Publish Xperience-10M minimal and neural task baseline cards

README.md

@@ -94,6 +94,7 @@ transfers them to H20 for manifest building, training, and evaluation.
 | `artifacts/episode_task_suite/neural_mlp/**/model.pt` | stores the neural MLP checkpoints |
 | `artifacts/**/metrics.json` | records the committed metric values |
 | `artifacts/**/feature_manifest.json` | maps feature blocks back to source modalities |
+| `artifacts/episode_task_suite/research_directions/` | maps every task to the four Ropedia research directions with minimal-vs-neural readouts |
 | `assets/task_architectures.png` | shows the shared pipeline and all 12 heads |
 | `assets/task_suite_infographic.png` | presents the 12 heads with public-sample modality thumbnails and verified metrics |
 
@@ -104,6 +105,7 @@ transfers them to H20 for manifest building, training, and evaluation.
 - `artifacts/episode_task_suite/neural_mlp/**/history.json`: neural training traces
 - `artifacts/**/metrics.json`: committed metrics
 - `artifacts/**/feature_manifest.json`: feature block boundaries where relevant
+- `artifacts/episode_task_suite/research_directions/*.json|*.csv|*.md`: four-track task taxonomy
 - `scripts/*.py`: training and visualization scripts
 - `notes/*.md`: interpretation and reproducibility notes
 
@@ -127,6 +129,21 @@ https://huggingface.co/collections/cy0307/ropedia-episode-task-suite
 
 ![Minimal 12-task architecture](assets/task_architectures.png)
 
+## Four Research Directions
+
+The baselines are also grouped by the four Ropedia research tracks:
+
+| Direction | Current status | Baseline evidence |
+| --- | --- | --- |
+| A. Human Modeling & Motion Understanding | partially implemented | hand trajectory forecasting improves from `0.8223` to `0.1116` MPJPE with the neural MLP; contact is degenerate in this sample |
+| B. 3D/4D Reconstruction & Neural Rendering | proxy tasks only | cross-modal retrieval, feature reconstruction, and misalignment are prerequisites, not full neural rendering |
+| C. Egocentric Vision & Interaction | strongest implemented track | action/subtask/transition/next-action/object/caption tasks plus alignment/order diagnostics |
+| D. Scene Reconstruction & World Modeling | early proxy tasks | state, object, retrieval, reconstruction, and temporal tasks are first probes before scene graphs or maps |
+
+Primary taxonomy file:
+
+`artifacts/episode_task_suite/research_directions/research_direction_taxonomy.json`
+
 ## Metrics Snapshot
 
