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FIGURE_INDEX.md

@@ -17,9 +17,9 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | Original task-suite infographic | `docs/assets/task_suite_infographic.png` | 1800 x 6600 | `scripts/render_task_suite_infographic.py` | Primary visual map of the original task families, verified metrics, and sample modalities; the unified public suite is now documented as 20 tasks. |
 | Episode-to-task pipeline diagram | `docs/assets/pipeline_diagram.png` | 1800 x 1120 | `scripts/generate_visualizations.py` | End-to-end data processing and evaluation pipeline overview. |
 | Qwen3-Omni LoRA training pipeline | `docs/assets/qwen3_omni_lora_pipeline.png` | 1536 x 1024 | `docs/assets/qwen3_omni_lora_pipeline.prompt.md` | Detailed raw-data-to-adapter flow for staged Xperience-10M Qwen3-Omni LoRA training. |
-| Spatial intelligence pipeline placeholder | `docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png` | 1672 x 941 | `docs/assets/foundation-pipelines/prompts.md` | ChatGPT image-generated placeholder for the spatial intelligence training pipeline track. |
-| Human-video world model pipeline placeholder | `docs/assets/foundation-pipelines/human-video-world-model-pipeline.png` | 1672 x 941 | `docs/assets/foundation-pipelines/prompts.md` | ChatGPT image-generated placeholder for the human-video world-model training pipeline track. |
-| Vision-language-action pipeline placeholder | `docs/assets/foundation-pipelines/vision-language-action-pipeline.png` | 1672 x 941 | `docs/assets/foundation-pipelines/prompts.md` | ChatGPT image-generated placeholder for the VLA/action-policy training pipeline track. |
+| Spatial intelligence task-training diagram | `docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png` | 1800 x 1012 | `scripts/render_foundation_pipeline_diagrams.py` | Readable inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence pipeline track. |
+| Human-video world model task-training diagram | `docs/assets/foundation-pipelines/human-video-world-model-pipeline.png` | 1800 x 1012 | `scripts/render_foundation_pipeline_diagrams.py` | Readable inputs-to-future-targets-to-training-to-evaluation diagram for the human-video world-model pipeline track. |
+| Vision-language-action task-training diagram | `docs/assets/foundation-pipelines/vision-language-action-pipeline.png` | 1800 x 1012 | `scripts/render_foundation_pipeline_diagrams.py` | Readable inputs-to-action-targets-to-VLA-training-to-evaluation diagram for the VLA/action-policy pipeline track. |
 | Minimal and neural task architecture map | `docs/assets/task_architectures.png` | 1800 x 2450 | `scripts/render_overview_figures.py` | Minimal and neural heads for the original task contracts and shared feature contracts. |
 | Video modality thumbnail | `docs/assets/modalities/video.jpg` | 880 x 520 | `scripts/export_modality_atlas_assets.py` | Derived thumbnail for synchronized camera streams. |
 | Audio modality thumbnail | `docs/assets/modalities/audio.png` | 880 x 520 | `scripts/export_modality_atlas_assets.py` | Derived waveform thumbnail for the MP4 AAC stream. |

PROJECT_README.md

@@ -881,14 +881,23 @@ so the public claims stay precise:
 | Human-video world models | Predict next action, next subtask, future object set, contact transition, and future state from observed interaction windows. | Partially evidenced by future-task probes and Cosmos-style branches; visual/latent future quality still needs stronger metrics. |
 | Vision-language-action models | Convert egocentric video, captions, hand/body motion, contacts, and objects into action chunks or policy-compatible targets. | Feasible, but gated by action-token conversion, normalization, retargeting evidence, and held-out policy metrics. |
 
-Placeholder visuals for the three tracks are published in
+Task-training diagrams for the three tracks are published in
 [`docs/assets/foundation-pipelines`](docs/assets/foundation-pipelines). They
-are ChatGPT image-generated communication assets, not completed model-quality
-evidence.
+replace the earlier concept-art images and show each direction as
+inputs -> task targets -> model training -> evaluation gates. They are
+communication assets, not completed model-quality evidence.
 
-| Spatial intelligence | Human-video world model | Vision-language-action |
-| --- | --- | --- |
-| ![Spatial intelligence pipeline placeholder](docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png) | ![Human-video world model pipeline placeholder](docs/assets/foundation-pipelines/human-video-world-model-pipeline.png) | ![Vision-language-action pipeline placeholder](docs/assets/foundation-pipelines/vision-language-action-pipeline.png) |
+**Spatial intelligence models**
+
+![Spatial intelligence task-training diagram](docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png)
+
+**Human-video world models**
+
+![Human-video world-model task-training diagram](docs/assets/foundation-pipelines/human-video-world-model-pipeline.png)
+
+**Vision-language-action models**
+
+![Vision-language-action task-training diagram](docs/assets/foundation-pipelines/vision-language-action-pipeline.png)
 
 See [`THREE_FOUNDATION_PIPELINES.md`](THREE_FOUNDATION_PIPELINES.md) and
 [`docs/data/three_foundation_pipelines.json`](docs/data/three_foundation_pipelines.json).

README.md

@@ -903,14 +903,23 @@ so the public claims stay precise:
 | Human-video world models | Predict next action, next subtask, future object set, contact transition, and future state from observed interaction windows. | Partially evidenced by future-task probes and Cosmos-style branches; visual/latent future quality still needs stronger metrics. |
 | Vision-language-action models | Convert egocentric video, captions, hand/body motion, contacts, and objects into action chunks or policy-compatible targets. | Feasible, but gated by action-token conversion, normalization, retargeting evidence, and held-out policy metrics. |
 
-Placeholder visuals for the three tracks are published in
+Task-training diagrams for the three tracks are published in
 [`docs/assets/foundation-pipelines`](docs/assets/foundation-pipelines). They
-are ChatGPT image-generated communication assets, not completed model-quality
-evidence.
+replace the earlier concept-art images and show each direction as
+inputs -> task targets -> model training -> evaluation gates. They are
+communication assets, not completed model-quality evidence.
 
-| Spatial intelligence | Human-video world model | Vision-language-action |
-| --- | --- | --- |
-| ![Spatial intelligence pipeline placeholder](docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png) | ![Human-video world model pipeline placeholder](docs/assets/foundation-pipelines/human-video-world-model-pipeline.png) | ![Vision-language-action pipeline placeholder](docs/assets/foundation-pipelines/vision-language-action-pipeline.png) |
+**Spatial intelligence models**
+
+![Spatial intelligence task-training diagram](docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png)
+
+**Human-video world models**
+
+![Human-video world-model task-training diagram](docs/assets/foundation-pipelines/human-video-world-model-pipeline.png)
+
+**Vision-language-action models**
+
+![Vision-language-action task-training diagram](docs/assets/foundation-pipelines/vision-language-action-pipeline.png)
 
 See [`THREE_FOUNDATION_PIPELINES.md`](THREE_FOUNDATION_PIPELINES.md) and
 [`docs/data/three_foundation_pipelines.json`](docs/data/three_foundation_pipelines.json).

THREE_FOUNDATION_PIPELINES.md

@@ -14,20 +14,25 @@ inertial signals, object/contact annotations, and language captions.
 | Human-video world models | Can the model predict what happens next? | Observed video/audio/sensor windows, hand/body motion, object/contact state, action/subtask labels, future windows. | Future-state and future-action probes over the existing split, then Cosmos-style or latent world-model training with separate dynamics metrics. | Partially evidenced through current future-task probes and Cosmos-style branch artifacts; still needs stronger visual/latent future metrics. |
 | Vision-language-action models | Can the model turn what it sees and reads into action? | Egocentric video, language captions, hand/body motion, contacts, objects, procedure/subtask labels. | Observation-language-to-action target conversion, action-chunk scoring, policy-token baselines, then VLA/policy model fine-tuning. | Feasible but gated by action-target conversion; do not claim policy quality until action tokens, normalization, and held-out policy metrics exist. |
 
-## Published Placeholder Figures
+## Published Task-Training Diagrams
 
-The repo and public mirrors now include three stable placeholder visuals for
-the foundation pipeline tracks. They are ChatGPT image-generated communication
-assets, not evidence of completed model-quality training. The exact technical
-scope remains the text and JSON contract in this document and
-`docs/data/three_foundation_pipelines.json`.
+The repo and public mirrors now include three stable diagrams for the
+foundation pipeline tracks. They replace the earlier concept-art images
+and explicitly show each direction as inputs -> task targets -> model training
+-> evaluation gates. They are communication assets, not evidence of completed
+model-quality training. The exact technical scope remains the text and JSON
+contract in this document and `docs/data/three_foundation_pipelines.json`.
 
-| Track | Placeholder asset | Prompt notes |
+| Track | Diagram asset | Source notes |
 | --- | --- | --- |
 | Spatial intelligence models | `docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png` | `docs/assets/foundation-pipelines/prompts.md` |
 | Human-video world models | `docs/assets/foundation-pipelines/human-video-world-model-pipeline.png` | `docs/assets/foundation-pipelines/prompts.md` |
 | Vision-language-action models | `docs/assets/foundation-pipelines/vision-language-action-pipeline.png` | `docs/assets/foundation-pipelines/prompts.md` |
 
+The deterministic renderer is
+`scripts/render_foundation_pipeline_diagrams.py`; it keeps the public labels
+readable and aligned with the machine-readable contract.
+
 ## 1. Spatial Intelligence Pipeline
 
 Purpose: train and evaluate models that turn flat video into spatial state and

assets/foundation-pipelines/README.md

@@ -1,12 +1,14 @@
-# Foundation Pipeline Placeholder Figures
+# Foundation Pipeline Task-Training Diagrams
 
-These three bitmap figures are ChatGPT image-generated placeholder visuals for
-the foundation pipeline tracks documented in `THREE_FOUNDATION_PIPELINES.md`
-and `docs/data/three_foundation_pipelines.json`.
+These three bitmap figures are task-training diagrams for the foundation
+pipeline tracks documented in `THREE_FOUNDATION_PIPELINES.md` and
+`docs/data/three_foundation_pipelines.json`.
 
