Publish Ropedia Xperience-10M derived artifacts

PROJECT_README.md

@@ -23,7 +23,7 @@ into:
 - lightweight neural MLP heads for the same 12 task contracts,
 - a generated four-direction research taxonomy matching the Ropedia job tracks,
 - four additional direction-extension probes with minimal and neural baselines,
-- human-readable research task cards and an interactive task walkthrough/player for every task,
+- human-readable research task cards and an interactive scrub/play walkthrough storyboard for every task,
 - a next-milestone track for Qwen3-Omni fine-tuning and sensor-bridge evaluation,
 - metrics, predictions, model weights, manifests, charts, and a static website,
 - a clear explanation of what a single episode can and cannot prove.
@@ -44,7 +44,7 @@ This repo is organized around an explicit proof boundary:
 | 12-task suite | `scripts/episode_task_suite.py`, per-task `metrics.json`, predictions | chronological single-episode split |
 | Neural heads | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/` | compact MLP heads, not a foundation model |
 | Research directions | `research_direction_taxonomy.json`, extension probe results | direct/proxy/diagnostic evidence, not full solutions |
-| Task surface integrity | `docs/data/task_surface_integrity.json`, `scripts/validate_task_surface.py` | public task cards stay human-readable, thumbnail-backed, and wired to the walkthrough/player data |
+| Task surface integrity | `docs/data/task_surface_integrity.json`, `scripts/validate_task_surface.py` | public task cards stay human-readable, thumbnail-backed, and wired to the scrub/play walkthrough storyboard |
 | Qwen3-Omni | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `MULTI_EPISODE_ACCESS_STATUS.md` | readiness-only until 32 valid episodes are available |
 | Scope claims guard | `scripts/validate_scope_claims.py`, `docs/data/scope_claims_audit.json` | historical `32ep` path strings are provenance, not 32-episode results |
 | Mirror parity | `scripts/validate_mirror_parity.py`, `docs/data/mirror_parity.json` | prepared GitHub/HF mirrors carry matching data, figure, website HTML, and validator files |
@@ -69,7 +69,7 @@ The current prepared-mirror parity report is at
 [`docs/data/mirror_parity.json`](docs/data/mirror_parity.json).
 The current scope-claims audit is at
 [`docs/data/scope_claims_audit.json`](docs/data/scope_claims_audit.json).
-The task-card and walkthrough-player integrity report is at
+The task-card and walkthrough-storyboard integrity report is at
 [`docs/data/task_surface_integrity.json`](docs/data/task_surface_integrity.json).
 The generated evaluation protocol is at
 [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md) and
@@ -238,7 +238,7 @@ Hugging Face Space app:
 | Figure index | `FIGURE_INDEX.md`, `docs/data/figure_index.json` | Makes public figures, charts, modality thumbnails, dimensions, hashes, and source scripts auditable |
 | Brand assets | `docs/data/brand_assets.json`, `docs/assets/brand/` | Makes the generated logo, favicon, README/HF card image, app icon, and social preview auditable |
 | Evaluation protocol | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json` | Defines the task unit, split, metrics, leakage controls, and unsupported interpretations |
-| Task surface integrity | `docs/data/task_surface_integrity.json` | Checks the public task cards, readable task names, representative modality thumbnails, and interactive task player |
+| Task surface integrity | `docs/data/task_surface_integrity.json` | Checks the public task cards, readable task names, representative modality thumbnails, and interactive walkthrough storyboard |
 | Minimal heads | softmax, ridge projection/regression, multi-label logistic heads | Keeps every input/output contract visible and debuggable |
 | Neural heads | PyTorch MLP classifiers/regressors under `neural_mlp/` | Checks whether nonlinear heads improve each task without changing features |
 | Evidence | metrics, predictions, confusion matrices, diagrams, dashboard | Makes the single-episode claims reviewable without rerunning first |
@@ -322,7 +322,7 @@ scripts/
   neural_task_models.py             # optional PyTorch MLP heads for all 12 tasks
   research_direction_taxonomy.py    # maps 12 tasks to the four research tracks
   research_direction_extension_tasks.py # one extra data-backed probe per track
-  task_walkthroughs.py              # human-readable task-card and walkthrough-player metadata
+  task_walkthroughs.py              # human-readable task-card and walkthrough-storyboard metadata
   generate_visualizations.py        # refreshes SVG charts + summary JSON
   render_task_suite_infographic.py  # renders the task-suite presentation PNG
   export_modality_atlas_assets.py   # exports responsive modality-card assets
@@ -332,7 +332,7 @@ scripts/
   build_quality_gates.py            # builds reviewer-facing publication gates
   validate_mirror_parity.py         # checks prepared GitHub/HF mirror file parity
   validate_scope_claims.py          # checks Qwen3-Omni readiness/result claim boundaries
-  validate_task_surface.py          # checks readable task cards and interactive player wiring
+  validate_task_surface.py          # checks readable task cards and interactive storyboard wiring
   validate_website_integrity.py     # checks local site links, anchors, JSON, images
   validate_publication_package.py   # checks public repo + HF bundle hygiene
   publish_hf_bundles.py             # uploads prepared HF Space/artifact/model bundles
@@ -362,13 +362,13 @@ docs/
   data/live_publication_status.json # live GitHub/HF publication verification
   data/quality_gates.json           # machine-readable publication gates
   data/publication_audit.json       # machine-readable publication hygiene check
-  data/task_surface_integrity.json  # machine-readable task-card/player integrity check
+  data/task_surface_integrity.json  # machine-readable task-card/storyboard integrity check
   data/website_integrity.json       # machine-readable website integrity check
   data/project_manifest.json        # machine-readable public-surface metadata
   data/reviewer_packet.json         # machine-readable reviewer path and proof boundary
   data/research_directions.json     # four-track website data bundle
   data/research_direction_extensions.json # four extra probe data bundle
-  data/task_walkthroughs.json       # human-readable task-card and walkthrough-player data
+  data/task_walkthroughs.json       # human-readable task-card and walkthrough-storyboard data
   data/modality_atlas.json          # responsive modality-card data
   assets/brand/*.png                # project logo, favicon, social card
   assets/task_suite_infographic.png # 12-task presentation graphic
@@ -573,10 +573,10 @@ Current direction-level coverage:
 
 | Direction | Current status | Covered task evidence | What is not solved yet |
 | --- | --- | --- | --- |
-| A. Human Modeling & Motion Understanding | Partially implemented | `hand_trajectory_forecast` and `contact_prediction` are direct; `timeline_action` and `object_relevance` are proxies. Neural MLP improves hand forecasting from `0.8223` to `0.1116` MPJPE. | No full body/shape model, SMPL/MANO target, deformation prior, or multi-episode motion-generation evaluation yet. |
-| B. 3D/4D Reconstruction & Neural Rendering | Proxy tasks only | `cross_modal_retrieval`, `modality_reconstruction`, and `misalignment_detection` test alignment/reconstruction prerequisites. | No NeRF, Gaussian Splatting, TSDF, mesh, novel-view synthesis, or calibrated 4D reconstruction model yet. |
+| A. Human Modeling & Motion Understanding | Partially implemented | Hand Trajectory Forecasting and Contact State Prediction are direct; Action Recognition and Object Relevance Prediction are proxies. Neural MLP improves hand forecasting from `0.8223` to `0.1116` MPJPE. | No full body/shape model, SMPL/MANO target, deformation prior, or multi-episode motion-generation evaluation yet. |
+| B. 3D/4D Reconstruction & Neural Rendering | Proxy tasks only | Cross-Modal Retrieval, Cross-Modal Reconstruction, and Multimodal Synchronization Detection test alignment/reconstruction prerequisites. | No NeRF, Gaussian Splatting, TSDF, mesh, novel-view synthesis, or calibrated 4D reconstruction model yet. |
 | C. Egocentric Vision & Interaction | Strongest implemented track | 6 direct tasks: action, subtask, transition, next-action, object relevance, and caption grounding, plus alignment/order diagnostics. | Single-episode chronological split limits generalization; audio and stronger video-language backbones still need to be added. |
-| D. Scene Reconstruction & World Modeling | Early proxy tasks | Subtask/next-action, object relevance, retrieval, reconstruction, temporal order, and misalignment provide state/world-model probes. | No persistent scene graph, object permanence task, long-term map, or held-out-episode world model yet. |
+| D. Scene Reconstruction & World Modeling | Early proxy tasks | Procedure Step Recognition, Next-Action Prediction, Object Relevance Prediction, Cross-Modal Retrieval, Cross-Modal Reconstruction, Temporal Order Verification, and Multimodal Synchronization Detection provide state/world-model probes. | No persistent scene graph, object permanence task, long-term map, or held-out-episode world model yet. |
 
 The important interpretation is that all four directions can be **started** from
 the Xperience-10M sample modalities, but only direction C is strongly represented
@@ -599,10 +599,10 @@ the reported numbers are real sample-derived metrics, not placeholder results.
 
