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PROJECT_README.md

@@ -1367,6 +1367,15 @@ 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 artifacts; 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. |
 
+For the single public sample, each direction is now shown as an explicit
+training-pair recipe:
+
+| Direction | One-sample input | One-sample output target |
+| --- | --- | --- |
+| Spatial intelligence | 20-frame windows from `windows.csv` / `shared_windows.npz`, joined with six MP4 camera streams plus `annotation.hdf5` depth, pose, SLAM/calibration, object/contact cues, and optional language questions. | Camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA targets. |
+| Human-video world model | Current observed window at time `t`: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only. | Shifted future targets: next action, next subtask, future object set, contact transition, time-to-transition, camera-motion delta, or latent/future feature. |
+| Vision-language-action | Egocentric/fisheye video, caption/object context, hand/body mocap, contact state, and current subtask text as observation-language input. | Action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. |
+
 High-resolution slide diagrams for the three tracks are published in
 [`docs/assets/foundation-pipelines`](docs/assets/foundation-pipelines). Spatial
 intelligence and human-video world modeling use the clean slide PNGs supplied

README.md

@@ -1389,6 +1389,15 @@ 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 artifacts; 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. |
 
+For the single public sample, each direction is now shown as an explicit
+training-pair recipe:
+
+| Direction | One-sample input | One-sample output target |
+| --- | --- | --- |
+| Spatial intelligence | 20-frame windows from `windows.csv` / `shared_windows.npz`, joined with six MP4 camera streams plus `annotation.hdf5` depth, pose, SLAM/calibration, object/contact cues, and optional language questions. | Camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA targets. |
+| Human-video world model | Current observed window at time `t`: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only. | Shifted future targets: next action, next subtask, future object set, contact transition, time-to-transition, camera-motion delta, or latent/future feature. |
+| Vision-language-action | Egocentric/fisheye video, caption/object context, hand/body mocap, contact state, and current subtask text as observation-language input. | Action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. |
+
 High-resolution slide diagrams for the three tracks are published in
 [`docs/assets/foundation-pipelines`](docs/assets/foundation-pipelines). Spatial
 intelligence and human-video world modeling use the clean slide PNGs supplied

THREE_FOUNDATION_PIPELINES.md

@@ -14,6 +14,23 @@ 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. |
 
+## One-Sample Training-Pair Recipes
+
+These recipes describe how to obtain input/output pairs from the **single public
+sample episode**. They are development contracts, not claims that the three full
+foundation models are already trained.
+
+| Track | Input from the one public sample | Output target from the same sample | Existing hooks |
+| --- | --- | --- | --- |
+| Spatial intelligence models | Slice `results/episode_task_suite/windows.csv` and `shared_windows.npz` into 20-frame windows, then join the six MP4 camera streams with `annotation.hdf5` depth, camera pose, SLAM/calibration, object cues, contacts, and optional language questions. | Camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers derived from the annotation timeline. | `object_relevance`, `modality_reconstruction`, `caption_grounding`, `object_set_forecast`, `camera_view_sync_retrieval`. |
+| Human-video world models | Use the current observed 20-frame window at time `t`: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only. | Shift the same episode timeline forward to create next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets. | `next_action`, `long_horizon_next_action`, `next_subtask_forecast`, `object_set_forecast`, `time_to_transition`, `ego_motion_forecast`. |
+| Vision-language-action models | Use egocentric/fisheye video windows, caption/object context, hand/body mocap, contact state, and current subtask text as the observation-language side. | Action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. | `timeline_action`, `next_action`, `hand_trajectory_forecast`, `contact_prediction`, `interaction_text_prediction`, `action_object_relation`. |
+
+The one-sample windowization is 5,821 frames, 1,161 overlapping 20-frame windows,
+5-frame stride, and about 20 FPS. Future labels or future windows must not leak
+into inputs for world-model targets. VLA/policy claims require a later action
+space converter, normalization, retargeting report, and held-out policy metrics.
+
 ## Published Direction Figures
 
 The repo and public mirrors include three high-resolution direction images from
@@ -46,12 +63,19 @@ Data contract:
 
 - Inputs: multiview RGB, egocentric RGB, depth, camera pose, calibration, object
   labels, contact labels, optional language queries.
+- One-sample input builder: slice 20-frame windows from `windows.csv` and
+  `shared_windows.npz`, then join the six MP4 camera streams with
+  `annotation.hdf5` depth, camera pose, SLAM/calibration, object cues, contacts,
+  and optional language questions.
 - Intermediate artifacts: synchronized camera window manifest, pose/depth
   availability report, scene/object memory records, object permanence targets,
   spatial relation targets, and spatial QA prompts.
 - Outputs: object count, object persistence, relative location, 3D geometry
   consistency, multiview retrieval, camera-motion-aware scene memory, and
   language answers grounded in the scene.
+- One-sample output builder: camera-view match, object relevance, object-set
+  memory, depth/pose reconstruction proxy, caption-grounded retrieval, and
+  spatial QA targets.
 
