80F4-8D900C454748.htm" }, { "label": "R4 Autodesk Revit 2027 release announcement 04072026", "url": "https://www.autodesk.com/blogs/aec/2026/04/07/whats-new-in-revit-2027/" }, { "label": "Wikipedia · Autodesk Revit", "url": "https://en.wikipedia.org/wiki/Autodesk_Revit" }, { "label": "Autodesk · Revit parametric relationships", "url": "https://help.autodesk.com/cloudhelp/2024/ENU/Revit-GetStarted/files/GUID-71F2C8EE-2A90-4076-A6C7-702082566DDF.htm" } ] }, { "id": "25", "stable_id": "IRM_OP_25", "software": "Beautiful.ai", "operation": "Slide AI image regeneration", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Beautiful.ai Slide AI regenerates one slide image while the surrounding slide remains outside the assessed endpoint.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The replacement image is learned synthesis. Preserved slide text does not reduce the image-generation coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "BA1 Beautiful.ai Create and Edit your Slides with Slide AI, updated 03262026", "url": "https://support.beautiful.ai/hc/en-us/articles/43350069148557-Create-and-Edit-your-Slides-with-Slide-AI" }, { "label": "BA2 Beautiful.ai Product Updates, releases 03172026 and 12152025", "url": "https://www.beautiful.ai/product-updates" }, { "label": "BA3 Beautiful.ai AI presentations and image style controls, consulted 09282026", "url": "https://www.beautiful.ai/ai-presentations" } ] }, { "id": "26", "stable_id": "IRM_OP_26", "software": "Beautiful.ai", "operation": "Smart Slide photo layout using generated images", "y": 6.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Beautiful.ai Photo Grid automatically fits and crops supplied photos into a selected layout.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The recorded behavior establishes automatic layout but does not identify whether learned saliency or a prescribed fit rule determines the crop. Resolve that mechanism before selecting an equation branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The recorded behavior establishes automatic layout but does not identify whether learned saliency or a prescribed fit rule determines the crop. Resolve that mechanism before selecting an equation branch." }, "sources": [ { "label": "PG1 Beautiful.ai Photo Grid, updated 04072026", "url": "https://support.beautiful.ai/hc/en-us/articles/40184565610893-Photo-Grid" }, { "label": "PG2 Beautiful.ai How do I add an image to a slide, consulted 09282026", "url": "https://support.beautiful.ai/hc/en-us/articles/360018624371-How-do-I-add-an-image-to-a-slide" }, { "label": "Beautiful.ai adaptive templates", "url": "https://www.beautiful.ai/slide-templates" } ] }, { "id": "401", "stable_id": "IRM_OP_401", "software": "Blackmagic Design DaVinci Resolve", "operation": "Manual primary master Gain adjustment", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "DaVinci Resolve applies a manually selected primary master Gain adjustment in a fixed color-management context.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The tonal operation is prescribed, not a learned estimate of a desired correction. The unpublished exact shader prevents claiming one universal channel multiplier but does not establish a trained predictor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Resolve21 manual, p3204", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "Resolve20 Colorist guide, pp28–31", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci-Resolve-20-Colorist-Guide.pdf" } ] }, { "id": "71", "stable_id": "IRM_OP_71", "software": "Blackmagic Design DaVinci Resolve", "operation": "Manual roll edit of one video edit point", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve's manual roll edit moves the shared boundary of adjacent clips while preserving their combined duration.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The edit applies a specified time offset without learned shot selection or frame synthesis.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Resolve21 manual", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "Resolve20 Editor guide, pp86–89", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci-Resolve-20-Editors-Guide.pdf" }, { "label": "Blackmagic Design · Edit/Color Core", "url": "https://www.blackmagicdesign.com/products/davinciresolve" }, { "label": "Blackmagic · Color nodes and revisable effects", "url": "https://www.blackmagicdesign.com/products/davinciresolve/color" }, { "label": "Blackmagic · Timeline editing", "url": "https://www.blackmagicdesign.com/products/davinciresolve/edit" } ] }, { "id": "73", "stable_id": "IRM_OP_73", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "AI Set Extender Announced Workflow", "y": null, "y_status": "not_assigned", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve AI Set Extender is documented as an announced scene-extension workflow.", "retained_learning": null, "rating_input": {}, "rating_rationale": "An announcement alone does not establish the available operation or its executed learned mechanism. Preserve the catalogue entry without presenting an installed-operation coordinate; intended synthesis would be a separate conditional comparison.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "An announcement alone does not establish the available operation or its executed learned mechanism. Preserve the catalogue entry without presenting an installed-operation coordinate; intended synthesis would be a separate conditional comparison." }, "sources": [ { "label": "P73", "url": "https://www.blackmagicdesign.com/media/partial/release/20250404-02" }, { "label": "N73", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_20_New_Features_Guide.pdf" }, { "label": "M73", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "NV73", "url": "https://blogs.nvidia.com/blog/studio-rtx-ai-garage-davinci-resolve-flux1-nim/" }, { "label": "Figure source", "url": "https://photography.tutsplus.com/articles/davinci-resolve-ai--cms-109186" }, { "label": "Blackmagic Design · Announced Set Extender workflow", "url": "https://www.blackmagicdesign.com/de/media/release/20250404-02" }, { "label": "Larry Jordan · Resolve 20 AI features", "url": "https://larryjordan.com/articles/ai-powered-features-in-davinci-resolve-20/" } ] }, { "id": "74", "stable_id": "IRM_OP_74", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "DaVinci Resolve AI IntelliScript Timeline Assembly", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve IntelliScript uses neural transcription and dialogue matching to assemble an initial timeline with alternatives.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned recognition and alignment determine retained clip relationships. The operation spans fidelity and relational estimation, supporting 8–10 before manual take changes.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "M74", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "N74", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_20_New_Features_Guide.pdf" }, { "label": "NV74", "url": "https://blogs.nvidia.com/blog/studio-rtx-ai-garage-davinci-resolve-flux1-nim/" }, { "label": "Blackmagic Design · Resolve Studio neural tools", "url": "https://www.blackmagicdesign.com/products/davinciresolve/studio" } ] }, { "id": "75", "stable_id": "IRM_OP_75", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "DaVinci Resolve Depth Map v2 with Map Adjustment", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve Depth Map v2 estimates scene depth; conventional map adjustments transform the retained depth-derived alpha.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Depth inference crosses learned spatial relations and signal reconstruction. Adjustment arithmetic retains that responsibility; retain 8–10 without an adjustment-strength weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "M75", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "N75", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_20_New_Features_Guide.pdf" }, { "label": "S75", "url": "https://www.blackmagicdesign.com/products/davinciresolve/studio" }, { "label": "Blackmagic Design · Depth map adjustment guide p 31", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci-Resolve-18-Colorist-Guide.pdf" } ] }, { "id": "76", "stable_id": "IRM_OP_76", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "DaVinci Resolve Magic Mask", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve Magic Mask v2 predicts a subject mask before tracking or direct paint correction.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Neural membership determination matches the region reference. Click quantity and the subject's appearance do not determine t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Resolve 20 guide pp 68–73", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_20_New_Features_Guide.pdf" }, { "label": "Resolve 21 manual pp 3338–3346", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "Apple WWDC22", "url": "https://developer.apple.com/videos/play/wwdc2022/10063/" }, { "label": "NVIDIA April 10 2025", "url": "https://blogs.nvidia.com/blog/studio-rtx-ai-garage-davinci-resolve-flux1-nim/" } ] }, { "id": "77", "stable_id": "IRM_OP_77", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "DaVinci Resolve Relight FX", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve Relight estimates surface orientation, derives illumination and applies a grade using the resulting alpha.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned geometry estimate governs the final illumination. Conventional Gain application transmits that dependency; spatial reconstruction supports 8–10.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "M77", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "O77", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_18.5_New_Features_Guide.pdf" }, { "label": "R77", "url": "https://forum.blackmagicdesign.com/viewtopic.php?f=21&t=179277" } ] }, { "id": "78", "stable_id": "IRM_OP_78", "software": "Blackmagic Design DaVinci Resolve Studio", "operation": "DaVinci Resolve Super Scale 2x Enhanced", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolve Super Scale 2x Enhanced uses its neural enlargement route with sharpness and noise controls.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned detail reconstruction spans perceptual reconstruction and fidelity restoration. The undisclosed training objective supports 5–8 without a precise point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Resolve 18.5 release", "url": "https://forum.blackmagicdesign.com/viewtopic.php?f=21&t=179277" }, { "label": "18.5 guide p11", "url": "https://documents.blackmagicdesign.com/SupportNotes/DaVinci_Resolve_18.5_New_Features_Guide.pdf" }, { "label": "Resolve 21 manual p297", "url": "https://documents.blackmagicdesign.com/UserManuals/DaVinci_Resolve_21_Reference_Manual.pdf" }, { "label": "Dong et al version 3 July 31 2015", "url": "https://arxiv.org/html/1501.00092v3" } ] }, { "id": "35", "stable_id": "IRM_OP_35", "software": "Blender (addon)", "operation": "ComfyUI-BlenderAI-node Material Generation and Baking", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ComfyUI-BlenderAI-node generates texture imagery and Blender bakes the generated appearance onto a model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned material appearance survives conventional baking. Adding the bake stage does not dilute the synthesis responsibility by stage counting.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "B46-M1 ComfyUI-BlenderAI-node README at inspected commit", "url": "https://github.com/AIGODLIKE/ComfyUI-BlenderAI-node/blob/b6729c96797e340c7688de00e547f1c41372ec76/README.md" }, { "label": "B46-M2 ComfyUI-BlenderAI-node v2.0.0 release dated 07252025", "url": "https://github.com/AIGODLIKE/ComfyUI-BlenderAI-node/releases/tag/v2.0.0" }, { "label": "B46-M3 EasyBakeNode mesh, pass and bake settings source", "url": "https://github.com/AIGODLIKE/EasyBakeNode/blob/1646538ff7e558d1fa8873bd07767a4a47d21fdc/src/node_tree/nodes.py" }, { "label": "B46-M4 EasyBakeNode Blender bake execution source", "url": "https://github.com/AIGODLIKE/EasyBakeNode/blob/1646538ff7e558d1fa8873bd07767a4a47d21fdc/src/node_tree/run.py" }, { "label": "B46-M5 Blender 4.5 manual: Render Baking", "url": "https://docs.blender.org/UATEST/manual/en/4.5/render/cycles/baking.html" }, { "label": "GitHub · ComfyUI-BlenderAI-node", "url": "https://github.com/AIGODLIKE/ComfyUI-BlenderAI-node" } ] }, { "id": "37", "stable_id": "IRM_OP_37", "software": "Blender (addon)", "operation": "StableGen TRELLIS.2 Image-to-Mesh", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "StableGen's TRELLIS.2 mesh-only route predicts a structured three-dimensional representation and decodes a mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned object geometry completion is an extensive-synthesis analogy. Mesh-only scope excludes separately invoked texturing and cleanup.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "https://github.com/sakalond/StableGen", "url": "https://github.com/sakalond/StableGen" }, { "label": "https://openaccess.thecvf.com/content/CVPR2026/papers/Xiang_Native_and_Compact_Structured_Latents_for_3D_Generation_CVPR_2026_paper.pdf", "url": "https://openaccess.thecvf.com/content/CVPR2026/papers/Xiang_Native_and_Compact_Structured_Latents_for_3D_Generation_CVPR_2026_paper.pdf" }, { "label": "https://arxiv.org/html/2512.14692v1#A6", "url": "https://arxiv.org/html/2512.14692v1#A6" }, { "label": "https://github.com/microsoft/TRELLIS.2", "url": "https://github.com/microsoft/TRELLIS.2" }, { "label": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/mesh_gen/workflows.py", "url": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/mesh_gen/workflows.py" }, { "label": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/core/properties.py", "url": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/core/properties.py" }, { "label": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/mesh_gen/trellis2.py", "url": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/mesh_gen/trellis2.py" }, { "label": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/ui/presets.py", "url": "https://github.com/sakalond/StableGen/blob/fae5474098c40ce149a93dbfffe02b33876b3cd4/stablegen/ui/presets.py" } ] }, { "id": "38", "stable_id": "IRM_OP_38", "software": "Blender (addon, Video Sequence Editor)", "operation": "Pallaidium FLUX.2 Klein Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Pallaidium invokes FLUX.2 Klein 4B image generation from Blender's video sequencer.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The host interface transports the generation request; synthesis remains the assessed responsibility. Hosting is not an additional inferential stage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "B47-P1 Pallaidium README at inspected commit", "url": "https://github.com/tin2tin/Pallaidium/blob/80083591cfe90ce7039d475265213467fc3ae4d5/README.md" }, { "label": "B47-P2 Pallaidium Klein 4B plugin input handling and generation", "url": "https://github.com/tin2tin/Pallaidium/blob/80083591cfe90ce7039d475265213467fc3ae4d5/models_plugins/image/flux2_klein_4b.py" }, { "label": "B47-P3 Black Forest Labs FLUX.2 Klein 4B model card", "url": "https://huggingface.co/black-forest-labs/FLUX.2-klein-4B" }, { "label": "B47-R Black Forest Labs Klein announcement dated 01152026", "url": "https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence" }, { "label": "GitHub · Pallaidium", "url": "https://github.com/tin2tin/Pallaidium" }, { "label": "BFL model family and deployment distinctions", "url": "https://docs.bfl.ai/flux_2/flux2_overview" } ] }, { "id": "27", "stable_id": "IRM_OP_27", "software": "Blender", "operation": "Conventional Geometry Nodes Construction", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender evaluates a conventional Geometry Nodes graph to construct geometry.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The declared primitive, field and instancing nodes implement prescribed geometry functions; no neural add-on is included.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/introduction.html", "url": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/introduction.html" }, { "label": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/mesh/primitives/mesh_line.html", "url": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/mesh/primitives/mesh_line.html" }, { "label": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/instances/instance_on_points.html", "url": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/instances/instance_on_points.html" }, { "label": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/input/scene/object_info.html", "url": "https://docs.blender.org/manual/en/4.5/modeling/geometry_nodes/input/scene/object_info.html" }, { "label": "Blender · Geometry Nodes", "url": "https://docs.blender.org/manual/en/latest/modeling/geometry_nodes/introduction.html" }, { "label": "Blender · Geometry Nodes modifier", "url": "https://docs.blender.org/manual/en/4.2/modeling/modifiers/generate/geometry_nodes.html" } ] }, { "id": "28", "stable_id": "IRM_OP_28", "software": "Blender", "operation": "Conventional Image Compositing Graph", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender Alpha Over composites two images using the supplied alpha and blend factor.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The inspected implementation performs compositing arithmetic. Image provenance does not change this isolated compositor's coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "BL1 Blender v4.5.0 Alpha Over node source, inspected 09282026", "url": "https://raw.githubusercontent.com/blender/blender/v4.5.0/source/blender/nodes/composite/nodes/node_composite_alpha_over.cc" }, { "label": "BL2 Blender 4.5 LTS Alpha Over node manual, consulted 09282026", "url": "https://docs.blender.org/UATEST/manual/en/4.5/compositing/types/color/mix/alpha_over.html" }, { "label": "BL3 Blender 4.5 LTS release announcement 07152025", "url": "https://www.blender.org/press/blender-4-5-lts-release/" }, { "label": "BL4 Blender 4.5 LTS compositor release notes, consulted 09282026", "url": "https://developer.blender.org/docs/release_notes/4.5/compositor/" }, { "label": "Blender · Compositor", "url": "https://docs.blender.org/manual/en/latest/compositing/introduction.html" }, { "label": "Blender · Compositing node workflow", "url": "https://docs.blender.org/UATEST/manual/en/4.5/compositing/introduction.html" } ] }, { "id": "29", "stable_id": "IRM_OP_29", "software": "Blender", "operation": "Correlation Tracking with Editable Markers", "y": 6.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender tracks a seeded marker through the selected correlation-based libmv tracking route.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional pattern matching and numerical optimization do not establish a trained predictor. Automated marker motion remains conventional in this scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B45-R Blender 4.5 LTS release history; 4.5.14 dated 09152026", "url": "https://www.blender.org/releases/4-5/" }, { "label": "B45-T1 Blender 4.4 manual: Track settings and pattern matching", "url": "https://docs.blender.org/manual/en/4.4/movie_clip/tracking/clip/toolbar/track.html" }, { "label": "B45-T2 Blender v4.5.14: TrackRegion options, coordinates and results", "url": "https://github.com/blender/blender/blob/v4.5.14/intern/libmv/libmv/tracking/track_region.h" }, { "label": "Blender · Motion Tracking", "url": "https://docs.blender.org/manual/en/latest/movie_clip/tracking/index.html" }, { "label": "Blender · Correlation tracking and markers", "url": "https://docs.blender.org/manual/en/latest/movie_clip/tracking/clip/toolbar/track.html" } ] }, { "id": "30", "stable_id": "IRM_OP_30", "software": "Blender", "operation": "Cycles Open Image Denoise Pass", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Cycles Open Image Denoise predicts a clean image from a noisy render, optionally using auxiliary passes.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The retained learned denoising responsibility matches fidelity restoration, independently of the host renderer.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "https://docs.blender.org/manual/en/4.5/render/cycles/render_settings/sampling.html", "url": "https://docs.blender.org/manual/en/4.5/render/cycles/render_settings/sampling.html" }, { "label": "https://www.openimagedenoise.org/", "url": "https://www.openimagedenoise.org/" }, { "label": "https://github.com/RenderKit/oidn/blob/v2.3.3/README.md", "url": "https://github.com/RenderKit/oidn/blob/v2.3.3/README.md" }, { "label": "https://github.com/blender/blender/blob/v4.5.0/intern/cycles/integrator/denoiser_oidn.cpp", "url": "https://github.com/blender/blender/blob/v4.5.0/intern/cycles/integrator/denoiser_oidn.cpp" }, { "label": "https://github.com/blender/blender/blob/v4.5.0/build_files/build_environment/cmake/versions.cmake", "url": "https://github.com/blender/blender/blob/v4.5.0/build_files/build_environment/cmake/versions.cmake" }, { "label": "https://github.com/RenderKit/oidn/blob/v2.3.3/training/model.py", "url": "https://github.com/RenderKit/oidn/blob/v2.3.3/training/model.py" }, { "label": "Blender · Cycles Denoising", "url": "https://docs.blender.org/manual/en/latest/render/cycles/render_settings/sampling.html" }, { "label": "Blender · Cycles denoising settings", "url": "https://docs.blender.org/UATEST/manual/en/4.5/render/cycles/render_settings/sampling.html" } ] }, { "id": "31", "stable_id": "IRM_OP_31", "software": "Blender", "operation": "MCP LLM Scene Object Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The Blender MCP integration executes an LLM's native scene-object construction commands.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned object and arrangement choices survive command execution. External asset generators are excluded; the semantic construction comparison supports 2–10 without a selected point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "B45-R Blender 4.5 LTS release history; 4.5.14 dated 09152026", "url": "https://www.blender.org/releases/4-5/" }, { "label": "B45-M1 MCP for Blender: README at inspected commit", "url": "https://github.com/ahujasid/mcp-for-blender/blob/41a184322db3ccdcb2fdbc1f6994afe71bf9163c/README.md" }, { "label": "B45-M2 MCP for Blender: scene, screenshot and code execution functions", "url": "https://github.com/ahujasid/mcp-for-blender/blob/41a184322db3ccdcb2fdbc1f6994afe71bf9163c/src/blender_mcp/server.py" }, { "label": "B45-M3 MCP for Blender: package version 2.1.1", "url": "https://github.com/ahujasid/mcp-for-blender/blob/41a184322db3ccdcb2fdbc1f6994afe71bf9163c/pyproject.toml" }, { "label": "GitHub · blender-mcp", "url": "https://github.com/ahujasid/blender-mcp" }, { "label": "Ahujasid · MCP for Blender scene operations", "url": "https://github.com/ahujasid/mcp-for-blender" } ] }, { "id": "32", "stable_id": "IRM_OP_32", "software": "Blender", "operation": "Mesh and Unapplied Modifier Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender evaluates direct mesh edits and conventional Mirror or Subdivision modifiers.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Mesh editing and subdivision implement geometric rules without a retained trained predictor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B45-R Blender 4.5 LTS release history; 4.5.14 dated 09152026", "url": "https://www.blender.org/releases/4-5/" }, { "label": "B45-G1 Blender 4.5 manual: Mirror modifier; official localized page with English operation text", "url": "https://docs.blender.org/manual/id/4.5/modeling/modifiers/generate/mirror.html" }, { "label": "B45-G2 Blender 4.5 manual: Subdivision Surface modifier", "url": "https://docs.blender.org/manual/en/4.5/modeling/modifiers/generate/subdivision_surface.html" }, { "label": "B45-G3 Blender 4.5 manual: modifier stack and order", "url": "https://docs.blender.org/manual/en/4.5/modeling/modifiers/introduction.html" }, { "label": "Blender · Mesh Modeling", "url": "https://docs.blender.org/manual/en/latest/modeling/meshes/editing/introduction.html" }, { "label": "Blender · Mesh editing operations", "url": "https://docs.blender.org/manual/en/4.5/modeling/meshes/editing/mesh/index.html" } ] }, { "id": "39", "stable_id": "IRM_OP_39", "software": "Blender (plugin + hosted service)", "operation": "Meshy for Blender Bridge", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Meshy Bridge downloads and imports an existing model through glTF import and conventional mesh/material setup.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The import command contains no trained generator. Earlier asset generation is separately scoped and does not give the import an AI score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B47-M1 Meshy Bridge documentation", "url": "https://docs.meshy.ai/en/webapp/plugins/blender/bridge-to-blender" }, { "label": "B47-M2 Official Meshy v0.6.1 archive: BridgePanel.py", "url": "https://cdn.meshy.ai/meshy-for-blender/meshy-blender-plugin-v0.6.1.zip" }, { "label": "B47-M3 Blender v4.5.14 mesh merge operator and default threshold", "url": "https://github.com/blender/blender/blob/v4.5.14/source/blender/editors/mesh/editmesh_tools.cc" }, { "label": "B47-M4 Meshy plugin introduction and host compatibility", "url": "https://docs.meshy.ai/en/webapp/plugins/blender/introduction" }, { "label": "Meshy · Meshy for Blender Bridge", "url": "https://www.meshy.ai/integrations/blender" }, { "label": "Meshy · Bridge import and cleanup workflow", "url": "https://intercom.help/meshy/en/articles/16100257-meshy-to-blender-cleanup-workflow" } ] }, { "id": "33", "stable_id": "IRM_OP_33", "software": "Blender", "operation": "Rigid Body Constraint Simulation", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender's rigid-body solver evaluates constraints, collisions and motion for the declared simulation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Numerical physics simulation uses prescribed dynamics. Emergent or unexpected motion is not evidence of a trained mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B46-R Blender 4.5 LTS release history; 4.5.14 dated 09152026", "url": "https://www.blender.org/releases/4-5/" }, { "label": "B46-P1 Blender 4.5 manual: Hinge constraint", "url": "https://docs.blender.org/UATEST/manual/en/4.5/physics/rigid_body/constraints/types/hinge.html" }, { "label": "B46-P2 Blender 4.5 manual: Rigid Body World", "url": "https://docs.blender.org/manual/pt/4.5/physics/rigid_body/world.html" }, { "label": "B46-P3 Blender v4.5.14: rigid body construction and simulation source", "url": "https://github.com/blender/blender/blob/v4.5.14/source/blender/blenkernel/intern/rigidbody.cc" }, { "label": "B46-P4 Blender 4.5 manual: constraint objects and settings", "url": "https://docs.blender.org/manual/id/4.5/physics/rigid_body/constraints/introduction.html" }, { "label": "Blender · Physics Simulation", "url": "https://docs.blender.org/manual/en/latest/physics/introduction.html" }, { "label": "Blender · Rigid body world and cache", "url": "https://docs.blender.org/manual/en/latest/physics/rigid_body/world.html" } ] }, { "id": "34", "stable_id": "IRM_OP_34", "software": "Blender", "operation": "Sculpting", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Blender sculpt brushes directly change mesh geometry through their selected conventional brush algorithms.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Brush geometry and remeshing are conventional within the defined operation; previous generated content does not change the brush mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B46-R Blender 4.5 LTS release history; 4.5.14 dated 09152026", "url": "https://www.blender.org/releases/4-5/" }, { "label": "B46-S1 Blender 4.5 manual: Grab brush", "url": "https://docs.blender.org/manual/id/4.5/sculpt_paint/sculpting/brushes/grab.html" }, { "label": "B46-S2 Blender v4.5.14: Grab brush deformation source", "url": "https://github.com/blender/blender/blob/v4.5.14/source/blender/editors/sculpt_paint/brushes/grab.cc" }, { "label": "B46-S3 Blender 4.5 manual: radius, strength and brush settings", "url": "https://docs.blender.org/manual/en/4.5/sculpt_paint/brush/brush_settings.html" }, { "label": "Blender · Sculpting", "url": "https://docs.blender.org/manual/en/latest/sculpt_paint/sculpting/index.html" }, { "label": "Blender · Sculpting modes", "url": "https://docs.blender.org/manual/en/2.90/sculpt_paint/sculpting/adaptive.html" } ] }, { "id": "36", "stable_id": "IRM_OP_36", "software": "Blender through StableGen", "operation": "FLUX.2 Klein Texturing and Projection", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "StableGen uses FLUX.2 Klein to synthesize texture views and projects those results onto existing geometry.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned surface realization remains consequential after projection. The model's architecture and projection path are recorded without a guidance-input bonus.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "B47-S1 StableGen Klein workflow template at inspected commit", "url": "https://github.com/sakalond/StableGen/blob/1d6e83fc966afc8b2c196c011b75ce7cdaa07c83/stablegen/util/workflow_templates.py" }, { "label": "B47-S2 StableGen v0.3.1 release dated 06122026", "url": "https://github.com/sakalond/StableGen/releases/tag/v0.3.1" }, { "label": "B47-S3 Black Forest Labs FLUX.2 Klein base 4B model card", "url": "https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B" }, { "label": "B47-S4 StableGen Klein reference conditioning and prompt construction", "url": "https://github.com/sakalond/StableGen/blob/1d6e83fc966afc8b2c196c011b75ce7cdaa07c83/stablegen/texturing/workflows.py" }, { "label": "B47-S5 StableGen camera projection and material blending", "url": "https://github.com/sakalond/StableGen/blob/1d6e83fc966afc8b2c196c011b75ce7cdaa07c83/stablegen/texturing/projection.py" }, { "label": "B47-R Black Forest Labs Klein announcement dated 01152026", "url": "https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence" }, { "label": "GitHub · StableGen", "url": "https://github.com/sakalond/StableGen" }, { "label": "StableGen releases and Klein support", "url": "https://github.com/sakalond/StableGen/releases" } ] }, { "id": "40", "stable_id": "IRM_OP_40", "software": "Browzwear platform", "operation": "AI Avatar Manual Pose Editing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Browzwear's pose editor manually changes an existing avatar pose.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The specified pose-editing operation uses conventional controls. AI-branded avatar provenance does not establish learning in the edit itself.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B48-R VStitcher 2026.2.1 release dated 09222026", "url": "https://help.browzwear.com/en/articles/16992808-vstitcher-2026-2-1-release-notes" }, { "label": "B48-P1 Designing Avatar Poses Overview", "url": "https://help.browzwear.com/en/articles/13065644-designing-avatar-poses-overview" }, { "label": "B48-P2 Editing a Pose", "url": "https://help.browzwear.com/en/articles/13065645-editing-a-pose" }, { "label": "B48-P3 AI Models in VStitcher", "url": "https://help.browzwear.com/en/articles/13065860-ai-models-in-vstitcher" }, { "label": "B48-P4 Dressing and Styling AI Avatars", "url": "https://help.browzwear.com/en/articles/13065862-dressing-and-styling-ai-avatars" }, { "label": "Browzwear · Product overview", "url": "https://browzwear.com/" } ] }, { "id": "41", "stable_id": "IRM_OP_41", "software": "Browzwear VStitcher", "operation": "Pattern and Sewing Garment Simulation", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "VStitcher simulates sewn garment patterns using the selected fabric and avatar configuration.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped cloth simulation is prescribed numerical modeling; separately offered learned avatar or image generation is excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B48-R VStitcher 2026.2.1 release dated 09222026", "url": "https://help.browzwear.com/en/articles/16992808-vstitcher-2026-2-1-release-notes" }, { "label": "B48-G1 VStitcher 2026.2 release dated 07302026", "url": "https://help.browzwear.com/en/articles/16111588-vstitcher-2026-2-release-notes" }, { "label": "B48-G2 Preparing the Garment on the Avatar", "url": "https://help.browzwear.com/en/articles/13065742-preparing-the-garment-on-the-avatar" }, { "label": "B48-G3 Stitching Overview", "url": "https://help.browzwear.com/en/articles/13065399-stitching-overview" }, { "label": "B48-G4 Dressing the Avatar", "url": "https://help.browzwear.com/en/articles/13065737-dressing-the-avatar" }, { "label": "B48-G5 Fabric Analyzer", "url": "https://help.browzwear.com/en/articles/13065916-fabric-analyzer" }, { "label": "B48-G6 Physics Reference", "url": "https://help.browzwear.com/en/articles/13065506-physics-reference" }, { "label": "B48-G7 Understanding the Grid", "url": "https://help.browzwear.com/en/articles/13065479-understanding-the-grid" }, { "label": "Browzwear · VStitcher garment simulation", "url": "https://browzwear.com/products/v-stitcher" } ] }, { "id": "42", "stable_id": "IRM_OP_42", "software": "Canva", "operation": "Conversational generation of an editable social graphic", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Canva's Design action generates a layered social graphic through its documented design model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned layout and visual composition survive in editable elements. The present image references bound this structured design comparison at 2–10 without establishing a precise point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "B48-C1 Canva AI 2.0 announcement and Design Model", "url": "https://www.canva.com/newsroom/news/canva-create-2026-ai/" }, { "label": "B48-C2 Canva AI assistant: editable components and brand context", "url": "https://www.canva.com/ai-assistant/" }, { "label": "B48-C3 Canva AI landing page and access information", "url": "https://www.canva.com/canva-ai/" }, { "label": "CMSWire · AI conversational design", "url": "https://www.cmswire.com/digital-experience/canva-ai-20-adds-agentic-design-tools/" }, { "label": "Canva Design Model and structured outputs", "url": "https://www.canva.com/newsroom/news/magic-layers/" } ] }, { "id": "44", "stable_id": "IRM_OP_44", "software": "Canva", "operation": "Magic Design template arrangement of generated imagery", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Design recommends templates and arranges supplied imagery into designs.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The sources do not separate learned recommendation or composition from prescribed template fitting. Template availability and AI branding cannot establish the equation branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The sources do not separate learned recommendation or composition from prescribed template fitting. Template availability and AI branding cannot establish the equation branch." }, "sources": [ { "label": "B49-C2 Canva Use Magic Design to generate design templates — consulted 09282026", "url": "https://www.canva.com/help/use-magic-design/" }, { "label": "OpenAI and Canva · Magic Design model and template combination", "url": "https://openai.com/index/canva/" }, { "label": "Vantage Labs AI · Canva Magic Studio review", "url": "https://www.vantagelabsai.com/blog/canva-ai-magic-studio-review" } ] }, { "id": "45", "stable_id": "IRM_OP_45", "software": "Canva", "operation": "Magic Edit regional replacement", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Edit synthesizes replacement content for a brushed image region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned replacement appearance and geometry support the same synthesis comparison as Generative Fill. Brush extent and prompt specificity supply no offset.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "https://www.canva.com/help/using-magic-edit/", "url": "https://www.canva.com/help/using-magic-edit/" }, { "label": "https://www.canva.com/features/ai-photo-editing/", "url": "https://www.canva.com/features/ai-photo-editing/" }, { "label": "https://www.canva.com/features/ai-replace/", "url": "https://www.canva.com/features/ai-replace/" }, { "label": "https://www.canva.com/newsroom/news/supercharging-the-visual-suite/", "url": "https://www.canva.com/newsroom/news/supercharging-the-visual-suite/" } ] }, { "id": "46", "stable_id": "IRM_OP_46", "software": "Canva", "operation": "Magic Expand image outpainting", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Expand synthesizes image content beyond the existing canvas boundary.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Outpainting retains learned scene completion and matches the synthesis reference. Expansion area is not a numerical weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Canva Help — Expand images; workflow and limitations, consulted 09212026", "url": "https://www.canva.com/help/using-magic-expand/" }, { "label": "Canva — AI Image Expander; intended color/style matching and frame controls", "url": "https://www.canva.com/features/ai-image-expander/" }, { "label": "Canva — Introducing Magic Studio; launch account, used as historical context", "url": "https://www.canva.com/en_in/newsroom/news/magic-studio/" }, { "label": "Canva — AI Photo Editor; object isolation and separate Magic Edit attribution", "url": "https://www.canva.com/features/ai-photo-editing/" } ] }, { "id": "47", "stable_id": "IRM_OP_47", "software": "Canva", "operation": "Magic Grab object extraction", "y": 6.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Grab extracts an object and completes the background exposed by extraction.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned completion of the exposed background matches generation at 2. Retaining an extracted object alongside that completion does not dilute synthesis through an extra-stage adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Canva Help — Magic Grab; object selection, extraction and background retention", "url": "https://www.canva.com/help/using-magic-grab/" }, { "label": "Canva — AI Photo Editor; object isolation and separate Magic Edit attribution", "url": "https://www.canva.com/features/ai-photo-editing/" }, { "label": "Zapier — How to use Canva AI; firsthand use account of background filling, not architecture evidence", "url": "https://zapier.com/blog/canva-ai/" }, { "label": "Canva Help — Magic Layers; separate operation, not a synonym for Magic Grab", "url": "https://www.canva.com/help/editable-magic-layers/" } ] }, { "id": "48", "stable_id": "IRM_OP_48", "software": "Canva", "operation": "Magic Media text-to-image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Media generates an image from the selected text-to-image action.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The assessed endpoint retains learned image synthesis. Other Canva media functions are outside the record, and no precise backend is inferred.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Canva Help — Use Magic Media; Images workflow, styles, reference and layout controls", "url": "https://www.canva.com/help/using-magic-media/" }, { "label": "Canva Indonesian product FAQ — explicit Magic Media Stable Diffusion attribution; model version not named", "url": "https://www.canva.com/id_id/generator-model-3d-ai/" }, { "label": "Canva Portuguese product page — corroborating Magic Media Stable Diffusion attribution", "url": "https://www.canva.com/pt_br/cartaz-ia/" }, { "label": "Rombach et al. — High-Resolution Image Synthesis with Latent Diffusion Models, CVPR 2022; family background, not Canva deployment specification", "url": "https://arxiv.org/abs/2112.10752" }, { "label": "Canva Learn — How to use AI for better presentations; original prompting and style walkthrough", "url": "https://www.canva.com/learn/how-to-use-AI-for-better-presentations/" }, { "label": "Canva Newsroom — AI image generation apps; Magic Media distinguished from DALL·E and Imagen", "url": "https://www.canva.com/newsroom/news/text-to-image-ai-image-generator/" }, { "label": "Canva Newsroom — TIME 2024 Best Inventions; Dream Lab Phoenix attribution, historical", "url": "https://www.canva.com/newsroom/news/time-best-inventions/" }, { "label": "Canva Magic Media image and graphics functions", "url": "https://www.canva.com/newsroom/news/what-happened-at-canva-create-2024/" } ] }, { "id": "43", "stable_id": "IRM_OP_43", "software": "Canva", "operation": "Manual layout using generated imagery", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Canva's manual editor places, sizes, groups and orders existing image and text elements.