-They are **pipeline placeholders**, not evidence of completed foundation-model
-training. Exact technical claims live in the surrounding Markdown, JSON, and
-website labels.
+They replace the earlier concept-art images. Each diagram spells out the
+direction, supported task targets, model-training route, and evaluation gates.
+They are still **pipeline communication assets**, not evidence of completed
+foundation-model quality. Exact technical claims live in the surrounding
+Markdown, JSON, and website labels.
 
 | Track | Asset |
 | --- | --- |
@@ -14,3 +16,6 @@ website labels.
 | Human-video world models | `human-video-world-model-pipeline.png` |
 | Vision-language-action models | `vision-language-action-pipeline.png` |
 
+The deterministic rendering script is
+`scripts/render_foundation_pipeline_diagrams.py`; prompt and image-generation
+notes are in `prompts.md`.

assets/foundation-pipelines/prompts.md

@@ -1,39 +1,49 @@
-# ChatGPT Image Prompts
+# Foundation Pipeline Diagram Prompts
+
+The first public pass used ChatGPT image-generated concept visuals. The second
+pass uses the same direction prompts for visual exploration, then renders the
+final public PNGs with `scripts/render_foundation_pipeline_diagrams.py` so the
+task names, model-training route, and evaluation gates stay exact and readable.
 
 ## Spatial Intelligence
 
 Use case: infographic-diagram. Asset type: 16:9 website figure for Ropedia
-Xperience-10M foundation pipeline track. Create a polished text-free diagram
-image for a spatial intelligence model training pipeline. Show multi-view video
-frames and depth/pose streams flowing into a scene-object memory module, then
-spatial reasoning outputs like 3D structure, object permanence, counting, and
-question answering. Use a premium dark research-product presentation style,
-high contrast, crisp geometric panels, subtle neon green/cyan/white accents,
-clean technical linework, no decorative blobs, no logos, no readable text, no
-watermark.
+Xperience-10M foundation pipeline track. Create a structured diagram, not
+concept art, for a spatial intelligence model training direction. Show four
+left-to-right zones: inputs, task targets, model training, and evaluation
+gates. The content should represent multiview RGB, egocentric video, depth,
+camera pose, calibration, object/contact/language cues, spatial QA, object
+counting, object permanence, relative location, multiview retrieval, 3D
+consistency, spatial-memory encoders, and held-out episode metrics. Use a
+premium dark research-product style, high contrast, crisp panels, clean
+technical linework, no decorative blobs, no logos, no watermark.
 
 ## Human-Video World Models
 
 Use case: infographic-diagram. Asset type: 16:9 website figure for Ropedia
-Xperience-10M foundation pipeline track. Create a polished text-free diagram
-image for a human-video world model training pipeline. Show observed egocentric
-video/audio/sensor windows flowing into a latent world-state model, then
-predicted future frames, future action bars, object/contact state changes, and
-uncertainty bands. Use a premium dark research-product presentation style,
-high contrast, crisp geometric panels, subtle neon green/teal/white accents
-with small amber highlights, clean technical linework, no decorative blobs, no
-logos, no readable text, no watermark.
+Xperience-10M foundation pipeline track. Create a structured diagram, not
+concept art, for a human-video world-model training direction. Show four
+left-to-right zones: observed interaction inputs, future task targets, model
+training, and held-out future evaluation. The content should represent
+observed video/audio/sensor windows, hand/body motion, camera pose,
+object/contact state, action/subtask labels, next action, next subtask, future
+object set, contact transition, camera-motion delta, latent future state, Qwen
+structured future probes, Cosmos/dynamics branches, rollout or latent
+reconstruction, no future leakage, and future-task metrics. Use a premium dark
+research-product style, high contrast, crisp panels, clean technical linework,
+no decorative blobs, no logos, no watermark.
 
 ## Vision-Language-Action
 
 Use case: infographic-diagram. Asset type: 16:9 website figure for Ropedia
-Xperience-10M foundation pipeline track. Create a polished text-free diagram
-image for a vision-language-action model training pipeline. Show egocentric
-video frames, language caption tokens, hand/body motion traces, object/contact
-cues, and procedure labels flowing into a multimodal action policy module, then
-predicted action chunks, hand trajectory curves, contact decisions, and policy
-evaluation panels. Use a premium dark research-product presentation style,
-high contrast, crisp geometric panels, subtle neon green/cyan/white accents
-with small magenta highlights, clean technical linework, no decorative blobs,
-no logos, no readable text, no watermark.
-
+Xperience-10M foundation pipeline track. Create a structured diagram, not
+concept art, for a vision-language-action model training direction. Show four
+left-to-right zones: observation/language inputs, action task targets,
+VLA/policy-compatible training, and held-out action evaluation. The content
+should represent egocentric video, captions, objects, contacts, procedures,
+hand/body motion windows, subtask labels, action-token vocabulary, next action,
+action chunks, object-conditioned action, contact state, subtask transition,
+action-space conversion, normalization, leakage and retargeting reports, VLA
+or policy heads, and held-out policy/action metrics. Use a premium dark
+research-product style, high contrast, crisp panels, clean technical linework,
+no decorative blobs, no logos, no watermark.

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-17T15:16:18+00:00",
+  "generated_at_utc": "2026-06-17T16:20:42+00:00",
   "status": "pass",
   "artifact_count": 204,
   "missing": [],
@@ -135,8 +135,8 @@
       "surface": "repo_hf",
       "shows": "Frames spatial intelligence, human-video world modeling, and vision-language-action as three pipeline tracks with explicit inputs, outputs, maturity, and next evidence gates.",
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       "id": "three_foundation_pipelines_json",
@@ -146,41 +146,41 @@
       "surface": "website_hf",
       "shows": "Machine-readable pipeline-track contract for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 6518,
-      "sha256": "e337901e7ddd2f8845987d4c41d9362e5fc780d3cb0659494576b7a0da53fb49"
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-      "title": "Spatial intelligence pipeline placeholder",
+      "id": "spatial_intelligence_task_training_diagram",
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       "path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the spatial intelligence model training pipeline.",
+      "shows": "Inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence model training pipeline.",
       "exists": true,
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-      "sha256": "ca98e2f5171497f6b97627ee8d0dee68f4aa929a2ba205e8b8e64e89f7f66f06"
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-      "id": "human_video_world_model_pipeline_placeholder",
-      "title": "Human-video world model pipeline placeholder",
+      "id": "human_video_world_model_task_training_diagram",
+      "title": "Human-video world model task-training diagram",
       "path": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the human-video world-model training pipeline.",
+      "shows": "Observed-window-to-future-targets-to-training-to-evaluation diagram for the human-video world-model training pipeline.",
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-      "bytes": 2356312,
-      "sha256": "cee2d717a97b88d8f5bae3e58fe202791a4f3073e488cb666acb0214117b735b"
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     {
-      "id": "vision_language_action_pipeline_placeholder",
-      "title": "Vision-language-action pipeline placeholder",
+      "id": "vision_language_action_task_training_diagram",
+      "title": "Vision-language-action task-training diagram",
       "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the vision-language-action training pipeline.",
+      "shows": "Observation-and-language-to-action-targets-to-VLA-training-to-evaluation diagram for the vision-language-action training pipeline.",
       "exists": true,
-      "bytes": 2421011,
-      "sha256": "f8554f7df26ab79fef348740ce45ac3da032cb4085c490d62910ad1147dd1ecf"
+      "bytes": 255706,
+      "sha256": "2efa63a771a9f5abf119207022a6a64a2b6763e529327399dff901d44d9b52d9"
     },
     {
       "id": "omni_model_extension_contract",
@@ -521,7 +521,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "5d013c6820fb7f582b4b4b9a55f98de20168ea1947d4bea64e11d16dbd521428"
+      "sha256": "f245383fc291681fcc62d1abd12bc37cf6d9806dcfcd8a46e48d41705bc87227"
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     {
       "id": "source_alignment_validator",
@@ -938,8 +938,8 @@
       "surface": "repo_hf",
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
       "exists": true,
-      "bytes": 6217,
-      "sha256": "5ec54cfb09114042ada7aad54b3c8143b9b305bcd1f8ffd65d9f5116f3f19481"
+      "bytes": 7116,
+      "sha256": "bfe0da08ee24878170206042daba4672bdff877f1d12577700a9c5fbe4adce25"
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     {
       "id": "figure_index_json",
@@ -949,8 +949,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 17287,
-      "sha256": "06a5854e14fb64e435d02e6fd5f476b37e9e3b981cf1d8b39fea3809f8ff5eaf"
+      "bytes": 19580,
+      "sha256": "7fd00f1d411f84c4816df435c025b2c3f419fd4c141c32faf8875dcf8caef679"
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     {
       "id": "figure_index_builder",
@@ -960,8 +960,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 16864,
-      "sha256": "df362654a5c65d7adedc924f2af93e1fbd248fa861f20aea1576473daaeb0b0d"
+      "bytes": 16943,
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     {
       "id": "brand_assets_json",
@@ -1130,7 +1130,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 149465,
+      "bytes": 161420,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1141,8 +1141,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 60253,
-      "sha256": "ad4b408e9e19339285e37e0c47bffac6a450ddd1a439bf11ab80a90cec27b1fb"
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+      "sha256": "f2f804eea4861a9a6f059fc3f5e7539b95ac28fd986f416020bbc345524eea90"
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     {
       "id": "reproducibility_contract",
@@ -1174,8 +1174,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 59218,
-      "sha256": "38985fa362861b3975240ec62cc186378c84a8d0e11727651dc3cd2a87bfdd11"
+      "bytes": 59294,
+      "sha256": "0980c49473eec3a58fc6dac65a5a94db7a60af79251c0303b9aeba641017b614"
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       "id": "publication_audit",
@@ -1186,7 +1186,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 8299,
+      "bytes": 8544,
       "hash_policy": "existence_and_size_only"
     },
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@@ -1210,7 +1210,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 902747,
+      "bytes": 910015,
       "hash_policy": "existence_and_size_only"
     },
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@@ -1222,7 +1222,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 19052,
+      "bytes": 19663,
       "hash_policy": "existence_and_size_only"
     },
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data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:16:21+00:00",
+  "generated_at_utc": "2026-06-17T16:20:24+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 29,
   "figures": [
@@ -108,53 +108,53 @@
       "source_script_exists": true
     },
     {
-      "id": "spatial_intelligence_pipeline_placeholder",
-      "title": "Spatial intelligence pipeline placeholder",
+      "id": "spatial_intelligence_task_training_diagram",
+      "title": "Spatial intelligence task-training diagram",
       "path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
-      "role": "ChatGPT image-generated placeholder for the spatial intelligence training pipeline track.",
-      "source_script": "docs/assets/foundation-pipelines/prompts.md",
+      "role": "Readable inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence pipeline track.",
+      "source_script": "scripts/render_foundation_pipeline_diagrams.py",
       "surface": "README, website, HF Space, artifact dataset, model card",
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         "first_pipeline": "Define the action space, use existing 20-task next-action/contact/object-conditioned tasks first, then add hand-trajectory or policy-compatible action chunks after conversion is traceable.",
         "id": "vision_language_action",
+        "image_alt": "Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.",
         "intermediate_artifacts": [
           "action-token vocabulary",
           "action-chunk windows",
@@ -3360,7 +3482,8 @@
           "policy or VLA held-out metrics"
         ],
         "question": "Can the model turn what it sees and reads into action?",
-        "title": "Vision-language-action models"
+        "title": "Vision-language-action models",
+        "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png"
       }
     ]
   },