 | Direction | New extension task | Input | Output | Minimal | Neural MLP | Why it matters |
 | --- | --- | --- | --- | ---: | ---: | --- |
-| A. Human Modeling & Motion Understanding | `body_motion_intensity` | non-mocap video/depth/pose/IMU/SLAM/language features | high vs low body/hand motion | `0.7827` macro-F1 | `0.7986` macro-F1 | Starts a human-motion-energy target without leaking mocap input. |
-| B. 3D/4D Reconstruction & Neural Rendering | `multi_view_consistency_retrieval` | fisheye camera feature query | synchronized stereo-left view rank | `0.5534` MRR | `0.3469` MRR | Tests whether multi-view features preserve synchronized 4D scene identity. |
-| C. Egocentric Vision & Interaction | `action_phase_progress` | non-caption multimodal window | progress inside current action segment | `0.3416` MAE | `0.3038` MAE | Adds a task-structure/intent-style target beyond class labels. |
-| D. Scene Reconstruction & World Modeling | `ego_motion_forecast` | current sensors excluding camera translation and captions | future camera-translation delta | `0.1989` MAE | `0.0989` MAE | Starts a short-horizon world-model target over wearer motion. |
+| A. Human Modeling & Motion Understanding | Body and Hand Motion Intensity | non-mocap video/depth/pose/IMU/SLAM/language features | high vs low body/hand motion | `0.7827` macro-F1 | `0.7986` macro-F1 | Starts a human-motion-energy target without leaking mocap input. |
+| B. 3D/4D Reconstruction & Neural Rendering | Multi-View Consistency Retrieval | fisheye camera feature query | synchronized stereo-left view rank | `0.5534` MRR | `0.3469` MRR | Tests whether multi-view features preserve synchronized 4D scene identity. |
+| C. Egocentric Vision & Interaction | Action Phase Progress Estimation | non-caption multimodal window | progress inside current action segment | `0.3416` MAE | `0.3038` MAE | Adds a task-structure/intent-style target beyond class labels. |
+| D. Scene Reconstruction & World Modeling | Short-Horizon Ego-Motion Forecasting | current sensors excluding camera translation and captions | future camera-translation delta | `0.1989` MAE | `0.0989` MAE | Starts a short-horizon world-model target over wearer motion. |
 
 Run:
 
@@ -637,18 +637,18 @@ Compact map:
 
 | Task | Case study | Input -> process -> output |
 | --- | --- | --- |
-| `timeline_action` | A pouring window should be named as the current action. | all-modality window -> action label builder + classifier -> action class |
-| `timeline_subtask` | A fine action is grouped into a broader drink-preparation stage. | all-modality window -> subtask label builder + classifier -> subtask label |
-| `transition_detection` | Detect the change from preparing to pouring. | window -> boundary builder + binary classifier -> boundary/steady |
-| `next_action` | A preparing window predicts what happens 20 frames later. | current window -> future-label shift + classifier -> next action |
-| `hand_trajectory_forecast` | A hand moving toward a cup becomes a future 3D hand path. | current window -> future mocap target + regressor -> hand trajectory |
-| `contact_prediction` | Decide whether hand/body contact is happening. | non-contact features -> contact target + binary classifier -> contact label |
-| `object_relevance` | Infer milk, cup, coffee, or related objects during pouring. | non-caption features -> multi-hot object target + sigmoid heads -> object set |
-| `caption_grounding` | Query Pour milk into coffee and retrieve the matching moment. | text-like query + candidates -> projection + cosine ranker -> ranked windows |
-| `cross_modal_retrieval` | Motion/IMU from pouring retrieves matching depth/video. | motion/IMU/camera -> projection + candidate index -> ranked depth/video windows |
-| `modality_reconstruction` | Infer depth/video features from motion, IMU, and camera pose. | source modalities -> scaler + regressor -> target modality vector |
-| `temporal_order` | Tell whether reaching then pouring was reversed. | adjacent window pair -> pair combiner + binary classifier -> correct/reversed |
-| `misalignment_detection` | Catch motion paired with visual/depth features shifted in time. | motion side + visual side -> aligned/shifted pair builder + classifier -> aligned/shifted |
+| Action Recognition | A pouring window should be named as the current action. | all-modality window -> action label builder + classifier -> action class |
+| Procedure Step Recognition | A fine action is grouped into a broader drink-preparation stage. | all-modality window -> subtask label builder + classifier -> subtask label |
+| Action Boundary Detection | Detect the change from preparing to pouring. | window -> boundary builder + binary classifier -> boundary/steady |
+| Next-Action Prediction | A preparing window predicts what happens 20 frames later. | current window -> future-label shift + classifier -> next action |
+| Hand Trajectory Forecasting | A hand moving toward a cup becomes a future 3D hand path. | current window -> future mocap target + regressor -> hand trajectory |
+| Contact State Prediction | Decide whether hand/body contact is happening. | non-contact features -> contact target + binary classifier -> contact label |
+| Object Relevance Prediction | Infer milk, cup, coffee, or related objects during pouring. | non-caption features -> multi-hot object target + sigmoid heads -> object set |
+| Language Grounding | Query Pour milk into coffee and retrieve the matching moment. | text-like query + candidates -> projection + cosine ranker -> ranked windows |
+| Cross-Modal Retrieval | Motion/IMU from pouring retrieves matching depth/video. | motion/IMU/camera -> projection + candidate index -> ranked depth/video windows |
+| Cross-Modal Reconstruction | Infer depth/video features from motion, IMU, and camera pose. | source modalities -> scaler + regressor -> target modality vector |
+| Temporal Order Verification | Tell whether reaching then pouring was reversed. | adjacent window pair -> pair combiner + binary classifier -> correct/reversed |
+| Multimodal Synchronization Detection | Catch motion paired with visual/depth features shifted in time. | motion side + visual side -> aligned/shifted pair builder + classifier -> aligned/shifted |
 
 ## Minimal 12-Task Architectures
 
@@ -668,10 +668,10 @@ There are four reusable head families:
 
 | Head family | Used by | What it means |
 | --- | --- | --- |
-| Linear softmax classifier | `timeline_action`, `timeline_subtask`, `transition_detection`, `next_action`, `contact_prediction`, `temporal_order`, `misalignment_detection` | z-score features, then `XW+b`, softmax, cross-entropy, L2 |
-| Dual ridge regression/projection | `hand_trajectory_forecast`, `modality_reconstruction` | z-score input/target, solve ridge regression with L2=10 |
-| Ridge + cosine ranking | `caption_grounding`, `cross_modal_retrieval` | project one modality into another feature space, then rank candidates by cosine |
-| Multi-label logistic regression | `object_relevance` | z-score non-caption features, sigmoid object heads, threshold at 0.5 |
+| Linear softmax classifier | Action Recognition, Procedure Step Recognition, Action Boundary Detection, Next-Action Prediction, Contact State Prediction, Temporal Order Verification, Multimodal Synchronization Detection | z-score features, then `XW+b`, softmax, cross-entropy, L2 |
+| Dual ridge regression/projection | Hand Trajectory Forecasting, Cross-Modal Reconstruction | z-score input/target, solve ridge regression with L2=10 |
+| Ridge + cosine ranking | Language Grounding, Cross-Modal Retrieval | project one modality into another feature space, then rank candidates by cosine |
+| Multi-label logistic regression | Object Relevance Prediction | z-score non-caption features, sigmoid object heads, threshold at 0.5 |
 
 The optional neural run keeps the same feature vectors, leakage filters,
 chronological splits, and metrics, but replaces the task heads with small
@@ -684,18 +684,18 @@ The task-specific heads are:
 
 | Task | Input | Minimal head | Output |
 | --- | --- | --- | --- |
-| `timeline_action` | all featurized modalities | linear softmax | current action class |
-| `timeline_subtask` | all featurized modalities | linear softmax | current subtask class |
-| `transition_detection` | all featurized modalities | linear softmax | steady vs action boundary |
-| `next_action` | all featurized modalities at `t` | linear softmax | action at `t+20` frames |
-| `hand_trajectory_forecast` | all featurized modalities at `t` | ridge regression | future 10-frame left/right hand joints |
-| `contact_prediction` | non-contact and non-caption feature blocks | linear softmax | any body contact |
-| `object_relevance` | non-caption feature blocks | multi-label logistic | relevant object set |
-| `caption_grounding` | sensor windows projected to text space | ridge projection + cosine ranking | matching time window for text query |
-| `cross_modal_retrieval` | motion/IMU/camera projected to visual space | ridge projection + cosine ranking | matching depth/video window |
-| `modality_reconstruction` | motion/IMU/camera | ridge regression | depth/video feature vector |
-| `temporal_order` | `[x_t, x_t+1, x_t+1-x_t]` | binary linear softmax | correct vs reversed order |
-| `misalignment_detection` | motion plus visual pair | binary linear softmax | aligned vs shifted by 8 windows |
+| Action Recognition | all featurized modalities | linear softmax | current action class |
+| Procedure Step Recognition | all featurized modalities | linear softmax | current subtask class |
+| Action Boundary Detection | all featurized modalities | linear softmax | steady vs action boundary |
+| Next-Action Prediction | all featurized modalities at `t` | linear softmax | action at `t+20` frames |
+| Hand Trajectory Forecasting | all featurized modalities at `t` | ridge regression | future 10-frame left/right hand joints |
+| Contact State Prediction | non-contact and non-caption feature blocks | linear softmax | any body contact |
+| Object Relevance Prediction | non-caption feature blocks | multi-label logistic | relevant object set |
+| Language Grounding | sensor windows projected to text space | ridge projection + cosine ranking | matching time window for text query |
+| Cross-Modal Retrieval | motion/IMU/camera projected to visual space | ridge projection + cosine ranking | matching depth/video window |
+| Cross-Modal Reconstruction | motion/IMU/camera | ridge regression | depth/video feature vector |
+| Temporal Order Verification | `[x_t, x_t+1, x_t+1-x_t]` | binary linear softmax | correct vs reversed order |
+| Multimodal Synchronization Detection | motion plus visual pair | binary linear softmax | aligned vs shifted by 8 windows |
 
 ## Key Results
 
@@ -721,18 +721,18 @@ same 8,378-d handcrafted window features.
 