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The layout commands implement the specified edits conventionally. Generated asset provenance does not change their mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B49-C1 Canva Group layer and align elements — consulted 09282026", "url": "https://www.canva.com/en_au/help/layer-group-align-variantb/" }, { "label": "Canva · Canva overview", "url": "https://www.canva.com/newsroom/news/canva-ai-launches/" } ] }, { "id": "49", "stable_id": "IRM_OP_49", "software": "CapCut (ByteDance)", "operation": "Auto Captions editable timed text", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "CapCut Auto Captions recognizes speech and places editable text on a timeline.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned transcription and alignment estimate structured content. The 8–10 comparison spans fidelity reconstruction and relational determination; caption editability is not an X adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "B49-A1 CapCut How do I recognise subtitles — January 2026 scope statement; consulted 09282026", "url": "https://www.capcut.com/help/how-to-recognise-subtitles" } ] }, { "id": "51", "stable_id": "IRM_OP_51", "software": "Capture One Pro", "operation": "Manual masked curves on generated imagery", "y": 9.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Capture One applies manually painted masks and specified curves to a compatible image.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped mask painting and tonal functions are conventional. AI Masking and Match Look are separate operations.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B50-R Capture One 16.7.7 release notes — released 04162026", "url": "https://support.captureone.com/hc/en-us/articles/35279291925277-Capture-One-16-7-7-release-notes" }, { "label": "B50-L Capture One Overview of Layers and Masks — consulted 09282026", "url": "https://support.captureone.com/hc/en-us/articles/360002601658-Overview-of-Layers-and-Masks" }, { "label": "B50-B Capture One Brush opacity and flow", "url": "https://support.captureone.com/hc/en-us/articles/360002617917-Modifying-the-Opacity-and-Flow-sliders-in-the-brush-settings" }, { "label": "B50-C Capture One Luma curve adjustments", "url": "https://support.captureone.com/hc/en-us/articles/360002604017-Improving-contrast-without-altering-saturation-in-the-Curve-tool" }, { "label": "Capture One · Capture One Layers and Curves", "url": "https://www.captureone.com/en/features" } ] }, { "id": "52", "stable_id": "IRM_OP_52", "software": "Capture One Pro", "operation": "Match Look reference-based adjustments", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Capture One Match Look estimates adjustment settings from a reference look and applies existing image controls.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "A trained appearance-mapping role lies between fixed-operator emulation and perceptual reconstruction. Platform correspondence is qualified; the record supports 5–12 rather than a precise t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "B50-R Capture One 16.7.7 release notes — released 04162026", "url": "https://support.captureone.com/hc/en-us/articles/35279291925277-Capture-One-16-7-7-release-notes" }, { "label": "B50-R2 Capture One 16.5.0 release notes — released 10232024", "url": "https://support.captureone.com/hc/en-us/articles/22162074930589-Capture-One-16-5-0-Release-Notes" }, { "label": "B50-K Capture One Match Look Tool", "url": "https://support.captureone.com/hc/en-us/articles/22188770298269-Match-Look-Tool" }, { "label": "B50-Q1 Qualcomm Neural reference matching on Snapdragon", "url": "https://www.qualcomm.com/news/onq/2024/10/coming-to-snapdragon-x-series-new-npu-powered-creator-tools-and-daw-apps" }, { "label": "B50-Q2 Qualcomm Match Look with DirectML and Hexagon NPU", "url": "https://www.qualcomm.com/developer/blog/2024/10/elevating-ai-capture-one-directml-qualcomm-hexagon-npu" }, { "label": "Michal Krause · Capture One Match Look", "url": "https://www.michalkrause.com/en/capture-one-165-brings-ai-people-selection-and-a-match-look-feature-to-copy-the-look-of-another-photo/" }, { "label": "Capture One Match Look", "url": "https://support.captureone.com/hc/en-us/articles/22266038214173-Match-Look" } ] }, { "id": "50", "stable_id": "IRM_OP_50", "software": "Capture One Pro", "operation": "Subject mask on generated imagery", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Capture One's Subject command predicts a foreground mask before image adjustments.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Learned subject membership matches region determination. Other masking modes and subsequent adjustments are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "B50-R Capture One 16.7.7 release notes — released 04162026", "url": "https://support.captureone.com/hc/en-us/articles/35279291925277-Capture-One-16-7-7-release-notes" }, { "label": "B50-M Capture One AI Masking — consulted 09282026", "url": "https://support.captureone.com/hc/en-us/articles/14055231933853-AI-Masking" }, { "label": "Life after Photoshop · Capture One AI Masking", "url": "https://lifeafterphotoshop.com/the-new-capture-one-ai-select-tool-and-how-it-works/" }, { "label": "Capture One layers and masks", "url": "https://support.captureone.com/hc/en-us/articles/360002601658-Overview-of-Layers-and-Masks" } ] }, { "id": "53", "stable_id": "IRM_OP_53", "software": "Cavalry", "operation": "Procedural animation of generated SVG artwork", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Cavalry imports SVG artwork and evaluates Duplicator rules and authored transform animation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed procedural distribution and animation contain no trained predictor within this scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "B51-C1 Cavalry 2.6 release notes — released 02042026", "url": "https://cavalry.studio/docs/tech-info/release-notes/2.6/2-6-0-release-notes/" }, { "label": "B51-C2 Cavalry SVG Shape — consulted 09282026", "url": "https://cavalry.studio/docs/nodes/shapes/svg-shape/" }, { "label": "B51-C3 Cavalry Vector Artwork Support — consulted 09282026", "url": "https://cavalry.studio/docs/nodes/general/vector-artwork-support/" }, { "label": "B51-C4 Cavalry Duplicator — consulted 09282026", "url": "https://cavalry.studio/docs/nodes/shapes/duplicator/" }, { "label": "B51-C5 Cavalry Linear Distribution — consulted 09282026", "url": "https://cavalry.studio/docs/nodes/general/distribution-types/linear-distribution/" }, { "label": "B51-C6 Cavalry Time Editor — consulted 09282026", "url": "https://cavalry.studio/docs/user-interface/menus/window-menu/scene-window/time-editor/" }, { "label": "Cavalry · Procedural Animation", "url": "https://cavalry.studio/" } ] }, { "id": "54", "stable_id": "IRM_OP_54", "software": "chaiNNer", "operation": "ESRGAN image upscaling chain", "y": 7.0, "y_status": "work_in_progress", "x": 5.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "chaiNNer loads an ESRGAN RRDB upscaler and applies it to an image through its node graph.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0" }, "rating_rationale": "The documented GAN-trained ESRGAN reconstruction matches the perceptual reconstruction reference. A different loaded checkpoint requires a new recorded configuration.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "5", "display": "5.0", "t": "0", "calculation": "X = 5 + 0 × (8 − 5) = 5." }, "sources": [ { "label": "B51-E1 chaiNNer v0.25.1 release — released 10232025", "url": "https://github.com/chaiNNer-org/chaiNNer/releases/tag/v0.25.1" }, { "label": "B51-E2 chaiNNer v0.25.1 Load Model source — consulted 09282026", "url": "https://github.com/chaiNNer-org/chaiNNer/blob/v0.25.1/backend/src/packages/chaiNNer_pytorch/pytorch/io/load_model.py" }, { "label": "B51-E3 chaiNNer v0.25.1 Upscale Image source — consulted 09282026", "url": "https://github.com/chaiNNer-org/chaiNNer/blob/v0.25.1/backend/src/packages/chaiNNer_pytorch/pytorch/processing/upscale_image.py" }, { "label": "B51-E4 ESRGAN original pretrained models — consulted 09282026", "url": "https://github.com/xinntao/ESRGAN/blob/master/models/README.md" }, { "label": "B51-E5 Wang et al. ESRGAN — revised 09172018", "url": "https://arxiv.org/abs/1809.00219" }, { "label": "GitHub · chaiNNer", "url": "https://github.com/chaiNNer-org/chaiNNer" }, { "label": "chaiNNer image processing and supported models", "url": "https://chainner.app/" }, { "label": "Original ESRGAN model and network", "url": "https://github.com/xinntao/ESRGAN" } ] }, { "id": "55", "stable_id": "IRM_OP_55", "software": "Chaos V-Ray", "operation": "NVIDIA AI Denoiser Image Pass", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "V-Ray uses the explicitly selected NVIDIA AI denoiser to reconstruct the noisy beauty pass.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The trained noise-removal responsibility matches fidelity restoration. A fallback to the default conventional engine would be a different scoped operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "B51-V1 V-Ray 7 for 3ds Max release notes — build 7.00.02 released 10312024", "url": "https://docs-chaos.atlassian.net/wiki/spaces/VMAX/pages/113580676" }, { "label": "B51-V2 Chaos RenderChannelDenoiser SDK reference — consulted 09282026", "url": "https://docs.chaos.com/vray_app_sdk/doc/csharp/class_v_ray_1_1_plugins_1_1_render_channel_denoiser.html" }, { "label": "B51-V3 NVIDIA OptiX AI Denoiser — consulted 09282026", "url": "https://developer.nvidia.com/optix-denoiser" }, { "label": "B51-V4 Chaitanya et al. Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder — 07012017", "url": "https://research.nvidia.com/publication/2017-07_interactive-reconstruction-monte-carlo-image-sequences-using-recurrent" }, { "label": "Chaos · V-Ray AI Denoiser", "url": "https://docs-chaos.atlassian.net/wiki/spaces/VMAYA/pages/111739233" } ] }, { "id": "56", "stable_id": "IRM_OP_56", "software": "ChatGPT", "operation": "Current conversational image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ChatGPT's image-generation service synthesizes the requested image through its managed image backend.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained image responsibility matches synthesis. Internal routing and a particular API checkpoint are not inferred from the conversational interface.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "B52-G1 OpenAI ChatGPT image generation documentation — consulted 09282026", "url": "https://learn.chatgpt.com/docs/image-generation" }, { "label": "B52-G2 OpenAI image generation guide, Responses API prompt revision — consulted 09282026", "url": "https://developers.openai.com/api/docs/guides/image-generation" }, { "label": "OpenAI · Images 2.0 Conversational Generation", "url": "https://openai.com/index/introducing-chatgpt-images-2-0/" } ] }, { "id": "57", "stable_id": "IRM_OP_57", "software": "Cinema 4D", "operation": "MCP LLM Scene Object Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The Cinema 4D MCP bridge executes an LLM's native primitive and transform commands.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned scene planning survives the conventional host commands. Current reference coverage supports 2–10; host compatibility is not a measure of inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "B52-M1 Cinema 4D MCP README at inspected commit — 03072026", "url": "https://github.com/ttiimmaacc/cinema4d-mcp/blob/6bab376f0adbbfe20698c79689a14547e6bd9bb9/README.md" }, { "label": "B52-M2 Cinema 4D MCP server tool definitions at inspected commit", "url": "https://github.com/ttiimmaacc/cinema4d-mcp/blob/6bab376f0adbbfe20698c79689a14547e6bd9bb9/src/cinema4d_mcp/server.py" }, { "label": "B52-M3 Cinema 4D MCP native object implementation at inspected commit", "url": "https://github.com/ttiimmaacc/cinema4d-mcp/blob/6bab376f0adbbfe20698c79689a14547e6bd9bb9/c4d_plugin/mcp_server_plugin.pyp" }, { "label": "GitHub · cinema4d-mcp", "url": "https://github.com/ttiimmaacc/cinema4d-mcp" } ] }, { "id": "58", "stable_id": "IRM_OP_58", "software": "Clip Studio Paint", "operation": "Colorize on generated line art", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Clip Studio Paint Colorize uses its machine-learning service to estimate colorization for line art.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned appearance allocation crosses the fixed-mapping and perceptual-reconstruction references. Optional color hints do not determine a numerical discount; retain 5–12.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "B52-C1 Clip Studio Paint 5.1.4 release notes — released 09082026", "url": "https://www.clipstudio.net/en/dl/release_note/latest/" }, { "label": "B52-C2 Celsys 1.8.4 announcement — 11292018", "url": "https://www.celsys.com/en/topic/2018112901" }, { "label": "B52-C3 Clip Studio Paint Colorize manual — consulted 09282026", "url": "https://help.clip-studio.com/en-us/manual_en/390_filters/Smart_Tools.htm" } ] }, { "id": "59", "stable_id": "IRM_OP_59", "software": "Clip Studio Paint", "operation": "Hand Scanner to Editable 3D Hand Pose", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Hand Scanner estimates a three-dimensional hand pose from camera imagery and applies it to the selected hand.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned pose reconstruction crosses relational determination and fidelity estimation. Native pose controls transmit the inference; retain 8–10.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "H1 Celsys — Clip Studio Paint 5.1.4 release 09082026", "url": "https://www.clipstudio.net/en/dl/release_note/latest/" }, { "label": "H2 Celsys — Version 2.0.0 release and Hand Scanner introduction 03142023", "url": "https://www.clipstudio.net/en/dl/release_note/v2/" }, { "label": "H3 Celsys — Posing 3D drawing figures, Hand Scanner; consulted 09282026", "url": "https://help.clip-studio.com/en-us/manual_en/660_3d/Posing_3D_drawing_figures.htm" }, { "label": "H4 Celsys — 3D hand models; consulted 09282026", "url": "https://help.clip-studio.com/en-us/manual_en/660_3d/3D_Hand_Models.htm" }, { "label": "Clip Studio Paint · Hand Scanner", "url": "https://www.clipstudio.net/en/functions/3d/" }, { "label": "CLIP STUDIO · AI posing tools", "url": "https://www.clipstudio.net/en/comics-manga/tool/3d/" } ] }, { "id": "60", "stable_id": "IRM_OP_60", "software": "Clip Studio Paint", "operation": "Manual vector stroke revision of generated artwork", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Clip Studio Paint moves a selected control point in an existing vector stroke.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional vector geometry evaluates the specified change. The provenance of the drawing reference is outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "V1 Celsys — Clip Studio Paint 5.1.4 release 09082026", "url": "https://www.clipstudio.net/en/dl/release_note/latest/" }, { "label": "V2 Celsys — Vector layers and Control point editing; consulted 09282026", "url": "https://help.clip-studio.com/en-us/manual_en/180_layers/Vector_layers.htm" }, { "label": "Clip Studio Paint · Layers/Vector", "url": "https://www.clipstudio.net/en/functions/" } ] }, { "id": "61", "stable_id": "IRM_OP_61", "software": "CLO 3D", "operation": "Sewn Pattern Garment Simulation", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "CLO simulates a garment from sewing relationships, fabric parameters and an avatar.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped cloth dynamics is conventional numerical simulation. Unanticipated drape does not establish a trained predictor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "C1 CLO — API/SDK Download, CLO 2026.1.224; consulted 09282026", "url": "https://developer.clo3d.com/download.html" }, { "label": "C2 CLO — SDK 11.0.0 changelog, August 2026", "url": "https://developer.clo3d.com/changelog.html" }, { "label": "C3 CLO — Simulation; updated 07052026", "url": "https://support.clo3d.com/hc/en-us/articles/53856382869657-Simulation" }, { "label": "C4 CLO — Segment Sewing; updated 12152025", "url": "https://support.clo3d.com/hc/en-us/articles/115000498767--2D-Tool-Segment-Sewing" }, { "label": "C5 CLO — Adjust Stretch Weft/Warp and Shear; updated 10232024", "url": "https://support.clo3d.com/hc/en-us/articles/115000483087-Adjust-Stretch-Weft-Warp-Shear" }, { "label": "C6 CLO — Adjust Bending Weft/Warp; consulted 09282026", "url": "https://support.clo3d.com/hc/en-us/articles/115000483187-Adjust-Bending-Weft-Warp" }, { "label": "C7 CLO — Edit Simulation Properties; consulted 09282026", "url": "https://support.clo3d.com/hc/en-us/articles/30201166554649-Edit-Simulation-Properties" }, { "label": "C8 CLO — Particle Distance; updated 06232025", "url": "https://support.clo3d.com/hc/en-us/articles/115012227227-Particle-Distance" }, { "label": "CLO · Garment Simulation", "url": "https://www.clo3d.com/en/" }, { "label": "CLO · Editable patterns and garment poses", "url": "https://support.clo3d.com/hc/en-us/articles/115000297567-Modes" } ] }, { "id": "400", "stable_id": "IRM_OP_400", "software": "ComfyUI", "operation": "ImageCompositeMasked with supplied images and mask", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ComfyUI ImageCompositeMasked combines supplied images and mask by the pinned node's conventional alpha arithmetic.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The node neither predicts the mask nor regenerates the source. The blend retains no newly learned determination within this isolated operation; generation-plus-compositing would be a different boundary.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Pinned implementation", "url": "https://github.com/Comfy-Org/ComfyUI/blob/9c90340bc26ea69c2029ee6e882acbbb06e157b4/comfy_extras/nodes_mask.py" }, { "label": "pinned helper", "url": "https://raw.githubusercontent.com/Comfy-Org/ComfyUI/9c90340bc26ea69c2029ee6e882acbbb06e157b4/node_helpers.py" } ] }, { "id": "67", "stable_id": "IRM_OP_67", "software": "ComfyUI", "operation": "SD3.5 Large ControlNet Blur Reconstruction", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SD3.5 Large Blur ControlNet reconstructs image detail through the documented blur-conditioned generation route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Its restoration task and generative detail prior cross fidelity restoration and perceptual reconstruction. Retain 5–8; the blur amount itself does not supply t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "R1 Stability AI — SD3.5 Large ControlNets announcement 11262024", "url": "https://stability.ai/news-updates/sd3-5-large-controlnets" }, { "label": "R2 Stability AI — dated release index 11262024", "url": "https://stability.ai/news-updates?category=Research" }, { "label": "C1 ComfyUI — official SD3.5 model and ControlNet examples; consulted 09282026", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "S1 ComfyUI — ControlNet application node, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/nodes.py" }, { "label": "S2 ComfyUI — SD3.5 ControlNet loading and input handling, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/comfy/controlnet.py" }, { "label": "M1 Stability AI — SD3.5 Large Blur model card; consulted 09282026", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-controlnet-blur" }, { "label": "M2 Stability AI — SD3.5 ControlNets model family, pinned documentation", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-controlnets/blob/bdc92dce20362ce2bdbbb48ae9e34c51695a43cd/README.md" }, { "label": "ControlNet project · Tile model", "url": "https://github.com/lllyasviel/ControlNet-v1-1-nightly" }, { "label": "ControlNet Tile purpose and model identifier", "url": "https://github.com/lllyasviel/ControlNet-v1-1-nightly#controlnet-11-tile" }, { "label": "ComfyUI ControlNet model loading", "url": "https://comfyanonymous.github.io/ComfyUI_examples/controlnet/" }, { "label": "Daydream SDXL ControlNet model choices", "url": "https://docs.daydream.live/api/parameters/SDXL" } ] }, { "id": "65", "stable_id": "IRM_OP_65", "software": "ComfyUI", "operation": "SD3.5 Large ControlNet Canny Generation", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SD3.5 Large Canny ControlNet processes a supplied edge map during learned generation and decoding.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The trained control path supports image synthesis; additional learned conditioning is not reduced inference merely because edges are supplied.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "R1 Stability AI — SD3.5 Large ControlNets announcement 11262024", "url": "https://stability.ai/news-updates/sd3-5-large-controlnets" }, { "label": "R2 Stability AI — dated release index 11262024", "url": "https://stability.ai/news-updates?category=Research" }, { "label": "C1 ComfyUI — official SD3.5 model and ControlNet examples; consulted 09282026", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "S1 ComfyUI — ControlNet application node, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/nodes.py" }, { "label": "S2 ComfyUI — SD3.5 ControlNet loading and input handling, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/comfy/controlnet.py" }, { "label": "M1 Stability AI — SD3.5 Large Canny model card; consulted 09282026", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-controlnet-canny" }, { "label": "C2 ComfyUI — official Canny example, workflow metadata inspected 09282026", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/sd3.5_large_canny_controlnet_example.png" }, { "label": "P1 Zhang, Rao and Agrawala — Adding Conditional Control to Text-to-Image Diffusion Models, 2023; introduction and methods context", "url": "https://arxiv.org/html/2302.05543v3" }, { "label": "DotSimulate · ControlNet through StreamDiffusionTD", "url": "https://dotsimulate.com/docs/streamdiffusiontd" }, { "label": "Daydream · Supported Canny Depth and Tile models", "url": "https://docs.daydream.live/api/parameters/SDXL" }, { "label": "ComfyUI ControlNet inputs and host workflow", "url": "https://docs.comfy.org/tutorials/controlnet/controlnet" }, { "label": "ControlNet 1.1 named model family", "url": "https://github.com/lllyasviel/ControlNet-v1-1-nightly" }, { "label": "Historical source: ComfyUI · ControlNet-Conditioned", "url": "https://comfyanonymous.github.io/ComfyUI_examples/controlnet/" }, { "label": "Historical source: ControlNet project · Neural conditioning architecture", "url": "https://github.com/lllyasviel/ControlNet" } ] }, { "id": "66", "stable_id": "IRM_OP_66", "software": "ComfyUI", "operation": "SD3.5 Large ControlNet Depth Generation", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SD3.5 Large Depth ControlNet processes a supplied depth map during learned generation and decoding.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The operation retains learned image synthesis. Upstream depth estimation is excluded, and the supplied depth map contributes no score offset.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "R1 Stability AI — SD3.5 Large ControlNets announcement 11262024", "url": "https://stability.ai/news-updates/sd3-5-large-controlnets" }, { "label": "R2 Stability AI — dated release index 11262024", "url": "https://stability.ai/news-updates?category=Research" }, { "label": "C1 ComfyUI — official SD3.5 model and ControlNet examples; consulted 09282026", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "S1 ComfyUI — ControlNet application node, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/nodes.py" }, { "label": "S2 ComfyUI — SD3.5 ControlNet loading and input handling, source snapshot 09282026", "url": "https://github.com/Comfy-Org/ComfyUI/blob/2139131e880cf07116f0d0238de8cfbb125d2114/comfy/controlnet.py" }, { "label": "M1 Stability AI — SD3.5 Large Depth model card; consulted 09282026", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-controlnet-depth" }, { "label": "ComfyUI · Depth ControlNet image workflow", "url": "https://docs.comfy.org/tutorials/controlnet/depth-controlnet" }, { "label": "StreamDiffusionTD TouchDesigner integration", "url": "https://dotsimulate.com/docs/streamdiffusiontd" } ] }, { "id": "62", "stable_id": "IRM_OP_62", "software": "ComfyUI", "operation": "SD3.5 Large Image to Image", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ComfyUI encodes an image, runs SD3.5 Large image-to-image sampling and decodes the generated result.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned transformation retains extensive synthesis responsibility. Denoise strength is a model control, not t or a percentage of inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Baeseok Jae · img2img", "url": "https://baeseokjae.github.io/posts/comfyui-workflow-ai-image-generation-guide-2026/" }, { "label": "ComfyUI · Image to image generation", "url": "https://comfyanonymous.github.io/ComfyUI_examples/img2img/" }, { "label": "ComfyUI · SD1.5 image-to-image example", "url": "https://docs.comfy.org/tutorials/basic/image-to-image" }, { "label": "ComfyUI SD3.5", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "Stability AI — SD3.5 Large model card; architecture, encoders and training disclosure", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large" }, { "label": "Stability AI — Introducing Stable Diffusion 3.5, 10222024; updated October 29", "url": "https://stability.ai/news-updates/introducing-stable-diffusion-3-5" }, { "label": "Esser et al. — Scaling Rectified Flow Transformers for High-Resolution Image Synthesis, 2024; architecture and flow sections inspected", "url": "https://arxiv.org/html/2403.03206v1" }, { "label": "Stability AI — SD3.5 reference implementation and model variants", "url": "https://github.com/Stability-AI/sd3.5" }, { "label": "Stability AI — reference inference code; initial-image route and sampling settings", "url": "https://github.com/Stability-AI/sd3.5/blob/main/sd3_infer.py" }, { "label": "ComfyUI — KSampler node reference", "url": "https://docs.comfy.org/built-in-nodes/sampling/ksampler" }, { "label": "ComfyUI — pinned nodes.py; VAE encoding, inpaint preparation and sampler input handling; 95539f56344958339e39b7582a476267d489b0ee", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/nodes.py" }, { "label": "ComfyUI — pinned samplers.py; partial-denoise schedule and latent inpaint blending", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy/samplers.py" }, { "label": "ComfyUI — pinned model_sampling.py; flow noise scaling and shifted schedule", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy/model_sampling.py" }, { "label": "Meng et al. — SDEdit, 2021/revised 2022; abstract inspected as editing context, not SD3.5 implementation", "url": "https://arxiv.org/abs/2108.01073" } ] }, { "id": "63", "stable_id": "IRM_OP_63", "software": "ComfyUI", "operation": "SD3.5 Large Masked Latent Inpainting", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ComfyUI applies a latent noise mask to ordinary SD3.5 Large sampling and decodes the inpainted image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Masked use of the base generator retains learned synthesis. A dedicated inpainting checkpoint is not asserted, and mask area does not adjust X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "ComfyUI · Masked inpainting and outpainting", "url": "https://comfyanonymous.github.io/ComfyUI_examples/inpaint/" }, { "label": "ComfyUI SD3.5", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "ComfyUI inpainting", "url": "https://docs.comfy.org/tutorials/basic/inpaint" }, { "label": "Stability AI — SD3.5 Large model card; architecture, encoders and training disclosure", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large" }, { "label": "Stability AI — SD3.5 reference implementation and model variants", "url": "https://github.com/Stability-AI/sd3.5" }, { "label": "ComfyUI — KSampler node reference", "url": "https://docs.comfy.org/built-in-nodes/sampling/ksampler" }, { "label": "ComfyUI — pinned nodes.py; VAE encoding, inpaint preparation and sampler input handling; 95539f56344958339e39b7582a476267d489b0ee", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/nodes.py" }, { "label": "ComfyUI — pinned samplers.py; partial-denoise schedule and latent inpaint blending", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy/samplers.py" }, { "label": "ComfyUI — pinned nodes_sd3.py; triple text encoders and 16-channel empty latent", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy_extras/nodes_sd3.py" }, { "label": "ComfyUI — workflows, links and reusable graph records", "url": "https://docs.comfy.org/basic-concepts/workflow" } ] }, { "id": "64", "stable_id": "IRM_OP_64", "software": "ComfyUI", "operation": "SD3.5 Large Text to Image", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ComfyUI runs SD3.5 Large text encoding, learned latent prediction and learned image decoding.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The complete generation endpoint matches image synthesis. Conventional numerical sampling between learned predictions does not dilute the coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Wikipedia · ComfyUI", "url": "https://en.wikipedia.org/wiki/ComfyUI" }, { "label": "ComfyUI · Text to image generation", "url": "https://docs.comfy.org/tutorials/basic/text-to-image" }, { "label": "ComfyUI SD3.5", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "Stability current models", "url": "https://stability.ai/core-models" }, { "label": "Stability AI — SD3.5 Large model card; architecture, encoders and training disclosure", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large" }, { "label": "Stability AI — Introducing Stable Diffusion 3.5, 10222024; updated October 29", "url": "https://stability.ai/news-updates/introducing-stable-diffusion-3-5" }, { "label": "Esser et al. — Scaling Rectified Flow Transformers for High-Resolution Image Synthesis, 2024; architecture and flow sections inspected", "url": "https://arxiv.org/html/2403.03206v1" }, { "label": "Stability AI — SD3.5 reference implementation and model variants", "url": "https://github.com/Stability-AI/sd3.5" }, { "label": "Stability AI — reference inference code; initial-image route and sampling settings", "url": "https://github.com/Stability-AI/sd3.5/blob/main/sd3_infer.py" }, { "label": "ComfyUI — KSampler node reference", "url": "https://docs.comfy.org/built-in-nodes/sampling/ksampler" }, { "label": "ComfyUI — workflows, links and reusable graph records", "url": "https://docs.comfy.org/basic-concepts/workflow" }, { "label": "ComfyUI — pinned nodes.py; VAE encoding, inpaint preparation and sampler input handling; 95539f56344958339e39b7582a476267d489b0ee", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/nodes.py" }, { "label": "ComfyUI — pinned samplers.py; partial-denoise schedule and latent inpaint blending", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy/samplers.py" }, { "label": "ComfyUI — pinned nodes_sd3.py; triple text encoders and 16-channel empty latent", "url": "https://github.com/Comfy-Org/ComfyUI/blob/95539f56344958339e39b7582a476267d489b0ee/comfy_extras/nodes_sd3.py" } ] }, { "id": "68", "stable_id": "IRM_OP_68", "software": "ComfyUI", "operation": "SD3.5 Large text-to-image generation through VAE decode", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SD3.5 Large text-to-image generation includes learned prompt encoding, latent prediction and VAE decoding.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "This overlaps the same generation mechanism recorded in ID64 and receives the same synthesis comparison. It is retained as a catalogue record, not an independent calibration observation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Release", "url": "https://stability.ai/news-updates/introducing-stable-diffusion-3-5" }, { "label": "model card", "url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large" }, { "label": "paper §§2–4 and 5.2.1", "url": "https://arxiv.org/html/2403.03206v1" }, { "label": "Vendor code: BaseModel, CFGDenoiser, sample_euler, SDVAE", "url": "https://raw.githubusercontent.com/Stability-AI/sd3.5/main/sd3_impls.py" }, { "label": "ComfyUI SD3 implementation", "url": "https://raw.githubusercontent.com/Comfy-Org/ComfyUI/master/comfy/model_base.py" }, { "label": "official examples", "url": "https://comfyanonymous.github.io/ComfyUI_examples/sd3/" }, { "label": "ComfyUI · Workflow nodes and media outputs", "url": "https://docs.comfy.org/basic-concepts/workflow" }, { "label": "ComfyUI source · ImageCompositeMasked", "url": "https://github.com/Comfy-Org/ComfyUI/blob/master/comfy_extras/nodes_mask.py" } ] }, { "id": "69", "stable_id": "IRM_OP_69", "software": "Corel Painter 2023", "operation": "Flat Color brushstroke using Enhanced Cover", "y": 9.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Painter's Flat Color brush applies the documented Enhanced Cover raster painting method.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Antialiasing and artifact-reduction noise are conventional brush processing. Randomness does not establish a trained mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "2022 release history", "url": "https://product.corel.com/help/Painter/540111155/Corel-Painter-en/Corel-Painter-Whats-new-in-Painter.html" }, { "label": "method documentation", "url": "https://product.corel.com/help/Painter/540111162/Corel-Painter-en/Corel-Painter-Methods-and-subcategories.html" }, { "label": "2023 manual, pp380 and419", "url": "https://product.corel.com/help/Painter/540111162/Corel-Painter-en/Corel-Painter-2023.pdf" }, { "label": "spacing documentation", "url": "https://product.corel.com/help/Painter/540111162/Corel-Painter-en/Corel-Painter-Spacing-controls.html" }, { "label": "Fluid Paint control", "url": "https://product.corel.com/help/Painter/540111162/Corel-Painter-en/Corel-Painter-Fluid-Paint-controls.html" }, { "label": "Corel Painter · Corel Painter Natural Media", "url": "https://www.painterartist.com/en/" }, { "label": "Corel Painter 2023 photo painting guide", "url": "https://product.corel.com/help/Painter/540111162/Corel-Painter-en/Corel-Painter-2023-Quick-Start-Guide.pdf" }, { "label": "Corel independently editable layers", "url": "https://product.corel.com/help/Painter/540111155/Corel-Painter-en/Corel-Painter-Getting-Started-with-Layers.html" }, { "label": "Corel current Painter", "url": "https://www.painterartist.com/en/product/painter/" } ] }, { "id": "70", "stable_id": "IRM_OP_70", "software": "CSM Cube", "operation": "Single Image to Mesh", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "CSM Cube Base Single Image to Mesh predicts an untextured three-dimensional asset.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned object geometry completion supports a coarse extensive-synthesis match. Company research context does not disclose this deployment's complete architecture.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "CSM publisher demonstration", "url": "https://www.linkedin.com/posts/common-sense-machines_in-this-quick-and-easy-video-we-will-show-activity-7356773035451768833-D6zN" }, { "label": "CSM API v3 introduction", "url": "https://docs.csm.ai/?trk=public_post_main-feed-card-text" }, { "label": "Founder interview", "url": "https://www.metavert.io/videos/artificial-intelligence-livestream-tejas-kulkarni-common-sense-machines" }, { "label": "Meta parts case study", "url": "https://ai.meta.com/blog/segment-anything-common-sense-machines-3d-assets/" }, { "label": "Magic123 version 1 June 30 2023 methods 2.1–2.2", "url": "https://arxiv.org/html/2306.17843v1" }, { "label": "CSM · Parts and asset generation workflow", "url": "https://www.csm.ai/academy/parts-based-mesh-generation" } ] }, { "id": "72", "stable_id": "IRM_OP_72", "software": "DaVinci Resolve Studio", "operation": "DaVinci Resolve MCP LLM Timeline Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "An external LLM selects and orders complete clips, and the Resolve MCP bridge appends them to a timeline.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned temporal composition remains in the conventional timeline. The choice among whole clips is substantive, with a 2–10 synthesis-to-relational comparison envelope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "R72", "url": "https://github.com/samuelgursky/davinci-resolve-mcp/blob/89da04b19b0dbcbf6e5a53a54fe80542a8d2ef23/README.md" }, { "label": "A72", "url": "https://github.com/samuelgursky/davinci-resolve-mcp/blob/89da04b19b0dbcbf6e5a53a54fe80542a8d2ef23/src/granular/media_pool.py" }, { "label": "C72", "url": "https://github.com/samuelgursky/davinci-resolve-mcp/blob/89da04b19b0dbcbf6e5a53a54fe80542a8d2ef23/src/granular/common.py" }, { "label": "H72", "url": "https://github.com/samuelgursky/davinci-resolve-mcp/blob/89da04b19b0dbcbf6e5a53a54fe80542a8d2ef23/src/utils/shot_assembly.py" }, { "label": "API72", "url": "https://github.com/samuelgursky/davinci-resolve-mcp/blob/89da04b19b0dbcbf6e5a53a54fe80542a8d2ef23/docs/reference/resolve_scripting_api.txt" }, { "label": "GitHub · davinci-resolve-mcp", "url": "https://github.com/samuelgursky/davinci-resolve-mcp" } ] }, { "id": "79", "stable_id": "IRM_OP_79", "software": "Descript", "operation": "Transcript-directed cuts on generated video", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Descript deletes or moves an exactly selected span using an already supplied aligned transcript.", "retained_learning": false, "rating_input": {}, "rating_rationale": "This isolated command applies an existing time mapping conventionally. Transcription, inferred range selection and separately invoked Regenerate are outside its scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "E79", "url": "https://help.descript.com/getting-started/edit-like-a-doc" }, { "label": "D79", "url": "https://help.descript.com/script-editing/delete-vs-ignore" }, { "label": "B79", "url": "https://help.descript.com/script-editing/edit-boundaries" }, { "label": "G79", "url": "https://help.descript.com/script-editing/regenerate-overview" }, { "label": "F79", "url": "https://help.descript.com/timeline/automatic-microfades" }, { "label": "Descript · Video editing overview", "url": "https://www.descript.com/video-editing" } ] }, { "id": "80", "stable_id": "IRM_OP_80", "software": "Descript", "operation": "Underlord rough-cut assembly of generated video", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Underlord uses an LLM to choose media ranges and apply an initial rough cut through editing tools.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned editorial range selection survives the cuts. The 2–10 comparison reflects semantic and temporal construction; supplying an aligned transcript does not remove that responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "U80", "url": "https://help.descript.com/getting-started/underlord-beta-your-ai-co-editor-in-descript" }, { "label": "P80", "url": "https://www.descript.com/blog/article/underlord-ai-video-editor-primer" }, { "label": "A80", "url": "https://help.descript.com/api-and-mcp/api" }, { "label": "E80", "url": "https://help.descript.com/getting-started/edit-like-a-doc" }, { "label": "G80", "url": "https://help.descript.com/script-editing/regenerate-overview" }, { "label": "Haiku 4.5 system card", "url": "https://assets.anthropic.com/m/99128ddd009bdcb/original/Claude-Haiku-4-5-System-Card.pdf" }, { "label": "Descript visual agent and editable scenes", "url": "https://www.descript.com/underlord" } ] }, { "id": "81", "stable_id": "IRM_OP_81", "software": "Dreamina by CapCut", "operation": "Seedance 2.5 Text to Video", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Dreamina invokes Seedance 2.5 to generate an initial video clip through its learned multimodal generation framework.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned scene and motion realization supports an extensive-synthesis analogy. Duration and the number of predicted frames are not X multipliers.