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "summary": {
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     "expected_task_count": 12,

data/three_foundation_pipelines.json

@@ -3,12 +3,14 @@
   "status": "pipeline_plan",
   "source_document": "THREE_FOUNDATION_PIPELINES.md",
   "claim_boundary": "These are supported pipeline directions, not three completed model-quality claims.",
-  "placeholder_assets": {
-    "status": "published_placeholders",
+  "diagram_assets": {
+    "status": "published_task_training_diagrams",
     "asset_root": "docs/assets/foundation-pipelines",
-    "source": "ChatGPT image generation with repo-local prompt notes",
+    "source": "ChatGPT image-generation prompt exploration with deterministic repo-rendered labels",
     "source_prompt_file": "docs/assets/foundation-pipelines/prompts.md",
-    "note": "Images are visual placeholders for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
+    "renderer_script": "scripts/render_foundation_pipeline_diagrams.py",
+    "diagram_type": "inputs_to_tasks_to_training_to_evaluation",
+    "note": "Images are task-training communication diagrams for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
   },
   "shared_principles": [
     "Use episode-level train/validation/test separation.",
@@ -51,9 +53,47 @@
       "first_pipeline": "Build a spatial-memory exporter, start with metric depth and pose consistency tasks, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.",
       "current_maturity": "Ready as a pipeline and evaluation contract.",
       "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
-      "image_alt": "Placeholder visual for the spatial intelligence pipeline: multiview video, depth, and pose inputs feeding scene memory and spatial reasoning outputs.",
+      "image_alt": "Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "multiview RGB plus egocentric video",
+            "metric depth and confidence",
+            "camera pose, calibration, SLAM",
+            "object, contact, and language cues"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "spatial QA and object count",
+            "object permanence across windows",
+            "relative location and retrieval",
+            "pose-aware 3D consistency"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "export scene/object memory records",
+            "train spatial-memory encoder",
+            "add geometry-aware QA and retrieval heads",
+            "keep episode-level split discipline"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out episode spatial metrics",
+            "count and relation accuracy",
+            "retrieval rank and consistency",
+            "saved predictions before public claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "full neural rendering",
         "full 3D reconstruction",
@@ -92,9 +132,47 @@
       "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before claiming world-model quality.",
       "current_maturity": "Partially evidenced by current future-task probes and Cosmos-style branch artifacts.",
       "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
-      "placeholder_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png",
-      "image_alt": "Placeholder visual for the human-video world model pipeline: observed interaction windows feeding temporal dynamics and future-state outputs.",
+      "image_alt": "Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "observed video/audio/sensor window",
+            "hand/body motion and camera pose",
+            "object/contact state",
+            "action and subtask labels"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "next action and next subtask",
+            "future object set",
+            "contact transition",
+            "camera-motion delta or latent future"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "Qwen structured future probes",
+            "Cosmos/dynamics branch separately",
+            "latent rollout or reconstruction loss",
+            "no target-side future leakage"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out future-task metrics",
+            "contact and object-set F1",
+            "rollout or latent consistency",
+            "per-episode breakdown and examples"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "strong world model from structured future-task scores alone",
         "visual future quality without visual or latent future metrics"
@@ -131,9 +209,47 @@
       "first_pipeline": "Define the action space, use existing 20-task next-action/contact/object-conditioned tasks first, then add hand-trajectory or policy-compatible action chunks after conversion is traceable.",
       "current_maturity": "Feasible but gated by action-target conversion.",
       "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
-      "image_alt": "Placeholder visual for the vision-language-action pipeline: video, language, motion, and contact cues feeding action-chunk outputs.",
+      "image_alt": "Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "egocentric video and captions",
+            "objects, contacts, and procedures",
+            "hand/body motion windows",
+            "subtask labels and language context"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "action-token vocabulary",
+            "next action and action chunks",
+            "object-conditioned actions",
+            "contact state and subtask transition"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "build action-space converter",
+            "normalize and audit action chunks",
+            "train VLA/policy-compatible head",
+            "track leakage and retargeting reports"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out action metrics",
+            "chunk and next-action accuracy",
+            "object/contact-conditioned scores",
+            "policy card before robot-policy claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "robot policy quality",
         "policy generalization before action-space evidence exists"

data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:21+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
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@@ -80,8 +80,8 @@
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       "status": "pass",
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-      "evidence_index": 119623
+      "overview_index": 89133,
+      "evidence_index": 119986
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@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
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       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 88890,
-      "protocol_index": 115804,
-      "evidence_index": 119623
+      "overview_index": 89133,
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@@ -301,7 +301,7 @@
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@@ -341,12 +341,12 @@
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@@ -391,7 +391,7 @@
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docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-17T15:16:18+00:00",
+  "generated_at_utc": "2026-06-17T16:20:42+00:00",
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@@ -135,8 +135,8 @@
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@@ -146,41 +146,41 @@
       "surface": "website_hf",
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-      "title": "Spatial intelligence pipeline placeholder",
+      "id": "spatial_intelligence_task_training_diagram",
+      "title": "Spatial intelligence task-training diagram",
       "path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the spatial intelligence model training pipeline.",
+      "shows": "Inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence model training pipeline.",
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-      "title": "Human-video world model pipeline placeholder",
+      "id": "human_video_world_model_task_training_diagram",
+      "title": "Human-video world model task-training diagram",
       "path": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the human-video world-model training pipeline.",
+      "shows": "Observed-window-to-future-targets-to-training-to-evaluation diagram for the human-video world-model training pipeline.",
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-      "bytes": 2356312,
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-      "id": "vision_language_action_pipeline_placeholder",
-      "title": "Vision-language-action pipeline placeholder",
+      "id": "vision_language_action_task_training_diagram",
+      "title": "Vision-language-action task-training diagram",
       "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the vision-language-action training pipeline.",
+      "shows": "Observation-and-language-to-action-targets-to-VLA-training-to-evaluation diagram for the vision-language-action training pipeline.",
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@@ -521,7 +521,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
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       "bytes": 4432,
-      "sha256": "5d013c6820fb7f582b4b4b9a55f98de20168ea1947d4bea64e11d16dbd521428"
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@@ -938,8 +938,8 @@
       "surface": "repo_hf",
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
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-      "sha256": "5ec54cfb09114042ada7aad54b3c8143b9b305bcd1f8ffd65d9f5116f3f19481"
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@@ -949,8 +949,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
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@@ -960,8 +960,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
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@@ -1130,7 +1130,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
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       "hash_policy": "existence_and_size_only"
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@@ -1141,8 +1141,8 @@
       "surface": "repo",
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@@ -1174,8 +1174,8 @@
       "surface": "repo_hf",
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@@ -1186,7 +1186,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
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       "hash_policy": "existence_and_size_only"
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@@ -1210,7 +1210,7 @@
       "volatile": true,
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@@ -1222,7 +1222,7 @@
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docs/data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:16:21+00:00",
+  "generated_at_utc": "2026-06-17T16:20:24+00:00",
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@@ -108,53 +108,53 @@
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-      "source_script": "docs/assets/foundation-pipelines/prompts.md",
+      "role": "Readable inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence pipeline track.",
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-      "title": "Human-video world model pipeline placeholder",
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-        "title": "Vision-language-action models"
+        "title": "Vision-language-action models",
+        "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png"
       }
     ]
   },