 | Task | Neural metric | Minimal metric | Readout |
 | --- | ---: | ---: | --- |
-| `timeline_action` | 0.0263 macro-F1 | 0.0500 macro-F1 | Still blocked by unseen future classes |
-| `timeline_subtask` | 0.0175 macro-F1 | 0.0495 macro-F1 | Same single-episode split limitation |
-| `transition_detection` | 0.6485 macro-F1 | 0.6552 macro-F1 | Similar to the linear baseline |
-| `next_action` | 0.0235 macro-F1 | 0.0593 macro-F1 | Same unseen-label issue |
-| `hand_trajectory_forecast` | 0.1116 MPJPE | 0.8223 MPJPE | Neural regression improves this target |
-| `contact_prediction` | 1.0000 macro-F1 | 1.0000 macro-F1 | Degenerate one-class sample |
-| `object_relevance` | 0.1798 micro-F1 | 0.1839 micro-F1 | Similar weak object signal |
-| `caption_grounding` | 0.0178 MRR | 0.0172 MRR | Similar ranking behavior |
-| `cross_modal_retrieval` | 0.1530 MRR | 0.2634 MRR | Linear ridge remains stronger here |
-| `modality_reconstruction` | -0.0102 R2 | -0.0160 R2 | Small improvement but still weak |
-| `temporal_order` | 0.8718 F1 | 0.5487 F1 | Neural head captures local temporal structure |
-| `misalignment_detection` | 0.7335 F1 | 0.4866 F1 | Neural head improves alignment detection |
+| Action Recognition | 0.0263 macro-F1 | 0.0500 macro-F1 | Still blocked by unseen future classes |
+| Procedure Step Recognition | 0.0175 macro-F1 | 0.0495 macro-F1 | Same single-episode split limitation |
+| Action Boundary Detection | 0.6485 macro-F1 | 0.6552 macro-F1 | Similar to the linear baseline |
+| Next-Action Prediction | 0.0235 macro-F1 | 0.0593 macro-F1 | Same unseen-label issue |
+| Hand Trajectory Forecasting | 0.1116 MPJPE | 0.8223 MPJPE | Neural regression improves this target |
+| Contact State Prediction | 1.0000 macro-F1 | 1.0000 macro-F1 | Degenerate one-class sample |
+| Object Relevance Prediction | 0.1798 micro-F1 | 0.1839 micro-F1 | Similar weak object signal |
+| Language Grounding | 0.0178 MRR | 0.0172 MRR | Similar ranking behavior |
+| Cross-Modal Retrieval | 0.1530 MRR | 0.2634 MRR | Linear ridge remains stronger here |
+| Cross-Modal Reconstruction | -0.0102 R2 | -0.0160 R2 | Small improvement but still weak |
+| Temporal Order Verification | 0.8718 F1 | 0.5487 F1 | Neural head captures local temporal structure |
+| Multimodal Synchronization Detection | 0.7335 F1 | 0.4866 F1 | Neural head improves alignment detection |
 
 The strongest single-episode self-supervised signal is cross-modal retrieval:
 motion/IMU/camera features retrieve matching depth/video windows substantially

README.md

@@ -41,10 +41,10 @@ responsive modality atlas backed by
 public-sample stream remains readable on mobile without shipping raw videos or
 annotations.
 The website task section now reads from `docs/data/task_walkthroughs.json` to
-render common research task names, larger task cards, and an interactive task walkthrough/player.
+render common research task names, larger task cards, and an interactive scrub/play walkthrough storyboard.
 `docs/data/task_surface_integrity.json` verifies that those task cards stay
 human-readable, use representative modality thumbnails, and keep the
-walkthrough/player wired to the generated task metadata.
+walkthrough storyboard wired to the generated task metadata.
 
 The artifact bundle now includes
 `docs/data/xperience10m_dataset_card_alignment.json` and
@@ -225,18 +225,18 @@ These are single-episode chronological-split metrics. They are useful for debugg
 
 | Task | Neural metric | Minimal metric |
 | --- | ---: | ---: |
-| `timeline_action` macro-F1 | 0.0263 | 0.0500 |
-| `timeline_subtask` macro-F1 | 0.0175 | 0.0495 |
-| `transition_detection` macro-F1 | 0.6485 | 0.6552 |
-| `next_action` macro-F1 | 0.0235 | 0.0593 |
-| `hand_trajectory_forecast` MPJPE, lower is better | 0.1116 | 0.8223 |
-| `contact_prediction` macro-F1 | 1.0000 | 1.0000 |
-| `object_relevance` micro-F1 | 0.1798 | 0.1839 |
-| `caption_grounding` MRR | 0.0178 | 0.0172 |
-| `cross_modal_retrieval` MRR | 0.1530 | 0.2634 |
-| `modality_reconstruction` R2 | -0.0102 | -0.0160 |
-| `temporal_order` F1 | 0.8718 | 0.5487 |
-| `misalignment_detection` F1 | 0.7335 | 0.4866 |
+| Action Recognition macro-F1 | 0.0263 | 0.0500 |
+| Procedure Step Recognition macro-F1 | 0.0175 | 0.0495 |
+| Action Boundary Detection macro-F1 | 0.6485 | 0.6552 |
+| Next-Action Prediction macro-F1 | 0.0235 | 0.0593 |
+| Hand Trajectory Forecasting MPJPE, lower is better | 0.1116 | 0.8223 |
+| Contact State Prediction macro-F1 | 1.0000 | 1.0000 |
+| Object Relevance Prediction micro-F1 | 0.1798 | 0.1839 |
+| Language Grounding MRR | 0.0178 | 0.0172 |
+| Cross-Modal Retrieval MRR | 0.1530 | 0.2634 |
+| Cross-Modal Reconstruction R2 | -0.0102 | -0.0160 |
+| Temporal Order Verification F1 | 0.8718 | 0.5487 |
+| Multimodal Synchronization Detection F1 | 0.7335 | 0.4866 |
 
 Primary NN artifact path:
 
@@ -266,10 +266,10 @@ the four-direction roadmap concrete.
 
 | Direction | Extension task | Minimal | Neural MLP |
 | --- | --- | ---: | ---: |
-| A. Human Modeling & Motion Understanding | `body_motion_intensity` | 0.7827 macro-F1 | 0.7986 macro-F1 |
-| B. 3D/4D Reconstruction & Neural Rendering | `multi_view_consistency_retrieval` | 0.5534 MRR | 0.3469 MRR |
-| C. Egocentric Vision & Interaction | `action_phase_progress` | 0.3416 MAE | 0.3038 MAE |
-| D. Scene Reconstruction & World Modeling | `ego_motion_forecast` | 0.1989 MAE | 0.0989 MAE |
+| A. Human Modeling & Motion Understanding | Body and Hand Motion Intensity | 0.7827 macro-F1 | 0.7986 macro-F1 |
+| B. 3D/4D Reconstruction & Neural Rendering | Multi-View Consistency Retrieval | 0.5534 MRR | 0.3469 MRR |
+| C. Egocentric Vision & Interaction | Action Phase Progress Estimation | 0.3416 MAE | 0.3038 MAE |
+| D. Scene Reconstruction & World Modeling | Short-Horizon Ego-Motion Forecasting | 0.1989 MAE | 0.0989 MAE |
 
 Primary extension artifact:
 
@@ -277,9 +277,11 @@ Primary extension artifact:
 
 ## Task Walkthroughs
 
-Each task has a human-readable research name, case study, input contract,
-middle process modules, output contract, modality list, metric, and current
-limitation. Start here when onboarding a junior researcher or engineer:
+Each task has a human-readable research name, task card, case study, input
+contract, middle process modules, output contract, modality list, metric, and
+current limitation. The website mirrors these records as an interactive
+scrub/play walkthrough storyboard for onboarding a junior researcher or
+engineer:
 
 `results/episode_task_suite/task_walkthroughs/TASK_WALKTHROUGHS.md`
 

docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-01T17:05:05+00:00",
+  "generated_at_utc": "2026-06-01T17:44:19+00:00",
   "status": "pass",
   "artifact_count": 52,
   "missing": [],
@@ -206,7 +206,7 @@
       "proves": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
       "bytes": 13435,
-      "sha256": "b9dd409ee8a3634b154b5b76fff83d8edff567e7f521727ccb3ddb5ba093f35b"
+      "sha256": "39f834cb99bec9436f475fdf185325ff6e48ac4f4c9b7e09cd1832d0a704c401"
     },
     {
       "id": "figure_index_builder",
@@ -272,7 +272,7 @@
       "proves": "Machine-readable release-gate summary for validators, mirrors, and reviewer surfaces.",
       "exists": true,
       "bytes": 6937,
-      "sha256": "9e7643f343cbe19769ddc302496c7a2fbed496d0259a060c1ea6bd82062cdbc0"
+      "sha256": "c390816215face110fed18c5e81e8b0e735188d625920eba987ce8635f4cb9e9"
     },
     {
       "id": "task_surface_integrity",
@@ -283,7 +283,7 @@
       "volatile": true,
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 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T16:58:01+00:00",
+  "generated_at_utc": "2026-06-01T17:42:54+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -164,8 +164,8 @@
     "github_repo": {
       "root": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/working_repo_copy",
       "exists": true,
-      "file_count": 319,
-      "text_file_count": 255,
+      "file_count": 320,
+      "text_file_count": 256,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 52601010