 | Task | Neural MLP metric | Minimal metric |

artifacts/episode_task_suite/research_directions/research_direction_summary.md

@@ -0,0 +1,71 @@
+# Four-Direction Task Taxonomy
+
+This file is generated by `scripts/research_direction_taxonomy.py` from the committed 12-task metrics.
+It maps the current Xperience-10M sample tasks to the four Ropedia research directions without claiming that a single episode solves any full direction.
+
+## Baseline Families
+
+| Baseline | Meaning |
+| --- | --- |
+| Minimal | Interpretable softmax, logistic, ridge, and retrieval heads over the 8,378-d window feature vector. |
+| Neural MLP | Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts. |
+
+## Direction Coverage
+
+| Direction | Current status | Direct | Proxy | Diagnostic | Current readout |
+| --- | --- | ---: | ---: | ---: | --- |
+| A. Human Modeling & Motion Understanding | partially implemented | 2 | 2 | 0 | The sample supports hand trajectory forecasting and contact/object probes, but it does not yet include a full body/shape model or multi-person priors. |
+| B. 3D/4D Reconstruction & Neural Rendering | proxy tasks only | 0 | 2 | 1 | The current suite checks cross-modal alignment and depth/video reconstruction proxies; it does not yet train a renderer or reconstruct geometry. |
+| C. Egocentric Vision & Interaction | strongest implemented track | 6 | 2 | 3 | Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment. |
+| D. Scene Reconstruction & World Modeling | early proxy tasks | 0 | 6 | 3 | The current tasks probe temporal structure, object relevance, cross-modal retrieval, and modality prediction, but they do not yet build persistent maps or scene graphs. |
+
+## Task Mapping With Two Baselines
+
+| Task | Primary direction | Related directions | Minimal | Neural MLP | Readout |
+| --- | --- | --- | ---: | ---: | --- |
+| `timeline_action` | C | C:direct, A:proxy | 0.0500 macro-F1 | 0.0263 macro-F1 | Minimal baseline is stronger. Chronological single-episode split creates unseen future action classes. |
+| `timeline_subtask` | C | C:direct, D:proxy | 0.0495 macro-F1 | 0.0175 macro-F1 | Minimal baseline is stronger. Single-episode ordering makes future subtasks hard to generalize. |
+| `transition_detection` | C | C:direct, D:diagnostic | 0.6552 macro-F1 | 0.6485 macro-F1 | Minimal baseline is stronger. Boundary class is sparse, so accuracy alone is misleading. |
+| `next_action` | C | C:direct, D:proxy | 0.0593 macro-F1 | 0.0235 macro-F1 | Minimal baseline is stronger. Unseen future labels dominate the single-episode chronological test. |
+| `hand_trajectory_forecast` | A | A:direct, C:proxy | 0.8223 MPJPE | 0.1116 MPJPE | Neural MLP is stronger. Forecasting is window-level and not yet a full sequence or policy model. |
+| `contact_prediction` | A | A:direct, C:proxy | 1.0000 macro-F1 | 1.0000 macro-F1 | Both baselines are tied. The public sample is degenerate for this target because one class dominates. |
+| `object_relevance` | C | C:direct, A:proxy, D:proxy | 0.1839 micro-F1 | 0.1798 micro-F1 | Minimal baseline is stronger. Object labels are language-derived and sparse in one episode. |
+| `caption_grounding` | C | C:direct, D:proxy | 0.0172 MRR | 0.0178 MRR | Neural MLP is stronger. Bag-of-objects language features are too weak for rich grounding. |
+| `cross_modal_retrieval` | C | C:diagnostic, B:proxy, D:proxy | 0.2634 MRR | 0.1530 MRR | Minimal baseline is stronger. Retrieval proves alignment signal, not geometric reconstruction. |
+| `modality_reconstruction` | B | B:proxy, D:proxy | -0.0160 R2 | -0.0102 R2 | Neural MLP is stronger. Feature-vector reconstruction is not pixel, depth-map, mesh, NeRF, or Gaussian reconstruction. |
+| `temporal_order` | C | C:diagnostic, D:diagnostic | 0.5487 F1 | 0.8718 F1 | Neural MLP is stronger. Only local adjacent ordering, not long-horizon causal modeling. |
+| `misalignment_detection` | C | C:diagnostic, B:diagnostic, D:diagnostic | 0.4866 F1 | 0.7335 F1 | Neural MLP is stronger. Synthetic shifts diagnose alignment but do not solve calibration or mapping. |
+
+## Next-Step Interpretation
+
+### A. Human Modeling & Motion Understanding
+
+The sample supports hand trajectory forecasting and contact/object probes, but it does not yet include a full body/shape model or multi-person priors.
+
+- Add SMPL/SMPL-X or MANO-style body/hand parameter targets where available.
+- Train sequence models over multi-episode motion trajectories instead of isolated windows.
+- Evaluate affordance prediction on held-out objects and held-out episodes.
+
+### B. 3D/4D Reconstruction & Neural Rendering
+
+The current suite checks cross-modal alignment and depth/video reconstruction proxies; it does not yet train a renderer or reconstruct geometry.
+
+- Use calibrated multi-view video plus SLAM pose to build per-episode camera trajectories.
+- Add depth-supervised point clouds, TSDF, Gaussian Splatting, or NeRF baselines.
+- Evaluate novel-view synthesis and temporal consistency across held-out views/time.
+
+### C. Egocentric Vision & Interaction
+
+Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment.
+
+- Move from single-episode chronological splits to held-out-episode splits.
+- Add audio features and stronger multimodal backbones for action, intent, and grounding.
+- Evaluate long-horizon task success prediction and action-conditioned generation.
+
+### D. Scene Reconstruction & World Modeling
+
+The current tasks probe temporal structure, object relevance, cross-modal retrieval, and modality prediction, but they do not yet build persistent maps or scene graphs.
+
+- Convert windows into persistent object/scene-state nodes with timestamps and camera poses.
+- Add map consistency, object permanence, and spatial relation prediction tasks.
+- Train held-out-episode world models that predict future observations and task state.