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

docs/data/three_foundation_pipelines.json

@@ -3,12 +3,14 @@
   "status": "pipeline_plan",
   "source_document": "THREE_FOUNDATION_PIPELINES.md",
   "claim_boundary": "These are supported pipeline directions, not three completed model-quality claims.",
-  "placeholder_assets": {
-    "status": "published_placeholders",
+  "diagram_assets": {
+    "status": "published_task_training_diagrams",
     "asset_root": "docs/assets/foundation-pipelines",
-    "source": "ChatGPT image generation with repo-local prompt notes",
+    "source": "ChatGPT image-generation prompt exploration with deterministic repo-rendered labels",
     "source_prompt_file": "docs/assets/foundation-pipelines/prompts.md",
-    "note": "Images are visual placeholders for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
+    "renderer_script": "scripts/render_foundation_pipeline_diagrams.py",
+    "diagram_type": "inputs_to_tasks_to_training_to_evaluation",
+    "note": "Images are task-training communication diagrams for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
   },
   "shared_principles": [
     "Use episode-level train/validation/test separation.",
@@ -51,9 +53,47 @@
       "first_pipeline": "Build a spatial-memory exporter, start with metric depth and pose consistency tasks, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.",
       "current_maturity": "Ready as a pipeline and evaluation contract.",
       "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
-      "image_alt": "Placeholder visual for the spatial intelligence pipeline: multiview video, depth, and pose inputs feeding scene memory and spatial reasoning outputs.",
+      "image_alt": "Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "multiview RGB plus egocentric video",
+            "metric depth and confidence",
+            "camera pose, calibration, SLAM",
+            "object, contact, and language cues"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "spatial QA and object count",
+            "object permanence across windows",
+            "relative location and retrieval",
+            "pose-aware 3D consistency"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "export scene/object memory records",
+            "train spatial-memory encoder",
+            "add geometry-aware QA and retrieval heads",
+            "keep episode-level split discipline"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out episode spatial metrics",
+            "count and relation accuracy",
+            "retrieval rank and consistency",
+            "saved predictions before public claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "full neural rendering",
         "full 3D reconstruction",
@@ -92,9 +132,47 @@
       "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before claiming world-model quality.",
       "current_maturity": "Partially evidenced by current future-task probes and Cosmos-style branch artifacts.",
       "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
-      "placeholder_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png",
-      "image_alt": "Placeholder visual for the human-video world model pipeline: observed interaction windows feeding temporal dynamics and future-state outputs.",
+      "image_alt": "Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "observed video/audio/sensor window",
+            "hand/body motion and camera pose",
+            "object/contact state",
+            "action and subtask labels"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "next action and next subtask",
+            "future object set",
+            "contact transition",
+            "camera-motion delta or latent future"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "Qwen structured future probes",
+            "Cosmos/dynamics branch separately",
+            "latent rollout or reconstruction loss",
+            "no target-side future leakage"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out future-task metrics",
+            "contact and object-set F1",
+            "rollout or latent consistency",
+            "per-episode breakdown and examples"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "strong world model from structured future-task scores alone",
         "visual future quality without visual or latent future metrics"
@@ -131,9 +209,47 @@
       "first_pipeline": "Define the action space, use existing 20-task next-action/contact/object-conditioned tasks first, then add hand-trajectory or policy-compatible action chunks after conversion is traceable.",
       "current_maturity": "Feasible but gated by action-target conversion.",
       "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
-      "image_alt": "Placeholder visual for the vision-language-action pipeline: video, language, motion, and contact cues feeding action-chunk outputs.",
+      "image_alt": "Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "egocentric video and captions",
+            "objects, contacts, and procedures",
+            "hand/body motion windows",
+            "subtask labels and language context"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "action-token vocabulary",
+            "next action and action chunks",
+            "object-conditioned actions",
+            "contact state and subtask transition"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "build action-space converter",
+            "normalize and audit action chunks",
+            "train VLA/policy-compatible head",
+            "track leakage and retargeting reports"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out action metrics",
+            "chunk and next-action accuracy",
+            "object/contact-conditioned scores",
+            "policy card before robot-policy claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "robot policy quality",
         "policy generalization before action-space evidence exists"

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:21+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 88890,
-      "evidence_index": 119623
+      "overview_index": 89133,
+      "evidence_index": 119986
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 88890,
-      "protocol_index": 115804,
-      "evidence_index": 119623
+      "overview_index": 89133,
+      "protocol_index": 116167,
+      "evidence_index": 119986
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 111262,
+      "bytes": 111336,
       "top_level_type": "dict"
     },
     {
@@ -331,7 +331,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 19501,
+      "bytes": 19580,
       "top_level_type": "dict"
     },
     {
@@ -341,12 +341,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 149465,
+      "bytes": 161420,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 902747,
+      "bytes": 910892,
       "top_level_type": "dict"
     },
     {
@@ -391,7 +391,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 8299,
+      "bytes": 8604,
       "top_level_type": "dict"
     },
     {
@@ -441,7 +441,7 @@
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 150170,
+      "bytes": 154846,
       "top_level_type": "dict"
     },
     {
@@ -506,7 +506,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 6518,
+      "bytes": 10084,
       "top_level_type": "dict"
     },
     {
@@ -521,7 +521,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19052,
+      "bytes": 19664,
       "top_level_type": "dict"
     },
     {
@@ -633,25 +633,25 @@
     {
       "path": "assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "exists": true,
-      "bytes": 2356312,
-      "width": 1672,
-      "height": 941,
+      "bytes": 249668,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {
       "path": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "exists": true,
-      "bytes": 2337155,
-      "width": 1672,
-      "height": 941,
+      "bytes": 252509,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {
       "path": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "exists": true,
-      "bytes": 2421011,
-      "width": 1672,
-      "height": 941,
+      "bytes": 255706,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {

docs/index.html

@@ -620,12 +620,14 @@
     }
     .foundation-pipeline-grid {
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: 1fr;
       gap: 18px;
       margin: 0 0 28px;
     }
     .foundation-pipeline-card {
       min-width: 0;
+      display: grid;
+      grid-template-columns: minmax(0, 1.45fr) minmax(320px, 0.55fr);
       border: 1px solid var(--line);
       border-radius: var(--radius);
       overflow: hidden;
@@ -637,9 +639,11 @@
     .foundation-pipeline-card img {
       display: block;
       width: 100%;
+      height: 100%;
+      min-height: 320px;
       aspect-ratio: 16 / 9;
-      object-fit: cover;
-      border-bottom: 1px solid var(--line);
+      object-fit: contain;
+      border-right: 1px solid var(--line);
       background: #020502;
     }
     .foundation-pipeline-body {
@@ -2702,7 +2706,12 @@
       .section-tabs { padding-top: 10px; }
       .figure-brief,
       .split-radar-grid,
-      .foundation-pipeline-grid { grid-template-columns: 1fr; }
+      .foundation-pipeline-card { display: block; }
+      .foundation-pipeline-card img {
+        min-height: 0;
+        border-right: 0;
+        border-bottom: 1px solid var(--line);
+      }
       .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .brief-panel-head { grid-template-columns: 1fr; align-items: start; }
       .task-player { grid-template-columns: 1fr; }
@@ -3263,13 +3272,13 @@
           <h2>Additional development directions.</h2>
           <p>Beyond the current task heads, Qwen3-Omni fine-tuning path, Cosmos/world-model branch, and future native pretraining goal, Xperience-10M can support three foundation pipeline tracks plus several concrete research-development tracks.</p>
         </div>
-        <div class="foundation-pipeline-grid" aria-label="Three foundation model training pipeline placeholders">
+        <div class="foundation-pipeline-grid" aria-label="Three foundation model task-training diagrams">
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/spatial-intelligence-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the spatial intelligence pipeline: multiview video, depth, and pose inputs feeding scene memory and spatial reasoning outputs.">
+            <img src="assets/foundation-pipelines/spatial-intelligence-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Spatial intelligence models</h3>
-              <p>Build scene/object memory targets from multiview RGB, depth, pose, calibration, object cues, and language prompts, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.</p>
+              <p>Train spatial-memory models from multiview RGB, egocentric video, depth, pose, calibration, object/contact cues, and language prompts; evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.</p>
               <div class="foundation-pipeline-links">
                 <a href="data/three_foundation_pipelines.json">Track JSON</a>
                 <a href="assets/foundation-pipelines/spatial-intelligence-pipeline.png">Open image</a>
@@ -3277,11 +3286,11 @@
             </div>
           </article>
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/human-video-world-model-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the human-video world model pipeline: observed interaction windows feeding temporal dynamics and future-state outputs.">
+            <img src="assets/foundation-pipelines/human-video-world-model-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Human-video world models</h3>
-              <p>Predict next action, next subtask, future object set, contact transitions, camera-motion deltas, and latent future state from observed interaction windows, with Cosmos-style dynamics metrics kept separate.</p>
+              <p>Train future-prediction models from observed interaction windows to score next action, next subtask, future object set, contact transition, camera-motion delta, and latent future state, with Qwen-style probes and Cosmos-style dynamics kept separate.</p>
               <div class="foundation-pipeline-links">
                 <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">Track note</a>
                 <a href="assets/foundation-pipelines/human-video-world-model-pipeline.png">Open image</a>
@@ -3289,11 +3298,11 @@
             </div>
           </article>
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/vision-language-action-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the vision-language-action pipeline: video, language, motion, and contact cues feeding action-chunk outputs.">
+            <img src="assets/foundation-pipelines/vision-language-action-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Vision-language-action models</h3>
-              <p>Convert egocentric video, captions, hand/body motion, contacts, and objects into traceable action chunks or policy-compatible targets before claiming VLA or robot policy quality.</p>
+              <p>Train VLA or policy-compatible heads only after converting egocentric video, captions, hand/body motion, contacts, objects, and procedures into traceable action tokens, chunks, and object-conditioned action targets.</p>
               <div class="foundation-pipeline-links">
                 <a href="data/three_foundation_pipelines.json">Track contract</a>
                 <a href="assets/foundation-pipelines/vision-language-action-pipeline.png">Open image</a>