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Publication Quality Gates",
   "status": "pass",
-  "generated_at_utc": "2026-06-01T16:56:38+00:00",
+  "generated_at_utc": "2026-06-01T17:42:23+00:00",
   "rule": "Do not present a release as current unless every automated gate passes, then verify live GitHub/HF mirrors after publishing.",
   "automated_gates": [
     {

docs/data/research_direction_extensions.json

@@ -30,7 +30,7 @@
     "body_motion_intensity": {
       "direction": "A",
       "direction_name": "Human Modeling & Motion Understanding",
-      "name": "Body/hand motion intensity",
+      "name": "Body and Hand Motion Intensity",
       "family": "classification",
       "case_study": "A window with a fast reach or pour should be classified as high motion; a steady holding window should be low motion.",
       "input": "Current non-mocap feature blocks: video, depth, camera pose/rotation, IMU, SLAM, calibration, and language context.",
@@ -46,7 +46,7 @@
     "multi_view_consistency_retrieval": {
       "direction": "B",
       "direction_name": "3D/4D Reconstruction & Neural Rendering",
-      "name": "Multi-view consistency retrieval",
+      "name": "Multi-View Consistency Retrieval",
       "family": "retrieval",
       "case_study": "Given the fisheye camera features for a pouring moment, retrieve the synchronized stereo-left view from the same time window.",
       "input": "Query side: fisheye_cam0 video feature block. Candidate side: stereo_left video feature block from held-out windows.",
@@ -62,7 +62,7 @@
     "action_phase_progress": {
       "direction": "C",
       "direction_name": "Egocentric Vision & Interaction",
-      "name": "Action phase progress",
+      "name": "Action Phase Progress Estimation",
       "family": "regression",
       "case_study": "Inside a Pour coffee action segment, estimate whether the current window is near the beginning, middle, or end of that action.",
       "input": "Current non-caption multimodal feature vector, so the label text cannot be copied directly from the language block.",
@@ -78,7 +78,7 @@
     "ego_motion_forecast": {
       "direction": "D",
       "direction_name": "Scene Reconstruction & World Modeling",
-      "name": "Short-horizon ego-motion forecast",
+      "name": "Short-Horizon Ego-Motion Forecasting",
       "family": "forecast",
       "case_study": "From the current sensors, predict how the camera translation will change over the next 20 frames while the wearer moves through the scene.",
       "input": "Current multimodal features excluding the camera-translation block and caption text.",
@@ -306,4 +306,4 @@
       }
     }
   }
-}
+}

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T16:53:58+00:00",
+  "generated_at_utc": "2026-06-01T17:37:41+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,
@@ -18,7 +18,7 @@
       "pose_slam": 11,
       "video": 12
     },
-    "interactive_surface": "task cards plus play/previous/next walkthrough player",
+    "interactive_surface": "task cards plus scrub/play/chapter walkthrough storyboard",
     "failure_count": 0
   },
   "checks": [
@@ -64,45 +64,45 @@
       "observed": "timeline_action"
     },
     {
-      "name": "timeline_action: public_field_display_name_is_human_readable",
+      "name": "timeline_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Action Recognition",
+      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_input_short_is_human_readable",
+      "name": "timeline_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "Egocentric Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_output_short_is_human_readable",
+      "name": "timeline_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "current action class",
+      "value": "Look at one short multimodal window and name what action is happening now.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_process_short_is_human_readable",
+      "name": "timeline_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "window features -> action label builder -> classifier",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_card_blurb_is_human_readable",
+      "name": "timeline_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
+      "value": "window features -> action label builder -> classifier",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_plain_goal_is_human_readable",
+      "name": "timeline_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Look at one short multimodal window and name what action is happening now.",
+      "value": "Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_research_name_is_human_readable",
+      "name": "timeline_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Egocentric Action Recognition",
+      "value": "current action class",
       "raw_hits": []
     },
     {
@@ -184,45 +184,45 @@
       "observed": "timeline_subtask"
     },
     {
-      "name": "timeline_subtask: public_field_display_name_is_human_readable",
+      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Procedure Step Recognition",
+      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_input_short_is_human_readable",
+      "name": "timeline_subtask: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "Temporal Subtask Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_output_short_is_human_readable",
+      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "current procedure step",
+      "value": "Predict the higher-level task stage for the current window.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_process_short_is_human_readable",
+      "name": "timeline_subtask: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "window features -> subtask label builder -> classifier",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
+      "name": "timeline_subtask: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
+      "value": "window features -> subtask label builder -> classifier",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
+      "name": "timeline_subtask: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Predict the higher-level task stage for the current window.",
+      "value": "Procedure Step Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_research_name_is_human_readable",
+      "name": "timeline_subtask: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Subtask Recognition",
+      "value": "current procedure step",
       "raw_hits": []
     },
     {
@@ -304,45 +304,45 @@
       "observed": "transition_detection"
     },
     {
-      "name": "transition_detection: public_field_display_name_is_human_readable",
+      "name": "transition_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Action Boundary Detection",
+      "value": "Detect the local moment where the episode changes from one action segment to the next.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_input_short_is_human_readable",
+      "name": "transition_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "current window with boundary target",
+      "value": "Temporal Action Segmentation",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_output_short_is_human_readable",
+      "name": "transition_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "boundary or steady",
+      "value": "Detect whether the current window is near a boundary between actions.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_process_short_is_human_readable",
+      "name": "transition_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "action changes -> boundary labels -> binary classifier",
+      "value": "current window with boundary target",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_card_blurb_is_human_readable",
+      "name": "transition_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Detect the local moment where the episode changes from one action segment to the next.",
+      "value": "action changes -> boundary labels -> binary classifier",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_plain_goal_is_human_readable",
+      "name": "transition_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Detect whether the current window is near a boundary between actions.",
+      "value": "Action Boundary Detection",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_research_name_is_human_readable",
+      "name": "transition_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Action Segmentation",
+      "value": "boundary or steady",
       "raw_hits": []
     },
     {
@@ -422,45 +422,45 @@
       "observed": "next_action"
     },
     {
-      "name": "next_action: public_field_display_name_is_human_readable",
+      "name": "next_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Next-Action Prediction",
+      "value": "Forecast the near-future action from the current observations only.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_input_short_is_human_readable",
+      "name": "next_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "current window at time t",
+      "value": "Short-Horizon Intention Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_output_short_is_human_readable",
+      "name": "next_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "action at t+20 frames",
+      "value": "Use the current window to guess the action that will happen shortly after it.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_process_short_is_human_readable",
+      "name": "next_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current features -> future label shift -> classifier",
+      "value": "current window at time t",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_card_blurb_is_human_readable",
+      "name": "next_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Forecast the near-future action from the current observations only.",
+      "value": "current features -> future label shift -> classifier",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_plain_goal_is_human_readable",
+      "name": "next_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Use the current window to guess the action that will happen shortly after it.",
+      "value": "Next-Action Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_research_name_is_human_readable",
+      "name": "next_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Short-Horizon Intention Prediction",
+      "value": "action at t+20 frames",
       "raw_hits": []
     },
     {
@@ -540,45 +540,45 @@
       "observed": "hand_trajectory_forecast"
     },
     {
-      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Hand Trajectory Forecasting",
+      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "current multimodal window",
+      "value": "3D Hand Motion Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "future hand-joint trajectory",
+      "value": "Predict where the hands will move over the next few frames.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current features -> future mocap target -> regression head",
+      "value": "current multimodal window",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
+      "value": "current features -> future mocap target -> regression head",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Predict where the hands will move over the next few frames.",
+      "value": "Hand Trajectory Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "3D Hand Motion Forecasting",
+      "value": "future hand-joint trajectory",
       "raw_hits": []
     },
     {
@@ -658,45 +658,45 @@
       "observed": "contact_prediction"
     },
     {
-      "name": "contact_prediction: public_field_display_name_is_human_readable",
+      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Contact State Prediction",
+      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_input_short_is_human_readable",
+      "name": "contact_prediction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "non-contact, non-caption features",
+      "value": "Human-Object Contact Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_output_short_is_human_readable",
+      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "contact or no contact",
+      "value": "Predict whether the body or hand is in contact with something.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_process_short_is_human_readable",
+      "name": "contact_prediction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "feature filter -> contact target -> binary classifier",
+      "value": "non-contact, non-caption features",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
+      "name": "contact_prediction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
+      "value": "feature filter -> contact target -> binary classifier",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
+      "name": "contact_prediction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Predict whether the body or hand is in contact with something.",
+      "value": "Contact State Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_research_name_is_human_readable",
+      "name": "contact_prediction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Human-Object Contact Prediction",
+      "value": "contact or no contact",
       "raw_hits": []
     },
     {
@@ -774,45 +774,45 @@
       "observed": "object_relevance"
     },
     {
-      "name": "object_relevance: public_field_display_name_is_human_readable",
+      "name": "object_relevance: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Object Relevance Prediction",
+      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_input_short_is_human_readable",
+      "name": "object_relevance: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "non-caption multimodal features",
+      "value": "Object-Centric Interaction Recognition",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_output_short_is_human_readable",
+      "name": "object_relevance: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "relevant object set",
+      "value": "Predict which objects matter in the current window.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_process_short_is_human_readable",
+      "name": "object_relevance: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
+      "value": "non-caption multimodal features",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_card_blurb_is_human_readable",
+      "name": "object_relevance: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
+      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_plain_goal_is_human_readable",
+      "name": "object_relevance: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Predict which objects matter in the current window.",
+      "value": "Object Relevance Prediction",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_research_name_is_human_readable",
+      "name": "object_relevance: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Object-Centric Interaction Recognition",
+      "value": "relevant object set",
       "raw_hits": []
     },
     {
@@ -892,45 +892,45 @@
       "observed": "caption_grounding"
     },
     {
-      "name": "caption_grounding: public_field_display_name_is_human_readable",
+      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Language Grounding",
+      "value": "Retrieve the matching time window for an annotation-derived text query.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_input_short_is_human_readable",
+      "name": "caption_grounding: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "text-like query and candidate windows",
+      "value": "Language-to-Moment Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_output_short_is_human_readable",
+      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "ranked matching moments",
+      "value": "Given a text-like query from annotation, find the matching time window.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_process_short_is_human_readable",
+      "name": "caption_grounding: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "query features -> candidate index -> cosine ranker",
+      "value": "text-like query and candidate windows",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
+      "name": "caption_grounding: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Retrieve the matching time window for an annotation-derived text query.",
+      "value": "query features -> candidate index -> cosine ranker",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
+      "name": "caption_grounding: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Given a text-like query from annotation, find the matching time window.",
+      "value": "Language Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_research_name_is_human_readable",
+      "name": "caption_grounding: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Language-to-Moment Grounding",
+      "value": "ranked matching moments",
       "raw_hits": []
     },
     {
@@ -1008,45 +1008,45 @@
       "observed": "cross_modal_retrieval"
     },
     {
-      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Retrieval",
+      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "motion/IMU/pose query; depth/video candidates",
+      "value": "Multimodal Representation Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "ranked visual windows",
+      "value": "Use one group of modalities to retrieve the matching window from another group.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "modality split -> projection -> nearest-neighbor ranker",
+      "value": "motion/IMU/pose query; depth/video candidates",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
+      "value": "modality split -> projection -> nearest-neighbor ranker",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Use one group of modalities to retrieve the matching window from another group.",
+      "value": "Cross-Modal Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Representation Retrieval",
+      "value": "ranked visual windows",
       "raw_hits": []
     },
     {
@@ -1126,45 +1126,45 @@
       "observed": "modality_reconstruction"
     },
     {
-      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Reconstruction",
+      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "motion, IMU, and camera/pose features",
+      "value": "Modality Feature Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "reconstructed depth/video vector",
+      "value": "Predict one modality feature block from other modality blocks.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "source-target split -> scaler -> regression head",
+      "value": "motion, IMU, and camera/pose features",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
+      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
+      "value": "source-target split -> scaler -> regression head",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
+      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Predict one modality feature block from other modality blocks.",
+      "value": "Cross-Modal Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Modality Feature Reconstruction",
+      "value": "reconstructed depth/video vector",
       "raw_hits": []
     },
     {
@@ -1244,45 +1244,45 @@
       "observed": "temporal_order"
     },
     {
-      "name": "temporal_order: public_field_display_name_is_human_readable",
+      "name": "temporal_order: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_input_short_is_human_readable",
+      "name": "temporal_order: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "two adjacent windows plus difference vector",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_output_short_is_human_readable",
+      "name": "temporal_order: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "correct or reversed",
+      "value": "Tell whether two nearby windows are in the correct time order.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_process_short_is_human_readable",
+      "name": "temporal_order: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "pair builder -> feature combiner -> binary classifier",
+      "value": "two adjacent windows plus difference vector",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_card_blurb_is_human_readable",
+      "name": "temporal_order: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
+      "value": "pair builder -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_plain_goal_is_human_readable",
+      "name": "temporal_order: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two nearby windows are in the correct time order.",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_research_name_is_human_readable",
+      "name": "temporal_order: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "correct or reversed",
       "raw_hits": []
     },
     {
@@ -1360,45 +1360,45 @@
       "observed": "misalignment_detection"
     },
     {
-      "name": "misalignment_detection: public_field_display_name_is_human_readable",
+      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Synchronization Detection",
+      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_input_short_is_human_readable",
+      "name": "misalignment_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "motion-side and visual/depth-side feature groups",
+      "value": "Cross-Modal Misalignment Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_output_short_is_human_readable",
+      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "aligned or shifted",
+      "value": "Detect when modalities that should match are shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_process_short_is_human_readable",
+      "name": "misalignment_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
+      "value": "motion-side and visual/depth-side feature groups",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
+      "name": "misalignment_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
+      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
+      "name": "misalignment_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "Detect when modalities that should match are shifted out of sync.",
+      "value": "Multimodal Synchronization Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_research_name_is_human_readable",
+      "name": "misalignment_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Misalignment Detection",
+      "value": "aligned or shifted",
       "raw_hits": []
     },
     {
@@ -1545,6 +1545,26 @@
       "status": "pass",
       "marker": "id=\"walkthroughSelector\""
     },
+    {
+      "name": "website_marker_present:id=\"playerStoryboard\"",
+      "status": "pass",
+      "marker": "id=\"playerStoryboard\""
+    },
+    {
+      "name": "website_marker_present:id=\"playerFrameChip\"",
+      "status": "pass",
+      "marker": "id=\"playerFrameChip\""
+    },
+    {
+      "name": "website_marker_present:id=\"playerFrameCaption\"",
+      "status": "pass",
+      "marker": "id=\"playerFrameCaption\""
+    },
+    {
+      "name": "website_marker_present:id=\"playerScrub\"",
+      "status": "pass",
+      "marker": "id=\"playerScrub\""
+    },
     {
       "name": "website_marker_present:fetch(\"data/task_walkthroughs.json\"",
       "status": "pass",
@@ -1560,6 +1580,16 @@
       "status": "pass",
       "marker": "class=\"task-card-media\""
     },
+    {
+      "name": "website_marker_present:class=\"story-button",
+      "status": "pass",
+      "marker": "class=\"story-button"
+    },
+    {
+      "name": "website_marker_present:class=\"flow-step",
+      "status": "pass",
+      "marker": "class=\"flow-step"
+    },
     {
       "name": "website_marker_present:id=\"playerPlay\"",
       "status": "pass",
@@ -1599,9 +1629,33 @@
       "name": "interactive_player_wired_to_task_metadata",
       "status": "pass"
     },
+    {
+      "name": "interactive_video_storyboard_controls_present",
+      "status": "pass"
+    },
     {
       "name": "selector_uses_human_names",
       "status": "pass"
+    },
+    {
+      "name": "extension_probe_uses_human_name:body_motion_intensity",
+      "status": "pass",
+      "expected": "Body and Hand Motion Intensity"
+    },
+    {
+      "name": "extension_probe_uses_human_name:multi_view_consistency_retrieval",
+      "status": "pass",
+      "expected": "Multi-View Consistency Retrieval"
+    },
+    {
+      "name": "extension_probe_uses_human_name:action_phase_progress",
+      "status": "pass",
+      "expected": "Action Phase Progress Estimation"
+    },
+    {
+      "name": "extension_probe_uses_human_name:ego_motion_forecast",
+      "status": "pass",
+      "expected": "Short-Horizon Ego-Motion Forecasting"
     }
   ],
   "failures": []