 First practical implementation:
 
@@ -75,11 +99,17 @@ Data contract:
 
 - Inputs: observed video/audio/sensor windows, hand/body motion, camera pose,
   object/contact state, action/subtask labels, and optional language context.
+- One-sample input builder: use only the current observed 20-frame window at
+  time `t`, including RGB/audio/sensor summaries, hand/body motion, camera pose,
+  current object/contact state, and current action/subtask context.
 - Intermediate artifacts: observed/future window pairs, future label targets,
   action-conditioned target records, visual or latent reconstruction targets,
   and temporal consistency metadata.
 - Outputs: next action, next subtask, future object set, future state embedding,
   camera-motion delta, contact transition, and future-window quality metrics.
+- One-sample output builder: shift the episode timeline forward for next-action,
+  next-subtask, future object-set, contact-transition, time-to-transition,
+  camera-motion delta, or latent/future-feature targets.
 
 First practical implementation:
 
@@ -104,11 +134,17 @@ Data contract:
 
 - Inputs: egocentric video, language captions, hand/body motion, object/contact
   state, action/subtask labels, and optional retargeting metadata.
+- One-sample input builder: use egocentric/fisheye video windows,
+  caption/object context, hand/body mocap, contact state, and current subtask
+  text as the observation-language side.
 - Intermediate artifacts: action-token vocabulary, action-chunk windows,
   normalization stats, retargeting report, leakage audit, and action-space
   model card.
 - Outputs: next action, action chunk, object-conditioned action, contact state,
   subtask transition, and policy/VLA held-out metrics.
+- One-sample output builder: action-token proxies such as current/next action,
+  object-conditioned action relation, contact state, interaction-text class,
+  subtask transition, or hand-trajectory/action-chunk proxy.
 
 First practical implementation:
 

assets/foundation-pipelines/README.md

@@ -20,6 +20,14 @@ Markdown, JSON, and website labels.
 | Human-video world models | `human-video-world-model-pipeline.png` | `source-slides/human-video-world-model-slide.png` |
 | Vision-language-action models | `vision-language-action-pipeline.png` | `source-slides/vision-language-action-slide.png` |
 
+The website places each figure beside a one-sample training I/O recipe:
+
+| Track | One-sample training pair |
+| --- | --- |
+| Spatial intelligence models | Current 20-frame multiview/depth/pose/object window -> spatial relation, retrieval, reconstruction-proxy, or QA target. |
+| Human-video world models | Current observed 20-frame window at time `t` -> shifted future action, subtask, object-set, contact, transition-time, or future-feature target. |
+| Vision-language-action models | Egocentric video + caption/object/motion/contact context -> action-token, object-action, contact, interaction-text, subtask, or hand-trajectory proxy target. |
+
 The deterministic restoration script is
 `scripts/render_foundation_pipeline_diagrams.py`; restoration notes and source
 mapping are in `prompts.md`.

data/mirror_parity.json

@@ -1,23 +1,16 @@
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-      "actual_sha256": "64f753a9f138b067b98ca278b8057e8be8ee906a37d42511534b5abd6de26f94"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_space",
-      "kind": "hash_mismatch",
-      "path": "hf_space:data/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_artifacts_data",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:data/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_artifacts",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:docs/data/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_model_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:data/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_model_docs_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:docs/data/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/public_surface_qa.json",
-      "surface": "hf_model",
-      "kind": "hash_mismatch",
-      "path": "hf_model:metrics/public_surface_qa.json",
-      "expected_sha256": "bd9828bc08a54593e365e69fd71a290b1d5bfb6652930764f667f1b18db3e8fe",
-      "actual_sha256": "cd783b0a8718c16a53160fab33f545d649dc6b54d065c713555ea0630b5e7f05"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_space",
-      "kind": "hash_mismatch",
-      "path": "hf_space:data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_artifacts_data",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_artifacts",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:docs/data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_docs_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:docs/data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model",
-      "kind": "hash_mismatch",
-      "path": "hf_model:metrics/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    }
-  ]
+  "failures": []
 }

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:19+00:00",
+  "generated_at_utc": "2026-06-21T20:01:39+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": "2026-06-21T19:32:05+00:00"
+          "generated_at_utc": "2026-06-21T20:01:18+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-21T19:27:12+00:00"
+          "generated_at_utc": "2026-06-21T19:57:53+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-21T19:29:46+00:00"
+          "generated_at_utc": "2026-06-21T20:00:18+00:00"
         }
       },
       "failures": {}
@@ -97,7 +97,7 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 22,
         "Xperience-10M": 173,
-        "20-task": 109,
+        "20-task": 110,
         "Qwen3-Omni": 246,
         "128-episode pilot": 1
       }
@@ -107,7 +107,7 @@
       "status": "pass",
       "reason": "Public cards should link the repo, Space, artifacts, model baselines, upstream dataset, and Ropedia dataset page.",
       "marker_counts": {
-        "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite": 90,
+        "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite": 92,
         "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite": 12,
         "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts": 14,
         "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines": 13,
@@ -131,10 +131,10 @@
         "data/public_surface_qa.json": 8,
         "data/research_roadmap.json": 15,
         "data/task_suite_enhancement_128.json": 22,
-        "data/task_suite_20.json": 36,
+        "data/task_suite_20.json": 37,
         "data/two_evidence_lines.json": 5,
         "data/two_evidence_line_result_summary.json": 10,
-        "data/unified_task_model_radar.json": 21,
+        "data/unified_task_model_radar.json": 22,
         "data/single_episode_task_model_radar.json": 17,
         "data/episode128_task_model_radar.json": 16,
         "data/task_method_20_result_matrix.json": 28,