index.html

@@ -620,12 +620,14 @@
     }
     .foundation-pipeline-grid {
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: 1fr;
       gap: 18px;
       margin: 0 0 28px;
     }
     .foundation-pipeline-card {
       min-width: 0;
+      display: grid;
+      grid-template-columns: minmax(0, 1.45fr) minmax(320px, 0.55fr);
       border: 1px solid var(--line);
       border-radius: var(--radius);
       overflow: hidden;
@@ -637,9 +639,11 @@
     .foundation-pipeline-card img {
       display: block;
       width: 100%;
+      height: 100%;
+      min-height: 320px;
       aspect-ratio: 16 / 9;
-      object-fit: cover;
-      border-bottom: 1px solid var(--line);
+      object-fit: contain;
+      border-right: 1px solid var(--line);
       background: #020502;
     }
     .foundation-pipeline-body {
@@ -2702,7 +2706,12 @@
       .section-tabs { padding-top: 10px; }
       .figure-brief,
       .split-radar-grid,
-      .foundation-pipeline-grid { grid-template-columns: 1fr; }
+      .foundation-pipeline-card { display: block; }
+      .foundation-pipeline-card img {
+        min-height: 0;
+        border-right: 0;
+        border-bottom: 1px solid var(--line);
+      }
       .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .brief-panel-head { grid-template-columns: 1fr; align-items: start; }
       .task-player { grid-template-columns: 1fr; }
@@ -3263,13 +3272,13 @@
           <h2>Additional development directions.</h2>
           <p>Beyond the current task heads, Qwen3-Omni fine-tuning path, Cosmos/world-model branch, and future native pretraining goal, Xperience-10M can support three foundation pipeline tracks plus several concrete research-development tracks.</p>
         </div>
-        <div class="foundation-pipeline-grid" aria-label="Three foundation model training pipeline placeholders">
+        <div class="foundation-pipeline-grid" aria-label="Three foundation model task-training diagrams">
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/spatial-intelligence-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the spatial intelligence pipeline: multiview video, depth, and pose inputs feeding scene memory and spatial reasoning outputs.">
+            <img src="assets/foundation-pipelines/spatial-intelligence-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Spatial intelligence models</h3>
-              <p>Build scene/object memory targets from multiview RGB, depth, pose, calibration, object cues, and language prompts, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.</p>
+              <p>Train spatial-memory models from multiview RGB, egocentric video, depth, pose, calibration, object/contact cues, and language prompts; evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.</p>
               <div class="foundation-pipeline-links">
                 <a href="data/three_foundation_pipelines.json">Track JSON</a>
                 <a href="assets/foundation-pipelines/spatial-intelligence-pipeline.png">Open image</a>
@@ -3277,11 +3286,11 @@
             </div>
           </article>
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/human-video-world-model-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the human-video world model pipeline: observed interaction windows feeding temporal dynamics and future-state outputs.">
+            <img src="assets/foundation-pipelines/human-video-world-model-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Human-video world models</h3>
-              <p>Predict next action, next subtask, future object set, contact transitions, camera-motion deltas, and latent future state from observed interaction windows, with Cosmos-style dynamics metrics kept separate.</p>
+              <p>Train future-prediction models from observed interaction windows to score next action, next subtask, future object set, contact transition, camera-motion delta, and latent future state, with Qwen-style probes and Cosmos-style dynamics kept separate.</p>
               <div class="foundation-pipeline-links">
                 <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">Track note</a>
                 <a href="assets/foundation-pipelines/human-video-world-model-pipeline.png">Open image</a>
@@ -3289,11 +3298,11 @@
             </div>
           </article>
           <article class="foundation-pipeline-card">
-            <img src="assets/foundation-pipelines/vision-language-action-pipeline.png?v=foundation-pipelines-v1" alt="Placeholder visual for the vision-language-action pipeline: video, language, motion, and contact cues feeding action-chunk outputs.">
+            <img src="assets/foundation-pipelines/vision-language-action-pipeline.png?v=foundation-pipelines-v2" alt="Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.">
             <div class="foundation-pipeline-body">
-              <span>Pipeline placeholder</span>
+              <span>Task-training diagram</span>
               <h3>Vision-language-action models</h3>
-              <p>Convert egocentric video, captions, hand/body motion, contacts, and objects into traceable action chunks or policy-compatible targets before claiming VLA or robot policy quality.</p>
+              <p>Train VLA or policy-compatible heads only after converting egocentric video, captions, hand/body motion, contacts, objects, and procedures into traceable action tokens, chunks, and object-conditioned action targets.</p>
               <div class="foundation-pipeline-links">
                 <a href="data/three_foundation_pipelines.json">Track contract</a>
                 <a href="assets/foundation-pipelines/vision-language-action-pipeline.png">Open image</a>

metrics/artifact_index.json

@@ -1,6 +1,6 @@
 {
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@@ -135,8 +135,8 @@
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@@ -146,41 +146,41 @@
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       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the spatial intelligence model training pipeline.",
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-      "title": "Vision-language-action pipeline placeholder",
+      "id": "vision_language_action_task_training_diagram",
+      "title": "Vision-language-action task-training diagram",
       "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "kind": "visual_asset",
       "surface": "website_hf",
-      "shows": "ChatGPT image-generated placeholder visual for the vision-language-action training pipeline.",
+      "shows": "Observation-and-language-to-action-targets-to-VLA-training-to-evaluation diagram for the vision-language-action training pipeline.",
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@@ -521,7 +521,7 @@
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@@ -938,8 +938,8 @@
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@@ -949,8 +949,8 @@
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@@ -960,8 +960,8 @@
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@@ -1130,7 +1130,7 @@
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@@ -1174,8 +1174,8 @@
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@@ -1186,7 +1186,7 @@
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@@ -1210,7 +1210,7 @@
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metrics/figure_index.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:16:21+00:00",
+  "generated_at_utc": "2026-06-17T16:20:24+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
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@@ -108,53 +108,53 @@
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-      "role": "ChatGPT image-generated placeholder for the spatial intelligence training pipeline track.",
-      "source_script": "docs/assets/foundation-pipelines/prompts.md",
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-      "title": "Human-video world model pipeline placeholder",
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     {
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-      "title": "Vision-language-action pipeline placeholder",
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+      "title": "Vision-language-action task-training diagram",
       "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
-      "role": "ChatGPT image-generated placeholder for the VLA/action-policy training pipeline track.",
-      "source_script": "docs/assets/foundation-pipelines/prompts.md",
+      "role": "Readable inputs-to-action-targets-to-VLA-training-to-evaluation diagram for the VLA/action-policy pipeline track.",
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metrics/mirror_parity.json

@@ -1,9 +1,9 @@
 {
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@@ -138,45 +138,45 @@
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@@ -334,45 +334,45 @@
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@@ -824,45 +824,45 @@
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@@ -874,44 +874,44 @@
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+          "sha256": "3992da548fa25edbf1d9634c094322c70f30c4d21ce14825ba15783f7f78926d"
         }
       },
       "failures": []

metrics/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:16:15+00:00",
+  "generated_at_utc": "2026-06-17T16:21:20+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T13:55:22+00:00"
+          "generated_at_utc": "2026-06-17T15:17:21+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,12 +28,12 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T13:55:20+00:00"
+          "generated_at_utc": "2026-06-17T15:17:20+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T13:55:20+00:00"
+          "generated_at_utc": "2026-06-17T15:17:20+00:00"
         },
         "scale_up_status": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T13:55:30+00:00"
+          "generated_at_utc": "2026-06-17T15:17:33+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T13:55:47+00:00"
+          "generated_at_utc": "2026-06-17T15:57:50+00:00"
         }
       },
       "failures": {}

metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:33+00:00",
+  "generated_at_utc": "2026-06-17T16:23:32+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -145,6 +145,7 @@
     "scripts/build_public_surface_qa.py": true,
     "scripts/build_rendered_site_check.py": true,
     "scripts/build_interactive_research_roadmap.py": true,
+    "scripts/render_foundation_pipeline_diagrams.py": true,
     "scripts/verify_live_publication.py": true,
     "scripts/validate_mirror_parity.py": true,
     "scripts/validate_scope_claims.py": true,
@@ -203,8 +204,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1216,
-      "text_file_count": 1018,
+      "file_count": 1217,
+      "text_file_count": 1019,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -214,8 +215,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 1005,
-      "text_file_count": 843,
+      "file_count": 1010,
+      "text_file_count": 845,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -225,8 +226,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2389,
-      "text_file_count": 1036,
+      "file_count": 2410,
+      "text_file_count": 1041,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -236,8 +237,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 2824,
-      "text_file_count": 1197,
+      "file_count": 2835,
+      "text_file_count": 1200,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