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T16:53:58+00:00",
+  "generated_at_utc": "2026-06-01T17:37:41+00:00",
   "docs_root": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/working_repo_copy/docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -24,8 +24,8 @@
       "name": "reviewer_scorecard_precedes_evidence_ledger",
       "status": "pass",
       "reason": "The reviewer scorecard should appear before the deeper evidence ledger.",
-      "scorecard_index": 44211,
-      "evidence_index": 50857
+      "scorecard_index": 46359,
+      "evidence_index": 53005
     },
     {
       "name": "reviewer_scorecard_links_json",
@@ -37,9 +37,9 @@
       "name": "evaluation_protocol_between_scorecard_and_evidence",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "scorecard_index": 44211,
-      "protocol_index": 48450,
-      "evidence_index": 50857
+      "scorecard_index": 46359,
+      "protocol_index": 50598,
+      "evidence_index": 53005
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -112,7 +112,7 @@
     },
     {
       "path": "index.html",
-      "id_count": 55,
+      "id_count": 59,
       "reference_count": 88,
       "image_count": 22
     }
@@ -180,7 +180,7 @@
     },
     {
       "path": "data/research_direction_extensions.json",
-      "bytes": 11872,
+      "bytes": 11891,
       "top_level_type": "dict"
     },
     {
@@ -215,7 +215,7 @@
     },
     {
       "path": "data/task_surface_integrity.json",
-      "bytes": 44190,
+      "bytes": 45780,
       "top_level_type": "dict"
     },
     {
@@ -295,7 +295,7 @@
     {
       "path": "assets/charts/research_direction_extension_tasks.svg",
       "exists": true,
-      "bytes": 6667,
+      "bytes": 6686,
       "format": "SVG",
       "has_viewbox": true
     },