artifacts/episode_task_suite/research_directions/research_direction_task_map.csv

@@ -0,0 +1,28 @@
+direction,direction_name,task,task_name,family,relationship,primary_direction,metric_name,minimal_metric,neural_mlp_metric,better_baseline,why,current_limit
+C,Egocentric Vision & Interaction,timeline_action,Timeline action recognition,supervised,direct,C,macro-F1,0.05,0.0263157894737,minimal,Reads egocentric sensor state as the current human action; also provides a weak human-motion readout.,Chronological single-episode split creates unseen future action classes.
+A,Human Modeling & Motion Understanding,timeline_action,Timeline action recognition,supervised,proxy,C,macro-F1,0.05,0.0263157894737,minimal,Reads egocentric sensor state as the current human action; also provides a weak human-motion readout.,Chronological single-episode split creates unseen future action classes.
+C,Egocentric Vision & Interaction,timeline_subtask,Timeline subtask recognition,supervised,direct,C,macro-F1,0.0495412112118,0.0175182481752,minimal,Segments egocentric task state and provides a first proxy for symbolic world/task state.,Single-episode ordering makes future subtasks hard to generalize.
+D,Scene Reconstruction & World Modeling,timeline_subtask,Timeline subtask recognition,supervised,proxy,C,macro-F1,0.0495412112118,0.0175182481752,minimal,Segments egocentric task state and provides a first proxy for symbolic world/task state.,Single-episode ordering makes future subtasks hard to generalize.
+C,Egocentric Vision & Interaction,transition_detection,Action transition detection,diagnostic,direct,C,macro-F1,0.655182926829,0.648484848485,minimal,Localizes egocentric task boundaries and diagnoses temporal state changes.,"Boundary class is sparse, so accuracy alone is misleading."
+D,Scene Reconstruction & World Modeling,transition_detection,Action transition detection,diagnostic,diagnostic,C,macro-F1,0.655182926829,0.648484848485,minimal,Localizes egocentric task boundaries and diagnoses temporal state changes.,"Boundary class is sparse, so accuracy alone is misleading."
+C,Egocentric Vision & Interaction,next_action,Short-horizon next action,supervised,direct,C,macro-F1,0.0592592592593,0.0235294117647,minimal,Tests action intention/task-flow prediction from egocentric context.,Unseen future labels dominate the single-episode chronological test.
+D,Scene Reconstruction & World Modeling,next_action,Short-horizon next action,supervised,proxy,C,macro-F1,0.0592592592593,0.0235294117647,minimal,Tests action intention/task-flow prediction from egocentric context.,Unseen future labels dominate the single-episode chronological test.
+A,Human Modeling & Motion Understanding,hand_trajectory_forecast,Hand trajectory forecasting,forecast,direct,A,MPJPE,0.822264492512,0.11163123697,neural_mlp,Directly predicts human hand motion and supports hand-object interaction modeling.,Forecasting is window-level and not yet a full sequence or policy model.
+C,Egocentric Vision & Interaction,hand_trajectory_forecast,Hand trajectory forecasting,forecast,proxy,A,MPJPE,0.822264492512,0.11163123697,neural_mlp,Directly predicts human hand motion and supports hand-object interaction modeling.,Forecasting is window-level and not yet a full sequence or policy model.
+A,Human Modeling & Motion Understanding,contact_prediction,Body/object contact prediction,supervised,direct,A,macro-F1,1,1,tie,"Targets physical interaction state, a core affordance and manipulation signal.",The public sample is degenerate for this target because one class dominates.
+C,Egocentric Vision & Interaction,contact_prediction,Body/object contact prediction,supervised,proxy,A,macro-F1,1,1,tie,"Targets physical interaction state, a core affordance and manipulation signal.",The public sample is degenerate for this target because one class dominates.
+C,Egocentric Vision & Interaction,object_relevance,Relevant object set prediction,supervised,direct,C,micro-F1,0.183930300097,0.179758308157,minimal,Connects egocentric activity to manipulated objects and early object-centric state.,Object labels are language-derived and sparse in one episode.
+A,Human Modeling & Motion Understanding,object_relevance,Relevant object set prediction,supervised,proxy,C,micro-F1,0.183930300097,0.179758308157,minimal,Connects egocentric activity to manipulated objects and early object-centric state.,Object labels are language-derived and sparse in one episode.