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "S81", "url": "https://seed.bytedance.com/en/blog/one-take-creation-flexible-referencing-introducing-seedance-2-5" }, { "label": "T81", "url": "https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/transparency/20260803/Public%20Summary%20of%20Training%20Data%20Content%20for%20Seedance%202.5.pdf" }, { "label": "D81", "url": "https://dreamina.capcut.com/seedance/seedance-2-5" }, { "label": "L81", "url": "https://dreamina.capcut.com/resource/seedance-2-5-launch" }, { "label": "P81", "url": "https://arxiv.org/pdf/2604.14148v1" }, { "label": "CapCut · AI video generator", "url": "https://www.capcut.com/tools/ai-video-generator" }, { "label": "CapCut Seedance 1.0 workflow", "url": "https://www.capcut.com/tools/seedance-1-0-ai-video-generator" }, { "label": "Dreamina current Seedance model", "url": "https://dreamina.capcut.com/seedance/seedance-model" } ] }, { "id": "83", "stable_id": "IRM_OP_83", "software": "DxO PureRAW 6", "operation": "Geometric lens-distortion correction", "y": 3.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PureRAW geometric correction applies a measured camera/lens module to correct distortion.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The isolated calibrated geometric transformation is conventional. DeepPRIME's separate reconstruction is not counted inside this stage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "DM", "url": "https://www.dxo.com/en/dxo-module/" }, { "label": "PG", "url": "https://userguides.dxo.com/pureraw/en/processing-tools-and-settings/" }, { "label": "DX", "url": "https://www.dxo.com/en/news/deepprime-xd3-fourth-generation/" }, { "label": "PC", "url": "https://www.dxo.com/en/news/photolab-committed-to-color/" }, { "label": "Life after Photoshop · DxO Optics Modules", "url": "https://lifeafterphotoshop.com/dxo-pureraw-5-review/" } ] }, { "id": "82", "stable_id": "IRM_OP_82", "software": "DxO PureRAW", "operation": "PureRAW 6 DeepPRIME XD3 Denoising and Demosaicing", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PureRAW 6 DeepPRIME XD3 jointly estimates denoised and demosaiced image information for the selected RAW route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "Trained photographic signal recovery matches fidelity restoration. Separate geometric corrections are excluded; current losses and weights are not inferred.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "DX", "url": "https://www.dxo.com/en/news/deepprime-xd3-fourth-generation/" }, { "label": "DT", "url": "https://www.dxo.com/en/technology/deepprime/" }, { "label": "PG", "url": "https://userguides.dxo.com/pureraw/en/processing-tools-and-settings/" }, { "label": "DM", "url": "https://www.dxo.com/en/dxo-module/" }, { "label": "French guide", "url": "https://userguides.dxo.com/pureraw/fr/outils-et-reglages-de-traitement/" }, { "label": "DxO · DxO DeepPRIME", "url": "https://www.dxo.com/news/deepprime-xd3-fourth-generation/" }, { "label": "DxO PureRAW 5.5 DeepPRIME 3 availability", "url": "https://www.dxo.com/en/news/introducing-pureraw-5-5/" }, { "label": "DxO PureRAW 6 changes", "url": "https://support.dxo.com/hc/en-us/articles/4574386545565-What-is-new-in-DxO-PureRAW-6" } ] }, { "id": "84", "stable_id": "IRM_OP_84", "software": "Figma", "operation": "Agent Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Figma's agent invokes image generation and places the generated raster or image fill in the design.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The scoped endpoint retains image synthesis. Other agent actions do not enter this operation's coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "FA", "url": "https://help.figma.com/hc/en-us/articles/37998629035799-Work-with-the-Figma-agent-in-design-files" }, { "label": "FI", "url": "https://help.figma.com/hc/en-us/articles/24004542669463-Make-or-edit-an-image-with-AI" }, { "label": "FC", "url": "https://help.figma.com/hc/en-us/articles/33459875669015-How-AI-credits-work" }, { "label": "GL", "url": "https://developers.googleblog.com/introducing-gemini-2-5-flash-image/" }, { "label": "GC", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-2-5-Flash-Model-Card.pdf" }, { "label": "Figma · AI overview", "url": "https://www.figma.com/ai/" } ] }, { "id": "85", "stable_id": "IRM_OP_85", "software": "Figma", "operation": "Weave Replace Background", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Figma's embedded Weave Replace Background tool synthesizes a replacement background for an image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained background content supports the synthesis reference; preserving foreground material supplies no numerical offset.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "FW", "url": "https://help.figma.com/hc/en-us/articles/40779260614935-Use-Weave-tools-in-Figma" }, { "label": "FB", "url": "https://www.figma.com/blog/connecting-figma-and-weave/" }, { "label": "FV", "url": "https://cdn.sanity.io/images/599r6htc/regionalized/9fc54af1cc3da306593da109eaeb2e69a1d2a152-3264x1836.png?rect=2,0,3261,1836&w=666&h=375&q=75&fit=max&auto=format" }, { "label": "Figma · Image versus video Weave outputs", "url": "https://help.figma.com/hc/en-us/articles/360038510693-Guide-to-the-Figma-Community" } ] }, { "id": "86", "stable_id": "IRM_OP_86", "software": "Final Cut Pro (Apple)", "operation": "Magnetic Mask Neural Selection", "y": 4.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Final Cut Pro Magnetic Mask predicts and tracks foreground membership over a video clip.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The retained mask matches learned region determination. Temporal analysis is recorded without treating repeated execution as greater X magnitude.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Apple Add Magnetic Masks", "url": "https://support.apple.com/guide/final-cut-pro/ver1d67e3a53/mac" }, { "label": "Apple Edit Magnetic Masks", "url": "https://support.apple.com/en-ca/guide/final-cut-pro/ver43e886e74/mac" }, { "label": "Apple Final Cut Pro 11 launch", "url": "https://www.apple.com/newsroom/2024/11/final-cut-pro-11-begins-a-new-chapter-for-video-editing-on-mac/" } ] }, { "id": "87", "stable_id": "IRM_OP_87", "software": "Final Cut Pro (Apple)", "operation": "Magnetic Timeline Video Editing", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Final Cut Pro performs a manually chosen insert move and the Magnetic Timeline's prescribed ripple consequences.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Automatic timing updates follow editing rules without learned content selection. Their convenience does not establish a trained predictor.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Apple Magnetic Timeline", "url": "https://support.apple.com/guide/final-cut-pro/intro-to-the-magnetic-timeline-verb8fcfc133/mac" }, { "label": "Apple Arrange Clips", "url": "https://support.apple.com/guide/final-cut-pro/arrange-clips-in-the-timeline-verc147f195/12.3/mac/15.6" } ] }, { "id": "88", "stable_id": "IRM_OP_88", "software": "Final Cut Pro (Apple)", "operation": "Transcribe to Captions / Generate Closed Captions", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Final Cut Pro recognizes speech and produces timed closed-caption clips before manual correction.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Transcription and temporal alignment cross fidelity and relational estimation, giving 8–10 with no selected midpoint.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Apple Final Cut Pro 11 launch", "url": "https://www.apple.com/newsroom/2024/11/final-cut-pro-11-begins-a-new-chapter-for-video-editing-on-mac/" }, { "label": "Apple Create Closed Captions", "url": "https://support.apple.com/guide/final-cut-pro/create-closed-captions-vere399dab5e/mac" } ] }, { "id": "89", "stable_id": "IRM_OP_89", "software": "Finch", "operation": "Enterprise AI Unit Plan Generation and Linked Editing", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Finch Generated Plans constructs a unit layout using the documented generative plan route and its design relationships.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned plan construction retains semantic and spatial choices. The current references support a broad 2–10 comparison rather than a specific architectural-generation point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Finch Enterprise Generate Unit Plan", "url": "https://docs.finch3d.com/docs/projects-and-variants/unit-editor/enterprise-generate-unit-plan" }, { "label": "Finch 101 Generating Unit Plans", "url": "https://docs.finch3d.com/courses/finch-101/finch-101-generating-unit-plans" }, { "label": "AEC Magazine · Finch3D Plan Generation", "url": "https://aecmag.com/ai/finch3d-starts-to-sing/" }, { "label": "Finch · BIM output and Revit handoff", "url": "https://docs.finch3d.com/docs/first-steps/revit/all-about-revit" }, { "label": "Finch · Building generation and BIM geometry", "url": "https://www.finch3d.com/product" } ] }, { "id": "100", "stable_id": "IRM_OP_100", "software": "FLUX (Black Forest Labs API / Playground)", "operation": "FLUX.2 pro multi-reference scene synthesis", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "FLUX.2 pro synthesizes a scene using its multi-reference generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned integration of scene content matches synthesis. Reference quantity does not add conventional-generation credit.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "BFL FLUX.2 launch", "url": "https://bfl.ai/blog/flux-2" }, { "label": "BFL public FLUX.2 sampler", "url": "https://raw.githubusercontent.com/black-forest-labs/flux2/main/src/flux2/sampling.py" }, { "label": "BFL FLUX.2 representation study", "url": "https://bfl.ai/techblog/representation-comparison/index.html" }, { "label": "BFL FLUX.2 overview", "url": "https://docs.bfl.ai/flux_2/flux2_overview" }, { "label": "BFL Pro Preview API", "url": "https://docs.bfl.ai/api-reference/models/generate-or-edit-an-image-with-flux2-%5Bpro%5D-preview" }, { "label": "BFL FLUX.2 image editing", "url": "https://docs.bfl.ai/flux_2/flux2_image_editing" } ] }, { "id": "101", "stable_id": "IRM_OP_101", "software": "FLUX (Black Forest Labs API / Playground)", "operation": "FLUX.2 pro text-to-image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "FLUX.2 pro generates a raster through its text-to-image route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The extensive learned synthesis responsibility matches the generation reference. Prompt expansion and sampling settings are recorded separately from rating inputs.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "BFL FLUX.2 launch", "url": "https://bfl.ai/blog/flux-2" }, { "label": "BFL public FLUX.2 sampler", "url": "https://raw.githubusercontent.com/black-forest-labs/flux2/main/src/flux2/sampling.py" }, { "label": "BFL FLUX.2 representation study", "url": "https://bfl.ai/techblog/representation-comparison/index.html" }, { "label": "BFL FLUX.2 overview", "url": "https://docs.bfl.ai/flux_2/flux2_overview" }, { "label": "BFL Pro Preview API", "url": "https://docs.bfl.ai/api-reference/models/generate-or-edit-an-image-with-flux2-%5Bpro%5D-preview" }, { "label": "BFL FLUX.2 image editing", "url": "https://docs.bfl.ai/flux_2/flux2_image_editing" }, { "label": "Higgsfield hosted image models", "url": "https://higgsfield.ai/ai-image" } ] }, { "id": "99", "stable_id": "IRM_OP_99", "software": "FLUX through BFL API or Playground", "operation": "FLUX.2 Pro Targeted Image Editing", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "FLUX.2 pro performs the selected targeted image edit through its learned image-editing route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generator remains responsible for realizing the edited image. A recoloring request does not convert the model into a prescribed color transform or reduce X through prompt content.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "BFL FLUX.2 launch", "url": "https://bfl.ai/blog/flux-2" }, { "label": "BFL public FLUX.2 sampler", "url": "https://raw.githubusercontent.com/black-forest-labs/flux2/main/src/flux2/sampling.py" }, { "label": "BFL FLUX.2 representation study", "url": "https://bfl.ai/techblog/representation-comparison/index.html" }, { "label": "BFL FLUX.2 overview", "url": "https://docs.bfl.ai/flux_2/flux2_overview" }, { "label": "BFL Pro Preview API", "url": "https://docs.bfl.ai/api-reference/models/generate-or-edit-an-image-with-flux2-%5Bpro%5D-preview" }, { "label": "BFL FLUX.2 image editing", "url": "https://docs.bfl.ai/flux_2/flux2_image_editing" }, { "label": "Black Forest Labs · Kontext / Multi-Reference Editing", "url": "https://bfl.ai/blog/flux-1-kontext" }, { "label": "BFL Kontext operations", "url": "https://docs.bfl.ai/kontext/kontext_overview" } ] }, { "id": "102", "stable_id": "IRM_OP_102", "software": "Foundry Katana", "operation": "Katana Node Lookdev/Lighting", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Katana's scoped GafferThree edit changes one existing light-exposure attribute before rendering.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The native attribute edit is conventional. The broader look-development application and a later renderer do not enter this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Foundry GafferThree reference", "url": "https://learn.foundry.com/katana/Content/rg/misc_nodes/gafferthree.html" }, { "label": "Foundry material assignment", "url": "https://learn.foundry.com/katana/Content/ug/adding_assigning_materials/adding_assigning_materials.html" }, { "label": "Foundry graph state variables", "url": "https://learn.foundry.com/katana/content/ug/graph_state_variables/graph_state_variables.html" }, { "label": "Foundry · Katana lighting and rendering", "url": "https://www.foundry.com/products/katana" } ] }, { "id": "103", "stable_id": "IRM_OP_103", "software": "Foundry Mari", "operation": "Mari Layer/Node Painting", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Mari bakes an already supplied paint buffer into an existing texture layer using the recorded view and UV mapping.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The bounded projection and bake apply prescribed image-to-surface mapping. Learned image creation and other paint tools are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Foundry Mari paint buffer", "url": "https://learn.foundry.com/mari/Content/user_guide/painting/paint_buffer.html" }, { "label": "Foundry Mari", "url": "https://www.foundry.com/products/mari" } ] }, { "id": "104", "stable_id": "IRM_OP_104", "software": "Foundry Nuke", "operation": "Cattery MiDaS Depth Estimation", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Nuke Cattery MiDaS predicts relative depth, followed by a retained affine depth-map adjustment.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned scene geometry remains consequential after remapping. The 8–10 spatial-estimation comparison is unaffected by adding arithmetic application.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Foundry Cattery MiDaS model entry, Downloads and Instructions inspected", "url": "https://community.foundry.com/cattery/37770/midas" }, { "label": "MiDaS original repository, README and model comparison", "url": "https://github.com/isl-org/MiDaS" }, { "label": "Ranftl, Bochkovskiy and Koltun, Vision Transformers for Dense Prediction, 2021", "url": "https://arxiv.org/html/2103.13413v1" }, { "label": "Ranftl et al., Towards Robust Monocular Depth Estimation", "url": "https://arxiv.org/abs/1907.01341" }, { "label": "MiDaS official hubconf.py model and preprocessing definitions", "url": "https://github.com/isl-org/MiDaS/blob/master/hubconf.py" }, { "label": "Foundry Cattery FAQ", "url": "https://community.foundry.com/cattery/faq" }, { "label": "Foundry Grade node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/color_nodes/grade.html" }, { "label": "Foundry · MiDaS depth-map workflow", "url": "https://www.foundry.com/ai-solutions" }, { "label": "Foundry · Cattery model library and tasks", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cattery.html" } ] }, { "id": "105", "stable_id": "IRM_OP_105", "software": "Foundry Nuke", "operation": "Nuke Node Compositing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Nuke evaluates a defined graph of conventional transforms, grades and merges.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The selected graph contains no trained node. Supplied images and their generation history do not change these compositing operations.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Foundry Merge node reference and training transcript", "url": "https://learn.foundry.com/nuke/content/reference_guide/merge_nodes/merge.html" }, { "label": "Foundry Transform node reference and training transcript", "url": "https://learn.foundry.com/nuke/content/reference_guide/transform_nodes/transform.html" }, { "label": "Foundry Grade node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/color_nodes/grade.html" }, { "label": "Foundry · Revising and disabling Nuke nodes", "url": "https://learn.foundry.com/nuke/content/getting_started/using_interface/working_nodes.html" }, { "label": "Foundry · Conventional image blending", "url": "https://learn.foundry.com/nuke/content/reference_guide/merge_nodes/dissolve.html" } ] }, { "id": "106", "stable_id": "IRM_OP_106", "software": "Foundry Nuke (NukeX)", "operation": "CopyCat Matte Extraction and Correction", "y": 5.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "CopyCat learns matte extraction from paired examples; its prediction is followed by Grade and RotoPaint cleanup with learned regions retained.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The endpoint still contains learned region determination, supporting a 10 match. Training-example count and the amount painted supply no numerical dilution rule.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Foundry CopyCat node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/air_nodes/copycat.html" }, { "label": "Foundry Create a Data Set for Training", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-data.html" }, { "label": "Foundry Train and Monitor the Network", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-train.html" }, { "label": "Foundry Inference node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/air_nodes/inference.html" }, { "label": "Foundry Apply and Improve the Results from Your Network", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-apply.html" }, { "label": "Foundry Using BigCat", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/using-bigcat.html" }, { "label": "Foundry Grade node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/color_nodes/grade.html" }, { "label": "Foundry · CopyCat neural training", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-intro.html" } ] }, { "id": "107", "stable_id": "IRM_OP_107", "software": "Foundry Nuke (NukeX)", "operation": "Nuke AIR Deblur", "y": 5.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Nuke AIR Deblur uses a pretrained network to reconstruct motion-blurred image detail.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The trained restoration task supports a fidelity-restoration match. Mask and mix controls do not directly supply t; exact cancellation would require separate proof.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Foundry Deblur node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/air_nodes/deblur.html" }, { "label": "Foundry Nuke 13.0 release overview", "url": "https://campaigns.foundry.com/products/nuke-family/releases/13-0" }, { "label": "Foundry A look at machine learning in Nuke, 03262021 page with later additions", "url": "https://www.foundry.com/insights/film-tv/copycat-machine-learning-nuke" }, { "label": "Foundry Your guide to CopyCat, 04302021", "url": "https://www.foundry.com/insights/machine-learning/copy-cat-guide" }, { "label": "Foundry ML Server repository and README", "url": "https://github.com/TheFoundryVisionmongers/nuke-ML-server" }, { "label": "Foundry Apply and Improve the Results from Your Network", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-apply.html" } ] }, { "id": "108", "stable_id": "IRM_OP_108", "software": "Foundry Nuke (NukeX)", "operation": "Nuke AIR Upscale", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Nuke AIR Upscale predicts enlarged image detail through its pretrained network.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The published task supports 5–8 between perceptual reconstruction and fidelity restoration; undisclosed objective details do not justify a precise midpoint.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Foundry Upscale node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/air_nodes/upscale.html" }, { "label": "Foundry Nuke 13.0 release overview", "url": "https://campaigns.foundry.com/products/nuke-family/releases/13-0" }, { "label": "Foundry A look at machine learning in Nuke, 03262021 page with later additions", "url": "https://www.foundry.com/insights/film-tv/copycat-machine-learning-nuke" }, { "label": "Foundry Apply and Improve the Results from Your Network", "url": "https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-apply.html" }, { "label": "Foundry ML Server repository and README", "url": "https://github.com/TheFoundryVisionmongers/nuke-ML-server" } ] }, { "id": "109", "stable_id": "IRM_OP_109", "software": "Foundry Nuke (NukeX)", "operation": "Nuke SmartVector Paint Propagation", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SmartVector estimates image motion and VectorDistort propagates reference paint through that motion field.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The recorded algorithm account does not conclusively identify whether the active estimator contains a trained mechanism. 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Retain the dependency description while leaving the equation branch unresolved." }, "sources": [ { "label": "Foundry SmartVector node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/time_nodes/smartvector.html" }, { "label": "Foundry Generating Motion Vectors", "url": "https://learn.foundry.com/nuke/content/comp_environment/smartpaint/generating_vectors.html" }, { "label": "Foundry VectorDistort node reference", "url": "https://learn.foundry.com/nuke/content/reference_guide/transform_nodes/vectordistort.html" }, { "label": "Foundry Applying Motion Vectors to the Source", "url": "https://learn.foundry.com/nuke/content/comp_environment/smartpaint/generating_distortion.html" }, { "label": "Foundry Warping Multiple Reference Frames", "url": "https://learn.foundry.com/nuke/content/comp_environment/smartpaint/multi_reference_frames.html" }, { "label": "Foundry Merge node reference and training transcript", "url": "https://learn.foundry.com/nuke/content/reference_guide/merge_nodes/merge.html" }, { "label": "Foundry · Multiple reference frames", "url": "https://learn.foundry.com/nuke/13.1v1/content/comp_environment/smartpaint/multi_reference_frames.html" } ] }, { "id": "110", "stable_id": "IRM_OP_110", "software": "Freepik AI Suite / Magnific", "operation": "Mystic 2.5 text-to-image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Mystic 2.5 generates a raster through the specifically selected Freepik or Magnific image-generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Image synthesis supports the generation comparison. 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No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Magnific image model catalogue", "url": "https://www.magnific.com/ai/docs/image-ai-models" }, { "label": "Magnific image generator", "url": "https://www.freepik.com/ai/image-generator" }, { "label": "Mystic API contract", "url": "https://docs.magnific.com/api-reference/mystic/post-mystic" }, { "label": "AIbase · Freepik Mystic Generation", "url": "https://news.aibase.com/news/11409" }, { "label": "Mystic operation", "url": "https://www.freepik.com/ai/mystic" } ] }, { "id": "116", "stable_id": "IRM_OP_116", "software": "Gamma", "operation": "AI visual presentation generation", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Gamma generates an illustrated native presentation through managed planning and selected AI image generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned composition and image synthesis remain in the presentation. The mixed responsibilities support 2–10 without a stage-count average or invented controller specification.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Gamma creation modes", "url": "https://help.gamma.app/en/articles/7838093-how-do-i-create-a-new-presentation-document-or-webpage-in-gamma" }, { "label": "Gamma visuals menu", "url": "https://help.gamma.app/en/articles/11856101-how-do-i-use-the-visuals-menu-in-gamma" }, { "label": "Gamma Generate API guide", "url": "https://developers.gamma.app/guides" }, { "label": "Gamma image model contract", "url": "https://developers.gamma.app/reference/image-model-accepted-values" }, { "label": "Gamma slide structure", "url": "https://help.gamma.app/en/articles/11016396-what-are-slides-in-gamma-and-how-do-they-work" }, { "label": "Gamma · Help center", "url": "https://help.gamma.app/en/" } ] }, { "id": "117", "stable_id": "IRM_OP_117", "software": "Glyphs 4", "operation": "Master interpolation of AI-informed glyph drawings", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Glyphs interpolates compatible vector masters at a specified instance coordinate.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Linear outline interpolation is conventional. 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No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Glyphs outline compatibility", "url": "https://handbook.glyphsapp.com/interpolation/outline-compatibility/" }, { "label": "Glyphs higher-order interpolation", "url": "https://handbook.glyphsapp.com/higher-order-interpolation/" }, { "label": "Glyphs other settings", "url": "https://handbook.glyphsapp.com/other-settings/" }, { "label": "Glyphs multiple-master tutorial", "url": "https://glyphsapp.com/learn/multiple-masters-part-2-keeping-your-outlines-compatible" }, { "label": "Glyphs · Glyphs Interpolation", "url": "https://glyphsapp.com/" }, { "label": "Glyphs current interpolation handbook", "url": "https://handbook.glyphsapp.com/interpolation/" } ] }, { "id": "118", "stable_id": "IRM_OP_118", "software": "Godot Engine", "operation": "MCP LLM 3D Scene Node Construction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "A Godot MCP LLM selects a scene-node insertion and invokes native construction in the existing scene.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "The chosen node and placement retain learned structural decisions, supporting a 2–10 envelope. The isolated native add_node command has a separate conventional scope; this entry includes LLM planning.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Coding-Solo Godot MCP README", "url": "https://github.com/Coding-Solo/godot-mcp/blob/1209744fad78f3998f98c7394fd0f6ef50da5281/README.md" }, { "label": "Godot MCP command dispatch", "url": "https://github.com/Coding-Solo/godot-mcp/blob/1209744fad78f3998f98c7394fd0f6ef50da5281/src/index.ts#L484-L544" }, { "label": "Godot add_node implementation", "url": "https://github.com/Coding-Solo/godot-mcp/blob/1209744fad78f3998f98c7394fd0f6ef50da5281/src/scripts/godot_operations.gd#L456-L564" }, { "label": "GitHub · godot-mcp", "url": "https://github.com/Coding-Solo/godot-mcp" } ] }, { "id": "119", "stable_id": "IRM_OP_119", "software": "Google Docs", "operation": "Gemini image and diagram generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Google Docs invokes Gemini image or diagram-image generation and inserts the resulting image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The assessed raster retains learned visual synthesis. A diagram subject does not establish native graph construction unless that different output route is documented.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Google Docs contextual visual generation release", "url": "https://workspaceupdates.googleblog.com/2026/07/generate-and-edit-visuals-with-gemini-in-Google-Docs.html" }, { "label": "July Google Workspace feature release", "url": "https://workspace.google.com/blog/product-announcements/july-2026-workspace-feature-drop" }, { "label": "Google Docs Generate an image help", "url": "https://support.google.com/docs/answer/15559738?hl=en" }, { "label": "Google Docs image editing with Pics", "url": "https://support.google.com/docs/answer/17499836?hl=en" } ] }, { "id": "120", "stable_id": "IRM_OP_120", "software": "Google Flow", "operation": "Insert and Remove edits to generated video", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Google Flow's documented edit route inserts or removes scene content through learned video revision.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Both listed edit actions retain video synthesis responsibility at the present resolution. The record preserves their distinction and does not infer a particular deployed snapshot from an older Veo example.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Google Veo 3.1 and Flow release", "url": "https://blog.google/innovation-and-ai/products/veo-updates-flow/" }, { "label": "Google Flow video editing help", "url": "https://support.google.com/labs/answer/16935718?hl=en" }, { "label": "Google Flow model capability table", "url": "https://support.google.com/flow/answer/16352836?hl=en" } ] }, { "id": "121", "stable_id": "IRM_OP_121", "software": "Google Flow", "operation": "Veo 3.1 video generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Google Flow invokes Veo 3.1 Fast in the recorded reference-conditioned video-generation mode.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The resulting scene and motion are learned synthesis. Reference images and camera wording do not contribute numerical offsets.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Veo 3 family model card", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Veo-3-Model-Card.pdf" }, { "label": "Veo technical report", "url": "https://storage.googleapis.com/deepmind-media/veo/Veo-3-Tech-Report.pdf" }, { "label": "Veo 3.1 ingredients update", "url": "https://blog.google/innovation-and-ai/technology/ai/veo-3-1-ingredients-to-video/" }, { "label": "Google Flow model capability table", "url": "https://support.google.com/flow/answer/16352836?hl=en" }, { "label": "Veo 3.1 launch in Flow", "url": "https://blog.google/innovation-and-ai/products/veo-updates-flow/" } ] }, { "id": "387", "stable_id": "IRM_OP_387", "software": "Google Nano Banana Pro", "operation": "Gemini 3 Pro Image full-image generation", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Google Nano Banana Pro uses the recorded Gemini 3 Pro Image model for full-image generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image synthesis supports 2. The model card's transformer lineage does not establish a particular image-decoder architecture; 4K output is not an inference multiplier.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Google model account", "url": "https://deepmind.google/models/gemini-image/pro/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Google model cards", "url": "https://deepmind.google/models/model-cards/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "122", "stable_id": "IRM_OP_122", "software": "Google Slides", "operation": "Gemini 3 Pro image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Google Slides invokes Gemini 3 Pro Image to create an image through Help me visualize.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The image-generation endpoint matches synthesis. Deck composition and later slide edits are outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Google Slides image-generation help", "url": "https://support.google.com/docs/answer/16443280?hl=en" }, { "label": "Gemini 3 Pro Image model card", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Image-Model-Card.pdf" }, { "label": "Gemini 3 Pro model card", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Model-Card.pdf" }, { "label": "Google · Gemini in Slides (deck generation)", "url": "https://workspaceupdates.googleblog.com/2026/04/enerate-beautiful-and-editable-slides-with-ease-in-Google-Slides.html" } ] }, { "id": "123", "stable_id": "IRM_OP_123", "software": "Grasshopper + LunchBox plugin", "operation": "LunchBoxML LightGBM Surface Regression", "y": 7.0, "y_status": "work_in_progress", "x": 12.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "LunchBoxML trains a LightGBM regressor to predict surface height and constructs the resulting surface in Grasshopper.", "retained_learning": true, "rating_input": { "lower": "REF12", "higher": "REF12", "t": "0" }, "rating_rationale": "A learned numerical predictor approximating a target relation supports the fixed-function reference at 12. Its tree architecture is non-neural but remains learned; non-neural must not be confused with conventional no-learning computation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF12", "REF12" ], "status": "point", "raw_x": "12", "display": "12.0", "t": "0", "calculation": "X = 12 + 0 × (12 − 12) = 12." }, "sources": [ { "label": "Proving Ground · Regression surface example and trainer", "url": "https://apps.provingground.io/docs/lunchbox-documentation/lunchbox-ml/regression-trainers-and-testers/" }, { "label": "Microsoft · LightGBM tree-based learning", "url": "https://github.com/microsoft/LightGBM" }, { "label": "Proving Ground · Grasshopper LunchBoxML Components", "url": "https://apps.provingground.io/docs/lunchbox-documentation/lunchbox-ml/introduction/" }, { "label": "Proving Ground · LunchBox algorithms and graph components", "url": "https://apps.provingground.io/docs/lunchbox-documentation/lunchbox-ml/grasshopper-components-2/" }, { "label": "Proving Ground — ML.NET Workflow Summary", "url": "https://apps.provingground.io/docs/lunchbox-documentation/lunchbox-ml/ml-training-and-testing-workflow/" }, { "label": "Microsoft — LightGbmRegressionTrainer API, library mechanism", "url": "https://learn.microsoft.com/en-us/dotnet/api/microsoft.ml.trainers.lightgbm.lightgbmregressiontrainer?view=ml-dotnet-preview" }, { "label": "LightGBM — Features, tree growth and histogram learning", "url": "https://lightgbm.readthedocs.io/en/stable/Features.html" }, { "label": "Ke and colleagues — LightGBM research abstract, December 2017", "url": "https://www.microsoft.com/en-us/research/publication/lightgbm-a-highly-efficient-gradient-boosting-decision-tree/" }, { "label": "Proving Ground — Predict Solar Radiation tutorial, 04292025", "url": "https://provingground.io/2025/04/29/weekly-workflow-use-lunchbox-ml-to-predict-solar-radiation/" }, { "label": "Nathan Miller — LunchBox development history, 06032025", "url": "https://provingground.io/2025/06/03/lunchbox-reflections-on-the-past-present-and-future-of-grasshoppers-most-downloaded-plugin/" }, { "label": "Proving Ground — Rhino 8 Mac compatibility limits", "url": "https://apps.provingground.io/docs/lunchbox-documentation/troubleshooting/installation-rhino-8-on-the-mac/" } ] }, { "id": "124", "stable_id": "IRM_OP_124", "software": "gsplat", "operation": "Training Pipeline", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The gsplat baseline directly fits Gaussian scene parameters with optional neural appearance extensions disabled.", "retained_learning": null, "rating_input": {}, "rating_rationale": "Direct scene fitting exposes an unresolved boundary between learned representation and conventional estimation. Absence of neural layers alone cannot justify 15 under a learned-determination equation; a representation-consistent branch criterion is needed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "Direct scene fitting exposes an unresolved boundary between learned representation and conventional estimation. Absence of neural layers alone cannot justify 15 under a learned-determination equation; a representation-consistent branch criterion is needed." }, "sources": [ { "label": "GitHub · gsplat", "url": "https://github.com/nerfstudio-project/gsplat" }, { "label": "gsplat · Baseline trainer and optional appearance model", "url": "https://github.com/nerfstudio-project/gsplat/blob/main/examples/simple_trainer.py" } ] }, { "id": "125", "stable_id": "IRM_OP_125", "software": "Hailuo by MiniMax", "operation": "MiniMax H3 video generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "MiniMax H3 generates audiovisual clips through the Hailuo generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned scene and motion realization supports the synthesis comparison. Sound presence does not add a separate numerical weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "MiniMax · Hailuo overview", "url": "https://www.minimax.io/news/minimax-hailuo-23" }, { "label": "MiniMax H3 release and Hailuo access", "url": "https://www.minimax.io/blog/minimax-h3" } ] }, { "id": "126", "stable_id": "IRM_OP_126", "software": "Hatch Embroidery 4", "operation": "PhotoStitch conversion with editable visual preview", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Hatch PhotoStitch converts an image into a visual stitch design and preview.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The reviewed conversion description does not establish a trained predictor or a wholly conventional algorithm. Automatic conversion cannot choose the equation branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The reviewed conversion description does not establish a trained predictor or a wholly conventional algorithm. Automatic conversion cannot choose the equation branch." }, "sources": [ { "label": "Hatch · Hatch Auto-Digitize/PhotoStitch", "url": "https://hatchembroidery.com/" }, { "label": "Hatch current PhotoStitch and editing scope", "url": "https://hatchembroidery.com/products/hatch-embroidery/digitizer" }, { "label": "Hatch TrueView visual output", "url": "https://hatchembroidery.com/faq/features" } ] }, { "id": "162", "stable_id": "IRM_OP_162", "software": "Higgsfield", "operation": "3D Jutsu agent-authored editable scene", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "3D Jutsu's scene-building agent constructs a native editable scene.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned object and spatial decisions survive native scene creation, supporting the current 2–10 semantic-construction envelope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Higgsfield 3d", "url": "https://higgsfield.ai/blog/higgsfield-3d-jutsu" } ] }, { "id": "164", "stable_id": "IRM_OP_164", "software": "Higgsfield", "operation": "3D Jutsu camera trajectory editing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "3D Jutsu's manual camera-trajectory tools revise the existing scene animation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed camera-path editing is conventional; final rendering and newly requested agent motion are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Higgsfield 3d", "url": "https://higgsfield.ai/blog/higgsfield-3d-jutsu" } ] }, { "id": "163", "stable_id": "IRM_OP_163", "software": "Higgsfield", "operation": "3D Jutsu manual layout after AI scene generation", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "3D Jutsu's manual layout tools transform existing scene objects after generation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped native transformations contain no newly executed learned planner. Earlier scene generation belongs to another operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Higgsfield 3d", "url": "https://higgsfield.ai/blog/higgsfield-3d-jutsu" } ] }, { "id": "133", "stable_id": "IRM_OP_133", "software": "Higgsfield", "operation": "AI Influencer attribute-conditioned character image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "AI Influencer generates a character image through its attribute-conditioned Builder.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The first generated character retains extensive learned realization. Menu attributes neither provide a numerical discount nor identify a previously trained Soul ID model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield influencer", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-ai-influencer" }, { "label": "Higgsfield AI Influencer guide, 06132026, retrieved September 22", "url": "https://higgsfield.ai/blog/how-to-create-ai-influencer" }, { "label": "Soul ID help, 08012026, including training and limitations", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-create-and-use-a-soul-id-character" } ] }, { "id": "143", "stable_id": "IRM_OP_143", "software": "Higgsfield", "operation": "AI Skin Enhancer portrait reconstruction", "y": null, "y_status": "not_assigned", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "AI Skin Enhancer reconstructs portrait surface detail through its selected enhancement mode.