docs/index.html

@@ -1034,7 +1034,9 @@
       display: block;
       font-family: var(--font-ui);
       font-size: clamp(20px, 2.4vw, 34px);
-      line-height: 1.02;
+      line-height: 1.08;
+      text-wrap: balance;
+      word-spacing: 0.06em;
     }
     .player-badge span {
       display: block;
@@ -1043,6 +1045,32 @@
       font-family: var(--font-mono);
       font-size: 12px;
     }
+    .player-frame-chip {
+      position: absolute;
+      left: 14px;
+      top: 14px;
+      border: 1px solid rgba(164, 242, 127, 0.36);
+      border-radius: 999px;
+      background: rgba(2, 5, 2, 0.74);
+      color: #dce8d6;
+      padding: 7px 10px;
+      font-family: var(--font-mono);
+      font-size: 11px;
+      font-weight: 740;
+      letter-spacing: 0.04em;
+      text-transform: uppercase;
+      backdrop-filter: blur(12px);
+    }
+    .player-frame-caption {
+      margin: 12px 0 0;
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.48);
+      color: #dce8d6;
+      padding: 11px 12px;
+      font-size: 13px;
+      line-height: 1.5;
+    }
     .player-controls {
       display: flex;
       align-items: center;
@@ -1089,6 +1117,48 @@
       background: linear-gradient(90deg, var(--green), var(--cyan));
       transition: width 260ms cubic-bezier(0.16, 1, 0.3, 1);
     }
+    .task-scrubber {
+      flex: 1 1 210px;
+      min-width: 180px;
+      accent-color: var(--green);
+      cursor: pointer;
+    }
+    .storyboard-steps {
+      display: grid;
+      grid-template-columns: repeat(4, minmax(0, 1fr));
+      gap: 8px;
+      margin-top: 12px;
+    }
+    .story-button {
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.52);
+      color: #dce8d6;
+      min-height: 48px;
+      padding: 9px 8px;
+      font: inherit;
+      text-align: left;
+      cursor: pointer;
+    }
+    .story-button strong {
+      display: block;
+      color: var(--ink);
+      font-family: var(--font-mono);
+      font-size: 11px;
+      text-transform: uppercase;
+      letter-spacing: 0.04em;
+    }
+    .story-button span {
+      display: block;
+      margin-top: 4px;
+      color: var(--muted);
+      font-size: 12px;
+      line-height: 1.25;
+    }
+    .story-button.active {
+      border-color: rgba(164, 242, 127, 0.72);
+      background: rgba(164, 242, 127, 0.12);
+    }
     .modality-strip {
       display: grid;
       grid-template-columns: repeat(auto-fit, minmax(96px, 1fr));
@@ -1150,7 +1220,7 @@
       grid-template-columns: repeat(3, minmax(0, 1fr));
       gap: 10px;
     }
-    .flow-steps span,
+    .flow-step,
     .module-list li {
       border: 1px solid var(--soft-line);
       background: rgba(164, 242, 127, 0.06);
@@ -1158,7 +1228,17 @@
       padding: 10px;
       min-width: 0;
     }
-    .flow-steps strong,
+    .flow-step {
+      color: inherit;
+      font: inherit;
+      text-align: left;
+      cursor: pointer;
+    }
+    .flow-step.active {
+      border-color: rgba(164, 242, 127, 0.72);
+      background: rgba(164, 242, 127, 0.12);
+    }
+    .flow-step strong,
     .module-list strong {
       display: block;
       margin-bottom: 5px;
@@ -1168,7 +1248,7 @@
       text-transform: uppercase;
       letter-spacing: 0.04em;
     }
-    .flow-steps em {
+    .flow-step em {
       display: block;
       color: #dce8d6;
       font-style: normal;
@@ -1299,6 +1379,7 @@
       .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .review-grid, .scorecard-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .task-player { grid-template-columns: 1fr; }
       .task-selector { grid-template-columns: repeat(3, minmax(0, 1fr)); }
+      .storyboard-steps { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .modality-atlas { grid-template-columns: 1fr; }
       .artifact-group-head { grid-template-columns: 1fr; align-items: start; }
       .chart-grid { grid-template-columns: 1fr; }
@@ -1310,7 +1391,7 @@
     }
     @media (max-width: 640px) {
       .wrap { width: min(100% - 28px, var(--max)); }
-      .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .review-grid, .scorecard-grid, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .task-selector, .atlas-rows { grid-template-columns: 1fr; }
+      .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .review-grid, .scorecard-grid, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .atlas-rows { grid-template-columns: 1fr; }
       .artifact-group { padding: 16px; }
       .modality-atlas-panel { padding: 14px; }
       .atlas-card { padding: 12px; }
@@ -1327,6 +1408,7 @@
       .signal { grid-template-columns: 1fr; }
       .signal strong { text-align: left; }
       .player-counter { width: 100%; margin-left: 0; }
+      .task-scrubber { flex-basis: 100%; }
       .figure-pan {
         margin-inline: 0;
         padding-inline: 0;
@@ -1884,7 +1966,7 @@
         <div class="extension-grid">
           <article class="extension-card">
             <span class="status-pill">A / motion</span>
-            <h3>body_motion_intensity</h3>
+            <h3>Body and Hand Motion Intensity</h3>
             <p><strong>Case:</strong> classify fast reach/pour windows as high motion and steady holding windows as low motion.</p>
             <p><strong>Input:</strong> non-mocap video, depth, pose, IMU, SLAM, calibration, and language features.</p>
             <p><strong>Output:</strong> high_motion or low_motion.</p>
@@ -1892,7 +1974,7 @@
           </article>
           <article class="extension-card">
             <span class="status-pill">B / views</span>
-            <h3>multi_view_consistency_retrieval</h3>
+            <h3>Multi-View Consistency Retrieval</h3>
             <p><strong>Case:</strong> retrieve the synchronized stereo-left window from a fisheye-camera query.</p>
             <p><strong>Input:</strong> fisheye_cam0 video features against stereo_left candidate features.</p>
             <p><strong>Output:</strong> ranked synchronized view candidates.</p>
@@ -1900,7 +1982,7 @@
           </article>
           <article class="extension-card">
             <span class="status-pill">C / phase</span>
-            <h3>action_phase_progress</h3>
+            <h3>Action Phase Progress Estimation</h3>
             <p><strong>Case:</strong> estimate whether a Pour coffee window is near the start, middle, or end of its action segment.</p>
             <p><strong>Input:</strong> non-caption multimodal features.</p>
             <p><strong>Output:</strong> 0-to-1 progress inside the current action.</p>
@@ -1908,7 +1990,7 @@
           </article>
           <article class="extension-card">
             <span class="status-pill">D / world</span>
-            <h3>ego_motion_forecast</h3>
+            <h3>Short-Horizon Ego-Motion Forecasting</h3>
             <p><strong>Case:</strong> predict how the camera translation changes over the next 20 frames.</p>
             <p><strong>Input:</strong> current sensors excluding camera translation and captions.</p>
             <p><strong>Output:</strong> future camera-translation delta vector.</p>
@@ -1948,18 +2030,27 @@
           <div class="player-stage">
             <div class="player-screen">
               <img id="playerPoster" src="assets/modalities/video.jpg" alt="Representative sample modality for the selected task">
+              <div class="player-frame-chip" id="playerFrameChip">Step 1 / 4 · Input</div>
               <div class="player-badge">
                 <strong id="playerBadgeTitle">Action Recognition</strong>
                 <span id="playerBadgeMeta">Egocentric Action Recognition</span>
               </div>
             </div>
+            <p class="player-frame-caption" id="playerFrameCaption">Input: inspect the 20-frame multimodal window before choosing the target.</p>
             <div class="modality-strip" id="playerModalities" aria-label="Selected task modality evidence"></div>
             <div class="player-controls">
               <button type="button" id="playerPrev">Previous</button>
               <button type="button" class="primary-control" id="playerPlay">Play</button>
               <button type="button" id="playerNext">Next</button>
+              <input class="task-scrubber" id="playerScrub" type="range" min="0" max="11" value="0" step="1" aria-label="Scrub through task cards">
               <span class="player-counter" id="playerCounter">01 / 12</span>
             </div>
+            <div class="storyboard-steps" id="playerStoryboard" aria-label="Interactive walkthrough chapters">
+              <button type="button" class="story-button active" data-stage="0" aria-pressed="true"><strong>Input</strong><span>What enters the model</span></button>
+              <button type="button" class="story-button" data-stage="1" aria-pressed="false"><strong>Process</strong><span>How the target is built</span></button>
+              <button type="button" class="story-button" data-stage="2" aria-pressed="false"><strong>Output</strong><span>What is predicted</span></button>
+              <button type="button" class="story-button" data-stage="3" aria-pressed="false"><strong>Evaluate</strong><span>Metric and limitation</span></button>
+            </div>
             <div class="player-progress" aria-hidden="true"><span id="playerProgress"></span></div>
           </div>
           <article class="player-copy" aria-live="polite">
@@ -1970,9 +2061,9 @@
             <h3 id="playerTitle">Action Recognition</h3>
             <p class="player-case" id="playerCase">In the coffee-making sample, a pouring window maps to the current action label.</p>
             <div class="flow-steps">
-              <span><strong>Input</strong><em id="playerInput">20-frame multimodal window</em></span>
-              <span><strong>Process</strong><em id="playerProcess">window features -> classifier</em></span>
-              <span><strong>Output</strong><em id="playerOutput">current action class</em></span>
+              <button type="button" class="flow-step active" data-stage="0" aria-pressed="true"><strong>Input</strong><em id="playerInput">20-frame multimodal window</em></button>
+              <button type="button" class="flow-step" data-stage="1" aria-pressed="false"><strong>Process</strong><em id="playerProcess">window features -> classifier</em></button>
+              <button type="button" class="flow-step" data-stage="2" aria-pressed="false"><strong>Output</strong><em id="playerOutput">current action class</em></button>
             </div>
             <ul class="module-list" id="playerModules"></ul>
             <p id="playerMetric">Metric: macro-F1. Minimal 0.0500; neural MLP 0.0263.</p>
@@ -2181,8 +2272,15 @@ python scripts/validate_publication_package.py</code></pre>
     };
     let taskEntries = [];
     let activeTaskIndex = 0;
+    let activeStageIndex = 0;
     let activeFilter = "all";
     let playerTimer = null;
+    const storyStages = [
+      { key: "input", label: "Input" },
+      { key: "process", label: "Process" },
+      { key: "output", label: "Output" },
+      { key: "evaluate", label: "Evaluate" }
+    ];
 
     const escapeHtml = (value) => String(value ?? "")
       .replaceAll("&", "&amp;")
@@ -2208,6 +2306,23 @@ python scripts/validate_publication_package.py</code></pre>
 
     const normalizeTasks = (payload) => Object.values(payload.tasks || {});
 