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:34:08+00:00",
+  "generated_at_utc": "2026-06-21T20:02:09+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

data/research_roadmap_interactive.json

@@ -2862,7 +2862,7 @@
     ],
     "status": "planning_artifact"
   },
-  "generated_at_utc": "2026-06-21T10:51:52+00:00",
+  "generated_at_utc": "2026-06-21T19:51:24+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -4269,6 +4269,26 @@
           "spatial QA prompts"
         ],
         "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
+        "one_sample_training_io": {
+          "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics.",
+          "existing_task_hooks": [
+            "object_relevance",
+            "modality_reconstruction",
+            "caption_grounding",
+            "object_set_forecast",
+            "camera_view_sync_retrieval"
+          ],
+          "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+          "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/feature_manifest.json",
+            "official sample annotation.hdf5",
+            "official sample six MP4 camera streams"
+          ],
+          "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers."
+        },
         "outputs": [
           "object count",
           "object persistence",
@@ -4347,6 +4367,26 @@
           "temporal consistency metadata"
         ],
         "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
+        "one_sample_training_io": {
+          "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves.",
+          "existing_task_hooks": [
+            "next_action",
+            "long_horizon_next_action",
+            "next_subtask_forecast",
+            "object_set_forecast",
+            "time_to_transition",
+            "ego_motion_forecast"
+          ],
+          "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+          "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+            "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+          ],
+          "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets."
+        },
         "outputs": [
           "next action",
           "next subtask",
@@ -4425,6 +4465,26 @@
           "action-space model card"
         ],
         "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
+        "one_sample_training_io": {
+          "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics.",
+          "existing_task_hooks": [
+            "timeline_action",
+            "next_action",
+            "hand_trajectory_forecast",
+            "contact_prediction",
+            "interaction_text_prediction",
+            "action_object_relation"
+          ],
+          "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+          "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+            "official sample annotation.hdf5"
+          ],
+          "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy."
+        },
         "outputs": [
           "next action",
           "action chunk",

data/three_foundation_pipelines.json

@@ -59,6 +59,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Spatial intelligence models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/feature_manifest.json",
+          "official sample annotation.hdf5",
+          "official sample six MP4 camera streams"
+        ],
+        "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+        "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers.",
+        "existing_task_hooks": [
+          "object_relevance",
+          "modality_reconstruction",
+          "caption_grounding",
+          "object_set_forecast",
+          "camera_view_sync_retrieval"
+        ],
+        "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -138,6 +158,26 @@
       "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": "High-resolution slide diagram showing the Human-video world models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+          "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+        ],
+        "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+        "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets.",
+        "existing_task_hooks": [
+          "next_action",
+          "long_horizon_next_action",
+          "next_subtask_forecast",
+          "object_set_forecast",
+          "time_to_transition",
+          "ego_motion_forecast"
+        ],
+        "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -215,6 +255,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Vision-language-action models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+          "official sample annotation.hdf5"
+        ],
+        "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+        "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy.",
+        "existing_task_hooks": [
+          "timeline_action",
+          "next_action",
+          "hand_trajectory_forecast",
+          "contact_prediction",
+          "interaction_text_prediction",
+          "action_object_relation"
+        ],
+        "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",

data/website_integrity.json

@@ -1,12 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:05+00:00",
+  "generated_at_utc": "2026-06-21T20:01:18+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 260,
-    "external_reference_count": 156,
+    "local_references": 267,
+    "external_reference_count": 158,
     "json_files": 55,
     "image_assets_referenced": 22,
     "failure_count": 0
@@ -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": 135119,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "evidence_index": 187740
     },
     {
       "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": 135119,
-      "protocol_index": 178478,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "protocol_index": 183928,
+      "evidence_index": 187740
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -175,6 +175,13 @@
       "reason": "The website should expose the main task-suite figure.",
       "marker_count": 4
     },
+    {
+      "name": "foundation_direction_cards_explain_one_sample_io",
+      "status": "pass",
+      "reason": "The three foundation direction cards should explain one-sample training inputs and outputs.",
+      "panel_count": 3,
+      "missing_terms": []
+    },
     {
       "name": "suite_task_map_precedes_radar_surface",
       "status": "pass",
@@ -265,7 +272,7 @@
       "name": "task_player_uses_walkthrough_json",
       "status": "pass",
       "reason": "The task player and task cards should read the generated walkthrough JSON.",
-      "marker_count": 2
+      "marker_count": 3
     },
     {
       "name": "task_cards_use_human_research_names",
@@ -284,7 +291,7 @@
     {
       "path": "index.html",
       "id_count": 99,
-      "reference_count": 232,
+      "reference_count": 239,
       "image_count": 28
     },
     {
@@ -363,7 +370,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1420747,
+      "bytes": 1420751,
       "top_level_type": "dict"
     },
     {
@@ -468,7 +475,7 @@
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 186755,
+      "bytes": 191028,
       "top_level_type": "dict"
     },
     {
@@ -538,7 +545,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 10312,
+      "bytes": 14465,
       "top_level_type": "dict"
     },
     {
@@ -563,7 +570,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19914,
+      "bytes": 20178,
       "top_level_type": "dict"
     },
     {

docs/assets/foundation-pipelines/README.md

@@ -20,6 +20,14 @@ Markdown, JSON, and website labels.
 | Human-video world models | `human-video-world-model-pipeline.png` | `source-slides/human-video-world-model-slide.png` |
 | Vision-language-action models | `vision-language-action-pipeline.png` | `source-slides/vision-language-action-slide.png` |
 