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The trained restoration and plausible-detail responsibilities support 5–8. A known depicted identity does not reduce the model's rating through input specificity.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield Skin Enhancer — modes and retouching workflow, 12062025", "url": "https://higgsfield.ai/blog/The-Skin-Enhancer-How-to-Retouch-Pictures-with-AI" }, { "label": "Higgsfield Skin Enhancer — generic architecture discussion and no-sliders description", "url": "https://higgsfield.ai/blog/AI-Skin-Enhancer-How-AI-Retouching-Works-in-Editing" }, { "label": "Higgsfield low-quality portrait enhancement — developer mechanism claims", "url": "https://higgsfield.ai/blog/How-AI-Skin-Enhancer-Retouches-Low-Quality-Photos-Images" }, { "label": "Higgsfield Skin Enhancer — public application entry", "url": "https://higgsfield.ai/apps/skin-enhancer" } ] }, { "id": "142", "stable_id": "IRM_OP_142", "software": "Higgsfield", "operation": "Angles image viewpoint synthesis", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Angles synthesizes a new image viewpoint, including views not directly observed.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned realization of viewpoint-dependent geometry and appearance supports synthesis. Rotation values are controls rather than measures of learned responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield / Mariam Barova — Angles guide with V2 updates; original publication 11112025", "url": "https://higgsfield.ai/blog/Change-the-Angle-of-Any-Image" }, { "label": "Higgsfield — Edit workflow guide, 08172026; confirms separate Relight and Angles tools", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield — Figma integration, feature scope only; not proof of identical web implementation", "url": "https://higgsfield.ai/plugins/figma" }, { "label": "Chase Jarvis — firsthand Angles 2.0 workflow report; backend attribution uncorroborated", "url": "https://chasejarvis.com/blog/higgsfield-angles-2-0-is-here-my-100-honest-review/" }, { "label": "Higgsfield — public Angles entry; login surface retrieved 09212026", "url": "https://higgsfield.ai/apps/angles" } ] }, { "id": "166", "stable_id": "IRM_OP_166", "software": "Higgsfield", "operation": "Blender plugin 3D Model mesh generation", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield's Blender 3D Model service generates mesh and material content.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned asset realization supports extensive synthesis. Native insertion does not reduce the generator's responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield blender", "url": "https://higgsfield.ai/plugins/blender" } ] }, { "id": "167", "stable_id": "IRM_OP_167", "software": "Higgsfield", "operation": "Blender plugin Character Animation rig and motion", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Character Animation provides rigging and timeline motion through its Blender integration.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The recorded feature description does not separate learned motion generation from conventional rigging or retargeting. Establish the selected processing path before issuing X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The recorded feature description does not separate learned motion generation from conventional rigging or retargeting. Establish the selected processing path before issuing X." }, "sources": [ { "label": "Higgsfield plugins", "url": "https://higgsfield.ai/creator-hub/help-center/integrations/external-integrations-higgsfield" } ] }, { "id": "165", "stable_id": "IRM_OP_165", "software": "Higgsfield", "operation": "Blender plugin Scene Builder scene authorship", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield's Blender Scene Builder constructs native scene content through its scene-authoring service.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned scene choices remain in the native objects. This host relationship supports 2–10 and is not an independent sample of the browser agent.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Higgsfield blender", "url": "https://higgsfield.ai/plugins/blender" }, { "label": "Higgsfield plugins", "url": "https://higgsfield.ai/creator-hub/help-center/integrations/external-integrations-higgsfield" } ] }, { "id": "161", "stable_id": "IRM_OP_161", "software": "Higgsfield", "operation": "Canvas Soul 2.0 to Seedance 2.5 video workflow", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Canvas chains Soul 2.0 image generation into Seedance 2.5 video generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The clip retains extensive learned synthesis across the chain. Two learned stages do not create an arithmetic sum or average of X values.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield canvas", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-canvas" }, { "label": "Higgsfield catalogue", "url": "https://open.higgsfield.ai/explore" } ] }, { "id": "127", "stable_id": "IRM_OP_127", "software": "Higgsfield", "operation": "Cinema Studio 4.0 directed shot generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Cinema Studio 4.0 synthesizes a directed video shot through its selected generative route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned realization of scene, appearance and motion matches extensive synthesis; camera and lens controls supply no inference discount.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Cinema Studio help and Seedance 2.5 attribution", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-cinema-studio" }, { "label": "Higgsfield Cinema Studio 4.0 feature guide", "url": "https://higgsfield.ai/blog/cinema-studio-4-0" }, { "label": "Higgsfield developer account of building Cinema Studio", "url": "https://higgsfield.ai/blog/how-we-built-cinema-studio" }, { "label": "Higgsfield Cinema Studio 4.0 API limits and billing formula", "url": "https://open.higgsfield.ai/models/higgsfield/cinema-studio/4.0/playground" }, { "label": "ByteDance Seedance 2.5 launch and architecture lineage", "url": "https://seed.bytedance.com/en/blog/one-take-creation-flexible-referencing-introducing-seedance-2-5" }, { "label": "ByteDance Seedance 2.5 training data summary version 1.0", "url": "https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/transparency/20260803/Public%20Summary%20of%20Training%20Data%20Content%20for%20Seedance%202.5.pdf" }, { "label": "Cinema Studio current public interface inspected 09212026", "url": "https://higgsfield.ai/generate" }, { "label": "Higgsfield Cinema Studio 3.5 documented controls", "url": "https://higgsfield.ai/blog/cinema-studio-3.5-full-tutorial" } ] }, { "id": "146", "stable_id": "IRM_OP_146", "software": "Higgsfield", "operation": "Cinema Studio 4.0 temporal extension", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Cinema Studio's selected Seedance 2.5 extension route generates a continuation of an existing clip.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "New temporal scene content retains synthesis responsibility. Supplied earlier frames do not calculate a smaller inference score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Cinema Studio version and extension help", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-cinema-studio" }, { "label": "Higgsfield Cinema Studio 4.0 feature guide", "url": "https://higgsfield.ai/blog/cinema-studio-4-0" }, { "label": "ByteDance Seedance 2.5 developer announcement", "url": "https://seed.bytedance.com/en/blog/one-take-creation-flexible-referencing-introducing-seedance-2-5" }, { "label": "Higgsfield developer account of Cinema Studio conditioning", "url": "https://higgsfield.ai/blog/how-we-built-cinema-studio" } ] }, { "id": "153", "stable_id": "IRM_OP_153", "software": "Higgsfield", "operation": "Color Palette grade of existing video", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Color Palette transfers a preset or reference grade to existing video.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The feature page does not distinguish a learned transfer from conventional color mapping. The platform's AI branding cannot resolve that branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The feature page does not distinguish a learned transfer from conventional color mapping. The platform's AI branding cannot resolve that branch." }, "sources": [ { "label": "Higgsfield vrelight", "url": "https://higgsfield.ai/blog/video-relight-color-palette-higgsfield" } ] }, { "id": "140", "stable_id": "IRM_OP_140", "software": "Higgsfield", "operation": "Expand image outpainting", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Expand synthesizes content beyond image boundaries.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The outpainting task supports the synthesis reference. No platform-wide model attribution or expansion-area adjustment is used.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield figma", "url": "https://higgsfield.ai/plugins/figma" }, { "label": "Higgsfield Expand Image — dedicated prompt-free application and FAQ", "url": "https://higgsfield.ai/apps/expand-image" }, { "label": "Higgsfield AI Image Extender — broader editor route", "url": "https://higgsfield.ai/ai-image-extender" }, { "label": "Higgsfield help — aspect ratios depend on the tool and model", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-lipsync-voiceover-and-aspect-ratios" }, { "label": "Higgsfield Figma plugin guide — separate integration route", "url": "https://higgsfield.ai/blog/higgsfield-figma-plugin" } ] }, { "id": "144", "stable_id": "IRM_OP_144", "software": "Higgsfield", "operation": "Face Swap identity replacement in an image", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Face Swap uses deep learning to realize a replacement identity within a target image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned synthesis of facial geometry and appearance supports the generation comparison. Two source images do not establish two conventional stages.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield swap", "url": "https://higgsfield.ai/blog/AI-Face-Character-Swap-in-Video-Photo-PRO-Guide" }, { "label": "Higgsfield figma", "url": "https://higgsfield.ai/plugins/figma" }, { "label": "Higgsfield — Face Swap product workflow and FAQ; consulted 09222026", "url": "https://higgsfield.ai/apps/face-swap" }, { "label": "Higgsfield — Next-Gen Face Swap guide, 10282025; current text consulted 09222026", "url": "https://higgsfield.ai/blog/a-simple-guide-to-face-swap" }, { "label": "Li et al. — FaceShifter, CVPR 2020; abstract inspected as general research context, not Higgsfield implementation", "url": "https://arxiv.org/abs/1912.13457" }, { "label": "Higgsfield — Cinematic Car Commercial, Face Swap section; written tutorial inspected 09222026", "url": "https://higgsfield.ai/blog/ai-car-commercial-youtube-guide" }, { "label": "Higgsfield — Account and Assets help, 07302026; generation metadata", "url": "https://higgsfield.ai/creator-hub/help-center/getting-started/whats-in-my-higgsfield-account" } ] }, { "id": "147", "stable_id": "IRM_OP_147", "software": "Higgsfield", "operation": "Genjutsu Motion Transfer video reinterpretation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Genjutsu Motion Transfer rebuilds character or scene imagery while following a source performance.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The video reinterpretation retains extensive learned synthesis, with no numerical discount for motion-reference information.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield genmotion", "url": "https://open.higgsfield.ai/models/higgsfiled/genjutsu/motion-transfer/v1.0" }, { "label": "Higgsfield genjutsu", "url": "https://higgsfield.ai/genjutsu" }, { "label": "Higgsfield — Genjutsu launch guide, 08312026; controls and workflow", "url": "https://higgsfield.ai/blog/higgsfield-genjutsu" }, { "label": "Higgsfield — Genjutsu Motion Transfer API reference; conflicting SDK examples retained", "url": "https://open.higgsfield.ai/models/higgsfiled/genjutsu/motion-transfer/v1.0/api-reference" }, { "label": "Higgsfield — Edit One Element in AI Video, 09162026; limits and reference roles", "url": "https://higgsfield.ai/blog/edit-ai-video-without-regenerating" }, { "label": "Higgsfield — API documentation index; consulted 09222026, not a model architecture disclosure", "url": "https://docs.higgsfield.ai/docs/llms.txt" } ] }, { "id": "148", "stable_id": "IRM_OP_148", "software": "Higgsfield", "operation": "Genjutsu Object Swap in existing footage", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Genjutsu Object Swap generates a replacement object or character in existing footage.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned replacement realization supports synthesis. Target locality does not by itself establish a smaller inferential coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield genjutsu", "url": "https://higgsfield.ai/genjutsu" }, { "label": "Higgsfield videoedit", "url": "https://higgsfield.ai/blog/edit-ai-video-without-regenerating" } ] }, { "id": "145", "stable_id": "IRM_OP_145", "software": "Higgsfield", "operation": "Image Upscale reconstructed image output", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield's recorded Topaz image-upscale route reconstructs a higher-resolution raster.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The task spans perceptual reconstruction and fidelity restoration, supporting 5–8. Exact Topaz model identity is still required for finer placement.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield — Restore Old Photos guide, 08022026; names Topaz High-Resolution Upscaler", "url": "https://higgsfield.ai/blog/restore-old-photos-ai" }, { "label": "Higgsfield — AI Image Upscaler product page; consulted 09222026", "url": "https://higgsfield.ai/ai-image-upscaler" }, { "label": "Topaz — Developer introduction; precision, generative and creative families; consulted 09222026", "url": "https://developer.topazlabs.com/" }, { "label": "Topaz — High Fidelity 2 developer reference; native model example, not confirmed Higgsfield backend", "url": "https://developer.topazlabs.com/image-models/gigapixel/high-fidelity-2" }, { "label": "Blau and Michaeli — The Perception-Distortion Tradeoff; research abstract inspected 09222026", "url": "https://arxiv.org/abs/1711.06077" }, { "label": "Higgsfield — Account and Assets help, 07302026; generation metadata", "url": "https://higgsfield.ai/creator-hub/help-center/getting-started/whats-in-my-higgsfield-account" }, { "label": "Higgsfield — Upscale Phone vs Camera; inspected and identified as VIDEO scope", "url": "https://higgsfield.ai/blog/Higgsfield-Upscale-Phone-vs-Camera" } ] }, { "id": "130", "stable_id": "IRM_OP_130", "software": "Higgsfield / Kling AI", "operation": "Kling 3.0 Motion Control performance transfer", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Kling 3.0 Motion Control synthesizes character footage using a performance reference.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The model generates the character's visual realization through time. Motion guidance does not subtract points from the synthesis comparison.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Kling 3.0 Motion Control Pro API schema", "url": "https://open.higgsfield.ai/models/kling-video/v3/motion-control/pro/api-reference" }, { "label": "Kling AI Motion Control guide dated 03052026", "url": "https://kling.ai/quickstart/motion-control-user-guide" }, { "label": "Kuaishou Kling Team MotionControl technical report March 2026", "url": "https://arxiv.org/html/2603.03160v1" }, { "label": "Runway guide to its Kling 3.0 Motion Control deployment", "url": "https://help.runwayml.com/hc/en-us/articles/50280558448147-Creating-with-Kling-3-0-Motion-Control" }, { "label": "Higgsfield Kling help and performance preparation", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-kling" }, { "label": "Higgsfield video edit and motion models", "url": "https://higgsfield.ai/ai-video-editor" }, { "label": "Higgsfield motion", "url": "https://open.higgsfield.ai/models/kling-video/v3/motion-control/pro/playground" } ] }, { "id": "132", "stable_id": "IRM_OP_132", "software": "Higgsfield / Kling AI", "operation": "Kling 3.0 prompt-directed clip generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Kling VIDEO 3.0 generates a clip through the specified generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned scene and motion synthesis matches the generation reference. Multi-shot controls and clip duration are configuration fields rather than scoring terms.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "PR Newswire · Kling 3.0 Generation", "url": "https://www.prnewswire.com/news-releases/kling-ai-launches-3-0-model-ushering-in-an-era-where-everyone-can-be-a-director-302679944.html" }, { "label": "Kuaishou Kling 3.0 model family", "url": "https://ir.kuaishou.com/news-releases/news-release-details/kling-ai-launches-30-model-ushering-era-where-everyone-can-be" }, { "label": "Krea Kling 3.0 endpoint boundaries", "url": "https://www.krea.ai/blog/kling-3-0-api-access-guide-pricing-code-examples-for-multi-shot-ai-video" }, { "label": "Higgsfield hosted video generation", "url": "https://higgsfield.ai/ai-image" }, { "label": "Higgsfield catalogue", "url": "https://open.higgsfield.ai/explore" }, { "label": "Kling VIDEO 3.0 Model User Guide, 02062026", "url": "https://kling.ai/quickstart/klingai-video-3-model-user-guide" } ] }, { "id": "131", "stable_id": "IRM_OP_131", "software": "Higgsfield / Kling AI", "operation": "Kling O3 targeted video appearance edit", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Kling O3 performs the documented targeted video appearance edit.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned video-editing operation retains synthesis responsibility; preservation intent is not proof of unchanged surrounding pixels.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Kling O1 generation versus edit modes", "url": "https://higgsfield.ai/blog/Kling-01-is-Here-A-Complete-Guide-to-Video-Model" }, { "label": "Kling Video 3.0 guide", "url": "https://app.klingai.com/cn/quickstart/klingai-video-3-model-user-guide" }, { "label": "Higgsfield omni", "url": "https://open.higgsfield.ai/models/kling-video/o3/video-edit/playground" }, { "label": "Kling VIDEO 3.0 Omni Model User Guide, 02062026", "url": "https://kling.ai/quickstart/klingai-video-3-omni-model-user-guide" }, { "label": "Kling VIDEO O1 User Guide, 12152025; editing guide linked by 3.0 Omni", "url": "https://kling.ai/quickstart/klingai-video-o1-user-guide" }, { "label": "Kling-Omni Technical Report, December 2025, version 1; predecessor O1 research", "url": "https://arxiv.org/html/2512.16776v1" } ] }, { "id": "136", "stable_id": "IRM_OP_136", "software": "Higgsfield", "operation": "Layers automatic image decomposition", "y": 6.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Layers decomposes an image into separately accessible elements.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned element separation can combine membership estimation with completion of hidden regions. The present evidence supports a 2–10 envelope rather than a point inferred from layer count.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Higgsfield layers", "url": "https://higgsfield.ai/higgsfield-layers" }, { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" } ] }, { "id": "138", "stable_id": "IRM_OP_138", "software": "Higgsfield", "operation": "Layers Edit Text replacement in a raster", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Layers Edit Text detects text and regenerates the raster with replacement wording and matched styling.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained raster is realized through learned visual regeneration, supporting generation at 2. Recognizing and supplying replacement text does not make its visual reconstruction conventional typography.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield official changelog, Layers release 08112026", "url": "https://higgsfield.ai/creator-hub/changelog" }, { "label": "Kush Sharma, Creative Pad Media, Layers Edit tutorial written summary, 08172026", "url": "https://www.creativepadmedia.com/fix-ai-generated-text-with-1-click-higgsfield-layers-edit/" }, { "label": "Higgsfield Layers product page; consulted 09222026", "url": "https://higgsfield.ai/higgsfield-layers" } ] }, { "id": "137", "stable_id": "IRM_OP_137", "software": "Higgsfield", "operation": "Layers manual recomposition after AI decomposition", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Layers permits movement, resizing and deletion after automatic decomposition.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The public account does not establish whether movement uses completed layers alone or invokes fresh learned filling. Clarify the active recomposition path before assigning conventional 15 or retained-learning X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The public account does not establish whether movement uses completed layers alone or invokes fresh learned filling. Clarify the active recomposition path before assigning conventional 15 or retained-learning X." }, "sources": [ { "label": "Higgsfield layers", "url": "https://higgsfield.ai/higgsfield-layers" }, { "label": "Higgsfield imageedit", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield official changelog, Layers release 08112026", "url": "https://higgsfield.ai/creator-hub/changelog" }, { "label": "Kush Sharma, Creative Pad Media, Layers Edit tutorial written summary, 08172026", "url": "https://www.creativepadmedia.com/fix-ai-generated-text-with-1-click-higgsfield-layers-edit/" } ] }, { "id": "139", "stable_id": "IRM_OP_139", "software": "Higgsfield", "operation": "Layers Remove Background on generated imagery", "y": 3.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Remove Background extracts a foreground cutout with transparency.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The documented extraction task supports a region-determination match. A new background is not included, and exact segmentation architecture remains undisclosed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Higgsfield layers", "url": "https://higgsfield.ai/higgsfield-layers" }, { "label": "Higgsfield Edit step-by-step guide, 08172026; consulted September 22", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield official changelog, Layers release 08112026", "url": "https://higgsfield.ai/creator-hub/changelog" }, { "label": "Higgsfield CLI model schema: image_background_remover; main branch consulted 09222026", "url": "https://github.com/higgsfield-ai/cli/blob/main/MODELS.md" }, { "label": "Higgsfield AI Background Generator product page; broader workflow, not dedicated Layers specification", "url": "https://higgsfield.ai/ai-background-generator" } ] }, { "id": "156", "stable_id": "IRM_OP_156", "software": "Higgsfield", "operation": "Marketing Studio campaign image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marketing Studio generates campaign imagery through its named image-generation service.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained campaign raster matches learned synthesis; product references and format presets supply no point offsets.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield marketing API", "url": "https://open.higgsfield.ai/models/marketing-studio/image/playground" } ] }, { "id": "159", "stable_id": "IRM_OP_159", "software": "Higgsfield", "operation": "Marketing Studio editable poster workflow", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marketing Studio generates a poster and exposes it through the Layers workflow.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The poster retains learned image synthesis and matches generation at 2. Decomposing it into editable layers does not remove that generated appearance or justify a higher coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield marketing", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-marketing-studio-to-create-video-ads" }, { "label": "Higgsfield layers", "url": "https://higgsfield.ai/higgsfield-layers" } ] }, { "id": "158", "stable_id": "IRM_OP_158", "software": "Higgsfield", "operation": "Marketing Studio reference-ad remake", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marketing Studio remakes a reference advertisement with newly generated content.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated ad retains learned visual realization. The reference's structure does not mathematically reduce inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield marketing", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-marketing-studio-to-create-video-ads" } ] }, { "id": "157", "stable_id": "IRM_OP_157", "software": "Higgsfield", "operation": "Marketing Studio UGC video generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marketing Studio generates an advertising video through its documented UGC route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned character and scene realization supports synthesis. Template choice does not establish a conventional video-generation mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield marketing", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-marketing-studio-to-create-video-ads" } ] }, { "id": "128", "stable_id": "IRM_OP_128", "software": "Higgsfield", "operation": "Nano Banana Pro masked image editing", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield invokes Nano Banana Pro for masked image revision.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned replacement content matches the generation reference. Host masking does not imply a conventional synthesis contribution or guaranteed pixel preservation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Nano Banana Pro Inpaint workflow guide", "url": "https://higgsfield.ai/blog/Top-Editing-Tool-in-2025-Nano-Banana-Pro-Inpaint" }, { "label": "Higgsfield image editing product description and entry link", "url": "https://higgsfield.ai/image-editing" }, { "label": "Higgsfield Nano Banana family help", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-nano-banana" }, { "label": "Google Gemini 3 Pro Image model card November 2025", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Image-Model-Card.pdf" }, { "label": "Google Gemini 3 Pro base model card updated May 2026", "url": "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Model-Card.pdf" }, { "label": "Higgsfield Layers public Inpaint entry inspected 09212026", "url": "https://higgsfield.ai/layers?model=nano_banana_pro_inpaint" } ] }, { "id": "160", "stable_id": "IRM_OP_160", "software": "Higgsfield", "operation": "Popcorn storyboard frame generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Popcorn generates storyboard-frame imagery before board arrangement.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The scoped frame retains image synthesis. Board organization and the number of alternatives are outside its coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield story", "url": "https://higgsfield.ai/blog/How-to-Use-AI-for-Storyboards-Higgsfield-Popcorn" } ] }, { "id": "154", "stable_id": "IRM_OP_154", "software": "Higgsfield", "operation": "Reframe video outpainting", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Reframe synthesizes additional surrounding footage for an expanded frame.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Video outpainting matches extensive synthesis. A crop-only command would be a different operation and is excluded here.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield lipsync", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-lipsync-voiceover-and-aspect-ratios" }, { "label": "Higgsfield plugins", "url": "https://higgsfield.ai/creator-hub/help-center/integrations/external-integrations-higgsfield" } ] }, { "id": "141", "stable_id": "IRM_OP_141", "software": "Higgsfield", "operation": "Relight image lighting revision", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Relight combines a documented generative/depth-based process to reconstruct illumination.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned lighting realization crosses fixed mapping and perceptual reconstruction, supporting 5–12. Interface light controls do not establish a recovered physical scene.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Higgsfield / Mariam Barova — Relight controls and developer mechanism description, 01062026; consulted 09212026", "url": "https://higgsfield.ai/blog/Relight-Director-Style-Cinematic-Lighting" }, { "label": "Higgsfield — Edit workflow guide, 08172026; confirms separate Relight and Angles tools", "url": "https://higgsfield.ai/blog/edit-photos-with-ai-higgsfield" }, { "label": "Higgsfield — Figma integration, feature scope only; not proof of identical web implementation", "url": "https://higgsfield.ai/plugins/figma" }, { "label": "Higgsfield — Video Relight and Color Palette guide, 08202026; separate operation comparison", "url": "https://higgsfield.ai/blog/video-relight-color-palette-higgsfield" }, { "label": "Higgsfield layers", "url": "https://higgsfield.ai/higgsfield-layers" } ] }, { "id": "149", "stable_id": "IRM_OP_149", "software": "Higgsfield", "operation": "Seedance 2.5 Draw to Edit video revision", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Seedance 2.5 Draw to Edit synthesizes revised footage through the named model route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Drawn indications guide learned generation; they are not a separately scored conventional contribution.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield videoedit", "url": "https://higgsfield.ai/blog/edit-ai-video-without-regenerating" } ] }, { "id": "129", "stable_id": "IRM_OP_129", "software": "Higgsfield", "operation": "Soul 2.0 image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Soul 2.0 synthesizes image content through Higgsfield's trained image model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned image-realization responsibility matches synthesis, independently of identity or style instructions.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield Soul product and model positioning", "url": "https://higgsfield.ai/soul-intro" }, { "label": "Higgsfield Soul family help and reference restrictions", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-soul-to-generate-images" }, { "label": "Higgsfield Soul v2 standard API input schema", "url": "https://open.higgsfield.ai/models/higgsfield-ai/soul/v2/standard/api-reference" }, { "label": "Higgsfield custom Soul 2.0 Moodboard guide", "url": "https://higgsfield.ai/blog/create-custom-ai-moodboard-soul-2" }, { "label": "Higgsfield native Soul versus hosted Nano Banana", "url": "https://higgsfield.ai/blog/soul-2-vs-nano-banana-pro" }, { "label": "Higgsfield catalogue", "url": "https://open.higgsfield.ai/explore" } ] }, { "id": "134", "stable_id": "IRM_OP_134", "software": "Higgsfield", "operation": "Soul 2.0 Photodump coordinated image set", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Soul Photodump generates coordinated images using an established Soul ID and preset.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Each generated image retains learned synthesis. Presets and image count do not turn the batch into independent calibration observations or change X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield photo", "url": "https://higgsfield.ai/blog/how-to-create-ai-photodump-higgsfield-soul" }, { "label": "Earlier Photodump Studio guide, 09182025", "url": "https://higgsfield.ai/blog/Higgsfield-Photodump-Studio-Your-Viral-Photoshoot" }, { "label": "Soul ID help, 08012026, including training and limitations", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-create-and-use-a-soul-id-character" }, { "label": "Earlier Higgsfield Soul ID explanation, 07062026, conflicting model-sharing claims", "url": "https://geo.higgsfield.ai/blog/higgsfield-soul-id-character-consistency-ai-videos" } ] }, { "id": "135", "stable_id": "IRM_OP_135", "software": "Higgsfield", "operation": "Soul Cinema cinematic still generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Soul Cinema generates a cinematic still through its named image-generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned visual realization matches synthesis. Cinematic controls and optional identity conditioning do not calculate t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield 3d", "url": "https://higgsfield.ai/blog/higgsfield-3d-jutsu" }, { "label": "Higgsfield influencer", "url": "https://higgsfield.ai/creator-hub/help-center/tools/how-do-i-use-ai-influencer" }, { "label": "Soul Cinema help, 08012026, controls and reference restrictions", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-soul-cinema" }, { "label": "Soul Cinema product page, retrieved 09222026", "url": "https://higgsfield.ai/soul-cinema" }, { "label": "Higgsfield Academy, Character sheets in Soul Cinema, worked prompts and rejected results", "url": "https://higgsfield.ai/academy/courses/santiago-cinematic/character-sheets-in-soul-cinema" }, { "label": "Soul family help, 08012026, settings and model distinctions", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-soul-to-generate-images" }, { "label": "Inside Higgsfield, How We Built Cinema Studio, product engineering account", "url": "https://higgsfield.ai/blog/how-we-built-cinema-studio" } ] }, { "id": "150", "stable_id": "IRM_OP_150", "software": "Higgsfield", "operation": "Speak 2.0 image-to-talking-video", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Speak 2.0 generates talking-character video from the selected image-to-video route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned facial motion and visual realization support synthesis. The exact managed architecture remains unassigned.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield lipsync", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-lipsync-voiceover-and-aspect-ratios" } ] }, { "id": "151", "stable_id": "IRM_OP_151", "software": "Higgsfield", "operation": "Sync Lipsync 3 mouth revision in existing video", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Sync Lipsync 3 generates revised mouth imagery and motion in an existing video.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The documented learned audiovisual synthesis is the retained responsibility. A mouth-only target does not supply an area-based reduction in X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Higgsfield — Lipsync help, 08012026; Sync Lipsync 3 listed for video input", "url": "https://higgsfield.ai/creator-hub/help-center/ai-models/how-do-i-use-lipsync-voiceover-and-aspect-ratios" }, { "label": "Sync Labs — sync-3 model guide; current native capabilities and options", "url": "https://sync.so/docs/models/sync-3" }, { "label": "Sync Labs — model comparison and caveats; distinguish sync-3 from lipsync-2-pro", "url": "https://sync.so/docs/models/lipsync" }, { "label": "Sync Labs — Sync Mode duration behavior; native API and Studio, not assumed host controls", "url": "https://sync.so/docs/developer-guides/sync-mode" }, { "label": "Sync Labs — improving lip sync quality; generic advice qualified by model-specific documentation", "url": "https://sync.so/docs/compatibility-and-tips/improving-lip-sync-quality" }, { "label": "Higgsfield CLI issue 54 — user-reported integration failure, 07272026; not a verified current service failure", "url": "https://github.com/higgsfield-ai/cli/issues/54" }, { "label": "Sync Labs — model-list API reference; separate identifiers for sync-3 and lipsync-2-pro", "url": "https://sync.so/docs/api-reference/api/models-api/list" }, { "label": "Higgsfield — public Lipsync Studio entry; login surface retrieved 09212026", "url": "https://higgsfield.ai/lipsync-studio" } ] }, { "id": "152", "stable_id": "IRM_OP_152", "software": "Higgsfield", "operation": "Video Relight footage lighting revision", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield Video Relight reconstructs video illumination through its documented AI service.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned appearance transformation supports a 5–12 mapping-to-perceptual envelope. Undisclosed stages prevent a precise comparison with the separately documented Resolve Relight mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Higgsfield vrelight", "url": "https://higgsfield.ai/blog/video-relight-color-palette-higgsfield" } ] }, { "id": "155", "stable_id": "IRM_OP_155", "software": "Higgsfield", "operation": "Video Upscale with Topaz Starlight Precise", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Higgsfield invokes Topaz Starlight Precise for video detail reconstruction.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The restoration task and generative prior support 5–8. The host route does not establish a new inference level relative to its underlying model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Higgsfield video restoration and Topaz hosted controls", "url": "https://higgsfield.ai/blog/ai-video-restoration-high-quality" }, { "label": "Topaz Starlight Precise 2.5 and 2.6 native documentation", "url": "https://docs.topazlabs.com/topaz-video/project-starlight-series/starlight-precise-25" }, { "label": "Topaz Starlight diffusion model family", "url": "https://docs.topazlabs.com/topaz-video/project-starlight-series" }, { "label": "Topaz March 2026 Precision update", "url": "https://www.topazlabs.com/news/the-precision-update---march-2026" } ] }, { "id": "168", "stable_id": "IRM_OP_168", "software": "Hypar", "operation": "Chat Beta door placement in an editable plan", "y": null, "y_status": "not_assigned", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Hypar Chat Beta selects and places doors in a native plan through its documented chat-assisted workflow.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned spatial decisions remain after native wall attachment. The task supports 2–10; the earlier text-to-BIM prototype is not the current implementation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "AEC Magazine · Text-to-BIM", "url": "https://aecmag.com/ai/hypar-text-to-bim-and-beyond/" }, { "label": "Hypar · Historical Text-to-BIM experiment", "url": "https://www.linkedin.com/posts/hypar-aec_hypar-text-to-bim-coming-soon-activity-7024371007851261953-Th-R" }, { "label": "Hypar · Current 3D planning scope", "url": "https://docs.hypar.io/" }, { "label": "Hypar current AI features", "url": "https://docs.hypar.io/guides-task-based-how-tos-for-everything-you-do-in-hypar./use-ai-features" }, { "label": "Historical source: AEC Magazine · Cloud Functions", "url": "https://aecmag.com/features/hypar-2-0/" }, { "label": "Historical source: Hypar · Functions and model dependencies", "url": "https://docs.hypar.io/3971e126b9ab4f5cb808023b6f386916" }, { "label": "Hypar editable plan relationships", "url": "https://docs.hypar.io/guides-task-based-how-tos-for-everything-you-do-in-hypar./model-your-plan" } ] }, { "id": "169", "stable_id": "IRM_OP_169", "software": "Ideogram", "operation": "Canvas Magic Fill and Extend", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Ideogram Canvas Magic Fill and Extend synthesize image content within or beyond a selected region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Both retained actions support extensive synthesis at the current resolution. The Canvas editing checkpoint is not assumed identical to the newest base model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "X · Canvas Magic Fill / Extend", "url": "https://x.com/ideogram_ai/status/1917985285679530232" }, { "label": "Ideogram Magic Fill", "url": "https://docs.ideogram.ai/canvas-and-editing/canvas/magic-fill" }, { "label": "Ideogram current platform", "url": "https://ideogram.ai/" } ] }, { "id": "170", "stable_id": "IRM_OP_170", "software": "Ideogram", "operation": "Ideogram 4.0 image generation", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Ideogram 4.0 generates image content through the named generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image realization matches the synthesis reference. Typography requests and style references do not add scoring terms.