metrics/research_roadmap_interactive.json

@@ -2222,7 +2222,7 @@
     ],
     "status": "planning_artifact"
   },
-  "generated_at_utc": "2026-06-17T13:02:21+00:00",
+  "generated_at_utc": "2026-06-17T16:20:57+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -3266,8 +3266,48 @@
           "language questions"
         ],
         "current_maturity": "Ready as a pipeline and evaluation contract.",
+        "diagram_flow": [
+          {
+            "items": [
+              "multiview RGB plus egocentric video",
+              "metric depth and confidence",
+              "camera pose, calibration, SLAM",
+              "object, contact, and language cues"
+            ],
+            "stage": "inputs"
+          },
+          {
+            "items": [
+              "spatial QA and object count",
+              "object permanence across windows",
+              "relative location and retrieval",
+              "pose-aware 3D consistency"
+            ],
+            "stage": "tasks_targets"
+          },
+          {
+            "items": [
+              "export scene/object memory records",
+              "train spatial-memory encoder",
+              "add geometry-aware QA and retrieval heads",
+              "keep episode-level split discipline"
+            ],
+            "stage": "train_models"
+          },
+          {
+            "items": [
+              "held-out episode spatial metrics",
+              "count and relation accuracy",
+              "retrieval rank and consistency",
+              "saved predictions before public claim"
+            ],
+            "stage": "evaluate_gates"
+          }
+        ],
+        "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
         "first_pipeline": "Build a spatial-memory exporter, start with metric depth and pose consistency tasks, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.",
         "id": "spatial_intelligence",
+        "image_alt": "Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.",
         "intermediate_artifacts": [
           "synchronized camera window manifest",
           "pose and depth availability report",
@@ -3287,7 +3327,8 @@
           "language answers grounded in the scene"
         ],
         "question": "Can the model recover and reason over space from video?",
-        "title": "Spatial intelligence models"
+        "title": "Spatial intelligence models",
+        "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png"
       },
       {
         "avoid_claiming_now": [
@@ -3304,8 +3345,48 @@
           "future windows"
         ],
         "current_maturity": "Partially evidenced by current future-task probes and Cosmos-style branch artifacts.",
+        "diagram_flow": [
+          {
+            "items": [
+              "observed video/audio/sensor window",
+              "hand/body motion and camera pose",
+              "object/contact state",
+              "action and subtask labels"
+            ],
+            "stage": "inputs"
+          },
+          {
+            "items": [
+              "next action and next subtask",
+              "future object set",
+              "contact transition",
+              "camera-motion delta or latent future"
+            ],
+            "stage": "tasks_targets"
+          },
+          {
+            "items": [
+              "Qwen structured future probes",
+              "Cosmos/dynamics branch separately",
+              "latent rollout or reconstruction loss",
+              "no target-side future leakage"
+            ],
+            "stage": "train_models"
+          },
+          {
+            "items": [
+              "held-out future-task metrics",
+              "contact and object-set F1",
+              "rollout or latent consistency",
+              "per-episode breakdown and examples"
+            ],
+            "stage": "evaluate_gates"
+          }
+        ],
+        "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
         "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before claiming world-model quality.",
         "id": "human_video_world_models",
+        "image_alt": "Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.",
         "intermediate_artifacts": [
           "observed and future window pairs",
           "future label targets",
@@ -3324,7 +3405,8 @@
           "future-window quality metrics"
         ],
         "question": "Can the model predict what happens next?",
-        "title": "Human-video world models"
+        "title": "Human-video world models",
+        "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png"
       },
       {
         "avoid_claiming_now": [
@@ -3340,8 +3422,48 @@
           "procedure and subtask labels"
         ],
         "current_maturity": "Feasible but gated by action-target conversion.",
+        "diagram_flow": [
+          {
+            "items": [
+              "egocentric video and captions",
+              "objects, contacts, and procedures",
+              "hand/body motion windows",
+              "subtask labels and language context"
+            ],
+            "stage": "inputs"
+          },
+          {
+            "items": [
+              "action-token vocabulary",
+              "next action and action chunks",
+              "object-conditioned actions",
+              "contact state and subtask transition"
+            ],
+            "stage": "tasks_targets"
+          },
+          {
+            "items": [
+              "build action-space converter",
+              "normalize and audit action chunks",
+              "train VLA/policy-compatible head",
+              "track leakage and retargeting reports"
+            ],
+            "stage": "train_models"
+          },
+          {
+            "items": [
+              "held-out action metrics",
+              "chunk and next-action accuracy",
+              "object/contact-conditioned scores",
+              "policy card before robot-policy claim"
+            ],
+            "stage": "evaluate_gates"
+          }
+        ],
+        "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
         "first_pipeline": "Define the action space, use existing 20-task next-action/contact/object-conditioned tasks first, then add hand-trajectory or policy-compatible action chunks after conversion is traceable.",
         "id": "vision_language_action",
+        "image_alt": "Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.",
         "intermediate_artifacts": [
           "action-token vocabulary",
           "action-chunk windows",
@@ -3360,7 +3482,8 @@
           "policy or VLA held-out metrics"
         ],
         "question": "Can the model turn what it sees and reads into action?",
-        "title": "Vision-language-action models"
+        "title": "Vision-language-action models",
+        "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png"
       }
     ]
   },

metrics/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

metrics/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:20+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

metrics/three_foundation_pipelines.json

@@ -3,12 +3,14 @@
   "status": "pipeline_plan",
   "source_document": "THREE_FOUNDATION_PIPELINES.md",
   "claim_boundary": "These are supported pipeline directions, not three completed model-quality claims.",
-  "placeholder_assets": {
-    "status": "published_placeholders",
+  "diagram_assets": {
+    "status": "published_task_training_diagrams",
     "asset_root": "docs/assets/foundation-pipelines",
-    "source": "ChatGPT image generation with repo-local prompt notes",
+    "source": "ChatGPT image-generation prompt exploration with deterministic repo-rendered labels",
     "source_prompt_file": "docs/assets/foundation-pipelines/prompts.md",
-    "note": "Images are visual placeholders for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
+    "renderer_script": "scripts/render_foundation_pipeline_diagrams.py",
+    "diagram_type": "inputs_to_tasks_to_training_to_evaluation",
+    "note": "Images are task-training communication diagrams for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
   },
   "shared_principles": [
     "Use episode-level train/validation/test separation.",
@@ -51,9 +53,47 @@
       "first_pipeline": "Build a spatial-memory exporter, start with metric depth and pose consistency tasks, then evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.",
       "current_maturity": "Ready as a pipeline and evaluation contract.",
       "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
-      "image_alt": "Placeholder visual for the spatial intelligence pipeline: multiview video, depth, and pose inputs feeding scene memory and spatial reasoning outputs.",
+      "image_alt": "Task-training diagram for the spatial intelligence pipeline: inputs, spatial task targets, model training route, and evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "multiview RGB plus egocentric video",
+            "metric depth and confidence",
+            "camera pose, calibration, SLAM",
+            "object, contact, and language cues"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "spatial QA and object count",
+            "object permanence across windows",
+            "relative location and retrieval",
+            "pose-aware 3D consistency"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "export scene/object memory records",
+            "train spatial-memory encoder",
+            "add geometry-aware QA and retrieval heads",
+            "keep episode-level split discipline"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out episode spatial metrics",
+            "count and relation accuracy",
+            "retrieval rank and consistency",
+            "saved predictions before public claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "full neural rendering",
         "full 3D reconstruction",
@@ -92,9 +132,47 @@
       "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before claiming world-model quality.",
       "current_maturity": "Partially evidenced by current future-task probes and Cosmos-style branch artifacts.",
       "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
-      "placeholder_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png",
-      "image_alt": "Placeholder visual for the human-video world model pipeline: observed interaction windows feeding temporal dynamics and future-state outputs.",
+      "image_alt": "Task-training diagram for the human-video world model pipeline: observed-window inputs, future targets, model training route, and held-out evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "observed video/audio/sensor window",
+            "hand/body motion and camera pose",
+            "object/contact state",
+            "action and subtask labels"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "next action and next subtask",
+            "future object set",
+            "contact transition",
+            "camera-motion delta or latent future"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "Qwen structured future probes",
+            "Cosmos/dynamics branch separately",
+            "latent rollout or reconstruction loss",
+            "no target-side future leakage"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out future-task metrics",
+            "contact and object-set F1",
+            "rollout or latent consistency",
+            "per-episode breakdown and examples"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "strong world model from structured future-task scores alone",
         "visual future quality without visual or latent future metrics"
@@ -131,9 +209,47 @@
       "first_pipeline": "Define the action space, use existing 20-task next-action/contact/object-conditioned tasks first, then add hand-trajectory or policy-compatible action chunks after conversion is traceable.",
       "current_maturity": "Feasible but gated by action-target conversion.",
       "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
-      "placeholder_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
+      "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
-      "image_alt": "Placeholder visual for the vision-language-action pipeline: video, language, motion, and contact cues feeding action-chunk outputs.",
+      "image_alt": "Task-training diagram for the vision-language-action pipeline: observation and language inputs, action targets, VLA training route, and action evaluation gates.",
+      "diagram_flow": [
+        {
+          "stage": "inputs",
+          "items": [
+            "egocentric video and captions",
+            "objects, contacts, and procedures",
+            "hand/body motion windows",
+            "subtask labels and language context"
+          ]
+        },
+        {
+          "stage": "tasks_targets",
+          "items": [
+            "action-token vocabulary",
+            "next action and action chunks",
+            "object-conditioned actions",
+            "contact state and subtask transition"
+          ]
+        },
+        {
+          "stage": "train_models",
+          "items": [
+            "build action-space converter",
+            "normalize and audit action chunks",
+            "train VLA/policy-compatible head",
+            "track leakage and retargeting reports"
+          ]
+        },
+        {
+          "stage": "evaluate_gates",
+          "items": [
+            "held-out action metrics",
+            "chunk and next-action accuracy",
+            "object/contact-conditioned scores",
+            "policy card before robot-policy claim"
+          ]
+        }
+      ],
       "avoid_claiming_now": [
         "robot policy quality",
         "policy generalization before action-space evidence exists"

metrics/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T15:17:21+00:00",
+  "generated_at_utc": "2026-06-17T16:23:24+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 88890,
-      "evidence_index": 119623
+      "overview_index": 89133,
+      "evidence_index": 119986
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 88890,
-      "protocol_index": 115804,
-      "evidence_index": 119623
+      "overview_index": 89133,
+      "protocol_index": 116167,
+      "evidence_index": 119986
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 111262,
+      "bytes": 111336,
       "top_level_type": "dict"
     },
     {
@@ -331,7 +331,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 19501,
+      "bytes": 19580,
       "top_level_type": "dict"
     },
     {
@@ -341,12 +341,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 149465,
+      "bytes": 161420,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 902747,
+      "bytes": 910892,
       "top_level_type": "dict"
     },
     {
@@ -391,7 +391,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 8299,
+      "bytes": 8604,
       "top_level_type": "dict"
     },
     {
@@ -441,7 +441,7 @@
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 150170,
+      "bytes": 154846,
       "top_level_type": "dict"
     },
     {
@@ -506,7 +506,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 6518,
+      "bytes": 10084,
       "top_level_type": "dict"
     },
     {
@@ -521,7 +521,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19052,
+      "bytes": 19664,
       "top_level_type": "dict"
     },
     {
@@ -633,25 +633,25 @@
     {
       "path": "assets/foundation-pipelines/human-video-world-model-pipeline.png",
       "exists": true,
-      "bytes": 2356312,
-      "width": 1672,
-      "height": 941,
+      "bytes": 249668,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {
       "path": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "exists": true,
-      "bytes": 2337155,
-      "width": 1672,
-      "height": 941,
+      "bytes": 252509,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {
       "path": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "exists": true,
-      "bytes": 2421011,
-      "width": 1672,
-      "height": 941,
+      "bytes": 255706,
+      "width": 1800,
+      "height": 1012,
       "format": "PNG"
     },
     {