+    const modalityLabels = (task) => (task.modalities || [])
+      .map((key) => modalityMeta[key]?.label)
+      .filter(Boolean)
+      .join(", ");
+
+    function stageNarration(task) {
+      const minimal = formatMetric(task.metric?.minimal);
+      const neural = formatMetric(task.metric?.neural_mlp);
+      const modules = (task.middle_modules || []).slice(0, 2).join(" ");
+      return [
+        `Input: ${task.input_short}. Evidence shown here comes from ${modalityLabels(task)}.`,
+        `Process: ${task.process_short}. ${modules}`,
+        `Output: ${task.output_short}. Case study: ${task.case_study}`,
+        `Evaluate: ${task.metric.name}, ${task.metric.direction} is better. Minimal ${minimal}; neural MLP ${neural}. Limitation: ${task.failure_mode}`
+      ];
+    }
+
     function renderTaskCards() {
       const grid = document.getElementById("taskGrid");
       grid.innerHTML = taskEntries.map((task, index) => {
@@ -2286,12 +2401,29 @@ python scripts/validate_publication_package.py</code></pre>
       )).join("");
       document.getElementById("playerMetric").textContent = `${task.metric.name} (${task.metric.direction} is better). Minimal ${formatMetric(task.metric.minimal)}; neural MLP ${formatMetric(task.metric.neural_mlp)}.`;
       document.getElementById("playerLimit").textContent = `Current limitation: ${task.failure_mode}`;
-      document.getElementById("playerCounter").textContent = `${String(index + 1).padStart(2, "0")} / ${String(taskEntries.length).padStart(2, "0")}`;
-      document.getElementById("playerProgress").style.width = `${((index + 1) / taskEntries.length) * 100}%`;
+      document.getElementById("playerScrub").max = Math.max(0, taskEntries.length - 1);
+      document.getElementById("playerScrub").value = index;
       document.getElementById("playerModalities").innerHTML = task.modalities.map((key) => {
         const modality = modalityMeta[key] || modalityMeta.video;
         return `<span class="modality-tile"><img src="${modality.src}" alt="${escapeHtml(modality.label)} sample thumbnail"><span>${escapeHtml(modality.label)}</span></span>`;
       }).join("");
+      renderStageFrame(task, index);
+    }
+
+    function renderStageFrame(task, index) {
+      const stage = storyStages[activeStageIndex] || storyStages[0];
+      const narration = stageNarration(task);
+      const totalFrames = Math.max(1, taskEntries.length * storyStages.length);
+      const currentFrame = index * storyStages.length + activeStageIndex + 1;
+      document.getElementById("playerFrameChip").textContent = `Step ${activeStageIndex + 1} / ${storyStages.length} · ${stage.label}`;
+      document.getElementById("playerFrameCaption").textContent = narration[activeStageIndex] || narration[0];
+      document.getElementById("playerCounter").textContent = `${String(index + 1).padStart(2, "0")} / ${String(taskEntries.length).padStart(2, "0")} · ${stage.label}`;
+      document.getElementById("playerProgress").style.width = `${(currentFrame / totalFrames) * 100}%`;
+      document.querySelectorAll("[data-stage]").forEach((button) => {
+        const active = Number(button.dataset.stage) === activeStageIndex;
+        button.classList.toggle("active", active);
+        button.setAttribute("aria-pressed", active ? "true" : "false");
+      });
     }
 
     function updateActiveMarkers() {
@@ -2307,13 +2439,28 @@ python scripts/validate_publication_package.py</code></pre>
       });
     }
 
-    function setActiveTask(index) {
+    function setActiveTask(index, options = {}) {
       if (!taskEntries.length) return;
       activeTaskIndex = (index + taskEntries.length) % taskEntries.length;
+      if (options.resetStage !== false) activeStageIndex = 0;
       renderPlayer(taskEntries[activeTaskIndex], activeTaskIndex);
       updateActiveMarkers();
     }
 
+    function setActiveStage(index) {
+      if (!taskEntries.length) return;
+      activeStageIndex = (index + storyStages.length) % storyStages.length;
+      renderStageFrame(taskEntries[activeTaskIndex], activeTaskIndex);
+    }
+
+    function advancePlayer() {
+      if (activeStageIndex < storyStages.length - 1) {
+        setActiveStage(activeStageIndex + 1);
+        return;
+      }
+      setActiveTask(activeTaskIndex + 1);
+    }
+
     function applyTaskFilter(filter) {
       activeFilter = filter;
       document.querySelectorAll(".filter").forEach((button) => {
@@ -2340,7 +2487,7 @@ python scripts/validate_publication_package.py</code></pre>
         return;
       }
       document.getElementById("playerPlay").textContent = "Pause";
-      playerTimer = window.setInterval(() => setActiveTask(activeTaskIndex + 1), 3200);
+      playerTimer = window.setInterval(advancePlayer, 2600);
     }
 
     async function initTaskSurface() {
@@ -2363,6 +2510,16 @@ python scripts/validate_publication_package.py</code></pre>
     document.getElementById("playerPrev").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex - 1); });
     document.getElementById("playerNext").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex + 1); });
     document.getElementById("playerPlay").addEventListener("click", togglePlayer);
+    document.getElementById("playerScrub").addEventListener("input", (event) => {
+      pausePlayer();
+      setActiveTask(Number(event.target.value));
+    });
+    document.querySelectorAll("[data-stage]").forEach((button) => {
+      button.addEventListener("click", () => {
+        pausePlayer();
+        setActiveStage(Number(button.dataset.stage));
+      });
+    });
     initTaskSurface();
 
     document.querySelectorAll("[data-copy]").forEach((button) => {

results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json

@@ -30,7 +30,7 @@
     "body_motion_intensity": {
       "direction": "A",
       "direction_name": "Human Modeling & Motion Understanding",
-      "name": "Body/hand motion intensity",
+      "name": "Body and Hand Motion Intensity",
       "family": "classification",
       "case_study": "A window with a fast reach or pour should be classified as high motion; a steady holding window should be low motion.",
       "input": "Current non-mocap feature blocks: video, depth, camera pose/rotation, IMU, SLAM, calibration, and language context.",
@@ -46,7 +46,7 @@
     "multi_view_consistency_retrieval": {
       "direction": "B",
       "direction_name": "3D/4D Reconstruction & Neural Rendering",
-      "name": "Multi-view consistency retrieval",
+      "name": "Multi-View Consistency Retrieval",
       "family": "retrieval",
       "case_study": "Given the fisheye camera features for a pouring moment, retrieve the synchronized stereo-left view from the same time window.",
       "input": "Query side: fisheye_cam0 video feature block. Candidate side: stereo_left video feature block from held-out windows.",
@@ -62,7 +62,7 @@
     "action_phase_progress": {
       "direction": "C",
       "direction_name": "Egocentric Vision & Interaction",
-      "name": "Action phase progress",
+      "name": "Action Phase Progress Estimation",
       "family": "regression",
       "case_study": "Inside a Pour coffee action segment, estimate whether the current window is near the beginning, middle, or end of that action.",
       "input": "Current non-caption multimodal feature vector, so the label text cannot be copied directly from the language block.",
@@ -78,7 +78,7 @@
     "ego_motion_forecast": {
       "direction": "D",
       "direction_name": "Scene Reconstruction & World Modeling",
-      "name": "Short-horizon ego-motion forecast",
+      "name": "Short-Horizon Ego-Motion Forecasting",
       "family": "forecast",
       "case_study": "From the current sensors, predict how the camera translation will change over the next 20 frames while the wearer moves through the scene.",
       "input": "Current multimodal features excluding the camera-translation block and caption text.",
@@ -306,4 +306,4 @@
       }
     }
   }
-}
+}

results/episode_task_suite/research_direction_extensions/research_direction_extension_summary.md

@@ -8,14 +8,14 @@ They do not claim cross-episode generalization or full direction completion.
 
 | Direction | Extension task | Minimal | Neural MLP | Meaning |
 | --- | --- | ---: | ---: | --- |
-| A. Human Modeling & Motion Understanding | `body_motion_intensity` | 0.7827 macro-F1 | 0.7986 macro-F1 | This is a motion-energy proxy, not a SMPL/MANO body model or a generative motion prior. |
-| B. 3D/4D Reconstruction & Neural Rendering | `multi_view_consistency_retrieval` | 0.5534 MRR | 0.3469 MRR | This checks calibrated multi-view signal, but it is still feature retrieval, not NeRF, Gaussian Splatting, or novel-view synthesis. |
-| C. Egocentric Vision & Interaction | `action_phase_progress` | 0.3416 MAE | 0.3038 MAE | This is an action-structure probe inside one episode, not a general intent model across homes, people, or tasks. |
-| D. Scene Reconstruction & World Modeling | `ego_motion_forecast` | 0.1989 MAE | 0.0989 MAE | This is a compact world-model proxy; it does not build a persistent map, scene graph, or object permanence model. |
+| A. Human Modeling & Motion Understanding | Body and Hand Motion Intensity | 0.7827 macro-F1 | 0.7986 macro-F1 | This is a motion-energy proxy, not a SMPL/MANO body model or a generative motion prior. |
+| B. 3D/4D Reconstruction & Neural Rendering | Multi-View Consistency Retrieval | 0.5534 MRR | 0.3469 MRR | This checks calibrated multi-view signal, but it is still feature retrieval, not NeRF, Gaussian Splatting, or novel-view synthesis. |
+| C. Egocentric Vision & Interaction | Action Phase Progress Estimation | 0.3416 MAE | 0.3038 MAE | This is an action-structure probe inside one episode, not a general intent model across homes, people, or tasks. |
+| D. Scene Reconstruction & World Modeling | Short-Horizon Ego-Motion Forecasting | 0.1989 MAE | 0.0989 MAE | This is a compact world-model proxy; it does not build a persistent map, scene graph, or object permanence model. |
 
 ## Task Details
 
-### A. Body/hand motion intensity
+### A. Body and Hand Motion Intensity
 
 - Case study: A window with a fast reach or pour should be classified as high motion; a steady holding window should be low motion.
 - Input: Current non-mocap feature blocks: video, depth, camera pose/rotation, IMU, SLAM, calibration, and language context.
@@ -27,7 +27,7 @@ They do not claim cross-episode generalization or full direction completion.
 - Neural result: 0.7986 macro-F1
 - Limitation: This is a motion-energy proxy, not a SMPL/MANO body model or a generative motion prior.
 