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "The Decoder · Prompt Generation", "url": "https://the-decoder.com/ideogram-3-0s-ai-image-generator-gets-a-realism-boost-and-new-editing-tools/" }, { "label": "Ideogram 4.0 current model and generation API", "url": "https://ideogram.ai/" } ] }, { "id": "174", "stable_id": "IRM_OP_174", "software": "Instant-NGP", "operation": "NeRF Field Training with Crop Controls", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Instant-NGP fits a neural radiance field to posed images and retains the active scene representation.", "retained_learning": null, "rating_input": {}, "rating_rationale": "Scene-specific fitting needs a representation-consistent calibration against explicit Gaussian fitting. Neural parameterization alone must not force a different branch for an equivalent fitted scene function.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "Scene-specific fitting needs a representation-consistent calibration against explicit Gaussian fitting. Neural parameterization alone must not force a different branch for an equivalent fitted scene function." }, "sources": [ { "label": "GitHub · instant-ngp", "url": "https://github.com/NVlabs/instant-ngp" }, { "label": "Original NeRF project and representation", "url": "https://www.matthewtancik.com/nerf" } ] }, { "id": "175", "stable_id": "IRM_OP_175", "software": "Invoke", "operation": "FLUX.2 Klein regional generation on Canvas", "y": 5.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Invoke Canvas invokes FLUX.2 Klein for regional image generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained pixels are learned synthesis. Canvas controls and the host implementation do not create an automatic intermediate score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Invoke · Control Canvas", "url": "https://support.invoke.ai/support/solutions/articles/151000096682-control-canvas" }, { "label": "Invoke current supported model families", "url": "https://github.com/invoke-ai/InvokeAI" }, { "label": "BFL current image and video branches", "url": "https://help.bfl.ai/articles/8667153955-what-is-the-bfl-playground" } ] }, { "id": "177", "stable_id": "IRM_OP_177", "software": "Jitter", "operation": "AI image to video", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Jitter image-to-video replaces an image layer with a generated clip.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned generation supplies motion and changing appearance, supporting synthesis. Automatically drafted prompt text has no numerical effect on the rating.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Jitter image-to-video output limits", "url": "https://help.jitter.video/en/articles/13671056-transform-images-into-video-with-ai" }, { "label": "Jitter: Image to Video launch, 02102026", "url": "https://jitter.video/changelog/2026-02-10-image-to-video/" }, { "label": "Jitter changelog: 03032026 prompting and 02182025 custom easing entries", "url": "https://jitter.video/changelog/" }, { "label": "Jitter: Understand operations vs keyframes, 08142026", "url": "https://help.jitter.video/en/articles/16415500-understand-operations-vs-keyframes" }, { "label": "Jitter: Animate with AI Superagents", "url": "https://help.jitter.video/en/articles/15656969-animate-with-ai-superagents" }, { "label": "Jitter: Choose an intelligence level with AI Superagents", "url": "https://help.jitter.video/en/articles/16597132-choose-an-intelligence-level-with-ai-superagents" }, { "label": "Jitter: Supported browsers and devices, 07152026", "url": "https://help.jitter.video/en/articles/5405284-understand-jitter-s-supported-browsers-and-devices" }, { "label": "Jitter: Current pricing and feature availability, consulted 09222026", "url": "https://jitter.video/pricing/" }, { "label": "Jitter: Create custom effects with AI; separate operation", "url": "https://help.jitter.video/en/articles/14969699-create-custom-effects-with-ai" } ] }, { "id": "178", "stable_id": "IRM_OP_178", "software": "Jitter", "operation": "Manual timing and motion revision of generated visuals", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Jitter's manual editor changes timing, easing and motion segments of existing visuals.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The specified animation rules execute conventionally; upstream Brainstorm or image generation is outside this edit.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Jitter custom animation controls", "url": "https://help.jitter.video/en/articles/14111550-make-your-first-animation" }, { "label": "Jitter timeline sequencing", "url": "https://help.jitter.video/en/articles/14111802-work-with-the-timeline" }, { "label": "Jitter: Create custom animations, 07232026", "url": "https://help.jitter.video/en/articles/14136797-create-custom-animations" }, { "label": "Jitter: Copy animations between layers, 04032026", "url": "https://help.jitter.video/en/articles/5367931-copy-animations-between-layers" }, { "label": "Jitter: Understand operations vs keyframes, 08142026", "url": "https://help.jitter.video/en/articles/16415500-understand-operations-vs-keyframes" }, { "label": "Jitter: Animate with AI Superagents", "url": "https://help.jitter.video/en/articles/15656969-animate-with-ai-superagents" }, { "label": "Jitter: Animate with AI Brainstorm, 05132026", "url": "https://help.jitter.video/en/articles/12932981-animate-with-ai-brainstorm" }, { "label": "Jitter changelog: 03032026 prompting and 02182025 custom easing entries", "url": "https://jitter.video/changelog/" }, { "label": "Jitter: What is Jitter?, 07222026", "url": "https://help.jitter.video/en/articles/12089209-what-is-jitter" }, { "label": "Jitter: Supported browsers and devices, 07152026", "url": "https://help.jitter.video/en/articles/5405284-understand-jitter-s-supported-browsers-and-devices" } ] }, { "id": "176", "stable_id": "IRM_OP_176", "software": "Jitter", "operation": "Oli Brainstorm editable animation generation", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Jitter Oli Brainstorm selects animation instructions and produces editable timeline operations.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned motion planning survives conventional animation execution. The source supports 2–10 while leaving the precise planning model undisclosed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Jitter · Product overview", "url": "https://jitter.video/" }, { "label": "Jitter Brainstorm model and editable results", "url": "https://help.jitter.video/en/articles/12932981-animate-with-ai-brainstorm" }, { "label": "Jitter timeline controls", "url": "https://help.jitter.video/en/articles/14111802-work-with-the-timeline" }, { "label": "Jitter: AI Brainstorm launch, 11252025", "url": "https://jitter.video/changelog/2025-11-25-ai-brainstorm/" }, { "label": "Jitter: Animate with AI Superagents", "url": "https://help.jitter.video/en/articles/15656969-animate-with-ai-superagents" }, { "label": "Jitter: Animate a logo", "url": "https://help.jitter.video/en/articles/8711145-animate-a-logo" }, { "label": "Jitter: Choose an intelligence level with AI Superagents", "url": "https://help.jitter.video/en/articles/16597132-choose-an-intelligence-level-with-ai-superagents" }, { "label": "Jitter: Understand operations vs keyframes, 08142026", "url": "https://help.jitter.video/en/articles/16415500-understand-operations-vs-keyframes" }, { "label": "Jitter: What is Jitter?, 07222026", "url": "https://help.jitter.video/en/articles/12089209-what-is-jitter" }, { "label": "Jitter: Supported browsers and devices, 07152026", "url": "https://help.jitter.video/en/articles/5405284-understand-jitter-s-supported-browsers-and-devices" }, { "label": "Jitter: Current pricing and feature availability, consulted 09222026", "url": "https://jitter.video/pricing/" }, { "label": "Jitter: Create custom effects with AI; separate operation", "url": "https://help.jitter.video/en/articles/14969699-create-custom-effects-with-ai" } ] }, { "id": "179", "stable_id": "IRM_OP_179", "software": "Kaedim", "operation": "Image Reconstruction and Artist Mesh Refinement", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Kaedim's documented service generates mesh content and then applies artist refinement.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Retained generated geometry supports the synthesis comparison. Human labor is recorded explicitly without inventing a fractional deduction; complete replacement of generated geometry would require separate evidence.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Kaedim · Image-to-3D", "url": "https://docs.kaedim3d.com/welcome/how-kaedim-works" }, { "label": "Kaedim — current enterprise FAQ and artist assurance", "url": "https://www.kaedim3d.com/faq" }, { "label": "Kaedim — Input Guidelines", "url": "https://docs.kaedim3d.com/creating-assets/input-guidelines" }, { "label": "Kaedim — product reference-image guidance", "url": "https://docs.kaedim3d.com/creating-assets/input-guidelines/input-image-examples/products" }, { "label": "Kaedim — Game-Ready Assets and configuration", "url": "https://docs.kaedim3d.com/creating-assets/game-ready-assets" }, { "label": "Kaedim — production stages and texture maps", "url": "https://docs.kaedim3d.com/creating-assets/game-ready-assets/stages-of-a-game-ready-asset" }, { "label": "Kaedim — image markups for modeling requirements", "url": "https://docs.kaedim3d.com/creating-assets/game-ready-assets/markups" }, { "label": "Kaedim — Prototype Assets, geometry-only route", "url": "https://docs.kaedim3d.com/creating-assets/prototype-assets" }, { "label": "Kaedim — documentation FAQ, unseen parts and manual iterations", "url": "https://docs.kaedim3d.com/welcome/faq" }, { "label": "Kaedim — polycount limits and detail tradeoffs", "url": "https://docs.kaedim3d.com/creating-assets/create-page/polycount" } ] }, { "id": "180", "stable_id": "IRM_OP_180", "software": "KeyShot Studio", "operation": "KeyShot AI Denoise", "y": 8.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot AI Denoise predicts clean image information from a partially converged render.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "Trained render restoration matches fidelity restoration. A blend control does not itself calculate a different X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "KeyShot · Learned denoising and AI modes", "url": "https://www.keyshot.com/blog/ai-shots-2026-1-introduction/" }, { "label": "KeyShot · Denoise toggle and blend", "url": "https://manual.keyshot.com/manual/image/image-styles/basic-image-style/" } ] }, { "id": "181", "stable_id": "IRM_OP_181", "software": "KeyShot Studio", "operation": "KeyShot Background", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot AI Shots Background generates surroundings around a rendered product.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned environmental realization supports synthesis. Keeping the product image does not provide a numerical discount.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "KeyShot research and implementation account", "url": "https://www.keyshot.com/blog/ai-shots-2026-1-introduction/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "182", "stable_id": "IRM_OP_182", "software": "KeyShot Studio", "operation": "KeyShot Imagine", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot AI Shots Imagine generates an image without CAD-driven scene construction.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The selected generative task matches synthesis. Its distinction from CAD-informed modes is preserved in the mechanism account rather than an unsupported point offset.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "KeyShot research and implementation account", "url": "https://www.keyshot.com/blog/ai-shots-2026-1-introduction/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "183", "stable_id": "IRM_OP_183", "software": "KeyShot Studio", "operation": "KeyShot Real-Time Rendering", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot evaluates conventional ray tracing with learned denoising and generative modes disabled.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Physical rendering and procedural material evaluation contain no trained predictor in this scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "KeyShot · Scene rendering and editing controls", "url": "https://www.keyshot.com/keyshot-studio/" }, { "label": "KeyShot · Editable material graph", "url": "https://manual.keyshot.com/manual/materials/material-graph/" }, { "label": "KeyShot · Animation controls", "url": "https://manual.keyshot.com/manual/animation/" } ] }, { "id": "184", "stable_id": "IRM_OP_184", "software": "KeyShot Studio", "operation": "KeyShot Replace", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot AI Shots Replace synthesizes content within a selected image region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The trained image-editing task supports synthesis. The mask determines execution scope but not the numerical amount of AI.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "KeyShot research and implementation account", "url": "https://www.keyshot.com/blog/ai-shots-2026-1-introduction/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "185", "stable_id": "IRM_OP_185", "software": "KeyShot Studio", "operation": "KeyShot Restyle", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KeyShot AI Shots Restyle uses its CAD-informed trained image model to realize materials, lighting and surroundings.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned visual realization matches synthesis. Fine-tuned CAD conditioning is documented machinery, not reduced inference because more information was supplied.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "KeyShot research and implementation account", "url": "https://www.keyshot.com/blog/ai-shots-2026-1-introduction/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "186", "stable_id": "IRM_OP_186", "software": "KIRI Engine", "operation": "3DGS to Mesh Neural Surface Reconstruction", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KIRI converts captured 3DGS material to a mesh through its documented neural surface-reconstruction route.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The retained surface fit is established, but its training/prior relationship and comparison with explicit scene fitting are insufficiently resolved for a defensible coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The retained surface fit is established, but its training/prior relationship and comparison with explicit scene fitting are insufficiently resolved for a defensible coordinate." }, "sources": [ { "label": "KIRI Engine · KIRI 3DGS Scan", "url": "https://www.kiriengine.app/tutorial/introduction-to-3dgs-scan" }, { "label": "KIRI · Scan mechanisms and neural mesh conversion", "url": "https://www.kiriengine.app/blog/from-2d-video-to-3d-magic" }, { "label": "KIRI · 3DGS mesh output options", "url": "https://docs.kiriengine.app/3dgs-scan/image-upload/" } ] }, { "id": "187", "stable_id": "IRM_OP_187", "software": "KIRI Engine", "operation": "Featureless Neural Surface Reconstruction", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "KIRI Featureless mode reconstructs a surface from capture through its neural surface-reconstruction method.", "retained_learning": null, "rating_input": {}, "rating_rationale": "This capture-fitting route must be distinguished from pretrained single-image asset generation. A consistent calibration of scene fitting is still required before a point is issued.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "This capture-fitting route must be distinguished from pretrained single-image asset generation. A consistent calibration of scene fitting is still required before a point is issued." }, "sources": [ { "label": "KIRI reconstruction methods", "url": "https://www.kiriengine.app/blog/from-2d-video-to-3d-magic", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "188", "stable_id": "IRM_OP_188", "software": "Kling AI (Kuaishou; hosted routes recorded)", "operation": "Image 3.0 text-to-image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Kling IMAGE 3.0 generates a still image through the selected text-to-image route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned visual realization matches synthesis. A video-model technical report is not treated as its image-network specification.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Kuaishou image and video model distinctions", "url": "https://ir.kuaishou.com/news-releases/news-release-details/kling-ai-launches-30-model-ushering-era-where-everyone-can-be" }, { "label": "Kling IMAGE 3.0 Model User Guide, 02042026", "url": "https://kling.ai/quickstart/klingai-image-3-model-user-guide" } ] }, { "id": "190", "stable_id": "IRM_OP_190", "software": "Krea", "operation": "Realtime live-feed visual restyling", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krea Realtime Edit transforms a live visual feed into generated imagery.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Model-driven visual realization supports synthesis. Repetition through time does not create an additional X multiplier.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Krea · Realtime Edit", "url": "https://www.krea.ai/blog/realtime-edit" } ] }, { "id": "189", "stable_id": "IRM_OP_189", "software": "Krea", "operation": "Realtime sketch-conditioned image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krea Realtime Draw transforms the evolving canvas through learned image generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated preview retains synthesis. Sketch detail and update frequency do not enter the coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Judy AI Lab · Realtime Canvas", "url": "https://judyailab.com/en/posts/krea-ai-realtime-creative-studio/" }, { "label": "Krea Realtime modes and separate input/output panels", "url": "https://www.krea.ai/docs/user-guide/features/realtime" }, { "label": "Krea Realtime Edit product explanation: drawing and live response", "url": "https://www.krea.ai/blog/realtime-edit" }, { "label": "Krea 2 technical report: genuine architecture evidence, not an established Photo Fast mapping", "url": "https://www.krea.ai/blog/krea-2-technical-report" }, { "label": "Krea 2 Open Source: released checkpoints and architecture, separate from unverified Realtime deployment", "url": "https://www.krea.ai/krea-2-open-source" }, { "label": "Krea documentation index: API coverage checked for deployment identifiers", "url": "https://www.krea.ai/docs/llms.txt" } ] }, { "id": "191", "stable_id": "IRM_OP_191", "software": "Krita", "operation": "Manual paintover of generated imagery on separate layers", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krita's conventional brushes paint on separate layers over an existing image.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped brush and layer operations contain no trained generator. Earlier image generation is not recounted in the paintover command.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Krita · Layers/Brush Engines", "url": "https://krita.org/en/features/" }, { "label": "Krita painting and layer workflow", "url": "https://docs.krita.org/en/user_manual/getting_started/basic_concepts.html" } ] }, { "id": "192", "stable_id": "IRM_OP_192", "software": "Krita with krita-ai-diffusion", "operation": "FLUX.2 Klein selected-region image editing", "y": 5.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krita's AI Diffusion plugin invokes FLUX.2 Klein for selected-region image revision.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned replacement pixels match synthesis. The plugin host and selection mask add no numerical offsets.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "GitHub · krita-ai-diffusion", "url": "https://github.com/Acly/krita-ai-diffusion" }, { "label": "Krita plugin supported current base models", "url": "https://docs.interstice.cloud/base-models/" }, { "label": "Krita plugin selection editing", "url": "https://docs.interstice.cloud/selections/" }, { "label": "BFL image and video branches", "url": "https://help.bfl.ai/articles/8667153955-what-is-the-bfl-playground" } ] }, { "id": "194", "stable_id": "IRM_OP_194", "software": "Krita with krita-ai-diffusion", "operation": "FLUX.2 Klein text to image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krita AI Diffusion invokes FLUX.2 Klein text-to-image generation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "This host-specific record matches synthesis and remains associated with the same model family elsewhere in the catalogue.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Krita plugin supported base models", "url": "https://docs.interstice.cloud/base-models/" } ] }, { "id": "193", "stable_id": "IRM_OP_193", "software": "Krita with krita-ai-diffusion", "operation": "Live painting generation preview", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Krita AI Diffusion repeatedly generates a preview from the live painted canvas.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The current checkpoint's learned image realization is the assessed responsibility. Update count and painting detail do not change X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Krita plugin current live generation and releases", "url": "https://github.com/Acly/krita-ai-diffusion" }, { "label": "Krita plugin current model support", "url": "https://docs.interstice.cloud/base-models/" } ] }, { "id": "195", "stable_id": "IRM_OP_195", "software": "Leonardo AI", "operation": "Realtime Canvas generation preview", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Leonardo Realtime Canvas converts a sketch and prompt into a generated image preview.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned realization supports synthesis. The supplied drawing is guidance and has no arithmetic specificity weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Knowara · Leonardo Realtime Canvas", "url": "https://knowara.com/ai-tools/image/leonardo-ai-review/" }, { "label": "Leonardo current Realtime Canvas guide", "url": "https://intercom.help/leonardo-ai/en/articles/8658301-realtime-canvas" } ] }, { "id": "199", "stable_id": "IRM_OP_199", "software": "LookX AI Cloud", "operation": "Image to Video Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "LookX generates moving imagery through its image-to-video service.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned motion and scene realization support synthesis. Architectural-host integrations do not make the returned video a conventional scene render.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "LookX · Image and video generation", "url": "https://www.lookx.ai/" } ] }, { "id": "200", "stable_id": "IRM_OP_200", "software": "LookX AI Cloud", "operation": "Sketch to Rendered Image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "LookX generates an architectural visualization from its sketch-to-image route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned visual realization supports synthesis. Source model completeness does not produce a numerical inference discount.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Parametric Architecture · LookX Render Generation", "url": "https://parametric-architecture.com/lookx-launched-an-ai-generated-tool-that-can-transform-sketch-or-squashed-paper-into-a-model/" } ] }, { "id": "202", "stable_id": "IRM_OP_202", "software": "Luma Dream Machine", "operation": "Ray3.2 image-to-video with supplied keyframes", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luma Ray3.2 generates video through its supplied-keyframe image-to-video route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned motion and appearance realization support synthesis. Image keyframes guide the model and are distinct from prescribed native transform interpolation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Luma Ray3.2 generation and multi-keyframes", "url": "https://lumalabs.ai/ray" } ] }, { "id": "201", "stable_id": "IRM_OP_201", "software": "Luma Dream Machine", "operation": "Ray3.2 Modify Video V2 full-clip restyling", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luma Ray3.2 Modify Video V2 generates a restyled version of the scoped clip.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The selected generative transformation supports synthesis. Retaining performance is a task property rather than an input-based score adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Luma · Ray overview", "url": "https://lumalabs.ai/ray" }, { "label": "Luma Ray3.2 release", "url": "https://lumalabs.ai/news/introducing-ray-3-2" } ] }, { "id": "203", "stable_id": "IRM_OP_203", "software": "Luminar Neo", "operation": "GenErase masked object removal", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luminar GenErase synthesizes content replacing a selected object.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image completion supports synthesis even without a written prompt. Prompt absence supplies no extra inference points.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "E1 Skylum GenErase user guide — consulted 09272026", "url": "https://support.skylum.com/extra-tools/generative-tools/generase" }, { "label": "E2 Skylum Generative Tools in Luminar Neo practical guidance — consulted 09272026", "url": "https://support.skylum.com/luminar-neo-tips/generative-tools-in-luminar-neo-everything-you-need-to-know" }, { "label": "E3 Skylum generative tools launch and Stable Diffusion attribution — 10262023", "url": "https://skylum.com/newsroom/luminar-neo-releases-generative-ai-technology-in-its-awardwinning-photo-editing-software" } ] }, { "id": "204", "stable_id": "IRM_OP_204", "software": "Luminar Neo", "operation": "GenExpand canvas outpainting", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luminar GenExpand generates content beyond the image boundary.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The outpainting responsibility matches synthesis. Expansion percentage describes a control limit, not an X coefficient.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "X1 Skylum GenExpand user guide — consulted 09272026", "url": "https://support.skylum.com/extra-tools/generative-tools/genexpand" }, { "label": "X2 Skylum Generative Tools in Luminar Neo practical guidance — consulted 09272026", "url": "https://support.skylum.com/luminar-neo-tips/generative-tools-in-luminar-neo-everything-you-need-to-know" }, { "label": "X3 Skylum generative tools launch and Stable Diffusion attribution — 10262023", "url": "https://skylum.com/newsroom/luminar-neo-releases-generative-ai-technology-in-its-awardwinning-photo-editing-software" } ] }, { "id": "205", "stable_id": "IRM_OP_205", "software": "Luminar Neo", "operation": "GenSwap masked object generation", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luminar GenSwap synthesizes inserted or replacement image content.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned object realization supports synthesis. Selection and descriptive wording do not calculate a reduced score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "S1 Skylum GenSwap user guide — consulted 09272026", "url": "https://support.skylum.com/extra-tools/generative-tools/genswap" }, { "label": "S2 Skylum Generative Tools in Luminar Neo practical guidance — consulted 09272026", "url": "https://support.skylum.com/luminar-neo-tips/generative-tools-in-luminar-neo-everything-you-need-to-know" }, { "label": "S3 Skylum generative tools launch and Stable Diffusion attribution — 10262023", "url": "https://skylum.com/newsroom/luminar-neo-releases-generative-ai-technology-in-its-awardwinning-photo-editing-software" } ] }, { "id": "206", "stable_id": "IRM_OP_206", "software": "Luminar Neo", "operation": "Sky AI replacement using a generated sky asset", "y": 7.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Luminar Sky AI estimates sky membership and composites an already supplied replacement sky.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Learned segmentation remains consequential through conventional asset placement, supporting region determination. Earlier generation of the sky asset is outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Skylum · Luminar SkyAI", "url": "https://skylum.com/luminar" }, { "label": "Skylum Sky AI controls and supplied sky", "url": "https://support.skylum.com/editing-tools/landscape-tools/sky-ai" }, { "label": "Skylum neural sky replacement description", "url": "https://blog.skylum.com/lightroom-mobile-vs-desktop/" } ] }, { "id": "207", "stable_id": "IRM_OP_207", "software": "Magnific (direct / Krea Enhancer)", "operation": "Creative image upscaling", "y": 3.0, "y_status": "work_in_progress", "x": 5.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magnific Creative Upscale synthesizes plausible detail through its creative enhancement route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0" }, "rating_rationale": "The trained plausible-detail responsibility matches perceptual reconstruction at 5. This is a task analogy, not a claim that Magnific uses ESRGAN architecture or losses.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "5", "display": "5.0", "t": "0", "calculation": "X = 5 + 0 × (8 − 5) = 5." }, "sources": [ { "label": "Magnific · Upscaler", "url": "https://www.magnific.com/blog/how-to-use-magnific-ai-image-upscaler-a-comprehensive-guide/" }, { "label": "Krea Magnific creative enhancement", "url": "https://www.krea.ai/blog/how-to-enhance-images-with-magnific" }, { "label": "Magnific Image Upscaler reference: Creative versus Precision and current direct engines", "url": "https://www.magnific.com/ai/docs/image-upscaler" }, { "label": "Krea Enhancer guide: model-specific controls and comparison viewer; consulted 09222026", "url": "https://www.krea.ai/docs/user-guide/features/enhancer" }, { "label": "Krea documentation index: API coverage checked for deployment identifiers", "url": "https://www.krea.ai/docs/llms.txt" } ] }, { "id": "208", "stable_id": "IRM_OP_208", "software": "Magnific (Krea Enhancer reference route)", "operation": "Precise image upscaling", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magnific Precise reconstructs enlarged image detail through its fidelity-oriented enhancement route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The task supports 5–8 between perceptual reconstruction and fidelity restoration. The name Precise alone does not establish a particular loss or exact point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Krea Magnific Precise controls and distinction", "url": "https://www.krea.ai/blog/how-to-enhance-images-with-magnific-precise" }, { "label": "Magnific Image Upscaler reference: Creative versus Precision and current direct engines", "url": "https://www.magnific.com/ai/docs/image-upscaler" }, { "label": "Krea Enhancer guide: model-specific controls and comparison viewer; consulted 09222026", "url": "https://www.krea.ai/docs/user-guide/features/enhancer" }, { "label": "Krea documentation index: API coverage checked for deployment identifiers", "url": "https://www.krea.ai/docs/llms.txt" }, { "label": "Krea walkthrough: Topaz Generative, consulted for author practice context only", "url": "https://www.krea.ai/blog/how-to-enhance-images-with-topaz-generative" } ] }, { "id": "209", "stable_id": "IRM_OP_209", "software": "Maket", "operation": "AI Floor Plan Generation to Editable Layout", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Maket generates a native floor-plan layout through its documented AI plan-generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned room and spatial arrangement decisions support 2–10. Editable layout structure does not determine a separate X bonus.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Maket · Floor Plan Generation", "url": "https://www.maket.ai/features" }, { "label": "Maket · Floor plan workflow", "url": "https://www.maket.ai/blog/how-to-use-maket" } ] }, { "id": "210", "stable_id": "IRM_OP_210", "software": "Marble", "operation": "Regional World Regeneration", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marble regenerates a selected region of a learned three-dimensional world.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial and appearance realization supports synthesis. Regional boundaries do not establish a linear reduction in inferential responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "World Labs generation and output states", "url": "https://www.worldlabs.ai/blog/marble-world-model", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "211", "stable_id": "IRM_OP_211", "software": "Marble", "operation": "World Generation with Splat Export", "y": 5.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Marble's world model generates a scene that can be exported as Gaussian splats.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The world-generation process retains learned synthesis. Splat export does not make this equivalent to conventional capture fitting.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "World Labs generation and output states", "url": "https://www.worldlabs.ai/blog/marble-world-model" } ] }, { "id": "212", "stable_id": "IRM_OP_212", "software": "Max (Max/MSP/Jitter)", "operation": "Jitter jit.gl.gridshape Working Patch", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Max/Jitter evaluates the supplied jit.gl.gridshape patch and its explicit parameters.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional shape generation contains no trained predictor in the bounded visual patch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Cycling 74 · Max/MSP Patching", "url": "https://cycling74.com/products/max" }, { "label": "Cycling 74 · Jitter video and graphics", "url": "https://cycling74.com/products/jitter" }, { "label": "Cycling 74 · jit.gl.gridshape", "url": "https://docs.cycling74.com/reference/jit.gl.gridshape" } ] }, { "id": "214", "stable_id": "IRM_OP_214", "software": "Maxon Cinema 4D iPad and Desktop", "operation": "HY 3D Base Mesh Generation Announced", "y": null, "y_status": "not_assigned", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Maxon describes an announced HY 3D mesh-generation integration for Cinema 4D.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The release status and selected implemented operation remain unverified. Preserve the announced case without issuing an installed-operation coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The release status and selected implemented operation remain unverified. Preserve the announced case without issuing an installed-operation coordinate." }, "sources": [ { "label": "The Rookies · Cinema 4D iPad announcement (feature unconfirmed)", "url": "https://www.therookies.co/blog/headlines/maxon-unveils-cinema-4d-ipad-edition" }, { "label": "Maxon · HY 3D integration announcement", "url": "https://www.maxon.net/en/article/maxon-and-tencent-cloud-partner-to-integrate-hy-3d-ai-engine-into-cinema-4d" } ] }, { "id": "213", "stable_id": "IRM_OP_213", "software": "Maxon Cinema 4D", "operation": "MoGraph Cloner and Effector Construction", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Cinema 4D MoGraph evaluates Cloner and Effector rules for the specified procedural construction.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Rule-driven distribution and animation are conventional. Procedural generation is not by itself learned synthesis.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Super Renders Farm · Cinema 4D Parametric/MoGraph", "url": "https://superrendersfarm.com/article/whats-new-cinema-4d-2026-features" }, { "label": "Maxon · MoGraph Cloner relationships", "url": "https://help.maxon.net/c4d/2026/en-us/Content/html/OMOGRAPH_CLONER.html" } ] }, { "id": "215", "stable_id": "IRM_OP_215", "software": "Maxon Red Giant Magic Bullet Looks", "operation": "Conventional grading of generated video", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Magic Bullet Looks evaluates the selected exposure, contrast and curve tools on existing footage.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The conventional grading functions contain no trained predictor in this declared stack.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Maxon · Magic Bullet Looks", "url": "https://support.maxon.net/hc/en-us/articles/23368967860636-Red-Giant-2026-0-0-September-10-2025" }, { "label": "Maxon Looks tool chain", "url": "https://help.maxon.net/rg/en-us/Content/html/03-MBL-overview-of-looks-interface.html" }, { "label": "Maxon Looks tools", "url": "https://help.maxon.net/rg/en-us/Content/html/09-MBL-tools-drawer.html" } ] }, { "id": "216", "stable_id": "IRM_OP_216", "software": "Maxon ZBrush", "operation": "ZBrush Sculpting", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ZBrush's conventional sculpting tools modify mesh geometry.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The selected brush and geometric operations are conventional; sculpt complexity does not establish learned inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Maxon · ZBrush Sculpting", "url": "https://www.maxon.net/en/zbrush" }, { "label": "Maxon · Sculpt layers", "url": "https://help.maxon.net/zbr/en-us/Content/html/reference-guide/tool/polymesh/layers/layers.html" } ] }, { "id": "399", "stable_id": "IRM_OP_399", "software": "Maxon ZBrush", "operation": "ZModeler Move Point — single Point target", "y": null, "y_status": "not_assigned", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ZBrush ZModeler Move with a single Point target displaces the selected point through a user-directed drag.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The recorded view and movement determine the geometric update. Broader targets, snapping, symmetry and later sculpting are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Maxon desktop ZModeler Point Actions, Move section", "url": "https://help.maxon.net/zbr/en-us/Content/html/user-guide/3d-modeling/modeling-basics/creating-meshes/zmodeler/zmodeler-actions/point-actions/point-actions.html" }, { "label": "Maxon desktop ZModeler Point Targets", "url": "https://help.maxon.net/zbr/en-us/Content/html/user-guide/3d-modeling/modeling-basics/creating-meshes/zmodeler/targets/point-targets/point-targets.html" } ] }, { "id": "217", "stable_id": "IRM_OP_217", "software": "Maxon ZBrush", "operation": "ZRemesher Retopology", "y": 3.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ZRemesher applies its documented topology reconstruction algorithms to a mesh.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The current evidence supports conventional algorithmic retopology. Automated topology choice alone does not establish a trained predictor; this remains an implementation-qualified assessment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Maxon ZRemesher reference, version-3 controls", "url": "https://help.maxon.net/zbr/en-us/Content/html/reference-guide/tool/polymesh/geometry/zremesher/zremesher.html" }, { "label": "Maxon ZRemesher 4.0 account", "url": "https://help.maxon.net/zbr/en-us/Content/html/user-guide/3d-modeling/topology/zremesher/zremesher-4-0/zremesher-4-0.html" }, { "label": "Maxon · ZBrush ZRemesher", "url": "https://www.maxon.net/en/article/maxon-adds-enhanced-functionality-and-unprecedented-value-to-maxon-one" }, { "label": "Maxon · ZRemesher topology controls", "url": "https://help.maxon.net/zbr/en-us/Content/html/user-guide/3d-modeling/topology/zremesher/zremesher.html" } ] }, { "id": "221", "stable_id": "IRM_OP_221", "software": "Meshroom (AliceVision)", "operation": "AliceVision Photogrammetry Graph", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Meshroom evaluates the classical AliceVision photogrammetry graph with learned extensions excluded.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The defined geometric reconstruction and optimization pathway is conventional. Optional MeshroomHub models are not attributed to the baseline graph.