+The website places each figure beside a one-sample training I/O recipe:
+
+| Track | One-sample training pair |
+| --- | --- |
+| Spatial intelligence models | Current 20-frame multiview/depth/pose/object window -> spatial relation, retrieval, reconstruction-proxy, or QA target. |
+| Human-video world models | Current observed 20-frame window at time `t` -> shifted future action, subtask, object-set, contact, transition-time, or future-feature target. |
+| Vision-language-action models | Egocentric video + caption/object/motion/contact context -> action-token, object-action, contact, interaction-text, subtask, or hand-trajectory proxy target. |
+
 The deterministic restoration script is
 `scripts/render_foundation_pipeline_diagrams.py`; restoration notes and source
 mapping are in `prompts.md`.

docs/data/mirror_parity.json

@@ -1,23 +1,16 @@
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@@ -31933,150 +31797,5 @@
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docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:19+00:00",
+  "generated_at_utc": "2026-06-21T20:01:39+00:00",
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@@ -18,7 +18,7 @@
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@@ -43,12 +43,12 @@
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@@ -97,7 +97,7 @@
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@@ -107,7 +107,7 @@
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@@ -131,10 +131,10 @@
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         "data/research_roadmap.json": 15,
         "data/task_suite_enhancement_128.json": 22,
-        "data/task_suite_20.json": 36,
+        "data/task_suite_20.json": 37,
         "data/two_evidence_lines.json": 5,
         "data/two_evidence_line_result_summary.json": 10,
-        "data/unified_task_model_radar.json": 21,
+        "data/unified_task_model_radar.json": 22,
         "data/single_episode_task_model_radar.json": 17,
         "data/episode128_task_model_radar.json": 16,
         "data/task_method_20_result_matrix.json": 28,

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:34:08+00:00",
+  "generated_at_utc": "2026-06-21T20:02:09+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

docs/data/research_roadmap_interactive.json

@@ -2862,7 +2862,7 @@
     ],
     "status": "planning_artifact"
   },
-  "generated_at_utc": "2026-06-21T10:51:52+00:00",
+  "generated_at_utc": "2026-06-21T19:51:24+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -4269,6 +4269,26 @@
           "spatial QA prompts"
         ],
         "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
+        "one_sample_training_io": {
+          "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics.",
+          "existing_task_hooks": [
+            "object_relevance",
+            "modality_reconstruction",
+            "caption_grounding",
+            "object_set_forecast",
+            "camera_view_sync_retrieval"
+          ],
+          "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+          "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/feature_manifest.json",
+            "official sample annotation.hdf5",
+            "official sample six MP4 camera streams"
+          ],
+          "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers."
+        },
         "outputs": [
           "object count",
           "object persistence",
@@ -4347,6 +4367,26 @@
           "temporal consistency metadata"
         ],
         "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
+        "one_sample_training_io": {
+          "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves.",
+          "existing_task_hooks": [
+            "next_action",
+            "long_horizon_next_action",
+            "next_subtask_forecast",
+            "object_set_forecast",
+            "time_to_transition",
+            "ego_motion_forecast"
+          ],
+          "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+          "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+            "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+          ],
+          "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets."
+        },
         "outputs": [
           "next action",
           "next subtask",
@@ -4425,6 +4465,26 @@
           "action-space model card"
         ],
         "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
+        "one_sample_training_io": {
+          "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics.",
+          "existing_task_hooks": [
+            "timeline_action",
+            "next_action",
+            "hand_trajectory_forecast",
+            "contact_prediction",
+            "interaction_text_prediction",
+            "action_object_relation"
+          ],
+          "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+          "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+            "official sample annotation.hdf5"
+          ],
+          "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy."
+        },
         "outputs": [
           "next action",
           "action chunk",

docs/data/three_foundation_pipelines.json

@@ -59,6 +59,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Spatial intelligence models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/feature_manifest.json",
+          "official sample annotation.hdf5",
+          "official sample six MP4 camera streams"
+        ],
+        "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+        "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers.",
+        "existing_task_hooks": [
+          "object_relevance",
+          "modality_reconstruction",
+          "caption_grounding",
+          "object_set_forecast",
+          "camera_view_sync_retrieval"
+        ],
+        "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -138,6 +158,26 @@
       "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": "High-resolution slide diagram showing the Human-video world models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+          "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+        ],
+        "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+        "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets.",
+        "existing_task_hooks": [
+          "next_action",
+          "long_horizon_next_action",
+          "next_subtask_forecast",
+          "object_set_forecast",
+          "time_to_transition",
+          "ego_motion_forecast"
+        ],
+        "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -215,6 +255,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Vision-language-action models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+          "official sample annotation.hdf5"
+        ],
+        "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+        "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy.",
+        "existing_task_hooks": [
+          "timeline_action",
+          "next_action",
+          "hand_trajectory_forecast",
+          "contact_prediction",
+          "interaction_text_prediction",
+          "action_object_relation"
+        ],
+        "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",