scripts/build_artifact_index.py

@@ -99,28 +99,28 @@ ARTIFACTS = [
         "shows": "Machine-readable pipeline-track contract for the website and Hugging Face mirrors.",
     },
     {
-        "id": "spatial_intelligence_pipeline_placeholder",
-        "title": "Spatial intelligence pipeline placeholder",
+        "id": "spatial_intelligence_task_training_diagram",
+        "title": "Spatial intelligence task-training diagram",
         "path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
         "kind": "visual_asset",
         "surface": "website_hf",
-        "shows": "ChatGPT image-generated placeholder visual for the spatial intelligence model training pipeline.",
+        "shows": "Inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence model training pipeline.",
     },
     {
-        "id": "human_video_world_model_pipeline_placeholder",
-        "title": "Human-video world model pipeline placeholder",
+        "id": "human_video_world_model_task_training_diagram",
+        "title": "Human-video world model task-training diagram",
         "path": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
         "kind": "visual_asset",
         "surface": "website_hf",
-        "shows": "ChatGPT image-generated placeholder visual for the human-video world-model training pipeline.",
+        "shows": "Observed-window-to-future-targets-to-training-to-evaluation diagram for the human-video world-model training pipeline.",
     },
     {
-        "id": "vision_language_action_pipeline_placeholder",
-        "title": "Vision-language-action pipeline placeholder",
+        "id": "vision_language_action_task_training_diagram",
+        "title": "Vision-language-action task-training diagram",
         "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
         "kind": "visual_asset",
         "surface": "website_hf",
-        "shows": "ChatGPT image-generated placeholder visual for the vision-language-action training pipeline.",
+        "shows": "Observation-and-language-to-action-targets-to-VLA-training-to-evaluation diagram for the vision-language-action training pipeline.",
     },
     {
         "id": "omni_model_extension_contract",

scripts/build_figure_index.py

@@ -67,27 +67,27 @@ FIGURES = [
         "surface": "README, website, HF Space, artifact dataset, model card",
     },
     {
-        "id": "spatial_intelligence_pipeline_placeholder",
-        "title": "Spatial intelligence pipeline placeholder",
+        "id": "spatial_intelligence_task_training_diagram",
+        "title": "Spatial intelligence task-training diagram",
         "path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
-        "role": "ChatGPT image-generated placeholder for the spatial intelligence training pipeline track.",
-        "source_script": "docs/assets/foundation-pipelines/prompts.md",
+        "role": "Readable inputs-to-tasks-to-training-to-evaluation diagram for the spatial intelligence pipeline track.",
+        "source_script": "scripts/render_foundation_pipeline_diagrams.py",
         "surface": "README, website, HF Space, artifact dataset, model card",
     },
     {
-        "id": "human_video_world_model_pipeline_placeholder",
-        "title": "Human-video world model pipeline placeholder",
+        "id": "human_video_world_model_task_training_diagram",
+        "title": "Human-video world model task-training diagram",
         "path": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
-        "role": "ChatGPT image-generated placeholder for the human-video world-model training pipeline track.",
-        "source_script": "docs/assets/foundation-pipelines/prompts.md",
+        "role": "Readable inputs-to-future-targets-to-training-to-evaluation diagram for the human-video world-model pipeline track.",
+        "source_script": "scripts/render_foundation_pipeline_diagrams.py",
         "surface": "README, website, HF Space, artifact dataset, model card",
     },
     {
-        "id": "vision_language_action_pipeline_placeholder",
-        "title": "Vision-language-action pipeline placeholder",
+        "id": "vision_language_action_task_training_diagram",
+        "title": "Vision-language-action task-training diagram",
         "path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
-        "role": "ChatGPT image-generated placeholder for the VLA/action-policy training pipeline track.",
-        "source_script": "docs/assets/foundation-pipelines/prompts.md",
+        "role": "Readable inputs-to-action-targets-to-VLA-training-to-evaluation diagram for the VLA/action-policy pipeline track.",
+        "source_script": "scripts/render_foundation_pipeline_diagrams.py",
         "surface": "README, website, HF Space, artifact dataset, model card",
     },
     {