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "GitHub · Meshroom v2025.1.0", "url": "https://github.com/alicevision/Meshroom/releases/tag/v2025.1.0" } ] }, { "id": "223", "stable_id": "IRM_OP_223", "software": "Meshy", "operation": "Meshy 7.1 Single Image to Mesh", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Meshy 7.1 predicts a mesh through the single-image geometry-generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned object geometry completion supports synthesis; direct mesh access does not change that responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Meshy · Image-to-3D", "url": "https://www.meshy.ai/features/image-to-3d" }, { "label": "Meshy API changelog", "url": "https://docs.meshy.ai/en/api/changelog" } ] }, { "id": "224", "stable_id": "IRM_OP_224", "software": "Meshy", "operation": "Meshy 7.1 Text to Mesh", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Meshy 7.1 generates a mesh through its text-to-geometry route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated three-dimensional form supports the extensive-synthesis analogy. Prompt detail and candidate count do not change X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Meshy · Text-to-3D", "url": "https://www.meshy.ai/features/text-to-3d" }, { "label": "Meshy API changelog", "url": "https://docs.meshy.ai/en/api/changelog" } ] }, { "id": "222", "stable_id": "IRM_OP_222", "software": "Meshy", "operation": "Meshy 7 Texture Base Color Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Meshy 7 Texture generates base-color appearance for a supplied mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned surface realization supports synthesis. Supplied UVs and geometry are not numerical deductions, and separate PBR-map work is excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Medium · Text-to-Texture", "url": "https://medium.com/data-science-in-your-pocket/meshy-ai-3d-generator-review-2026-the-complete-production-workflow-tested-272df212c925" }, { "label": "Meshy · Prompted texture generation on an existing mesh", "url": "https://help.meshy.ai/en/articles/9996850-how-to-use-the-text-to-texture-feature" }, { "label": "Meshy current texture release", "url": "https://docs.meshy.ai/en/webapp/changelog" } ] }, { "id": "225", "stable_id": "IRM_OP_225", "software": "Microsoft PowerPoint", "operation": "Copilot agent arrangement of generated visual assets", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PowerPoint Copilot's selected Claude route decides and authors the arrangement of supplied visual assets.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned composition survives native slide commands, supporting 2–10. This excludes separately invoked image generation and does not equate all Copilot actions.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Microsoft · Copilot Agent Mode release", "url": "https://www.microsoft.com/en-us/microsoft-365/blog/2026/04/22/copilots-agentic-capabilities-in-word-excel-and-powerpoint-are-generally-available/" }, { "label": "Microsoft Copilot model choice", "url": "https://support.microsoft.com/en-us/office/choose-your-model-when-editing-with-copilot-in-powerpoint" }, { "label": "Microsoft agentic editing", "url": "https://support.microsoft.com/en-us/office/copilot-frontier/get-started-editing-with-copilot-in-office" } ] }, { "id": "226", "stable_id": "IRM_OP_226", "software": "Microsoft PowerPoint", "operation": "Designer layout suggestions for generated imagery", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PowerPoint Designer recommends and applies slide layouts.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The source does not resolve learned ranking, template matching and prescribed placement for the selected route. Copilot's separate model evidence cannot establish Designer's branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The source does not resolve learned ranking, template matching and prescribed placement for the selected route. Copilot's separate model evidence cannot establish Designer's branch." }, "sources": [ { "label": "Microsoft · PowerPoint Designer (Design Suggestions)", "url": "https://support.microsoft.com/en-us/office/create-professional-slide-layouts-with-designer-53c77d7b-dc40-45c2-b684-81415eac0617" }, { "label": "Microsoft Designer visual arrangement", "url": "https://support.microsoft.com/en-us/powerpoint/create-professional-slide-layouts-with-designer" } ] }, { "id": "227", "stable_id": "IRM_OP_227", "software": "Microsoft PowerPoint", "operation": "Master-layout revision of slides containing generated visuals", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PowerPoint propagates a manually revised Slide Master layout and visual attributes.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Native layout inheritance is prescribed computation without a trained planner in this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Microsoft · PowerPoint Slide Master", "url": "https://support.microsoft.com/en-us/office/what-is-a-slide-master-b9abb2a0-7aef-4257-a14e-4329c904da54" }, { "label": "Microsoft master and dependent layout edits", "url": "https://support.microsoft.com/en-us/powerpoint/training/customize-a-slide-master" } ] }, { "id": "228", "stable_id": "IRM_OP_228", "software": "Midjourney", "operation": "V8.2 Draft Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Midjourney V8.2 Draft generates draft images through its selected generation mode.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image synthesis supports 2. Lower resolution, batch size and generation speed do not imply less inferential responsibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Midjourney · Draft Mode", "url": "https://updates.midjourney.com/" }, { "label": "Midjourney · Draft modes and V7 Enhance", "url": "https://docs.midjourney.com/hc/en-us/articles/35577175650957-Draft-Conversational-Modes" }, { "label": "Midjourney versions", "url": "https://docs.midjourney.com/hc/en-us/articles/32199405667853-Version" } ] }, { "id": "229", "stable_id": "IRM_OP_229", "software": "Midjourney", "operation": "V8.2 Edit Model Masked Image Revision", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Midjourney V8.2 Edit Model generates revised content for a masked image region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned edit matches synthesis. Layer preparation and Smart Select are excluded and do not supply a hybrid score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Midjourney · Editor guide", "url": "https://docs.midjourney.com/hc/en-us/articles/32764383466893-Editor" }, { "label": "Midjourney Edit Model compatibility", "url": "https://docs.midjourney.com/hc/en-us/articles/48495453462797-Edit-Model" } ] }, { "id": "230", "stable_id": "IRM_OP_230", "software": "Midjourney", "operation": "V8.2 Edit Model Reference Conditioned Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Midjourney V8.2 Edit Model generates an image through its reference-conditioned route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image realization supports synthesis. Legacy V7 Omni parameters are not silently transferred to this model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "AI Wiki · Omni Reference", "url": "https://aiwiki.ai/wiki/midjourney_v7" }, { "label": "Midjourney official Omni Reference documentation", "url": "https://docs.midjourney.com/hc/en-us/articles/36285124473997-Omni-Reference" }, { "label": "Midjourney versions", "url": "https://docs.midjourney.com/hc/en-us/articles/32199405667853-Version" }, { "label": "Midjourney Edit Model", "url": "https://docs.midjourney.com/hc/en-us/articles/48495453462797-Edit-Model" } ] }, { "id": "232", "stable_id": "IRM_OP_232", "software": "Midjourney", "operation": "V8.2 Edit Model Whole Image Restyling", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Midjourney V8.2 Edit Model performs whole-image restyling.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned reinterpretation of appearance and detail supports synthesis. Intended structural preservation does not establish a prescribed color transform.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Midjourney legacy Retexture boundary", "url": "https://docs.midjourney.com/hc/en-us/articles/33329788681101-Legacy-Features" }, { "label": "Midjourney Edit Model", "url": "https://docs.midjourney.com/hc/en-us/articles/48495453462797-Edit-Model" } ] }, { "id": "231", "stable_id": "IRM_OP_231", "software": "Midjourney V8.2", "operation": "Text-to-image generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Midjourney V8.2 generates an image through its text-to-image route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The retained visual realization matches synthesis; parameter choices are recorded without converting them into t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Midjourney · Prompt Generation", "url": "https://updates.midjourney.com/v8-1-is-now-the-default-model/" }, { "label": "Midjourney current and historical version definitions", "url": "https://docs.midjourney.com/hc/en-us/articles/32199405667853-Version" }, { "label": "Midjourney Prompt Basics: supplied description and unspecified particulars", "url": "https://docs.midjourney.com/hc/en-us/articles/32023408776205-Prompt-Basics" }, { "label": "Midjourney Stylize: default, range and prompt interpretation", "url": "https://docs.midjourney.com/hc/en-us/articles/32196176868109-Stylize" }, { "label": "Midjourney Raw: Standard and Raw modes", "url": "https://docs.midjourney.com/hc/en-us/articles/32634113811853-Raw" }, { "label": "Midjourney Personalization: account defaults and evolving profile codes", "url": "https://docs.midjourney.com/hc/en-us/articles/32433330574221-Personalization" }, { "label": "Midjourney Seeds: repeatability and limits", "url": "https://docs.midjourney.com/hc/en-us/articles/32604356340877-Seeds" }, { "label": "Midjourney Chaos / Variety: variation control and default", "url": "https://docs.midjourney.com/hc/en-us/articles/32099348346765-Chaos-Variety" } ] }, { "id": "234", "stable_id": "IRM_OP_234", "software": "Miro", "operation": "AI Diagram Digitization with Current Models", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Miro's documented diagram-digitization route interprets a diagram image and constructs native elements.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Recognition and reconstruction of relationships support 8–10. The current digitization route is distinct from earlier unconstrained Sidekicks diagram generation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Miro Sidekicks visual formats", "url": "https://help.miro.com/hc/en-us/articles/30139627329042-Sidekicks" }, { "label": "Miro model selector", "url": "https://help.miro.com/hc/en-us/articles/29484232754578-Select-Your-Own-Model-Beta" }, { "label": "Miro available model families", "url": "https://help.miro.com/hc/en-us/articles/20970362792210-Miro-AI-reference" }, { "label": "Miro native diagram output", "url": "https://help.miro.com/hc/en-us/articles/25275263961874-Miro-Diagrams" } ] }, { "id": "233", "stable_id": "IRM_OP_233", "software": "Miro", "operation": "AI Flows Diagram Generation", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Miro AI Flows uses a selected language model to construct diagram content and relationships.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned relational and semantic construction supports 2–10. Workflow routing and native diagram editability do not add scoring terms.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Miro · Flow models and editable blocks", "url": "https://help.miro.com/hc/en-us/articles/29484232754578-Select-Your-Own-Model-Beta" }, { "label": "Miro · Diagram generation operations", "url": "https://help.miro.com/hc/en-us/articles/20970362792210-Miro-AI-reference" }, { "label": "Miro · AI overview", "url": "https://miro.com/ai/" }, { "label": "Miro · Editable Flows", "url": "https://help.miro.com/hc/en-us/articles/29681832191378-Flows-overview" }, { "label": "Miro · Reruns and output restoration", "url": "https://help.miro.com/hc/en-us/articles/29687970855442-Flows" }, { "label": "Miro editable diagram format", "url": "https://help.miro.com/hc/en-us/articles/25275263961874-Miro-Diagrams" } ] }, { "id": "236", "stable_id": "IRM_OP_236", "software": "mnml.ai", "operation": "Render Enhancer Image Output", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "mnml.ai Render Enhancer transforms an existing rendering through its learned enhancement service.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "The named appearance-enhancement task supports 5–12 between perceptual reconstruction and fixed mapping. General platform generation and separate upscaling are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "mnml.ai · Render Generation and Enhancement", "url": "https://mnml.ai/app/render-enhancer" } ] }, { "id": "235", "stable_id": "IRM_OP_235", "software": "MoGe by Microsoft", "operation": "MoGe 3 CLI Depth Image Prediction", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "MoGe 3 predicts the selected depth-image output through its documented CLI route.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned spatial reconstruction crosses relational determination and fidelity estimation, supporting 8–10 without assuming current Meshroom-plugin compatibility.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "conditional_range": [ "8", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "8.0–10.0", "calculation": "X = 8 + t × (10 − 8); t = 0–1; X = 8–10. No midpoint assigned." }, "sources": [ { "label": "Meshroom Hub · Depth estimation extension", "url": "https://github.com/meshroomHub/mrDepthEstimation" }, { "label": "Microsoft · MoGe models", "url": "https://github.com/microsoft/MoGe" } ] }, { "id": "237", "stable_id": "IRM_OP_237", "software": "Nerfstudio", "operation": "Nerfacto Neural Field Reconstruction", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Nerfacto fits a neural field to posed capture images and retains that scene representation.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The current calibration needs a consistent treatment of scene fitting across neural and explicit representations. A neural architecture alone cannot settle the distinction from Gaussian fitting.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The current calibration needs a consistent treatment of scene fitting across neural and explicit representations. A neural architecture alone cannot settle the distinction from Gaussian fitting." }, "sources": [ { "label": "Radiance Fields · Nerfacto", "url": "https://radiancefields.com/nerfstudio-1-0-and-splatfacto-released" }, { "label": "Nerfstudio Nerfacto method", "url": "https://docs.nerf.studio/nerfology/methods/nerfacto.html" }, { "label": "Original NeRF project", "url": "https://www.matthewtancik.com/nerf" } ] }, { "id": "238", "stable_id": "IRM_OP_238", "software": "Nerfstudio", "operation": "Splatfacto Explicit Gaussian Optimization", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Splatfacto optimizes explicit Gaussian parameters from posed images and seed points, with learned extensions excluded.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The retained fitted scene requires the same representation-consistent branch decision as gsplat and NeRF fitting. The historical non-neural label is not sufficient for 15.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The retained fitted scene requires the same representation-consistent branch decision as gsplat and NeRF fitting. The historical non-neural label is not sufficient for 15." }, "sources": [ { "label": "Nerfstudio Splatfacto representation and export limits", "url": "https://docs.nerf.studio/nerfology/methods/splat.html" } ] }, { "id": "239", "stable_id": "IRM_OP_239", "software": "Notch", "operation": "3D Object Material and Transform Graph", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Notch evaluates explicit object transforms and material nodes in the defined scene graph.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped graph excludes AI models, learned trackers and denoisers; its native functions are conventional.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Notch · Node Graph", "url": "https://manual.notch.one/2026.1/en/docs/reference/nodes/" }, { "label": "Notch · Node properties", "url": "https://manual.notch.one/2026.1/en/docs/reference/user-interface/properties/" }, { "label": "Notch · 3D object materials and transform relationships", "url": "https://manual.notch.one/1.0/en/docs/reference/nodes/3d/3d-object/" } ] }, { "id": "240", "stable_id": "IRM_OP_240", "software": "NVIDIA Audio2Face-3D in Autodesk Maya ACE", "operation": "Audio2Face Diffusion to Maya Animation Curves", "y": 8.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Audio2Face Diffusion generates facial animation that is mapped to Maya animation curves.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned motion realization supports a synthesis analogy. Conventional rig mapping and editable curves retain, rather than erase, that contribution.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "GitHub · Audio2Face-3D", "url": "https://github.com/NVIDIA/Audio2Face-3D" } ] }, { "id": "241", "stable_id": "IRM_OP_241", "software": "NVIDIA DLSS in Real Time Engines", "operation": "DLSS 4.5 Super Resolution Render Pass", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "DLSS 4.5 Super Resolution reconstructs the rendered image using learned spatial and temporal processing.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The restoration task spans fidelity and perceptual reconstruction, supporting 5–8. Frame Generation and Ray Reconstruction are separately scoped.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "NVIDIA · NVIDIA DLSS 4 Render Pass", "url": "https://www.nvidia.com/en-us/geforce/technologies/dlss/" }, { "label": "NVIDIA Research · DLSS 4 mechanisms", "url": "https://research.nvidia.com/labs/adlr/DLSS4/" }, { "label": "NVIDIA DLSS 4.5", "url": "https://www.nvidia.com/en-us/geforce/news/dlss-4-5-dynamic-multi-frame-gen-6x-2nd-gen-transformer-super-res/" } ] }, { "id": "242", "stable_id": "IRM_OP_242", "software": "NVIDIA Omniverse (Kit SDK)", "operation": "USD Asset Composition", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Omniverse composes USD layers, references and variants through the native composition system.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed scene composition contains no trained content generator within this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "NVIDIA · Omniverse USD Composition", "url": "https://docs.nvidia.com/omniverse/index.html" }, { "label": "NVIDIA · USD composition and asset hierarchy", "url": "https://docs.omniverse.nvidia.com/usd/latest/learn-openusd/independent/modularity-guide.html" } ] }, { "id": "243", "stable_id": "IRM_OP_243", "software": "ON1 Photo RAW 2026", "operation": "ON1 Generative Erase", "y": 5.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ON1 Local Gen AI Erase synthesizes replacement content in the selected region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The named generative mode matches synthesis; other erase modes and downstream layering are not silently included.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "ON1 · Generative Eraser modes and separate layers", "url": "https://on1.clickhelp.co/articles/#!photo-raw-2026-user-guide/generative-eraser" } ] }, { "id": "244", "stable_id": "IRM_OP_244", "software": "ON1 Photo RAW 2026", "operation": "ON1 NoNoise AI", "y": 8.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ON1 NoNoise AI applies the learned denoising route, excluding the conventional Classic tab.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The trained noise-removal responsibility matches fidelity restoration. Retained settings do not give an editability-based X adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "ON1 · NoNoise neural model and Apply Adjust controls", "url": "https://on1.clickhelp.co/articles/#!photo-raw-2026-user-guide/noise-sharpening" } ] }, { "id": "245", "stable_id": "IRM_OP_245", "software": "ON1 Photo RAW 2026", "operation": "ON1 Resize AI", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ON1 Resize AI Highest Quality reconstructs enlarged imagery through its generative model.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Detail reconstruction spans the perceptual and fidelity references, supporting 5–8. The standard faster mode is outside this record.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "ON1 · Resize AI 2026 model choices", "url": "https://www.on1.com/press/on1-announces-photo-raw-2026-with-advanced-ai-tools-masking-layers-and-new-creative-filters/" }, { "label": "ON1 · Resize adjustment and saved output workflow", "url": "https://on1.clickhelp.co/articles/#!photo-raw-2026-user-guide/basic-resize-ai-workflow" } ] }, { "id": "246", "stable_id": "IRM_OP_246", "software": "ON1 Photo RAW 2026", "operation": "ON1 Super Select AI", "y": 6.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "ON1 Super Select predicts semantic membership and uses the mask for a conventional exposure adjustment.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Learned region determination survives the applied adjustment, supporting a 10 match. The two stages are not averaged.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "ON1 · Machine-learning masking", "url": "https://www.on1.com/products/plugins/" }, { "label": "ON1 · Super Select and local adjustments", "url": "https://www.on1.com/videos/how-to-select-edit-anything-in-on1-photo-raw-super-select-ai-and-masking-in-action/" }, { "label": "ON1 · Super Select editable masks and adjustments", "url": "https://on1.clickhelp.co/articles/#!photo-raw-2026-user-guide/super-select-ai-tool" } ] }, { "id": "247", "stable_id": "IRM_OP_247", "software": "Onshape", "operation": "Parametric Part Studio Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Onshape evaluates native Part Studio features and parametric relationships.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped geometric construction contains no trained predictor; its informational AI Advisor is excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Onshape · Parametric Modeling", "url": "https://www.onshape.com/en/" }, { "label": "Onshape · Feature editing and rollback", "url": "https://cad.onshape.com/help/Content/PartStudio/features_and_parts_lists.htm" } ] }, { "id": "248", "stable_id": "IRM_OP_248", "software": "OpenAI API", "operation": "GPT Image 2.5 Sunburst masked image editing", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The OpenAI Images API invokes the recorded GPT Image 2.5 Sunburst masked-edit route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The image model retains learned synthesis. API model selection is distinct from ChatGPT's managed routing, and masks do not supply numerical inference fractions.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "OpenAI · GPT Image Edit (image + mask)", "url": "https://developers.openai.com/api/docs/models/gpt-image-2" }, { "label": "OpenAI current GPT Image 2.5 Sunburst", "url": "https://developers.openai.com/api/docs/models/gpt-image-2.5-sunburst" }, { "label": "OpenAI mask editing guide", "url": "https://developers.openai.com/api/docs/guides/image-generation" } ] }, { "id": "249", "stable_id": "IRM_OP_249", "software": "OTOY OctaneRender", "operation": "AI Upsampling Image Pass", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Octane's AI upsampler reconstructs a higher-resolution render from a lower-resolution image.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned enlargement supports the 5–8 reconstruction envelope. The preceding conventional renderer is outside the isolated pass.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "OTOY · Octane AI Up-Sampler", "url": "https://docs.otoy.com/cinema4d/AIUp-sampler.html" } ] }, { "id": "250", "stable_id": "IRM_OP_250", "software": "OTOY OctaneRender", "operation": "Spectral AI Denoised Beauty Pass", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Octane's Spectral AI denoiser predicts the clean beauty image using render buffers.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The trained restoration task matches fidelity restoration; auxiliary-buffer count is not an X weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "OTOY · Octane Spectral AI Denoiser", "url": "https://docs.otoy.com/standaloneSE/Denoiser.html" }, { "label": "OTOY · Deep learning denoised passes", "url": "https://docs.otoy.com/standaloneSE/DenoisedAOVs.html" } ] }, { "id": "251", "stable_id": "IRM_OP_251", "software": "Penpot", "operation": "Manual masking of an imported generated image", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Penpot applies manually specified masks to imported image layers.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped mask geometry and clipping are conventional. The imported image's earlier production process does not change this operation's X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Penpot · Penpot overview", "url": "https://penpot.app/" }, { "label": "Penpot paths images and masking", "url": "https://help.penpot.app/user-guide/designing/layers/" } ] }, { "id": "260", "stable_id": "IRM_OP_260", "software": "Pika", "operation": "Pika 2.5 prompt to video", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Pika 2.5 generates a video through its recorded prompt-to-video route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned synthesis determines the clip's appearance and motion. The generation-reference match extends across time as a stated task analogy, not a count of frames or neural layers.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Pika · Pika overview", "url": "https://pika.art" }, { "label": "Pika current 2.5 generation route", "url": "https://create.pika.art/apps/pika-2-5" } ] }, { "id": "261", "stable_id": "IRM_OP_261", "software": "Plasticity", "operation": "Direct Solid and Surface Editing", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Plasticity performs direct solid and surface editing through its geometric kernel.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The specified construction uses conventional geometry operations with no documented trained prediction.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "CG Channel · Direct Modeling", "url": "https://www.cgchannel.com/2026/04/plasticity-2026-1-is-out/" }, { "label": "Plasticity · Parasolid direct modeling", "url": "https://www.plasticity.xyz/" } ] }, { "id": "262", "stable_id": "IRM_OP_262", "software": "Playground", "operation": "GPT Image 2.5 Flare design with Smart Layers", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Playground combines its recorded GPT Image generation route with Smart Layers separation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generated design retains the image model's learned synthesis, supporting generation at 2. Smart Layers separation does not remove that responsibility or introduce an arithmetic averaging rule.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Playground · Design Generation", "url": "https://playgroundai.com/blog/introducing-playground-v3" }, { "label": "Playground current models and Smart Layers", "url": "https://playgroundai.com/design/whats-new" } ] }, { "id": "263", "stable_id": "IRM_OP_263", "software": "Point-E", "operation": "Text to Point Cloud Research Example", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Point-E's research example generates a point cloud through its learned diffusion pipeline.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial synthesis matches the generation reference by task responsibility. Separate point-cloud-to-mesh processing is excluded, and the cross-domain match is a calibration judgment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "OpenAI · Point-E point cloud diffusion", "url": "https://github.com/openai/point-e" } ] }, { "id": "265", "stable_id": "IRM_OP_265", "software": "Polycam", "operation": "LiDAR Depth to Mesh Reconstruction", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Polycam LiDAR mode constructs a mesh from captured depth and imagery.", "retained_learning": null, "rating_input": {}, "rating_rationale": "Depth sensing does not by itself establish a conventional-only pipeline. The retained contribution of trained image or depth processing is not established for the complete scoped service; no branch or coordinate is asserted.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "Depth sensing does not by itself establish a conventional-only pipeline. The retained contribution of trained image or depth processing is not established for the complete scoped service; no branch or coordinate is asserted." }, "sources": [ { "label": "Polycam · Gaussian Splat Mode", "url": "https://poly.cam/object-capture", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Polycam · Separate capture modes", "url": "https://learn.poly.cam/hc/en-us/articles/48565771018772-Which-Capture-Mode-Should-I-Use", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Polycam · Crop and Gaussian export limitation", "url": "https://learn.poly.cam/hc/en-us/articles/29647360522516-How-to-Crop-a-Capture-in-Polycam", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "264", "stable_id": "IRM_OP_264", "software": "Polycam", "operation": "Object Mode Gaussian capture with crop preview", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Polycam Gaussian Object Mode constructs a fitted scene representation from captures.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The full route does not separate pose estimation, learned preprocessing and per-scene representation fitting. A coordinate requires that separation and the same fitting criterion used for the other radiance-field cases.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The full route does not separate pose estimation, learned preprocessing and per-scene representation fitting. A coordinate requires that separation and the same fitting criterion used for the other radiance-field cases." }, "sources": [ { "label": "Polycam · Gaussian Splat Mode", "url": "https://poly.cam/object-capture" }, { "label": "Polycam · Separate capture modes", "url": "https://learn.poly.cam/hc/en-us/articles/48565771018772-Which-Capture-Mode-Should-I-Use" }, { "label": "Polycam · Crop and Gaussian export limitation", "url": "https://learn.poly.cam/hc/en-us/articles/29647360522516-How-to-Crop-a-Capture-in-Polycam" }, { "label": "Polycam capture export types", "url": "https://learn.poly.cam/hc/en-us/articles/27756102599572-What-File-Types-Can-Polycam-Export" } ] }, { "id": "266", "stable_id": "IRM_OP_266", "software": "Polycam", "operation": "Object Mode Photogrammetric Mesh", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Polycam Object photogrammetry mode reconstructs a mesh from photographs.", "retained_learning": null, "rating_input": {}, "rating_rationale": "Classical photogrammetry is a plausible component, but the saved service documentation does not isolate trained feature or depth estimation. A conventional-only result cannot be assumed for the entire managed pipeline.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "Classical photogrammetry is a plausible component, but the saved service documentation does not isolate trained feature or depth estimation. A conventional-only result cannot be assumed for the entire managed pipeline." }, "sources": [ { "label": "Polycam · Gaussian Splat Mode", "url": "https://poly.cam/object-capture", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Polycam · Separate capture modes", "url": "https://learn.poly.cam/hc/en-us/articles/48565771018772-Which-Capture-Mode-Should-I-Use", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Polycam · Crop and Gaussian export limitation", "url": "https://learn.poly.cam/hc/en-us/articles/29647360522516-How-to-Crop-a-Capture-in-Polycam", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "267", "stable_id": "IRM_OP_267", "software": "Polycam", "operation": "Web Single Image to Model", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Polycam's web 3D Generator predicts a mesh from a single image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned shape construction supports a cross-domain match to generation at 2. Supplied imagery does not establish a smaller numerical inference share.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Polycam · 3D Generator", "url": "https://poly.cam/tools/3d-model-generator" }, { "label": "Polycam · Image to editable model", "url": "https://learn.poly.cam/hc/en-us/articles/35116894902292-How-to-Create-a-Model-from-the-3D-Generator" } ] }, { "id": "268", "stable_id": "IRM_OP_268", "software": "Postshot", "operation": "Explicit Splat Optimization and Cleanup", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Postshot's Splat mode fits an explicit Gaussian scene with supplied camera poses.", "retained_learning": null, "rating_input": {}, "rating_rationale": "This excludes camera tracking and NeRF mode. The per-scene fitting criterion remains unresolved across explicit splats and neural fields; representing the fit without neural layers does not alone justify 15.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "This excludes camera tracking and NeRF mode. The per-scene fitting criterion remains unresolved across explicit splats and neural fields; representing the fit without neural layers does not alone justify 15." }, "sources": [ { "label": "Radiance Fields · Splat Training", "url": "https://radiancefields.com/postshot-launches-v1-0-out-of-beta" }, { "label": "Jawset · Splat selection deletion and export", "url": "https://www.jawset.com/docs/d/Postshot%2BUser%2BGuide/Release%2BNotes/v0.3" } ] }, { "id": "274", "stable_id": "IRM_OP_274", "software": "Prezi", "operation": "AI visual presentation generation", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Prezi AI constructs the initial visual presentation; unspecified later revisions are excluded.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned organization and content construction remain in the presentation. The task crosses the synthesis and region references, so 2–10 is retained as a comparison envelope rather than an invented point.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Prezi · Prezi overview", "url": "https://prezi.com/features/ai/" }, { "label": "Prezi AI native presentation workflow", "url": "https://support.prezi.com/hc/en-us/articles/22436099469847-How-to-use-Prezi-AI-to-design-your-presentation" } ] }, { "id": "276", "stable_id": "IRM_OP_276", "software": "Procreate Dreams", "operation": "Move and Scale Keyframe Interpolation in Dreams 2.0", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Procreate Dreams evaluates a specified Move and Scale animation with conventional interpolation.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Keyframe interpolation is prescribed computation. Automatic playback and intermediate-frame evaluation do not establish learned inference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Procreate · Keyframing", "url": "https://procreate.com/dreams" }, { "label": "Procreate Dreams 2.0 · Keyframe interpolation", "url": "https://help.procreate.com/dreams/handbook/2.0/keyframes-and-performing/keyframes" } ] }, { "id": "275", "stable_id": "IRM_OP_275", "software": "Procreate (iPad)", "operation": "Manual Painting on Separate Layers Comparison", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Procreate performs manually directed painting in the native layered document.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional brush and layer operations contain no documented trained determination in this scope. Imported generated references do not change the branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Procreate · Painting", "url": "https://procreate.com/ai" }, { "label": "Procreate · Paint Smudge and Erase", "url": "https://help.procreate.com/procreate/handbook/brushes/paint-smudge-erase" }, { "label": "Procreate · Separate layer edits", "url": "https://help.procreate.com/procreate/handbook/5.1/layers" } ] }, { "id": "277", "stable_id": "IRM_OP_277", "software": "PromeAI", "operation": "Sketch to Rendered Image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "PromeAI Sketch Rendering synthesizes a rendered appearance from the supplied sketch.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image construction matches generation at 2. The sketch's detail is not an inference weight, and preservation of exact geometry is not established by the coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "PromeAI · Sketch Rendering", "url": "https://www.promeai.pro/architecture-sketch-transformation" } ] }, { "id": "278", "stable_id": "IRM_OP_278", "software": "Radiant Photo 2", "operation": "Automatic scene-based photo correction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Radiant Photo 2 performs scene analysis followed by automatic image correction.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The available account does not separate learned scene decisions from fixed correction rules or establish their retained contribution. Product claims about intelligent enhancement cannot determine a defensible t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The available account does not separate learned scene decisions from fixed correction rules or establish their retained contribution. Product claims about intelligent enhancement cannot determine a defensible t." }, "sources": [ { "label": "Radiant Photo current workflow description", "url": "https://radiantimaginglabs.com/radiant-photo-2-workflows/", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Digital Camera World · Radiant Photo Auto Corrections", "url": "https://www.digitalcameraworld.com/cameras/radiant-photo-2-review" }, { "label": "Radiant Photo 2 operation and controls", "url": "https://radiantimaginglabs.com/radiant-photo-2/" } ] }, { "id": "279", "stable_id": "IRM_OP_279", "software": "Real-ESRGAN", "operation": "RealESR General x4v3 image upscaling", "y": 5.0, "y_status": "work_in_progress", "x": 5.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Real-ESRGAN real-general-x4v3 performs native fourfold enlargement with face enhancement disabled.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0" }, "rating_rationale": "Its trained perceptual reconstruction matches the ESRGAN-family reference at 5. This is a task and objective comparison, not an assumption of identical network architecture or an upscaling-factor score.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "5", "display": "5.0", "t": "0", "calculation": "X = 5 + 0 × (8 − 5) = 5." }, "sources": [ { "label": "GitHub · Real-ESRGAN", "url": "https://github.com/xinntao/Real-ESRGAN" } ] }, { "id": "280", "stable_id": "IRM_OP_280", "software": "RealityKit by Apple", "operation": "Current Object Capture Photo to Model", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Apple RealityKit Object Capture reconstructs an object through a managed capture pipeline.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The available documentation does not establish which trained feature or depth predictions survive in the reconstructed object. Neural Engine availability is insufficient evidence for either the learned branch or a coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The available documentation does not establish which trained feature or depth predictions survive in the reconstructed object. Neural Engine availability is insufficient evidence for either the learned branch or a coordinate." }, "sources": [ { "label": "Apple · Apple Object Capture", "url": "https://developer.apple.com/documentation/realitykit/realitykit-object-capture/" }, { "label": "Apple WWDC21 · Object Capture", "url": "https://developer.apple.com/videos/play/wwdc2021/10076/" }, { "label": "Apple photogrammetry workflow", "url": "https://developer.apple.com/documentation/realitykit/creating-3d-objects-from-photographs/" } ] }, { "id": "281", "stable_id": "IRM_OP_281", "software": "RealityScan by Epic Games", "operation": "2.2 AI Masks and Photogrammetric Mesh", "y": 5.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "RealityScan 2.2 reconstructs a mesh using enabled AI-generated masks.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The documented learned region decisions constrain the reconstruction and remain consequential in it, supporting 10. Matching and mesh construction do not dilute that responsibility through an automatic hybrid adjustment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "RealityScan · Photogrammetry", "url": "https://www.realityscan.com/news/realityscan-20-new-release-brings-powerful-new-features-to-a-rebranded-realityscan" }, { "label": "RealityScan · AI masking in production", "url": "https://www.realityscan.com/spotlights/how-cd-projekt-red-used-realityscan-to-create-the-witcher-4-unreal-engine-5-tech-demo" }, { "label": "RealityScan · Reconstruction commands", "url": "https://dev.epicgames.com/documentation/realityscan/all-commands" }, { "label": "RealityScan current 2.2 release", "url": "https://www.realityscan.com/news/realityscan-2-2-is-here-with-full-amd-gpu-support-download-today" } ] }, { "id": "282", "stable_id": "IRM_OP_282", "software": "Rebelle 8.3", "operation": "Layered manual painting over a generated reference", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rebelle 8.3 simulates manually directed painting over an imported reference, before NanoPixel export.