docs/data/website_integrity.json

@@ -1,12 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:05+00:00",
+  "generated_at_utc": "2026-06-21T20:01:18+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 260,
-    "external_reference_count": 156,
+    "local_references": 267,
+    "external_reference_count": 158,
     "json_files": 55,
     "image_assets_referenced": 22,
     "failure_count": 0
@@ -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": 135119,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "evidence_index": 187740
     },
     {
       "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": 135119,
-      "protocol_index": 178478,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "protocol_index": 183928,
+      "evidence_index": 187740
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -175,6 +175,13 @@
       "reason": "The website should expose the main task-suite figure.",
       "marker_count": 4
     },
+    {
+      "name": "foundation_direction_cards_explain_one_sample_io",
+      "status": "pass",
+      "reason": "The three foundation direction cards should explain one-sample training inputs and outputs.",
+      "panel_count": 3,
+      "missing_terms": []
+    },
     {
       "name": "suite_task_map_precedes_radar_surface",
       "status": "pass",
@@ -265,7 +272,7 @@
       "name": "task_player_uses_walkthrough_json",
       "status": "pass",
       "reason": "The task player and task cards should read the generated walkthrough JSON.",
-      "marker_count": 2
+      "marker_count": 3
     },
     {
       "name": "task_cards_use_human_research_names",
@@ -284,7 +291,7 @@
     {
       "path": "index.html",
       "id_count": 99,
-      "reference_count": 232,
+      "reference_count": 239,
       "image_count": 28
     },
     {
@@ -363,7 +370,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1420747,
+      "bytes": 1420751,
       "top_level_type": "dict"
     },
     {
@@ -468,7 +475,7 @@
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 186755,
+      "bytes": 191028,
       "top_level_type": "dict"
     },
     {
@@ -538,7 +545,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 10312,
+      "bytes": 14465,
       "top_level_type": "dict"
     },
     {
@@ -563,7 +570,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19914,
+      "bytes": 20178,
       "top_level_type": "dict"
     },
     {

docs/index.html

@@ -989,6 +989,57 @@
       font-size: 13px;
       line-height: 1.55;
     }
+    .foundation-io-panel {
+      display: grid;
+      gap: 10px;
+      margin-top: 14px;
+      padding-top: 14px;
+      border-top: 1px solid var(--soft-line);
+    }
+    .foundation-io-row {
+      border: 1px solid rgba(204, 255, 160, 0.13);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.34);
+      padding: 10px 11px;
+    }
+    .foundation-io-row strong {
+      display: block;
+      margin-bottom: 5px;
+      color: var(--ink);
+      font-family: var(--font-ui);
+      font-size: 12px;
+      font-weight: 850;
+      line-height: 1.2;
+    }
+    .foundation-io-row p {
+      color: var(--muted);
+      font-size: 12.5px;
+      line-height: 1.45;
+    }
+    .foundation-io-row code {
+      color: var(--accent-2);
+      font-size: 0.95em;
+    }
+    .foundation-io-tasks {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 6px;
+    }
+    .foundation-io-tasks a {
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      color: var(--accent-2);
+      font-size: 11px;
+      font-weight: 800;
+      line-height: 1;
+      padding: 6px 7px;
+      text-decoration: none;
+      background: rgba(2, 5, 2, 0.52);
+    }
+    .foundation-io-tasks a:hover {
+      border-color: var(--green);
+      color: var(--ink);
+    }
     .foundation-pipeline-links {
       display: flex;
       flex-wrap: wrap;
@@ -4034,7 +4085,9 @@
       .split-radar-grid,
       .foundation-pipeline-card { display: block; }
       .foundation-pipeline-card img {
+        height: auto;
         min-height: 0;
+        aspect-ratio: auto;
         border-right: 0;
         border-bottom: 1px solid var(--line);
       }
@@ -5255,6 +5308,24 @@
               <span>High-resolution direction slide</span>
               <h3>Spatial intelligence models</h3>
               <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-io-panel" aria-label="Spatial intelligence one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Use <code>windows.csv</code> and <code>shared_windows.npz</code> to slice each 20-frame window, then join six MP4 RGB streams with <code>annotation.hdf5</code> depth, camera pose, SLAM/calibration, object cues, contacts, and optional language questions.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>Build targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers derived from the same public annotation timeline.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 8/10/12/14/16</a>
+                    <a href="data/task_suite_20.json">20-task JSON</a>
+                    <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window manifest</a>
+                  </div>
+                </div>
+              </div>
               <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>
@@ -5267,6 +5338,24 @@
               <span>High-resolution direction slide</span>
               <h3>Human-video world models</h3>
               <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-io-panel" aria-label="Human-video world-model one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Take the current 20-frame observed window at time <code>t</code> from <code>shared_windows.npz</code>: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>Shift the same episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets. Future labels stay out of the input.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 4/13/14/17/20</a>
+                    <a href="data/unified_task_model_radar.json">model scores</a>
+                    <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json">future probes</a>
+                  </div>
+                </div>
+              </div>
               <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>
@@ -5279,6 +5368,24 @@
               <span>High-resolution direction slide</span>
               <h3>Vision-language-action models</h3>
               <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-io-panel" aria-label="Vision-language-action one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Use egocentric/fisheye video windows, caption/object context from <code>annotation.hdf5</code>, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>For the one-sample suite, output action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. Robot action chunks need a later retargeting converter.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 1/4/5/6/15/18</a>
+                    <a href="data/task_walkthroughs.json">task walkthroughs</a>
+                    <a href="data/three_foundation_pipelines.json">track contract</a>
+                  </div>
+                </div>
+              </div>
               <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