+D,Scene Reconstruction & World Modeling,object_relevance,Relevant object set prediction,supervised,proxy,C,micro-F1,0.183930300097,0.179758308157,minimal,Connects egocentric activity to manipulated objects and early object-centric state.,Object labels are language-derived and sparse in one episode.
+C,Egocentric Vision & Interaction,caption_grounding,Caption-to-window grounding,retrieval,direct,C,MRR,0.0171839460838,0.0178111116104,neural_mlp,Grounds language annotation into egocentric sensor time and task state.,Bag-of-objects language features are too weak for rich grounding.
+D,Scene Reconstruction & World Modeling,caption_grounding,Caption-to-window grounding,retrieval,proxy,C,MRR,0.0171839460838,0.0178111116104,neural_mlp,Grounds language annotation into egocentric sensor time and task state.,Bag-of-objects language features are too weak for rich grounding.
+C,Egocentric Vision & Interaction,cross_modal_retrieval,Cross-modal retrieval,retrieval,diagnostic,C,MRR,0.263359840066,0.15300700222,minimal,"Tests whether synchronized modalities identify the same 4D moment, a prerequisite for reconstruction and world modeling.","Retrieval proves alignment signal, not geometric reconstruction."
+B,3D/4D Reconstruction & Neural Rendering,cross_modal_retrieval,Cross-modal retrieval,retrieval,proxy,C,MRR,0.263359840066,0.15300700222,minimal,"Tests whether synchronized modalities identify the same 4D moment, a prerequisite for reconstruction and world modeling.","Retrieval proves alignment signal, not geometric reconstruction."
+D,Scene Reconstruction & World Modeling,cross_modal_retrieval,Cross-modal retrieval,retrieval,proxy,C,MRR,0.263359840066,0.15300700222,minimal,"Tests whether synchronized modalities identify the same 4D moment, a prerequisite for reconstruction and world modeling.","Retrieval proves alignment signal, not geometric reconstruction."
+B,3D/4D Reconstruction & Neural Rendering,modality_reconstruction,Modality reconstruction,forecast,proxy,B,R2,-0.0160228467711,-0.0101981718914,neural_mlp,Predicts visual/depth state from non-target sensors as a weak reconstruction/world-model objective.,"Feature-vector reconstruction is not pixel, depth-map, mesh, NeRF, or Gaussian reconstruction."
+D,Scene Reconstruction & World Modeling,modality_reconstruction,Modality reconstruction,forecast,proxy,B,R2,-0.0160228467711,-0.0101981718914,neural_mlp,Predicts visual/depth state from non-target sensors as a weak reconstruction/world-model objective.,"Feature-vector reconstruction is not pixel, depth-map, mesh, NeRF, or Gaussian reconstruction."
+C,Egocentric Vision & Interaction,temporal_order,Temporal order verification,diagnostic,diagnostic,C,F1,0.548736462094,0.871794871795,neural_mlp,Checks whether features encode local time direction and task progression.,"Only local adjacent ordering, not long-horizon causal modeling."
+D,Scene Reconstruction & World Modeling,temporal_order,Temporal order verification,diagnostic,diagnostic,C,F1,0.548736462094,0.871794871795,neural_mlp,Checks whether features encode local time direction and task progression.,"Only local adjacent ordering, not long-horizon causal modeling."
+C,Egocentric Vision & Interaction,misalignment_detection,Cross-modal misalignment detection,diagnostic,diagnostic,C,F1,0.486567164179,0.733524355301,neural_mlp,"Detects temporal desynchronization, a key data-quality gate for multimodal reconstruction and world models.",Synthetic shifts diagnose alignment but do not solve calibration or mapping.
+B,3D/4D Reconstruction & Neural Rendering,misalignment_detection,Cross-modal misalignment detection,diagnostic,diagnostic,C,F1,0.486567164179,0.733524355301,neural_mlp,"Detects temporal desynchronization, a key data-quality gate for multimodal reconstruction and world models.",Synthetic shifts diagnose alignment but do not solve calibration or mapping.
+D,Scene Reconstruction & World Modeling,misalignment_detection,Cross-modal misalignment detection,diagnostic,diagnostic,C,F1,0.486567164179,0.733524355301,neural_mlp,"Detects temporal desynchronization, a key data-quality gate for multimodal reconstruction and world models.",Synthetic shifts diagnose alignment but do not solve calibration or mapping.