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Paint-flow and pigment simulation are conventional within this scope. The reference image's production history does not introduce an in-scope learned mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Escape Motions · Rebelle overview", "url": "https://www.escapemotions.com/products/rebelle/about" }, { "label": "Rebelle 8 — painting from a reference", "url": "https://escapemotions.com/products/rebelle/manual/8/starting-painting/workflow-options/" }, { "label": "Rebelle 8 — simulation description", "url": "https://escapemotions.com/products/rebelle/manual/8/" }, { "label": "Escape Motions — 8.3 release", "url": "https://www.escapemotions.com/community/forum/t/45927/" } ] }, { "id": "283", "stable_id": "IRM_OP_283", "software": "Rebelle 8.3 Pro", "operation": "NanoPixel 2 enlarged raster export", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rebelle NanoPixel 2 enlarges the painted image for raster export.", "retained_learning": null, "rating_input": {}, "rating_rationale": "Earlier NanoPixel machine-learning claims do not establish the current NanoPixel 2 mechanism or its trained contribution. Neither the word fractal nor the absence of a published neural architecture resolves the branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "Earlier NanoPixel machine-learning claims do not establish the current NanoPixel 2 mechanism or its trained contribution. Neither the word fractal nor the absence of a published neural architecture resolves the branch." }, "sources": [ { "label": "Rebelle current mechanism documentation", "url": "https://escapemotions.com/products/rebelle/documents/Rebelle_Manual_v.8.2.pdf", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "Rebelle 8 — NanoPixel 2", "url": "https://escapemotions.com/products/rebelle/manual/8/" }, { "label": "Rebelle 8 — export documentation", "url": "https://escapemotions.com/products/rebelle/documents/Rebelle_Manual_v.8.1.pdf" }, { "label": "Historical mechanism description only", "url": "https://www.escapemotions.com/products/rebelle/manual/7/" } ] }, { "id": "284", "stable_id": "IRM_OP_284", "software": "Recraft Studio V4.1", "operation": "Text to raster image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Recraft V4.1 produces a raster image through the recorded generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image synthesis matches 2. Subsequent editing is excluded; palette and layout instructions do not create separate numerical offsets.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Recraft · Prompt Generation (raster + native vector)", "url": "https://www.recraft.ai/blog/recraft-introduces-a-revolutionary-ai-model-that-thinks-in-design-language" }, { "label": "Recraft — V4.1 release and branches", "url": "https://www.recraft.ai/blog/recraft-v4-1-more-beautiful-by-nature" } ] }, { "id": "285", "stable_id": "IRM_OP_285", "software": "Recraft Studio V4.1 Vector", "operation": "Text to editable SVG", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Recraft V4.1 Vector generates editable SVG paths and layers.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned visual construction matches generation at 2. Editable representation does not automatically reduce the inferred construction or change X; later designer interaction belongs to a separate inquiry.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Recraft — current native vector generator", "url": "https://www.recraft.ai/ai-vector-generator" }, { "label": "Recraft — V4.1 branches", "url": "https://www.recraft.ai/blog/recraft-v4-1-more-beautiful-by-nature" } ] }, { "id": "286", "stable_id": "IRM_OP_286", "software": "Remini", "operation": "Photo Enhance accepted image", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Remini Photo Enhance reconstructs image detail through the managed learned enhancement service.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The documented reconstruction task supports the fidelity-to-perceptual envelope 5–8. Public service evidence does not establish an objective or checkpoint sufficient for a narrower position.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Remini · Enhance", "url": "https://remini.ai/" }, { "label": "Remini — photo-quality enhancement", "url": "https://remini.ai/low-quality-enhancer" } ] }, { "id": "287", "stable_id": "IRM_OP_287", "software": "Resolume Arena / Avenue", "operation": "Layered Alpha compositing of generated video", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolume Arena or Avenue composites supplied video layers with Alpha blending.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional blending and triggering define this operation. Generation of the supplied clips occurs outside the scope, and external neural effects are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Resolume · Software overview", "url": "https://resolume.com/software" }, { "label": "Resolume — blend-mode mechanisms", "url": "https://resolume.com/support/en/blend-modes" }, { "label": "Resolume — layer controls", "url": "https://www.resolume.com/support/en/layers" } ] }, { "id": "288", "stable_id": "IRM_OP_288", "software": "Resolume Wire", "operation": "Video Mixer Alpha composite in a working patch", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Resolume Wire evaluates a two-source Video Mixer patch in Alpha mode.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The bounded composition is conventional arithmetic over the supplied sources. Node-based organization and earlier source generation do not alter X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Resolume — Wire blend modes", "url": "https://resolume.com/support/en/blend-modes" }, { "label": "Resolume — patch connections", "url": "https://resolume.com/support/en/fast-patching" } ] }, { "id": "291", "stable_id": "IRM_OP_291", "software": "Rhino 8", "operation": "Direct SubD Geometry Editing", "y": 4.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rhino 8 performs direct editing of SubD geometry.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped control-point and surface evaluation operations are conventional, with no retained trained prediction.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Rhino · Rhino features", "url": "https://www.rhino3d.com/features/" }, { "label": "McNeel · Direct SubD editing", "url": "https://www.rhino3d.com/features/subd/" } ] }, { "id": "292", "stable_id": "IRM_OP_292", "software": "Rhino 8 / Grasshopper", "operation": "Conventional Parametric Geometry Definition", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Grasshopper evaluates a specified graph of conventional geometry components.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Graph complexity does not establish learning. Third-party learned components are outside this defined configuration.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "McNeel · Grasshopper component system", "url": "https://www.rhino3d.com/6/new/grasshopper/" } ] }, { "id": "293", "stable_id": "IRM_OP_293", "software": "Rhino 8 / Grasshopper", "operation": "Galapagos Evolutionary Geometry Search", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Grasshopper Galapagos searches parameters of a conventional geometry definition with an evolutionary solver.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The specified search uses no learned surrogate. Its generated alternatives remain conventional under the equation's retained-learning criterion.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "David Rutten · Galapagos evolutionary computation", "url": "https://www.grasshopper3d.com/group/galapagos" } ] }, { "id": "294", "stable_id": "IRM_OP_294", "software": "Rhino 8 / Grasshopper", "operation": "RhinoMCP LLM Grasshopper Graph Construction", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "RhinoMCP uses an LLM to choose components, connections and parameters for a Grasshopper graph.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned structural decisions survive as the graph. The synthesis-to-region envelope records a cross-domain comparison; later conventional graph evaluation does not cancel those decisions.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "RhinoMCP developer documentation", "url": "https://github.com/jingcheng-chen/rhinomcp", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" } ] }, { "id": "295", "stable_id": "IRM_OP_295", "software": "Rive", "operation": "State-machine animation of generated visual assets", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rive evaluates supplied animations through an explicitly constructed state machine.", "retained_learning": false, "rating_input": {}, "rating_rationale": "States, transitions and bindings determine the visual behavior conventionally. The imported assets' earlier generation is outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Rive · State Machine", "url": "https://rive.app/" }, { "label": "Rive current state-machine construction", "url": "https://rive.app/docs/editor/state-machine/state-machine" } ] }, { "id": "296", "stable_id": "IRM_OP_296", "software": "Rodin by Hyper3D", "operation": "Gen 2.5 Image to Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rodin Gen 2.5 generates mesh geometry from images, before separate material editing or inpainting.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial construction supports the generation reference by cross-domain analogy. The selected tier must be retained because an omitted tier can invoke a different engine.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "The Pilot News · Image-to-3D", "url": "https://business.thepilotnews.com/thepilotnews/article/pressadvantage-2026-5-26-hyper3d-launches-rodin-gen-25-bringing-sculpt-level-detail-and-production-control-to-ai-3d-generation" }, { "label": "Hyper3D · Diffusion meshes and regional 3D editing", "url": "https://hyper3d.ai/blog/rodin-gen-2" }, { "label": "Hyper3D · Image mesh output formats", "url": "https://hyper3d.ai/features/image-to-3d" }, { "label": "Rodin Gen 2.5 API", "url": "https://docs.hyper3d.ai/en/api-specification/rodin-gen2-5" } ] }, { "id": "297", "stable_id": "IRM_OP_297", "software": "Rodin by Hyper3D", "operation": "Gen 2.5 Text to Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Rodin Gen 2.5 generates mesh geometry from text, before separate material editing or inpainting.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned construction responsibility matches the related image-conditioned operation at 2. Prompt versus image conditioning does not automatically change the coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Hyper3D · Text mesh output formats", "url": "https://hyper3d.ai/features/text-to-3d" }, { "label": "Rodin Gen 2.5 API", "url": "https://docs.hyper3d.ai/en/api-specification/rodin-gen2-5" } ] }, { "id": "298", "stable_id": "IRM_OP_298", "software": "Runway", "operation": "Act-Two Character Video Performance Transfer", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Act-Two synthesizes character-video facial performance from a driving performance.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned appearance and motion synthesis supports the generation comparison. This targeted task is explicitly narrower than whole-scene generation, but no validated distance adjustment follows from that restriction.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Runway · Act-Two input modes and motion preservation", "url": "https://help.runwayml.com/hc/en-us/articles/42311337895827-Performance-Capture-with-Act-Two" } ] }, { "id": "299", "stable_id": "IRM_OP_299", "software": "Runway", "operation": "Aleph 2.0 / Edit Studio", "y": null, "y_status": "not_assigned", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Edit Studio edits a reference frame with Gen-4 Image and produces revised footage through Aleph 2.0.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image and temporal synthesis remain in the clip, supporting 2. Two learned stages do not generate an automatic hybrid coordinate or arithmetic sum.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Runway · Aleph 2.0 keyframe edits and prompt versions", "url": "https://help.runwayml.com/hc/en-us/articles/51683104370451-Creating-with-Edit-Studio" }, { "label": "Runway · Aleph selective changes and preservation claims", "url": "https://help.runwayml.com/hc/en-us/articles/52150503729171-Aleph-2-0-Prompting-Guide" } ] }, { "id": "301", "stable_id": "IRM_OP_301", "software": "Runway", "operation": "Gen-4.5 Video Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Gen-4.5 synthesizes a video in the scoped generation-only route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned appearance and motion construction supports the generation analogy at 2. Duration, camera instructions and conditioning format are not additional X weights.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Runway · Gen-4.5 modes and iteration", "url": "https://help.runwayml.com/hc/en-us/articles/46974685288467-Creating-with-Gen-4-5" }, { "label": "Runway hosted video-model catalogue", "url": "https://help.runwayml.com/hc/en-us/sections/54758948966547-Third-Party-Video-Models" } ] }, { "id": "300", "stable_id": "IRM_OP_300", "software": "Runway", "operation": "Gen-4 Image reference-conditioned synthesis", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Gen-4 Image synthesizes an image using the recorded reference-conditioned route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned visual construction matches generation. Saving or supplying references does not determine an inference percentage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Runway Gen-4 Image References", "url": "https://help.runwayml.com/hc/en-us/articles/40042718905875-Creating-with-Gen-4-Image-References" } ] }, { "id": "302", "stable_id": "IRM_OP_302", "software": "Runway", "operation": "Ruby SDR-to-HDR video conversion", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Ruby predicts an HDR appearance from SDR video.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned appearance-mapping task is compared with fixed-function transformation and perceptual reconstruction, supporting 5–12. The public workflow account does not establish a precise training objective or t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Runway Ruby conversion and output state", "url": "https://help.runwayml.com/hc/en-us/articles/54749891346579-Creating-HDR-videos-with-Runway-Ruby" } ] }, { "id": "303", "stable_id": "IRM_OP_303", "software": "Runway", "operation": "Workflows Video Generation and Stitching", "y": 7.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Runway Workflows combines two learned video-generation nodes with a conventional stitching node.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The stitched sequence retains the generators' learned synthesis. Assembly does not dilute or average away that responsibility, so this configuration remains at the generation reference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Runway · Workflows graph editing and output locks", "url": "https://help.runwayml.com/hc/en-us/articles/45763528999699-Introduction-to-Workflows" } ] }, { "id": "304", "stable_id": "IRM_OP_304", "software": "Shap-E", "operation": "Text to Mesh Research Example", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Shap-E's research pipeline predicts an implicit representation and decodes a generated mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial synthesis supports a cross-domain generation match. Conventional decoding does not remove the dependence on the learned representation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "GitHub · shap-e", "url": "https://github.com/openai/shap-e" } ] }, { "id": "305", "stable_id": "IRM_OP_305", "software": "Shapr3D", "operation": "History Based Parametric Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Shapr3D evaluates a native history-based parametric modeling sequence.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped feature history and geometric constraints use conventional computation. This does not classify every imported or externally generated asset as a learned operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Shapr3D · Modeling", "url": "https://support.shapr3d.com/hc/en-us/articles/13444210101788-5-590-History-Based-Parametric-Modeling-is-here" } ] }, { "id": "306", "stable_id": "IRM_OP_306", "software": "SHIMA SEIKI APEXFiz Design-Knit V-10", "operation": "Virtual fabric preview with pattern and color revision", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SHIMA SEIKI APEXFiz Design-Knit V-10 previews fabric with pattern and color changes.", "retained_learning": null, "rating_input": {}, "rating_rationale": "The current preview solver and any retained trained component are not established. A generative-image handoff is unnecessary for eligibility, but it would not resolve this mechanism gap either.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "raw_x": null, "display": "Unresolved", "reason": "The current preview solver and any retained trained component are not established. A generative-image handoff is unnecessary for eligibility, but it would not resolve this mechanism gap either." }, "sources": [ { "label": "Shima Seiki · Shima Seiki APEX Virtual Sampling", "url": "https://www.shimaseiki.com/product/design/" }, { "label": "SHIMA SEIKI · Fabric simulation and virtual samples", "url": "https://www.shimaseiki.com/product/design/software/" }, { "label": "SHIMA SEIKI · Virtual sampling", "url": "https://www2.shimaseiki.com/virtual_sampling/" }, { "label": "SHIMA SEIKI — Design-Knit scope", "url": "https://www.shimaseiki.com/product/design/software/features/" }, { "label": "SHIMANAVI — fabric and mapping operations", "url": "https://acf001.shimaseiki.com/pdfjs/site_home/FlatKnitDesign_ENG.pdf" } ] }, { "id": "307", "stable_id": "IRM_OP_307", "software": "SideFX Houdini", "operation": "Houdini Copernicus", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini Copernicus evaluates the specified conventional image-processing graph.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped nodes perform prescribed image computation. Learned-model nodes are excluded; node count supplies no inference points.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "SideFX · Copernicus image-processing scope", "url": "https://www.sidefx.com/docs/houdini/copernicus/index.html" }, { "label": "SideFX · COP nodes and bypass controls", "url": "https://www.sidefx.com/docs/houdini/nodes/cop/index.html" } ] }, { "id": "308", "stable_id": "IRM_OP_308", "software": "SideFX Houdini", "operation": "Houdini MCP Node Graph Construction", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The Houdini MCP workflow uses an LLM to construct a visual node graph.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned graph structure survives native node creation. The synthesis-to-region envelope records the cross-domain comparison; later execution of the graph is outside this authorship scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Project developer · Houdini MCP node creation and modification", "url": "https://github.com/capoomgit/houdini-mcp" }, { "label": "SideFX · Editable node networks and controls", "url": "https://www.sidefx.com/docs/houdini/network/flags" } ] }, { "id": "309", "stable_id": "IRM_OP_309", "software": "SideFX Houdini", "operation": "Houdini ML Deformer", "y": 7.0, "y_status": "work_in_progress", "x": 12.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini 21 ML Deform predicts weights that combine residual blend shapes to approximate the trained deformation mapping.", "retained_learning": true, "rating_input": { "lower": "REF12", "higher": "REF12", "t": "0" }, "rating_rationale": "The fixed-function emulation reference at 12 is the closer task comparison. Explicit blend-shape evaluation retains the learned weights and does not justify a conventional-only result.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF12", "REF12" ], "status": "point", "raw_x": "12", "display": "12.0", "t": "0", "calculation": "X = 12 + 0 × (12 − 12) = 12." }, "sources": [ { "label": "SideFX · ML Deform weights and residual shapes", "url": "https://www.sidefx.com/docs/houdini/nodes/sop/ml_deform.html" }, { "label": "SideFX · Houdini 21 deformer introduction", "url": "https://www.sidefx.com/docs/houdini/news/21/ml.html" } ] }, { "id": "310", "stable_id": "IRM_OP_310", "software": "SideFX Houdini", "operation": "Houdini ML Pyro Upres", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini ML Volume Upres predicts higher-resolution density tiles from a lower-resolution pyro simulation.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned reconstruction supports the fidelity-to-perceptual envelope 5–8. Optional sharpening and other-field upsampling are excluded, and current evidence does not fix a point within that envelope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "SideFX · Neural regression and volume-upres training", "url": "https://www.sidefx.com/docs/houdini/nodes/top/ml_trainregression.html" }, { "label": "SideFX · ML Volume Upres inputs and processing controls", "url": "https://www.sidefx.com/docs/houdini/nodes/sop/ml_volumeupres.html" }, { "label": "SideFX · Volume training and inference branches", "url": "https://www.sidefx.com/docs/houdini/ml/ml_trainvolumeupres.html" } ] }, { "id": "311", "stable_id": "IRM_OP_311", "software": "SideFX Houdini", "operation": "Houdini ML Style Transfer", "y": 4.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini trains and runs the documented GAN and U-Net style-transfer or colorization mapping.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned image transformation changes appearance. The fixed-function-to-perceptual envelope 5–12 accommodates the documented task family without assuming unrestricted scene generation or a particular loss weighting.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "SideFX · Style-transfer neural architecture and output tasks", "url": "https://www.sidefx.com/docs/houdini/nodes/top/ml_trainstyletransfer.html" } ] }, { "id": "312", "stable_id": "IRM_OP_312", "software": "SideFX Houdini", "operation": "Houdini Neural Point Surface", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini Neural Point Surface predicts a VDB surface using a pretrained convolutional model.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The surface reconstruction preserves geometry while resolving missing detail and material-specific appearance. A cross-domain fidelity-to-perceptual envelope 5–8 is more defensible than equating every CNN with unrestricted generation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "SideFX · Neural Point Surface model and revision controls", "url": "https://www.sidefx.com/docs/houdini/nodes/sop/neuralpointsurface.html" } ] }, { "id": "313", "stable_id": "IRM_OP_313", "software": "SideFX Houdini", "operation": "Houdini SOP Networks", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Houdini evaluates a specified conventional SOP network for geometry and attributes.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The defined nodes contain no learned model. Procedural complexity and downstream propagation do not change the branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "SideFX · SOP network structure", "url": "https://www.sidefx.com/docs/houdini/nodes/sop/index.html" } ] }, { "id": "314", "stable_id": "IRM_OP_314", "software": "SideFX Houdini", "operation": "ONNX Inference SOP ML Terrain Example", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SideFX's ML Terrain example uses a specified Pix2Pix ONNX model to predict detailed terrain from a painted heightfield.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned terrain transformation supports the fixed-function-to-perceptual envelope 5–12 by cross-domain analogy. ONNX is an execution format and supplies no coordinate by itself.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "SideFX · Pix2Pix ONNX terrain and painted-input workflow", "url": "https://www.sidefx.com/contentlibrary/ml-terrain/" }, { "label": "SideFX · ONNX Inference inputs and visual examples", "url": "https://www.sidefx.com/docs/houdini/nodes/sop/onnx.html" } ] }, { "id": "315", "stable_id": "IRM_OP_315", "software": "Sketch", "operation": "Native image vector and layer editing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Sketch performs direct native image, vector and layer editing with conventional layout relationships.", "retained_learning": false, "rating_input": {}, "rating_rationale": "External assistants are excluded. The earlier whole-application label is confined here to these documented native editing operations, without requiring generated source material.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Sketch · Sketch overview", "url": "https://www.sketch.com/blog/sketch-and-ai/" }, { "label": "Sketch · Symbols and overrides", "url": "https://www.sketch.com/docs/symbols-and-styles/symbols/" }, { "label": "Sketch image editing", "url": "https://www.sketch.com/docs/designing/images/" } ] }, { "id": "316", "stable_id": "IRM_OP_316", "software": "SketchUp", "operation": "AI Generate Object Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SketchUp AI Generate predicts a mesh asset through the documented image or text route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial construction supports a generation-reference match. Product documentation establishes the task; the exact deployed architecture remains undisclosed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Engineering.com · AI Object Generation", "url": "https://www.engineering.com/trimble-launches-sketchup-ai-for-rendering-and-object-generation/" }, { "label": "SketchUp · AI object generation", "url": "https://help.sketchup.com/en/ai-generate" } ] }, { "id": "317", "stable_id": "IRM_OP_317", "software": "SketchUp", "operation": "AI Render Image Output", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SketchUp AI Render synthesizes an image from a viewport and the selected generation controls.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned renderer determines image appearance, supporting the generation comparison. The existing editable source model does not convert synthesized image detail into conventional output.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Trimble · AI Render (formerly Diffusion)", "url": "https://sketchup.trimble.com/blog/en-US/article/realize-your-vision-faster-with-diffusion" }, { "label": "SketchUp · AI rendering and optional overlays", "url": "https://help.sketchup.com/en/using-ai-render" } ] }, { "id": "318", "stable_id": "IRM_OP_318", "software": "SketchUp", "operation": "Component Definition and Instance Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SketchUp evaluates manually constructed component definitions and instances.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Conventional geometry and instance relationships determine this scoped model operation; inference snapping is not evidence of a trained model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Focused SketchUp · Native Modeling", "url": "https://focusedsketchup.com/sketchup-2025-review-useful-updates-or-just-another-version-number/" }, { "label": "SketchUp · Component definitions and instances", "url": "https://help.sketchup.com/en/sketchup/creating-basic-component" } ] }, { "id": "319", "stable_id": "IRM_OP_319", "software": "Sloyd", "operation": "AI Template Assembly from Text", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Sloyd's Template Editor uses AI to choose and assemble parametric components from text.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned structural decisions remain in a conventionally evaluated model. The synthesis-to-region envelope 2–10 records that cross-domain responsibility without giving native parameters an automatic X bonus.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Sloyd · Product overview", "url": "https://www.sloyd.ai" }, { "label": "Sloyd · Text generation and template assembly distinction", "url": "https://www.sloyd.ai/text-to-3d" } ] }, { "id": "320", "stable_id": "IRM_OP_320", "software": "Sloyd", "operation": "Image to Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Sloyd's image-to-3D route predicts a mesh before later sculpting, rigging or assembly.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned shape construction supports generation at 2. Optional preceding image editing is excluded from the scoped pass.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Sloyd · Mesh generation output settings", "url": "https://api-dashboard.sloyd.ai/documentation/endpoints/3d-generation" } ] }, { "id": "321", "stable_id": "IRM_OP_321", "software": "Sloyd", "operation": "Manual Parametric Template Editing", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Sloyd evaluates manually chosen parametric template controls.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The manual template route is conventional; the separate AI assembly and free-form mesh generators are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Sloyd · Parametric templates and separate generated meshes", "url": "https://www.sloyd.ai/" } ] }, { "id": "322", "stable_id": "IRM_OP_322", "software": "SolidWorks", "operation": "Parametric Feature Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SolidWorks evaluates a native parametric feature model with its design history.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Feature and constraint evaluation are conventional within the scoped operation. This does not classify separate learned assistants or simulation surrogates.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Wikipedia · SolidWorks", "url": "https://en.wikipedia.org/wiki/SolidWorks" }, { "label": "SOLIDWORKS · Rollback and editable feature history", "url": "https://help.solidworks.com/2025/English/SolidWorks/sldworks/c_rollback_bar.htm" } ] }, { "id": "323", "stable_id": "IRM_OP_323", "software": "SPAR3D (Stable Point Aware 3D)", "operation": "Single Image to Mesh Neural Reconstruction", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SPAR3D predicts a point representation and reconstructs a mesh from a single image.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial synthesis supports a generation-reference match. Optional point-cloud edits before a later reconstruction are outside this initial pass.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Stability AI · Single-Image Reconstruction", "url": "https://stability.ai/news/stable-point-aware-3d" }, { "label": "Stability AI · Point diffusion and triplane mesh generation", "url": "https://stability.ai/news-updates/stable-point-aware-3d" } ] }, { "id": "324", "stable_id": "IRM_OP_324", "software": "Spline", "operation": "AI Generated Mesh in Editor", "y": 4.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Spline's AI Generate route produces a mesh through a hosted learned generator.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The common generation task supports 2 provisionally across the recorded engine family. A deployed-model comparison must name its engine; native parametric object editing is a separate operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Spline · AI 3D Generation", "url": "https://spline.design/ai-generate" } ] }, { "id": "325", "stable_id": "IRM_OP_325", "software": "Spline", "operation": "Parametric Object Editing", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Spline evaluates manually specified native parametric objects and their properties.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The defined editing route is conventional. Generated mesh imports and Spell world generation are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Spline · Design Editor", "url": "https://spline.design" }, { "label": "Spline · Parametric object editing", "url": "https://docs.spline.design/designing-in-3-d/objects/working-with-parametric-objects" } ] }, { "id": "326", "stable_id": "IRM_OP_326", "software": "Spline (Spell)", "operation": "Spell World Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Spline Spell uses diffusion to generate an initial world volume.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial construction matches the generation reference by cross-domain analogy. Later editing is outside this initial-generation scope.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Spline · Spell World Generation", "url": "https://blog.spline.design/introducing-spell" } ] }, { "id": "327", "stable_id": "IRM_OP_327", "software": "Stable Diffusion WebUI Forge", "operation": "SD3.5 Large text-to-image output", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Forge executes SD3.5 Large text-to-image generation without optional LoRAs, ControlNets or later editing.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generator matches 2. This host route overlaps the ComfyUI example and is retained as a catalogue entry, not counted as independent evidence for a different mechanism.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "GitHub · stable-diffusion-webui-forge", "url": "https://github.com/lllyasviel/stable-diffusion-webui-forge" }, { "label": "Forge — SD3.5 engine", "url": "https://github.com/lllyasviel/stable-diffusion-webui-forge/blob/main/backend/diffusion_engine/sd35.py" }, { "label": "Stability AI — current core models", "url": "https://stability.ai/core-models" } ] }, { "id": "331", "stable_id": "IRM_OP_331", "software": "SuperSplat", "operation": "Direct Gaussian Selection and Transform", "y": 5.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SuperSplat performs direct selection, transformation and cleanup of supplied Gaussian primitives.", "retained_learning": false, "rating_input": {}, "rating_rationale": "These editing operations are conventional even when the supplied representation was learned earlier. This boundary excludes fitting the scene representation itself.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "PlayCanvas · Editing", "url": "https://playcanvas.com/products/supersplat" }, { "label": "PlayCanvas · Direct selection and splat transforms", "url": "https://developer.playcanvas.com/user-manual/supersplat/editor/transform-measure-align/" }, { "label": "PlayCanvas Gaussian scene workflow", "url": "https://developer.playcanvas.com/user-manual/gaussian-splatting/" } ] }, { "id": "332", "stable_id": "IRM_OP_332", "software": "SWAPP (over Autodesk Revit / Archicad)", "operation": "Frank Native Drawing Annotation Automation", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "SWAPP Frank predicts and places native drawing annotations using the BIM model and learned documentation practices.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned visual organization survives in the drawings. The synthesis-to-region envelope is a cross-domain comparison; it does not classify the separate 2D-to-3D modeling product.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "SWAPP · Documentation Automation", "url": "https://swapp.ai/" }, { "label": "SWAPP · Frank documentation automation", "url": "https://swapp.ai/products/documentation/" }, { "label": "SWAPP · Native application workflow", "url": "https://swapp.ai/faq/" } ] }, { "id": "333", "stable_id": "IRM_OP_333", "software": "TattoosAI", "operation": "Prompt to tattoo design image", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TattoosAI generates a tattoo-concept image from text and style controls.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The documented learned image-generation service supports 2 provisionally. No particular model architecture or physical tattooing process is claimed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "TattoosAI · Prompt Generation", "url": "https://tattoosai.com/" }, { "label": "TattoosAI prompt-to-design workflow", "url": "https://www.tattoosai.com/" } ] }, { "id": "334", "stable_id": "IRM_OP_334", "software": "TestFit", "operation": "Site Solver Live Parametric Layout", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TestFit Site Solver evaluates its documented procedural layout engine under the chosen site constraints.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The vendor explicitly states that its engine does not run on a trained model. That evidence supports 15 for the solver-only boundary; deterministic execution alone would not establish absence of learning.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "TestFit engine and trained-model distinction", "url": "https://www.testfit.io/blog/testfit-mcp-feasibility-studies-in-plain-language", "provenance": "primary_source_refreshed_or_recovered_2026-09-29" }, { "label": "TestFit · Site Solver", "url": "https://www.testfit.io/product/site-solver" }, { "label": "TestFit · Deterministic design engine", "url": "https://www.testfit.io/mcp" }, { "label": "TestFit · Solver and manual modes", "url": "https://support.testfit.io/knowledge/testfit-glossary" } ] }, { "id": "335", "stable_id": "IRM_OP_335", "software": "Topaz Gigapixel", "operation": "Core Upscale — Standard 2", "y": 3.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Gigapixel Standard 2 reconstructs an enlarged image with Face Recovery disabled.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The core model's detail-preserving reconstruction supports 5–8 between fidelity and perceptual references. The published model description does not supply a training objective precise enough to select one t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Gigapixel Core Upscale", "url": "https://docs.topazlabs.com/topaz-gigapixel/enhancements/ai-models/core-models" }, { "label": "Krea Topaz API model choices", "url": "https://www.krea.ai/docs/api-reference/image-enhance/topaz" } ] }, { "id": "336", "stable_id": "IRM_OP_336", "software": "Topaz Gigapixel", "operation": "Face Recovery 3 with Strength Adjustment", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Gigapixel Face Recovery 3 reconstructs selected faces and applies the result with strength adjustment.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned face reconstruction supports 5–8. The conventional application stage retains the prediction and supplies no automatic reduction or score proportional to strength.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Gigapixel face selection and recovery", "url": "https://docs.topazlabs.com/topaz-gigapixel/enhancements/face-recovery" } ] }, { "id": "337", "stable_id": "IRM_OP_337", "software": "Topaz Gigapixel", "operation": "Redefine Creative Upscale", "y": 3.0, "y_status": "work_in_progress", "x": 5.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Gigapixel Redefine performs the documented creative diffusion-based reconstruction of an image.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0" }, "rating_rationale": "The perceptual-reconstruction reference at 5 is the closest task comparison. A diffusion architecture does not by itself equate a creative enhancement task with unrestricted scene synthesis.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "5", "display": "5.0", "t": "0", "calculation": "X = 5 + 0 × (8 − 5) = 5." }, "sources": [ { "label": "Topaz Gigapixel current generative model guide", "url": "https://docs.topazlabs.com/topaz-gigapixel/enhancements/ai-models/generative-models" }, { "label": "Topaz explanation of precision and diffusion enhancement", "url": "https://www.topazlabs.com/learn/responsible-nonfiction-ai-enhancement" }, { "label": "Topaz staff release notes for Gigapixel 8.4 Redefine modes", "url": "https://community.topazlabs.com/t/gigapixel-v8-4-0/91353" }, { "label": "Gigapixel settings and Auto Mode behavior", "url": "https://docs.topazlabs.com/topaz-gigapixel/enhancements/settings" }, { "label": "Topaz staff Gigapixel 8.2 release and local versus cloud changes", "url": "https://community.topazlabs.com/t/gigapixel-v8-2-0/87090" }, { "label": "Krea Topaz Generative model enum", "url": "https://www.krea.ai/docs/api-reference/image-enhance/topaz-generative" }, { "label": "Krea Topaz Generative UI", "url": "https://www.krea.ai/blog/how-to-enhance-images-with-topaz-generative" } ] }, { "id": "338", "stable_id": "IRM_OP_338", "software": "Topaz Photo", "operation": "Adjust Lighting — Contextual Correction", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Adjust Lighting v2 predicts a contextual illumination correction at full application.