@@ -989,6 +989,57 @@
       font-size: 13px;
       line-height: 1.55;
     }
+    .foundation-io-panel {
+      display: grid;
+      gap: 10px;
+      margin-top: 14px;
+      padding-top: 14px;
+      border-top: 1px solid var(--soft-line);
+    }
+    .foundation-io-row {
+      border: 1px solid rgba(204, 255, 160, 0.13);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.34);
+      padding: 10px 11px;
+    }
+    .foundation-io-row strong {
+      display: block;
+      margin-bottom: 5px;
+      color: var(--ink);
+      font-family: var(--font-ui);
+      font-size: 12px;
+      font-weight: 850;
+      line-height: 1.2;
+    }
+    .foundation-io-row p {
+      color: var(--muted);
+      font-size: 12.5px;
+      line-height: 1.45;
+    }
+    .foundation-io-row code {
+      color: var(--accent-2);
+      font-size: 0.95em;
+    }
+    .foundation-io-tasks {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 6px;
+    }
+    .foundation-io-tasks a {
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      color: var(--accent-2);
+      font-size: 11px;
+      font-weight: 800;
+      line-height: 1;
+      padding: 6px 7px;
+      text-decoration: none;
+      background: rgba(2, 5, 2, 0.52);
+    }
+    .foundation-io-tasks a:hover {
+      border-color: var(--green);
+      color: var(--ink);
+    }
     .foundation-pipeline-links {
       display: flex;
       flex-wrap: wrap;
@@ -4034,7 +4085,9 @@
       .split-radar-grid,
       .foundation-pipeline-card { display: block; }
       .foundation-pipeline-card img {
+        height: auto;
         min-height: 0;
+        aspect-ratio: auto;
         border-right: 0;
         border-bottom: 1px solid var(--line);
       }
@@ -5255,6 +5308,24 @@
               <span>High-resolution direction slide</span>
               <h3>Spatial intelligence models</h3>
               <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-io-panel" aria-label="Spatial intelligence one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Use <code>windows.csv</code> and <code>shared_windows.npz</code> to slice each 20-frame window, then join six MP4 RGB streams with <code>annotation.hdf5</code> depth, camera pose, SLAM/calibration, object cues, contacts, and optional language questions.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>Build targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers derived from the same public annotation timeline.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 8/10/12/14/16</a>
+                    <a href="data/task_suite_20.json">20-task JSON</a>
+                    <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window manifest</a>
+                  </div>
+                </div>
+              </div>
               <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>
@@ -5267,6 +5338,24 @@
               <span>High-resolution direction slide</span>
               <h3>Human-video world models</h3>
               <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-io-panel" aria-label="Human-video world-model one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Take the current 20-frame observed window at time <code>t</code> from <code>shared_windows.npz</code>: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>Shift the same episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets. Future labels stay out of the input.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 4/13/14/17/20</a>
+                    <a href="data/unified_task_model_radar.json">model scores</a>
+                    <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json">future probes</a>
+                  </div>
+                </div>
+              </div>
               <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>
@@ -5279,6 +5368,24 @@
               <span>High-resolution direction slide</span>
               <h3>Vision-language-action models</h3>
               <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-io-panel" aria-label="Vision-language-action one-sample training input and output">
+                <div class="foundation-io-row">
+                  <strong>Sample input</strong>
+                  <p>Use egocentric/fisheye video windows, caption/object context from <code>annotation.hdf5</code>, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Training output</strong>
+                  <p>For the one-sample suite, output action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. Robot action chunks need a later retargeting converter.</p>
+                </div>
+                <div class="foundation-io-row">
+                  <strong>Existing hooks</strong>
+                  <div class="foundation-io-tasks">
+                    <a href="#suite">Tasks 1/4/5/6/15/18</a>
+                    <a href="data/task_walkthroughs.json">task walkthroughs</a>
+                    <a href="data/three_foundation_pipelines.json">track contract</a>
+                  </div>
+                </div>
+              </div>
               <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/mirror_parity.json