scripts/render_foundation_pipeline_diagrams.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""Render clear task/training diagrams for the three foundation pipeline tracks."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from textwrap import wrap
+
+from PIL import Image, ImageDraw, ImageFilter, ImageFont
+
+
+ROOT = Path(__file__).resolve().parents[1]
+OUT_DIR = ROOT / "docs/assets/foundation-pipelines"
+
+W, H = 1800, 1012
+SCALE = 2
+FONT_REG = "/System/Library/Fonts/Supplemental/Arial.ttf"
+FONT_BOLD = "/System/Library/Fonts/Supplemental/Arial Bold.ttf"
+
+
+def font(size: int, bold: bool = False) -> ImageFont.FreeTypeFont:
+    return ImageFont.truetype(FONT_BOLD if bold else FONT_REG, size * SCALE)
+
+
+def sc(v: int | float) -> int:
+    return int(round(v * SCALE))
+
+
+def rounded(draw: ImageDraw.ImageDraw, box, radius, fill, outline=None, width=1):
+    box = tuple(sc(x) for x in box)
+    draw.rounded_rectangle(box, radius=sc(radius), fill=fill, outline=outline, width=sc(width))
+
+
+def line(draw: ImageDraw.ImageDraw, xy, fill, width=1):
+    draw.line(tuple(sc(x) for p in xy for x in p), fill=fill, width=sc(width))
+
+
+def text(draw: ImageDraw.ImageDraw, xy, value, size, fill, bold=False, anchor=None):
+    draw.text((sc(xy[0]), sc(xy[1])), value, font=font(size, bold), fill=fill, anchor=anchor)
+
+
+def multiline(draw, xy, value, size, fill, bold=False, max_chars=28, leading=1.24):
+    y = xy[1]
+    for raw in value.split("\n"):
+        lines = wrap(raw, max_chars) or [""]
+        for line_value in lines:
+            text(draw, (xy[0], y), line_value, size, fill, bold)
+            y += size * leading
+    return y
+
+
+def arrow(draw, start, end, color):
+    line(draw, [start, end], color, 4)
+    x1, y1 = start
+    x2, y2 = end
+    head = 16
+    if x2 >= x1:
+        pts = [(x2, y2), (x2 - head, y2 - 10), (x2 - head, y2 + 10)]
+    else:
+        pts = [(x2, y2), (x2 + head, y2 - 10), (x2 + head, y2 + 10)]
+    draw.polygon([(sc(x), sc(y)) for x, y in pts], fill=color)
+
+
+def draw_frame_stack(draw, x, y, accent):
+    for i in range(3):
+        rounded(draw, (x + i * 18, y + i * 13, x + 150 + i * 18, y + 92 + i * 13), 10, (10, 18, 18, 230), accent, 2)
+        line(draw, [(x + 12 + i * 18, y + 70 + i * 13), (x + 138 + i * 18, y + 34 + i * 13)], accent, 2)
+        rounded(draw, (x + 22 + i * 18, y + 20 + i * 13, x + 64 + i * 18, y + 52 + i * 13), 5, (80, 160, 140, 170))
+
+
+def draw_depth_pose(draw, x, y, accent):
+    rounded(draw, (x, y, x + 170, y + 105), 12, (8, 14, 20, 235), accent, 2)
+    for i in range(8):
+        c = (30 + i * 18, 80 + i * 12, 160 + i * 8)
+        draw.rectangle((sc(x + 12 + i * 18), sc(y + 14), sc(x + 30 + i * 18), sc(y + 92)), fill=c)
+    line(draw, [(x + 108, y + 86), (x + 142, y + 36)], (230, 240, 255), 3)
+    line(draw, [(x + 142, y + 36), (x + 156, y + 62)], (230, 240, 255), 3)
+    line(draw, [(x + 142, y + 36), (x + 119, y + 48)], (230, 240, 255), 3)
+
+
+def draw_world_frames(draw, x, y, accent):
+    for i, lab in enumerate(["t", "t+1", "t+2"]):
+        bx = x + i * 100
+        rounded(draw, (bx, y, bx + 78, y + 78), 10, (12, 18, 18, 230), accent if i == 0 else (150, 170, 170), 2)
+        if i > 0:
+            line(draw, [(bx + 14, y + 52), (bx + 62, y + 26)], accent, 2)
+        text(draw, (bx + 16, y + 14), lab, 18, (240, 248, 238), True)
+    arrow(draw, (x + 82, y + 39), (x + 96, y + 39), accent)
+    arrow(draw, (x + 182, y + 39), (x + 196, y + 39), accent)
+
+
+def draw_action_tokens(draw, x, y, accent):
+    labels = ["look", "reach", "grasp", "move", "place"]
+    for i, lab in enumerate(labels):
+        bx = x + (i % 3) * 94
+        by = y + (i // 3) * 54
+        rounded(draw, (bx, by, bx + 82, by + 38), 8, (12, 18, 15, 235), accent, 2)
+        text(draw, (bx + 41, by + 11), lab, 16, (238, 248, 232), True, "ma")
+
+
+def draw_column(draw, box, title, items, accent, icon_kind):
+    x1, y1, x2, y2 = box
+    rounded(draw, box, 18, (7, 12, 13, 230), (62, 82, 76), 1)
+    text(draw, (x1 + 22, y1 + 22), title.upper(), 19, accent, True)
+
+    icon_y = y1 + 62
+    if icon_kind == "frames":
+        draw_frame_stack(draw, x1 + 22, icon_y, accent)
+    elif icon_kind == "depth":
+        draw_depth_pose(draw, x1 + 22, icon_y, accent)
+    elif icon_kind == "world":
+        draw_world_frames(draw, x1 + 22, icon_y + 10, accent)
+    elif icon_kind == "tokens":
+        draw_action_tokens(draw, x1 + 22, icon_y + 8, accent)
+    elif icon_kind == "model":
+        cx, cy = x1 + 112, icon_y + 54
+        for r in [64, 46, 28]:
+            draw.ellipse((sc(cx - r), sc(cy - r), sc(cx + r), sc(cy + r)), outline=accent, width=sc(2))
+        for dx, dy in [(-62, -18), (-44, 48), (0, -58), (46, -36), (58, 36), (4, 60)]:
+            draw.ellipse((sc(cx + dx - 5), sc(cy + dy - 5), sc(cx + dx + 5), sc(cy + dy + 5)), fill=accent)
+    else:
+        rounded(draw, (x1 + 22, icon_y, x1 + 178, icon_y + 96), 14, (10, 20, 18, 230), accent, 2)
+        for i in range(5):
+            line(draw, [(x1 + 42, icon_y + 78 - i * 12), (x1 + 162, icon_y + 30 + i * 5)], accent, 2)
+
+    y = y1 + 198
+    for item in items:
+        rounded(draw, (x1 + 22, y + 2, x1 + 34, y + 14), 3, accent)
+        y = multiline(draw, (x1 + 48, y - 4), item, 18, (224, 232, 224), False, max_chars=27)
+        y += 10
+
+
+def background(accent_a, accent_b):
+    img = Image.new("RGB", (W * SCALE, H * SCALE), (3, 7, 7))
+    draw = ImageDraw.Draw(img, "RGBA")
+    for y in range(H * SCALE):
+        ratio = y / (H * SCALE)
+        r = int(4 + ratio * 9)
+        g = int(8 + ratio * 18)
+        b = int(9 + ratio * 15)
+        draw.line((0, y, W * SCALE, y), fill=(r, g, b, 255))
+
+    for i in range(0, W, 72):
+        line(draw, [(i, 0), (i + 400, H)], (40, 65, 58, 38), 1)
+    for j in range(100, H, 115):
+        line(draw, [(0, j), (W, j - 90)], (52, 83, 74, 22), 1)
+
+    glow = Image.new("RGBA", img.size, (0, 0, 0, 0))
+    gd = ImageDraw.Draw(glow, "RGBA")
+    gd.ellipse((sc(1100), sc(-260), sc(2180), sc(760)), fill=accent_a + (34,))
+    gd.ellipse((sc(-320), sc(480), sc(600), sc(1300)), fill=accent_b + (26,))
+    glow = glow.filter(ImageFilter.GaussianBlur(sc(56)))
+    img = Image.alpha_composite(img.convert("RGBA"), glow)
+    return img
+
+
+TRACKS = [
+    {
+        "file": "spatial-intelligence-pipeline.png",
+        "accent": (120, 244, 214),
+        "accent2": (204, 255, 160),
+        "title": "Spatial Intelligence",
+        "subtitle": "Train models to convert video, depth, and pose into scene memory and spatial reasoning.",
+        "status": "Pipeline contract: ready. Strong claims need raw depth/pose artifacts plus held-out metrics.",
+        "columns": [
+            ("inputs", "Inputs", ["multiview RGB + egocentric video", "metric depth and confidence", "camera pose, calibration, SLAM", "object, contact, and language cues"], "depth"),
+            ("targets", "Tasks / targets", ["spatial QA and object count", "object permanence across windows", "relative location and retrieval", "pose-aware 3D consistency"], "frames"),
+            ("train", "Train models", ["export scene/object memory records", "train spatial-memory encoder", "add geometry-aware QA/retrieval heads", "keep episode-level split discipline"], "model"),
+            ("eval", "Evaluate gates", ["held-out episode spatial metrics", "count/relation accuracy", "retrieval rank and consistency", "saved predictions before public claim"], "metrics"),
+        ],
+    },
+    {
+        "file": "human-video-world-model-pipeline.png",
+        "accent": (255, 196, 116),
+        "accent2": (112, 226, 240),
+        "title": "Human-Video World Model",
+        "subtitle": "Train models to predict future interaction state from observed human video windows.",
+        "status": "Partially evidenced by future probes and Cosmos-style branches; visual/latent future metrics remain gated.",
+        "columns": [
+            ("inputs", "Inputs", ["observed video/audio/sensor window", "hand/body motion and camera pose", "object/contact state", "action and subtask labels"], "world"),
+            ("targets", "Tasks / targets", ["next action and next subtask", "future object set", "contact transition", "camera-motion delta or latent future"], "frames"),
+            ("train", "Train models", ["Qwen structured future probes", "Cosmos/dynamics branch separately", "latent rollout or reconstruction loss", "no target-side future leakage"], "model"),
+            ("eval", "Evaluate gates", ["held-out future-task metrics", "contact and object-set F1", "rollout or latent consistency", "per-episode breakdown and examples"], "metrics"),
+        ],
+    },
+    {
+        "file": "vision-language-action-pipeline.png",
+        "accent": (164, 255, 159),
+        "accent2": (178, 142, 255),
+        "title": "Vision-Language-Action",
+        "subtitle": "Train models that map egocentric video and language into traceable action chunks.",
+        "status": "Feasible but gated by action-token conversion, normalization, retargeting, and policy metrics.",
+        "columns": [
+            ("inputs", "Inputs", ["egocentric video and captions", "objects, contacts, and procedures", "hand/body motion windows", "subtask labels and language context"], "tokens"),
+            ("targets", "Tasks / targets", ["action-token vocabulary", "next action and action chunks", "object-conditioned actions", "contact state and subtask transition"], "frames"),
+            ("train", "Train models", ["build action-space converter", "normalize and audit action chunks", "train VLA/policy-compatible head", "track leakage and retargeting reports"], "model"),
+            ("eval", "Evaluate gates", ["held-out action metrics", "chunk and next-action accuracy", "object/contact-conditioned scores", "policy card before robot-policy claim"], "metrics"),
+        ],
+    },
+]
+
+
+def render(track):
+    img = background(track["accent"], track["accent2"])
+    draw = ImageDraw.Draw(img, "RGBA")
+    accent = track["accent"] + (255,)
+    accent2 = track["accent2"] + (255,)
+
+    rounded(draw, (42, 38, W - 42, H - 38), 28, (3, 8, 8, 182), (84, 112, 104), 2)
+    text(draw, (82, 74), "Ropedia Xperience-10M", 19, (222, 232, 222), True)
+    text(draw, (82, 110), track["title"], 48, (248, 252, 246), True)
+    multiline(draw, (84, 172), track["subtitle"], 24, (194, 211, 202), False, max_chars=82)
+    rounded(draw, (1140, 72, 1688, 152), 18, (10, 18, 16, 220), accent2, 2)
+    text(draw, (1165, 98), "Direction -> task targets -> model training -> evaluation", 22, (246, 255, 239), True)
+
+    col_w = 390
+    gap = 34
+    start_x = 82
+    y1 = 250
+    y2 = 808
+    centers = []
+    for i, (_, title_value, items, icon_kind) in enumerate(track["columns"]):
+        x1 = start_x + i * (col_w + gap)
+        draw_column(draw, (x1, y1, x1 + col_w, y2), title_value, items, accent, icon_kind)
+        centers.append((x1 + col_w, (y1 + y2) / 2))
+        if i > 0:
+            prev_x = start_x + (i - 1) * (col_w + gap) + col_w
+            arrow(draw, (prev_x + 10, (y1 + y2) / 2), (x1 - 12, (y1 + y2) / 2), accent)
+
+    rounded(draw, (82, 846, W - 82, 930), 18, (8, 14, 12, 230), accent2, 2)
+    text(draw, (110, 872), "Claim boundary", 21, accent2, True)
+    multiline(draw, (310, 868), track["status"], 21, (232, 240, 232), False, max_chars=104)
+
+    out = img.convert("RGB").resize((W, H), Image.Resampling.LANCZOS)
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    out.save(OUT_DIR / track["file"], optimize=True, quality=95)
+
+
+def main():
+    for track in TRACKS:
+        render(track)
+    print("Rendered foundation pipeline diagrams:")
+    for track in TRACKS:
+        path = OUT_DIR / track["file"]
+        print(f"- {path} ({path.stat().st_size} bytes)")
+
+
+if __name__ == "__main__":
+    main()

scripts/validate_mirror_parity.py

@@ -140,6 +140,7 @@ SCRIPT_FILES = [
     "build_public_surface_qa.py",
     "build_rendered_site_check.py",
     "build_interactive_research_roadmap.py",
+    "render_foundation_pipeline_diagrams.py",
     "build_single_episode_explorer.py",
     "build_task_method_20_gap_audit.py",
     "build_research_takeaways.py",

scripts/validate_publication_package.py

@@ -356,6 +356,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "scripts/build_public_surface_qa.py",
         "scripts/build_rendered_site_check.py",
         "scripts/build_interactive_research_roadmap.py",
+        "scripts/render_foundation_pipeline_diagrams.py",
         "scripts/verify_live_publication.py",
         "scripts/validate_mirror_parity.py",
         "scripts/validate_scope_claims.py",

scripts/verify_live_publication.py

@@ -439,8 +439,8 @@ HASH_GROUPS = [
         },
     },
     {
-        "id": "spatial_intelligence_pipeline_placeholder",
-        "title": "Spatial intelligence pipeline placeholder",
+        "id": "spatial_intelligence_task_training_diagram",
+        "title": "Spatial intelligence task-training diagram",
         "local_path": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
         "urls": {
             "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
@@ -450,8 +450,8 @@ HASH_GROUPS = [
         },
     },
     {
-        "id": "human_video_world_model_pipeline_placeholder",
-        "title": "Human-video world model pipeline placeholder",
+        "id": "human_video_world_model_task_training_diagram",
+        "title": "Human-video world model task-training diagram",
         "local_path": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
         "urls": {
             "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/foundation-pipelines/human-video-world-model-pipeline.png",
@@ -461,8 +461,8 @@ HASH_GROUPS = [
         },
     },
     {
-        "id": "vision_language_action_pipeline_placeholder",
-        "title": "Vision-language-action pipeline placeholder",
+        "id": "vision_language_action_task_training_diagram",
+        "title": "Vision-language-action task-training diagram",
         "local_path": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
         "urls": {
             "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/foundation-pipelines/vision-language-action-pipeline.png",