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned appearance mapping supports the fixed-function-to-perceptual envelope 5–12. Exposure-like controls do not establish a conventional curve or an exact comparison position.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Topaz · Contextual lighting adjustment", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/adjust-lighting" } ] }, { "id": "339", "stable_id": "IRM_OP_339", "software": "Topaz Photo", "operation": "Autopilot Denoise and Sharpen Workflow", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Autopilot selects and applies Denoise and Sharpen on non-RAW imagery in the defined filter session.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned restoration and automatic model selection remain in the result. The 5–8 reconstruction envelope accommodates the unspecified reconstruction objectives without averaging the stages.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Photo AI Autopilot", "url": "https://www.topazlabs.com/topaz-photo" }, { "label": "Topaz · Autopilot filter configuration", "url": "https://docs.topazlabs.com/topaz-photo/functions/autopilot-and-configuration" }, { "label": "Topaz · Photo AI and Topaz Photo distinction", "url": "https://docs.topazlabs.com/topaz-photo/tpai-vs-tp" } ] }, { "id": "340", "stable_id": "IRM_OP_340", "software": "Topaz Photo", "operation": "Balance Color with Source Blending", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Balance Color predicts a correction and blends it with the source at nonzero intermediate opacity.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "The correction retains learned appearance determination, supporting 5–12. Opacity is not t; the reference position concerns the mechanism's responsibility, not a pixel contribution percentage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Topaz · Balance Color and opacity", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/balance-color" } ] }, { "id": "341", "stable_id": "IRM_OP_341", "software": "Topaz Photo", "operation": "Denoise — Non-RAW Normal Model", "y": 8.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Non-RAW Normal Denoise predicts a noise-reduced image at the scoped filter stage.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The fidelity-restoration task matches 8 provisionally. RAW demosaicing and other model variants are excluded, and training-loss equivalence with DnCNN is not claimed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Topaz · Non-RAW denoise controls", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/denoise-raw-and-non-raw" }, { "label": "Topaz · RAW denoise and demosaicing", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/raw-denoise" } ] }, { "id": "342", "stable_id": "IRM_OP_342", "software": "Topaz Photo", "operation": "Dust and Scratch Removal", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Dust and Scratch v2 reconstructs regions affected by image damage.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The repair task supports the fidelity-to-perceptual envelope 5–8. It is not automatically unrestricted generation merely because damaged pixels are replaced.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Dust and Scratch reconstruction", "url": "https://docs.topazlabs.com/topaz-photo/dust-and-scratch" } ] }, { "id": "343", "stable_id": "IRM_OP_343", "software": "Topaz Photo", "operation": "Preserve Text — Masked Restoration", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Preserve Text restores manually masked lettering and applies the restoration over other filters.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned image-detail reconstruction supports 5–8. The explicit mask retains the restoration's influence and does not add a conventional-component bonus; OCR and text authorship are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Preserve Text mask and models", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/preserve-text" } ] }, { "id": "344", "stable_id": "IRM_OP_344", "software": "Topaz Photo", "operation": "Recover Faces with Source Blending", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Recover Faces v3 reconstructs selected faces and blends the reconstruction with the source.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The fidelity-to-perceptual envelope 5–8 follows the retained face-reconstruction task. This overlaps Gigapixel's recovery family; blending does not determine t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Recover Faces controls", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/recover-faces" } ] }, { "id": "345", "stable_id": "IRM_OP_345", "software": "Topaz Photo", "operation": "Recover v3 Upscale", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Recover v3 enlarges and reconstructs degraded imagery with optional pre-downscaling excluded.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Restorative enlargement supports 5–8. The current host and model are retained explicitly rather than attributing the mechanism to the older Gigapixel route.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Gigapixel generative models", "url": "https://docs.topazlabs.com/gigapixel-ai/filters-panel/generative-models" }, { "label": "Topaz Photo current upscale models", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/upscale-and-resize" } ] }, { "id": "346", "stable_id": "IRM_OP_346", "software": "Topaz Photo", "operation": "Remove Object — Regional Reconstruction", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Remove v2 synthesizes replacement content for a selected object region.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Semantic removal and completion of the surrounding scene supports the generation reference at 2. Selection size does not supply an inference percentage.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "X · Photo AI Remove", "url": "https://x.com/topazlabs/status/1920680835130233140" }, { "label": "Topaz · Remove Object and revision limits", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/remove-object" } ] }, { "id": "347", "stable_id": "IRM_OP_347", "software": "Topaz Photo", "operation": "Sharpen — Standard Model", "y": 8.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Standard Sharpen estimates detail lost through blur, without a separate denoise pass.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "Its scoped restoration responsibility matches the fidelity-restoration reference at 8 provisionally. This is distinct from prescribed edge-contrast sharpening.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Topaz · Sharpen models and selection", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/sharpen" } ] }, { "id": "348", "stable_id": "IRM_OP_348", "software": "Topaz Photo", "operation": "Super Focus with Post-Render Selection", "y": 5.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Super Focus v3 reconstructs blurred detail and permits subsequent selection and strength adjustment.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned detail reconstruction supports 5–8. Post-render application controls retain the learned result and do not independently change the comparison coordinate.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Photo AI Super Focus", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/super-focus" } ] }, { "id": "349", "stable_id": "IRM_OP_349", "software": "Topaz Photo", "operation": "Wonder v3.5 Image Repair", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Photo Wonder v3.5 performs an integrated learned repair and enlargement pass.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The fidelity-to-perceptual reconstruction envelope 5–8 is supported; the available objective evidence does not select a precise t. Multiple repairs within one model do not require an arithmetic sum.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Wonder image repair", "url": "https://docs.topazlabs.com/topaz-photo/enhancements/wonder" } ] }, { "id": "350", "stable_id": "IRM_OP_350", "software": "Topaz Video", "operation": "Deinterlace — Dione DV", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Dione DV deinterlaces and enlarges footage with added noise disabled.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned temporal and spatial reconstruction supports 5–8 by comparison with fidelity and perceptual reconstruction. Field order and output dimensions do not set t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Video enhancement and deinterlacing models", "url": "https://docs.topazlabs.com/topaz-video/filters/enhancement" } ] }, { "id": "351", "stable_id": "IRM_OP_351", "software": "Topaz Video", "operation": "Frame Interpolation — Apollo", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Apollo estimates motion and constructs intermediate frames for the scoped twofold rate conversion.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned temporal reconstruction supports a cross-domain 5–8 envelope. Duplicate replacement and slow motion are excluded; frame-count multiplication is not an inference weight.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz Video frame interpolation guide", "url": "https://docs.topazlabs.com/topaz-video/filters/frame-interpolation" }, { "label": "Topaz Video importing previewing and exporting", "url": "https://docs.topazlabs.com/topaz-video/reference-guide/importing-previewing-and-exporting" }, { "label": "Topaz staff May 2023 video roadmap and Apollo comparison", "url": "https://community.topazlabs.com/t/video-roadmap-update-may-2023/45285" }, { "label": "Topaz developer Apollo training and frame generation explanation September 2022", "url": "https://community.topazlabs.com/t/video-ai-3-0-0-5-b/34458/125" }, { "label": "Topaz current and previous desktop model versions", "url": "https://docs.topazlabs.com/topaz-video/advanced-functions/previous-model-versions" } ] }, { "id": "354", "stable_id": "IRM_OP_354", "software": "Topaz Video", "operation": "Hyperion 2.5 SDR to HDR", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Hyperion 2.5 predicts an expanded luminance representation from SDR footage.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF12", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned appearance mapping supports 5–12 between fixed-function transformation and perceptual reconstruction. The change is more than export metadata, but its model identity does not determine t.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF12" ], "conditional_range": [ "5", "12" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–12.0", "calculation": "X = 5 + t × (12 − 5); t = 0–1; X = 5–12. No midpoint assigned." }, "sources": [ { "label": "Topaz · Hyperion SDR to HDR", "url": "https://docs.topazlabs.com/topaz-video/filters/sdr-to-hdr/hyperion" }, { "label": "Topaz current Hyperion 2.5", "url": "https://docs.topazlabs.com/topaz-video/filters/sdr-to-hdr/hyperion-2" } ] }, { "id": "352", "stable_id": "IRM_OP_352", "software": "Topaz Video", "operation": "Motion Deblur — Themis", "y": 2.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Themis performs learned motion-deblur restoration with other filters disabled.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The stated fidelity-restoration responsibility supports 8 provisionally. Stabilization and frame interpolation are different scoped tasks.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Topaz · Themis motion deblur", "url": "https://docs.topazlabs.com/topaz-video/filters/motion-deblur" } ] }, { "id": "353", "stable_id": "IRM_OP_353", "software": "Topaz Video", "operation": "Proteus Natural Progressive Video Enhancement", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Proteus Natural reconstructs progressive footage without separate source blending or other filters.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned image-detail enhancement supports the fidelity-to-perceptual envelope 5–8. Public task documentation does not establish one training-objective position.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Video AI overview", "url": "https://www.topazlabs.com/topaz-video-ai" }, { "label": "Topaz current enhancement models", "url": "https://docs.topazlabs.com/topaz-video/filters/enhancement" } ] }, { "id": "355", "stable_id": "IRM_OP_355", "software": "Topaz Video", "operation": "Stabilization with Full-Frame Border Synthesis", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Full Frame stabilization combines geometric stabilization with learned reconstruction of exposed borders.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The border-reconstruction task supports 5–8 provisionally. Conventional geometric motion does not dilute the retained prediction; Auto Crop is outside this scoped branch.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz · Stabilization geometry and generated borders", "url": "https://docs.topazlabs.com/topaz-video/filters/stabilization" } ] }, { "id": "356", "stable_id": "IRM_OP_356", "software": "Topaz Video", "operation": "Starlight Precise 2.6 Enhancement", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Topaz Video Starlight Precise 2.6 reconstructs degraded video through its documented diffusion-based enhancement route.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The restorative task supports 5–8. Diffusion architecture, processing cost and pixel-count increase do not automatically establish unrestricted generation or a numerical inference amount.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Topaz Video Starlight Precise 2.5 and 2.6 desktop guide", "url": "https://docs.topazlabs.com/topaz-video/project-starlight-series/starlight-precise-25" }, { "label": "Topaz staff Video 1.7 release introducing Precise 2.6", "url": "https://community.topazlabs.com/t/topaz-video-1-7-0/104328" }, { "label": "Topaz Starlight model family and execution distinction", "url": "https://docs.topazlabs.com/topaz-video/project-starlight-series" }, { "label": "Topaz explanation of precision and diffusion enhancement", "url": "https://www.topazlabs.com/learn/responsible-nonfiction-ai-enhancement" }, { "label": "Higgsfield hosted Topaz restoration guide for comparison", "url": "https://higgsfield.ai/blog/ai-video-restoration-high-quality" }, { "label": "Topaz · Original Starlight diffusion and export workflow", "url": "https://docs.topazlabs.com/video-ai/project-starlight" }, { "label": "Topaz · Video filter families", "url": "https://docs.topazlabs.com/topaz-video/filters" } ] }, { "id": "357", "stable_id": "IRM_OP_357", "software": "TouchDesigner", "operation": "MediaPipe Segmentation and Video Cutout", "y": 7.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TouchDesigner uses MediaPipe image segmentation to key source video and composite a background.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "The retained learned region determination matches 10. Conventional compositing does not erase the predicted mask's role.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "GitHub · mediapipe-touchdesigner", "url": "https://github.com/torinmb/mediapipe-touchdesigner" }, { "label": "Derivative · Composite TOP image construction", "url": "https://derivative.ca/UserGuide/Composite_TOP" }, { "label": "Plugin implementation: src/imageSegmentation.js, main branch consulted 09222026", "url": "https://github.com/torinmb/mediapipe-touchdesigner/blob/main/src/imageSegmentation.js" }, { "label": "Google Image Segmenter guide, updated 08172026", "url": "https://developers.google.com/edge/mediapipe/solutions/vision/image_segmenter" }, { "label": "Google Multiclass Segmentation model card, model date 05102023", "url": "https://storage.googleapis.com/mediapipe-assets/Model%20Card%20Multiclass%20Segmentation.pdf" }, { "label": "Derivative Matte TOP reference, channel selection and input roles", "url": "https://derivative.ca/UserGuide/Matte_TOP" } ] }, { "id": "358", "stable_id": "IRM_OP_358", "software": "TouchDesigner", "operation": "NVIDIA Background Segmentation and Composite", "y": 7.0, "y_status": "work_in_progress", "x": 10.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TouchDesigner uses NVIDIA Background TOP to predict a mask for a synchronized video composite.", "retained_learning": true, "rating_input": { "lower": "REF08", "higher": "REF10", "t": "1" }, "rating_rationale": "Learned region determination matches 10; the following composite adds no hybrid offset. Face tracking is not the mechanism assessed here.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF08", "REF10" ], "status": "point", "raw_x": "10", "display": "10.0", "t": "1", "calculation": "X = 8 + 1 × (10 − 8) = 10." }, "sources": [ { "label": "Derivative · NVIDIA Maxine Operators", "url": "https://docs.derivative.ca/Face_Track_CHOP" }, { "label": "Derivative · NVIDIA Background TOP", "url": "https://docs.derivative.ca/Nvidia_Background_TOP" }, { "label": "Derivative NVIDIA Background TOP reference; retrieved through indexed official page", "url": "https://derivative.ca/UserGuide/NVIDIA_Background_TOP" }, { "label": "NVIDIA AI Green Screen filter guide, updated 09102026", "url": "https://docs.nvidia.com/maxine/vfx/latest/Filters/AIGreenScreen.html" }, { "label": "Derivative experimental release notes, build 2025.30280 SDK update", "url": "https://docs.derivative.ca/Release_Notes/Experimental" }, { "label": "Derivative Matte TOP reference, channel selection and input roles", "url": "https://derivative.ca/UserGuide/Matte_TOP" }, { "label": "Derivative Composite TOP reference, Over formula and alignment controls", "url": "https://derivative.ca/UserGuide/Composite_TOP" } ] }, { "id": "359", "stable_id": "IRM_OP_359", "software": "TouchDesigner", "operation": "NVIDIA Denoise TOP — Video Noise Removal", "y": 7.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TouchDesigner NVIDIA Denoise TOP predicts noise-reduced video at unchanged resolution in Denoising mode.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The fidelity-restoration responsibility matches 8. Artifact Reduction and later compositing are excluded; source subject matter and image size supply no score components.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Derivative · NVIDIA Denoise TOP", "url": "https://derivative.ca/UserGuide/NVIDIA_Denoise_TOP" }, { "label": "NVIDIA Webcam Denoise filter guide, updated 09102026", "url": "https://docs.nvidia.com/maxine/vfx/latest/Filters/WebcamDenoiser.html" }, { "label": "NVIDIA Video Noise Reduction collection; linked model page failed retrieval", "url": "https://catalog.ngc.nvidia.com/orgs/nvidia/maxine/collections/nvvfxdenoising/-" }, { "label": "NVIDIA Maxine VFX SDK repository, feature and sample-app descriptions", "url": "https://github.com/NVIDIA-Maxine/Maxine-VFX-SDK" }, { "label": "Derivative experimental release notes, build 2025.30280 SDK update", "url": "https://docs.derivative.ca/Release_Notes/Experimental" } ] }, { "id": "360", "stable_id": "IRM_OP_360", "software": "TouchDesigner", "operation": "NVIDIA Upscaler TOP — Super-Resolution", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TouchDesigner NVIDIA Upscaler TOP Super Res 2 reconstructs higher-resolution frames with Artifact Reduction disabled.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "The learned reconstruction task supports 5–8. Available documentation does not establish a precise loss-function comparison; the separate Upscale filter is not silently substituted.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "Derivative · NVIDIA Upscaler TOP modes", "url": "https://derivative.ca/UserGuide/NVIDIA_Upscaler_TOP" }, { "label": "NVIDIA · Video Effects SDK filters", "url": "https://docs.nvidia.com/deeplearning/maxine/vfx-sdk-programming-guide/index.html" }, { "label": "NVIDIA Super Resolution filter guide, updated 01082026", "url": "https://docs.nvidia.com/maxine/vfx/latest/Filters/SuperResolution.html" }, { "label": "NVIDIA NGC Super Resolution collection; model-card route did not resolve", "url": "https://catalog.ngc.nvidia.com/orgs/nvidia/maxine/collections/nvvfxsuperres/-" }, { "label": "NVIDIA Video Super Resolution guide: separate newer filter, not assigned to this host operation", "url": "https://docs.nvidia.com/maxine/vfx/latest/Filters/VideoSuperResolution.html" } ] }, { "id": "361", "stable_id": "IRM_OP_361", "software": "TouchDesigner", "operation": "Transform and Composite Two Images", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TouchDesigner transforms one supplied image and composites it over another with the specified TOP nodes.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed geometry and compositing arithmetic contain no in-scope trained prediction. External image generation is outside this operation.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Derivative · Network Editor", "url": "https://docs.derivative.ca/Network_Editor" }, { "label": "Derivative · Transform TOP", "url": "https://derivative.ca/UserGuide/Transform_TOP" }, { "label": "Derivative Composite TOP: Over equation, layer order, native sizing", "url": "https://derivative.ca/UserGuide/Composite_TOP" } ] }, { "id": "362", "stable_id": "IRM_OP_362", "software": "Tripo AI", "operation": "H3.1 Image to Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Tripo H3.1 predicts mesh geometry from an image before retopology, rigging or segmentation.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial synthesis supports generation at 2 by cross-domain analogy. The current recorded model identity is retained; exact architecture is not established.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Technology.org · Image-to-3D", "url": "https://www.technology.org/2026/03/26/tripo-ai-introduces-h3-1-advancing-high-fidelity-ai-3d-generation-for-production/" }, { "label": "Tripo · v3.0 model generation", "url": "https://www.tripo3d.ai/blog/introducing-tripo-new-algorithm3" }, { "label": "Tripo H3.1 model", "url": "https://developers.tripo3d.ai/en/models/v3-1" } ] }, { "id": "363", "stable_id": "IRM_OP_363", "software": "Tripo AI", "operation": "H3.1 Text to Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Tripo H3.1 predicts a mesh from text with optional part generation disabled.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The construction responsibility matches the related image route at 2. Conditioning format does not create an automatic coordinate difference.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Tripo · Text to model and optional segmented parts", "url": "https://docs.tripo3d.ai/model-generation/text-to-model-v3-0-v3-1.html" }, { "label": "Tripo H3.1 model", "url": "https://developers.tripo3d.ai/en/models/v3-1" } ] }, { "id": "364", "stable_id": "IRM_OP_364", "software": "TripoSR (open-source code)", "operation": "Single Image to Mesh Reconstruction", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "TripoSR predicts a 3D representation from one image and decodes a mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned spatial-construction task supports a generation comparison. Separate preprocessing and subsequent scene assembly are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Stability AI · Single-Image Reconstruction", "url": "https://stability.ai/news-updates/triposr-3d-generation" }, { "label": "VAST and Stability AI · TripoSR single image reconstruction", "url": "https://github.com/VAST-AI-Research/TripoSR" } ] }, { "id": "365", "stable_id": "IRM_OP_365", "software": "Unity", "operation": "AI Sprite Generation and Inpainting", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unity's Sprite Generator synthesizes or inpaints sprite imagery through the selected learned image model.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The generation responsibility matches 2. Integration inside a game engine does not reduce the model's retained determination.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Unity · Sprite inpainting and alternative assets", "url": "https://unity.com/blog/unity-ai-sprite-generator" }, { "label": "CG Channel · Unity AI in 6.2", "url": "https://www.cgchannel.com/2025/08/unity-rolls-out-unity-ai-in-unity-6-2/" }, { "label": "Unity · Visual generators and output types", "url": "https://docs.unity3d.com/Packages/com.unity.ai.assistant@2.19/manual/Sprite/visual-assets.html" } ] }, { "id": "366", "stable_id": "IRM_OP_366", "software": "Unity", "operation": "Assistant Scene Object Placement", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unity Assistant selects and places scene objects through its documented Run action.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned structural choices remain in the scene. The synthesis-to-region envelope 2–10 is a cross-domain comparison; informational answers and file organization are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "Unity · Assistant scene actions", "url": "https://docs.unity.cn/6000.3/Documentation/Manual/com.unity.ai.assistant.html" } ] }, { "id": "368", "stable_id": "IRM_OP_368", "software": "Unity Editor", "operation": "MCP LLM Scene Hierarchy Construction", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The CoplayDev Unity MCP integration uses an LLM to construct GameObjects and their hierarchy.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned scene-structure choices survive conventional API execution. The 2–10 envelope records that responsibility without treating the MCP transport as a model.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "GitHub · unity-mcp", "url": "https://github.com/CoplayDev/unity-mcp" } ] }, { "id": "367", "stable_id": "IRM_OP_367", "software": "Unity", "operation": "GameObject Hierarchy and Component Editing", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unity evaluates manually edited GameObjects, components and hierarchy relationships.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped native editing is conventional. Runtime learned behavior and prior asset generation are separate operations.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Unity · Unity overview", "url": "https://docs.unity3d.com/6000.2/Documentation/Manual/unity-editor.html" } ] }, { "id": "369", "stable_id": "IRM_OP_369", "software": "Unreal Engine", "operation": "Blueprint Scene Construction", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unreal Engine evaluates explicitly authored Blueprint scene construction and actor-property edits.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped graph and property operations are conventional. Runtime neural inference and performance capture are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Epic Games · Unreal Blueprint / Level Editing", "url": "https://dev.epicgames.com/documentation/en-us/unreal-engine/blueprints-visual-scripting-in-unreal-engine" }, { "label": "Epic Games · Editor simulation", "url": "https://dev.epicgames.com/documentation/unreal-engine/playing-and-simulating-in-unreal-engine" } ] }, { "id": "374", "stable_id": "IRM_OP_374", "software": "Unreal Engine", "operation": "MCP LLM Blueprint Graph Construction", "y": 7.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "The experimental Unreal MCP integration uses an LLM to construct Blueprint nodes and connections.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF10", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned graph choices support a synthesis-to-region envelope 2–10. This is a documentation-based proposed configuration; current host compatibility has not been executed or verified.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF10" ], "conditional_range": [ "2", "10" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "2.0–10.0", "calculation": "X = 2 + t × (10 − 2); t = 0–1; X = 2–10. No midpoint assigned." }, "sources": [ { "label": "GitHub · unreal-mcp", "url": "https://github.com/chongdashu/unreal-mcp" } ] }, { "id": "373", "stable_id": "IRM_OP_373", "software": "Unreal Engine MetaHuman", "operation": "Current Animator Video and Depth Facial Solve", "y": 8.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "MetaHuman Animator solves facial animation through the current video-and-depth workflow.", "retained_learning": true, "rating_input": {}, "rating_rationale": "The saved evidence establishes learned dependencies but does not separate their retained roles from performer fitting and geometric solving sufficiently to select comparison references. Audio-only routes are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": "Current mechanism or scope calibration unresolved" }, "result": { "status": "unresolved", "branch": "retained_learning", "raw_x": null, "display": "Unresolved", "reason": "The saved evidence establishes learned dependencies but does not separate their retained roles from performer fitting and geometric solving sufficiently to select comparison references. Audio-only routes are excluded." }, "sources": [ { "label": "Epic Games · Unreal MetaHuman Animator", "url": "https://dev.epicgames.com/documentation/en-us/metahuman/metahuman-animator" }, { "label": "Epic · MetaHuman facial performance solving", "url": "https://www.metahuman.com/news/delivering-high-quality-facial-animation-in-minutes-metahuman-animator-is-now-available" }, { "label": "Epic CTO interview · Machine learning core", "url": "https://80.lv/articles/the-epic-games-team-on-lumen-metahuman-ue5-2-demo-ai" }, { "label": "MetaHuman current depth animation", "url": "https://dev.epicgames.com/documentation/en-us/metahuman/animation-from-depth-data" } ] }, { "id": "370", "stable_id": "IRM_OP_370", "software": "Unreal Engine", "operation": "Neural Morph Correction with Skinning", "y": 7.0, "y_status": "work_in_progress", "x": 12.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unreal Neural Morph predicts corrective deformation combined with conventional skinning.", "retained_learning": true, "rating_input": { "lower": "REF12", "higher": "REF12", "t": "0" }, "rating_rationale": "The trained approximation of a deformation mapping supports the fixed-function emulation reference at 12. Skinning retains the predicted correction and does not establish a conventional-only output.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF12", "REF12" ], "status": "point", "raw_x": "12", "display": "12.0", "t": "0", "calculation": "X = 12 + 0 × (12 − 12) = 12." }, "sources": [ { "label": "Epic Games · Unreal ML Deformer", "url": "https://dev.epicgames.com/documentation/unreal-engine/ml-deformer-framework-in-unreal-engine" }, { "label": "Epic · Neural Morph and skinning framework", "url": "https://dev.epicgames.com/documentation/en-us/unreal-engine/ml-deformer-framework-in-unreal-engine" } ] }, { "id": "371", "stable_id": "IRM_OP_371", "software": "Unreal Engine", "operation": "NNE Intel Open Image Denoise Spatial Pass", "y": 3.0, "y_status": "work_in_progress", "x": 8.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unreal's specified NNE route runs Intel Open Image Denoise as a spatial image pass.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "1" }, "rating_rationale": "The learned fidelity-restoration task matches 8. NNE is an execution framework, and neither its name nor other possible models are part of this assignment.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "8", "display": "8.0", "t": "1", "calculation": "X = 5 + 1 × (8 − 5) = 8." }, "sources": [ { "label": "Epic Games · Unreal Neural Network Engine (NNE)", "url": "https://dev.epicgames.com/documentation/en-us/unreal-engine/neural-network-engine-overview-in-unreal-engine" }, { "label": "Unreal · Path tracer NNE denoiser", "url": "https://dev.epicgames.com/documentation/unreal-engine/path-tracer-in-unreal-engine" } ] }, { "id": "372", "stable_id": "IRM_OP_372", "software": "Unreal Engine", "operation": "Sequencer 3D Transform Interpolation", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Unreal Sequencer interpolates specified object and camera transform keys.", "retained_learning": false, "rating_input": {}, "rating_rationale": "Prescribed temporal interpolation contains no trained predictor in this scope. Intermediate poses are computed without learned reconstruction.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Epic · Transform channels and quaternion interpolation", "url": "https://dev.epicgames.com/documentation/en-us/unreal-engine/transform-and-property-tracks?application_version=4.27" } ] }, { "id": "375", "stable_id": "IRM_OP_375", "software": "Upscayl", "operation": "RealESRGAN AnimeVideo v3 still-image upscale", "y": 2.0, "y_status": "work_in_progress", "x": 5.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Upscayl runs the bundled RealESRGAN AnimeVideo v3 model for native fourfold still-image enlargement.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t": "0" }, "rating_rationale": "The model's perceptual reconstruction supports the ESRGAN-family reference at 5. The video-derived model name does not make the scoped output a video, and this host overlaps the same mechanism family.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "status": "point", "raw_x": "5", "display": "5.0", "t": "0", "calculation": "X = 5 + 0 × (8 − 5) = 5." }, "sources": [ { "label": "GitHub · upscayl", "url": "https://github.com/upscayl/upscayl" }, { "label": "Upscayl bundled model files", "url": "https://github.com/upscayl/upscayl/tree/main/models" } ] }, { "id": "376", "stable_id": "IRM_OP_376", "software": "Vectorworks", "operation": "AI Visualizer Generate Image", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Vectorworks AI Visualizer Generate synthesizes an image from a supplied view.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned image appearance construction matches generation at 2. Inpainting and the separate native BIM model are outside this route.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Vectorworks · AI Visualizer", "url": "https://www.vectorworks.net/en-US/newsroom/2025-update-4" }, { "label": "Vectorworks · Stable Diffusion image generation", "url": "https://app-help.vectorworks.net/2025/eng/VW2025_Guide/Rendering2/Generating_AI_images.htm" } ] }, { "id": "377", "stable_id": "IRM_OP_377", "software": "Vectorworks Architect", "operation": "Native BIM Element Modeling", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Vectorworks Architect evaluates manually constructed native BIM elements and their parameters.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped model construction is conventional. AI Visualizer and automatic documentation are separate operations.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "Vectorworks · Vectorworks BIM Modeling", "url": "https://www.vectorworks.net/en-US/architect" } ] }, { "id": "378", "stable_id": "IRM_OP_378", "software": "Veras in supported CAD hosts", "operation": "Image to Video Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Veras produces video from an image through the selected hosted generation route.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned temporal image synthesis supports 2 provisionally at task-family level. The specific provider and model remain necessary execution metadata; no deployed-model equivalence is claimed.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Chaos · Veras image video and 3D workflows", "url": "https://www.chaos.com/veras" } ] }, { "id": "379", "stable_id": "IRM_OP_379", "software": "Veras in supported CAD hosts", "operation": "Veras 5.0 Trellis 2 Image to 3D Mesh", "y": 3.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Veras 5.0 invokes the specified Trellis 2 image-to-3D option to generate a mesh.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "Learned spatial construction supports generation by cross-domain analogy. HY3D and retrieval of an existing Cosmos asset are different routes and are excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "Chaos · Veras 5.0 model choices", "url": "https://blog.chaos.com/veras-5.0" }, { "label": "Chaos · Image-to-3D beta workflow", "url": "https://www.chaos.com/veras/whats-new" } ] }, { "id": "380", "stable_id": "IRM_OP_380", "software": "Veras in supported CAD hosts", "operation": "Viewport to Image Generation", "y": 2.0, "y_status": "work_in_progress", "x": 2.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Veras uses the recorded Nano Banana image-model option to synthesize an image from a viewport.", "retained_learning": true, "rating_input": { "lower": "REF02", "higher": "REF05", "t": "0" }, "rating_rationale": "The learned generation responsibility matches 2. Geometry-override controls do not become inference percentages, and optional compositing is excluded.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF02", "REF05" ], "status": "point", "raw_x": "2", "display": "2.0", "t": "0", "calculation": "X = 2 + 0 × (5 − 2) = 2." }, "sources": [ { "label": "EvolveLAB · Viewport Rendering", "url": "https://www.evolvelab.io/veras" } ] }, { "id": "381", "stable_id": "IRM_OP_381", "software": "vvvv gamma", "operation": "Stride Scene Graph Construction", "y": 7.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "vvvv gamma evaluates an explicitly constructed VL.Stride scene graph.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The bounded native scene construction is conventional. The platform's ability to host learned processing does not establish learning in this graph.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "status": "point", "branch": "no_retained_learning", "raw_x": "15", "display": "15.0", "calculation": "X = 15 by the no retained learning branch." }, "sources": [ { "label": "vvvv · Patching", "url": "https://vvvv.org/" }, { "label": "vvvv · Stride 3D scene graph", "url": "https://thegraybook.vvvv.org/reference/libraries/graphics-3d.html" } ] }, { "id": "382", "stable_id": "IRM_OP_382", "software": "waifu2x in nunif", "operation": "SwinUNet image upscaling", "y": 2.0, "y_status": "work_in_progress", "x": null, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "waifu2x in nunif runs the specified SwinUNet art model at native twofold enlargement.", "retained_learning": true, "rating_input": { "lower": "REF05", "higher": "REF08", "t_interval": [ "0", "1" ] }, "rating_rationale": "Learned detail reconstruction supports 5–8. The current model family is retained without assigning the older implementation's architecture or an undisclosed training objective to it.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. No application execution in this reassessment.", "pending_issue": null }, "result": { "branch": "retained_learning", "references": [ "REF05", "REF08" ], "conditional_range": [ "5", "8" ], "t_interval": [ "0", "1" ], "status": "range", "raw_x": null, "display": "5.0–8.0", "calculation": "X = 5 + t × (8 − 5); t = 0–1; X = 5–8. No midpoint assigned." }, "sources": [ { "label": "GitHub · waifu2x", "url": "https://github.com/nagadomi/waifu2x" }, { "label": "waifu2x current model selection", "url": "https://github.com/nagadomi/nunif/blob/master/waifu2x/hub.py" }, { "label": "waifu2x current GUI model guide", "url": "https://github.com/nagadomi/nunif/blob/master/waifu2x/docs/gui_ja.md" } ] }, { "id": "383", "stable_id": "IRM_OP_383", "software": "Wilcom EmbroideryStudio 2026", "operation": "Manual digitizing and visual stitch-design revision", "y": 8.0, "y_status": "work_in_progress", "x": 15.0, "assessment": { "assessment_date": "2026-09-29", "equation_version": "reference-rating-1", "profile_version": "irm-x-coordinate-series-1", "scope_and_mechanism": "Wilcom EmbroideryStudio evaluates manually specified outlines, stitch types, densities and sequencing for visual design revision.", "retained_learning": false, "rating_input": {}, "rating_rationale": "The scoped digitizing and preview construction contains no documented trained predictor. The artwork's earlier provenance does not change X.", "review_basis": "Reassessment of preserved catalogue evidence; selected primary sources refreshed. 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