@@ -1,23 +1,16 @@
 {
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-  "generated_at_utc": "2026-06-21T19:34:52+00:00",
+  "status": "pass",
+  "generated_at_utc": "2026-06-21T20:00:18+00:00",
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+      "status": "pass"
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     {
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@@ -974,187 +967,101 @@
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-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_docs_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:docs/data/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model",
-      "kind": "hash_mismatch",
-      "path": "hf_model:metrics/website_integrity.json",
-      "expected_sha256": "31450a9bad0d2e3c51183f4b7c08d49f8242726138bb42d6a92e8e41e9c4fe21",
-      "actual_sha256": "2f06634c03a7e528c2207932be59c4b9c1c1e450cc51b3537e19fd55439c9aa1"
-    }
-  ]
+  "failures": []
 }

metrics/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:19+00:00",
+  "generated_at_utc": "2026-06-21T20:01:39+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": "2026-06-21T19:32:05+00:00"
+          "generated_at_utc": "2026-06-21T20:01:18+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-21T19:27:12+00:00"
+          "generated_at_utc": "2026-06-21T19:57:53+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-21T19:29:46+00:00"
+          "generated_at_utc": "2026-06-21T20:00:18+00:00"
         }
       },
       "failures": {}
@@ -97,7 +97,7 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 22,
         "Xperience-10M": 173,
-        "20-task": 109,
+        "20-task": 110,
         "Qwen3-Omni": 246,
         "128-episode pilot": 1
       }
@@ -107,7 +107,7 @@
       "status": "pass",
       "reason": "Public cards should link the repo, Space, artifacts, model baselines, upstream dataset, and Ropedia dataset page.",
       "marker_counts": {
-        "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite": 90,
+        "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite": 92,
         "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite": 12,
         "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts": 14,
         "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines": 13,
@@ -131,10 +131,10 @@
         "data/public_surface_qa.json": 8,
         "data/research_roadmap.json": 15,
         "data/task_suite_enhancement_128.json": 22,
-        "data/task_suite_20.json": 36,
+        "data/task_suite_20.json": 37,
         "data/two_evidence_lines.json": 5,
         "data/two_evidence_line_result_summary.json": 10,
-        "data/unified_task_model_radar.json": 21,
+        "data/unified_task_model_radar.json": 22,
         "data/single_episode_task_model_radar.json": 17,
         "data/episode128_task_model_radar.json": 16,
         "data/task_method_20_result_matrix.json": 28,

metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:34:08+00:00",
+  "generated_at_utc": "2026-06-21T20:02:09+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

metrics/research_roadmap_interactive.json

@@ -2862,7 +2862,7 @@
     ],
     "status": "planning_artifact"
   },
-  "generated_at_utc": "2026-06-21T10:51:52+00:00",
+  "generated_at_utc": "2026-06-21T19:51:24+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -4269,6 +4269,26 @@
           "spatial QA prompts"
         ],
         "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
+        "one_sample_training_io": {
+          "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics.",
+          "existing_task_hooks": [
+            "object_relevance",
+            "modality_reconstruction",
+            "caption_grounding",
+            "object_set_forecast",
+            "camera_view_sync_retrieval"
+          ],
+          "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+          "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/feature_manifest.json",
+            "official sample annotation.hdf5",
+            "official sample six MP4 camera streams"
+          ],
+          "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers."
+        },
         "outputs": [
           "object count",
           "object persistence",
@@ -4347,6 +4367,26 @@
           "temporal consistency metadata"
         ],
         "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
+        "one_sample_training_io": {
+          "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves.",
+          "existing_task_hooks": [
+            "next_action",
+            "long_horizon_next_action",
+            "next_subtask_forecast",
+            "object_set_forecast",
+            "time_to_transition",
+            "ego_motion_forecast"
+          ],
+          "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+          "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+            "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+          ],
+          "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets."
+        },
         "outputs": [
           "next action",
           "next subtask",
@@ -4425,6 +4465,26 @@
           "action-space model card"
         ],
         "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
+        "one_sample_training_io": {
+          "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics.",
+          "existing_task_hooks": [
+            "timeline_action",
+            "next_action",
+            "hand_trajectory_forecast",
+            "contact_prediction",
+            "interaction_text_prediction",
+            "action_object_relation"
+          ],
+          "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+          "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+          "source_artifacts": [
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/shared_windows.npz",
+            "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+            "official sample annotation.hdf5"
+          ],
+          "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy."
+        },
         "outputs": [
           "next action",
           "action chunk",