-### B. Multi-view consistency retrieval
+### B. Multi-View Consistency Retrieval
 
 - Case study: Given the fisheye camera features for a pouring moment, retrieve the synchronized stereo-left view from the same time window.
 - Input: Query side: fisheye_cam0 video feature block. Candidate side: stereo_left video feature block from held-out windows.
@@ -39,7 +39,7 @@ They do not claim cross-episode generalization or full direction completion.
 - Neural result: 0.3469 MRR
 - Limitation: This checks calibrated multi-view signal, but it is still feature retrieval, not NeRF, Gaussian Splatting, or novel-view synthesis.
 
-### C. Action phase progress
+### C. Action Phase Progress Estimation
 
 - Case study: Inside a Pour coffee action segment, estimate whether the current window is near the beginning, middle, or end of that action.
 - Input: Current non-caption multimodal feature vector, so the label text cannot be copied directly from the language block.
@@ -51,7 +51,7 @@ They do not claim cross-episode generalization or full direction completion.
 - Neural result: 0.3038 MAE
 - Limitation: This is an action-structure probe inside one episode, not a general intent model across homes, people, or tasks.
 
-### D. Short-horizon ego-motion forecast
+### D. Short-Horizon Ego-Motion Forecasting
 
 - Case study: From the current sensors, predict how the camera translation will change over the next 20 frames while the wearer moves through the scene.
 - Input: Current multimodal features excluding the camera-translation block and caption text.

scripts/research_direction_extension_tasks.py

@@ -38,7 +38,7 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
             {
                 "direction": "A",
                 "direction_name": "Human Modeling & Motion Understanding",
-                "name": "Body/hand motion intensity",
+                "name": "Body and Hand Motion Intensity",
                 "family": "classification",
                 "case_study": "A window with a fast reach or pour should be classified as high motion; a steady holding window should be low motion.",
                 "input": "Current non-mocap feature blocks: video, depth, camera pose/rotation, IMU, SLAM, calibration, and language context.",
@@ -57,7 +57,7 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
             {
                 "direction": "B",
                 "direction_name": "3D/4D Reconstruction & Neural Rendering",
-                "name": "Multi-view consistency retrieval",
+                "name": "Multi-View Consistency Retrieval",
                 "family": "retrieval",
                 "case_study": "Given the fisheye camera features for a pouring moment, retrieve the synchronized stereo-left view from the same time window.",
                 "input": "Query side: fisheye_cam0 video feature block. Candidate side: stereo_left video feature block from held-out windows.",
@@ -76,7 +76,7 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
             {
                 "direction": "C",
                 "direction_name": "Egocentric Vision & Interaction",
-                "name": "Action phase progress",
+                "name": "Action Phase Progress Estimation",
                 "family": "regression",
                 "case_study": "Inside a Pour coffee action segment, estimate whether the current window is near the beginning, middle, or end of that action.",
                 "input": "Current non-caption multimodal feature vector, so the label text cannot be copied directly from the language block.",
@@ -95,7 +95,7 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
             {
                 "direction": "D",
                 "direction_name": "Scene Reconstruction & World Modeling",
-                "name": "Short-horizon ego-motion forecast",
+                "name": "Short-Horizon Ego-Motion Forecasting",
                 "family": "forecast",
                 "case_study": "From the current sensors, predict how the camera translation will change over the next 20 frames while the wearer moves through the scene.",
                 "input": "Current multimodal features excluding the camera-translation block and caption text.",
@@ -664,7 +664,7 @@ def write_markdown(payload: dict[str, Any]) -> None:
         min_value = task_main_metric(task, result, "minimal")
         nn_value = task_main_metric(task, result, "neural_mlp")
         lines.append(
-            f"| {spec['direction']}. {spec['direction_name']} | `{task}` | {fmt_metric(min_value, key)} {spec['metric_name']} | {fmt_metric(nn_value, key)} {spec['metric_name']} | {spec['current_limit']} |"
+            f"| {spec['direction']}. {spec['direction_name']} | {spec['name']} | {fmt_metric(min_value, key)} {spec['metric_name']} | {fmt_metric(nn_value, key)} {spec['metric_name']} | {spec['current_limit']} |"
         )
 
     lines.extend(["", "## Task Details", ""])

scripts/validate_publication_package.py

@@ -71,7 +71,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "12,103 episode folders",
             "all 12 task families before the",
             "Public-sample modality thumbnails remain enlarged below",
-            "interactive task walkthrough/player",
+            "interactive scrub/play walkthrough storyboard",
             "task_surface_integrity.json",
         ],
     },
@@ -92,7 +92,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "12,103 episode folders",
             "task-first 12-task infographic",
             "native responsive modality atlas",
-            "interactive task walkthrough/player",
+            "interactive scrub/play task walkthrough storyboard",
             "website HTML",
             "task_surface_integrity.json",
         ],
@@ -113,7 +113,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "cc-by-nc-4.0",
             "12,103 episode folders",
             "task-first 12-task map",
-            "interactive task walkthrough/player",
+            "interactive scrub/play walkthrough storyboard",
             "including critical website HTML",
             "task_surface_integrity.json",
         ],
@@ -135,7 +135,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "12,103 episode folders",
             "all 12 task families before the",
             "Public-sample modality thumbnails remain enlarged below",
-            "interactive task walkthrough/player",
+            "interactive scrub/play walkthrough storyboard",
             "task_surface_integrity.json",
         ],
     },
@@ -156,7 +156,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "12,103 episode folders",
             "task-first 12-head",
             "responsive modality atlas",
-            "interactive task player",
+            "interactive scrub/play storyboard",
             "website HTML",
             "task_surface_integrity.json",
         ],

scripts/validate_task_surface.py

@@ -37,6 +37,13 @@ EXPECTED_TASKS = {
     "misalignment_detection": "Multimodal Synchronization Detection",
 }
 
+EXPECTED_EXTENSION_NAMES = {
+    "body_motion_intensity": "Body and Hand Motion Intensity",
+    "multi_view_consistency_retrieval": "Multi-View Consistency Retrieval",
+    "action_phase_progress": "Action Phase Progress Estimation",
+    "ego_motion_forecast": "Short-Horizon Ego-Motion Forecasting",
+}
+
 REQUIRED_TASK_FIELDS = {
     "display_name",
     "research_name",
@@ -293,9 +300,15 @@ def validate_website(source: str, failures: list[dict[str, Any]]) -> list[dict[s
         'id="taskPlayer"',
         'id="taskGrid"',
         'id="walkthroughSelector"',
+        'id="playerStoryboard"',
+        'id="playerFrameChip"',
+        'id="playerFrameCaption"',
+        'id="playerScrub"',
         'fetch("data/task_walkthroughs.json"',
         'class="task-card"',
         'class="task-card-media"',
+        'class="story-button',
+        'class="flow-step',
         'id="playerPlay"',
         'id="playerPrev"',
         'id="playerNext"',
@@ -344,12 +357,23 @@ def validate_website(source: str, failures: list[dict[str, Any]]) -> list[dict[s
     )
     checks.append(
         check(
-            all(needle in player_renderer for needle in ["playerPoster", "middle_modules", "playerProgress"])
+            all(
+                needle in player_renderer
+                for needle in ["playerPoster", "middle_modules"]
+            )
+            and all(needle in source for needle in ["playerProgress", "renderStageFrame(task, index)"])
             and all(needle in source for needle in ['id="playerPlay"', 'id="playerPrev"', 'id="playerNext"']),
             "interactive_player_wired_to_task_metadata",
             failures,
         )
     )
+    checks.append(
+        check(
+            all(needle in source for needle in ["function setActiveStage", "function advancePlayer", "playerScrub"]),
+            "interactive_video_storyboard_controls_present",
+            failures,
+        )
+    )
     checks.append(
         check(
             "task.display_name" in selector_renderer and "artifact_id" not in selector_renderer,
@@ -357,6 +381,15 @@ def validate_website(source: str, failures: list[dict[str, Any]]) -> list[dict[s
             failures,
         )
     )
+    for artifact_id, display_name in EXPECTED_EXTENSION_NAMES.items():
+        checks.append(
+            check(
+                f"<h3>{artifact_id}</h3>" not in source and display_name in source,
+                f"extension_probe_uses_human_name:{artifact_id}",
+                failures,
+                expected=display_name,
+            )
+        )
     return checks
 
 
@@ -412,7 +445,7 @@ def build_report() -> dict[str, Any]:
             "expected_task_count": len(EXPECTED_TASKS),
             "task_family_counts": dict(sorted(task_families.items())),
             "modality_usage_counts": dict(sorted(task_modalities.items())),
-            "interactive_surface": "task cards plus play/previous/next walkthrough player",
+            "interactive_surface": "task cards plus scrub/play/chapter walkthrough storyboard",
             "failure_count": len(failures),
         },
         "checks": checks,