metrics/three_foundation_pipelines.json

@@ -59,6 +59,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Spatial intelligence models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: 5,821 frames, 1,161 overlapping 20-frame windows, 5-frame stride, about 20 FPS.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/feature_manifest.json",
+          "official sample annotation.hdf5",
+          "official sample six MP4 camera streams"
+        ],
+        "input_builder": "Slice each 20-frame window, then join multiview RGB summaries with depth, camera pose, SLAM/calibration, object cues, contact cues, and optional language questions from the public annotation timeline.",
+        "target_builder": "Create spatial targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers.",
+        "existing_task_hooks": [
+          "object_relevance",
+          "modality_reconstruction",
+          "caption_grounding",
+          "object_set_forecast",
+          "camera_view_sync_retrieval"
+        ],
+        "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -138,6 +158,26 @@
       "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": "High-resolution slide diagram showing the Human-video world models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: current observed windows are paired with shifted future labels or future-window features from the same timeline.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+          "results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json"
+        ],
+        "input_builder": "Use the current 20-frame observed window at time t: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.",
+        "target_builder": "Shift the episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets.",
+        "existing_task_hooks": [
+          "next_action",
+          "long_horizon_next_action",
+          "next_subtask_forecast",
+          "object_set_forecast",
+          "time_to_transition",
+          "ego_motion_forecast"
+        ],
+        "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",
@@ -215,6 +255,26 @@
       "diagram_image": "docs/assets/foundation-pipelines/vision-language-action-pipeline.png",
       "website_image": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "image_alt": "High-resolution slide diagram showing the Vision-language-action models direction for Xperience-10M.",
+      "one_sample_training_io": {
+        "sample_basis": "Single public sample episode: observation-language windows are paired with action-token proxies because robot retargeted action chunks are not part of the public sample yet.",
+        "source_artifacts": [
+          "results/episode_task_suite/windows.csv",
+          "results/episode_task_suite/shared_windows.npz",
+          "results/episode_task_suite/task_walkthroughs/task_walkthroughs.json",
+          "official sample annotation.hdf5"
+        ],
+        "input_builder": "Use egocentric/fisheye video windows, caption and object context, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.",
+        "target_builder": "Create action-token proxy targets: current or next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy.",
+        "existing_task_hooks": [
+          "timeline_action",
+          "next_action",
+          "hand_trajectory_forecast",
+          "contact_prediction",
+          "interaction_text_prediction",
+          "action_object_relation"
+        ],
+        "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics."
+      },
       "diagram_flow": [
         {
           "stage": "inputs",

metrics/website_integrity.json

@@ -1,12 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T19:32:05+00:00",
+  "generated_at_utc": "2026-06-21T20:01:18+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 260,
-    "external_reference_count": 156,
+    "local_references": 267,
+    "external_reference_count": 158,
     "json_files": 55,
     "image_assets_referenced": 22,
     "failure_count": 0
@@ -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": 135119,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "evidence_index": 187740
     },
     {
       "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": 135119,
-      "protocol_index": 178478,
-      "evidence_index": 182290
+      "overview_index": 136413,
+      "protocol_index": 183928,
+      "evidence_index": 187740
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -175,6 +175,13 @@
       "reason": "The website should expose the main task-suite figure.",
       "marker_count": 4
     },
+    {
+      "name": "foundation_direction_cards_explain_one_sample_io",
+      "status": "pass",
+      "reason": "The three foundation direction cards should explain one-sample training inputs and outputs.",
+      "panel_count": 3,
+      "missing_terms": []
+    },
     {
       "name": "suite_task_map_precedes_radar_surface",
       "status": "pass",
@@ -265,7 +272,7 @@
       "name": "task_player_uses_walkthrough_json",
       "status": "pass",
       "reason": "The task player and task cards should read the generated walkthrough JSON.",
-      "marker_count": 2
+      "marker_count": 3
     },
     {
       "name": "task_cards_use_human_research_names",
@@ -284,7 +291,7 @@
     {
       "path": "index.html",
       "id_count": 99,
-      "reference_count": 232,
+      "reference_count": 239,
       "image_count": 28
     },
     {
@@ -363,7 +370,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1420747,
+      "bytes": 1420751,
       "top_level_type": "dict"
     },
     {
@@ -468,7 +475,7 @@
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 186755,
+      "bytes": 191028,
       "top_level_type": "dict"
     },
     {
@@ -538,7 +545,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 10312,
+      "bytes": 14465,
       "top_level_type": "dict"
     },
     {
@@ -563,7 +570,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19914,
+      "bytes": 20178,
       "top_level_type": "dict"
     },
     {

scripts/validate_website_integrity.py

@@ -379,6 +379,12 @@ def validate(docs_root: Path, site_base: str) -> dict:
             None,
             "The website should expose the main task-suite figure.",
         ),
+        (
+            "foundation_direction_cards_explain_one_sample_io",
+            'class="foundation-io-panel"',
+            None,
+            "The three foundation direction cards should explain one-sample training inputs and outputs.",
+        ),
         (
             "suite_task_map_precedes_radar_surface",
             '<div class="figure-pan" id="task-suite-map">',
@@ -562,6 +568,19 @@ def validate(docs_root: Path, site_base: str) -> dict:
                 "modality_count": len(present_terms),
                 "missing_modalities": [term for term in modality_terms if term not in present_terms],
             }
+        elif name == "foundation_direction_cards_explain_one_sample_io":
+            panel_count = index_text.count(marker)
+            required_terms = [
+                "Sample input",
+                "Training output",
+                "Existing hooks",
+                "Spatial intelligence models",
+                "Human-video world models",
+                "Vision-language-action models",
+            ]
+            missing_terms = [term for term in required_terms if term not in index_text]
+            passed = panel_count == 3 and not missing_terms
+            detail = {"panel_count": panel_count, "missing_terms": missing_terms}
         elif name.startswith("dataset_card_section_"):
             marker_count = dataset_text.count(marker)
             passed = marker_count >= 1