artifacts/episode_task_suite/research_directions/research_direction_taxonomy.json

@@ -0,0 +1,384 @@
+{
+  "source": "results/episode_task_suite/summary_report.json",
+  "dataset_scope": {
+    "sample_episode_count": 1,
+    "num_frames": 5821,
+    "num_windows": 1161,
+    "feature_dim": 8378,
+    "warning": "Single public sample episode; this supports pipeline/task evidence, not cross-episode generalization claims."
+  },
+  "baselines": {
+    "minimal": "Interpretable softmax, logistic, ridge, and retrieval heads over the 8,378-d window feature vector.",
+    "neural_mlp": "Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts."
+  },
+  "directions": {
+    "A": {
+      "id": "human_motion",
+      "name": "Human Modeling & Motion Understanding",
+      "focus": "Human/hand/body motion, deformation priors, human-object interaction, affordance modeling.",
+      "preferred_background": "Human pose/shape estimation, SMPL-style models, motion capture, or motion generation.",
+      "current_status": "partially implemented",
+      "current_readout": "The sample supports hand trajectory forecasting and contact/object probes, but it does not yet include a full body/shape model or multi-person priors.",
+      "next_steps": [
+        "Add SMPL/SMPL-X or MANO-style body/hand parameter targets where available.",
+        "Train sequence models over multi-episode motion trajectories instead of isolated windows.",
+        "Evaluate affordance prediction on held-out objects and held-out episodes."
+      ],
+      "tasks": [
+        "timeline_action",
+        "hand_trajectory_forecast",
+        "contact_prediction",
+        "object_relevance"
+      ],
+      "counts": {
+        "direct": 2,
+        "proxy": 2,
+        "diagnostic": 0,
+        "total_links": 4
+      }
+    },
+    "B": {
+      "id": "reconstruction_rendering",
+      "name": "3D/4D Reconstruction & Neural Rendering",
+      "focus": "Multi-view dynamic scene reconstruction, NeRF/Gaussian Splatting, novel-view synthesis.",
+      "preferred_background": "3D reconstruction, neural rendering, camera calibration, and bundle adjustment.",
+      "current_status": "proxy tasks only",
+      "current_readout": "The current suite checks cross-modal alignment and depth/video reconstruction proxies; it does not yet train a renderer or reconstruct geometry.",
+      "next_steps": [
+        "Use calibrated multi-view video plus SLAM pose to build per-episode camera trajectories.",
+        "Add depth-supervised point clouds, TSDF, Gaussian Splatting, or NeRF baselines.",
+        "Evaluate novel-view synthesis and temporal consistency across held-out views/time."
+      ],
+      "tasks": [
+        "cross_modal_retrieval",
+        "modality_reconstruction",
+        "misalignment_detection"
+      ],
+      "counts": {
+        "direct": 0,
+        "proxy": 2,
+        "diagnostic": 1,
+        "total_links": 3
+      }
+    },
+    "C": {
+      "id": "egocentric_interaction",
+      "name": "Egocentric Vision & Interaction",
+      "focus": "Egocentric action and intention understanding, hand-object interaction, gaze/attention modeling, task structure modeling.",
+      "preferred_background": "Video understanding, action recognition, or egocentric vision.",
+      "current_status": "strongest implemented track",
+      "current_readout": "Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment.",
+      "next_steps": [
+        "Move from single-episode chronological splits to held-out-episode splits.",
+        "Add audio features and stronger multimodal backbones for action, intent, and grounding.",
+        "Evaluate long-horizon task success prediction and action-conditioned generation."
+      ],
+      "tasks": [
+        "timeline_action",
+        "timeline_subtask",
+        "transition_detection",
+        "next_action",
+        "hand_trajectory_forecast",
+        "contact_prediction",
+        "object_relevance",
+        "caption_grounding",
+        "cross_modal_retrieval",
+        "temporal_order",
+        "misalignment_detection"
+      ],
+      "counts": {
+        "direct": 6,
+        "proxy": 2,
+        "diagnostic": 3,
+        "total_links": 11
+      }
+    },
+    "D": {
+      "id": "world_modeling",
+      "name": "Scene Reconstruction & World Modeling",
+      "focus": "Long-term consistent 3D/4D scene mapping, scene graphs, object- and space-centric representations, spatial reasoning.",
+      "preferred_background": "Large-scale mapping, semantic reconstruction, or agent world models.",
+      "current_status": "early proxy tasks",
+      "current_readout": "The current tasks probe temporal structure, object relevance, cross-modal retrieval, and modality prediction, but they do not yet build persistent maps or scene graphs.",
+      "next_steps": [
+        "Convert windows into persistent object/scene-state nodes with timestamps and camera poses.",
+        "Add map consistency, object permanence, and spatial relation prediction tasks.",
+        "Train held-out-episode world models that predict future observations and task state."
+      ],
+      "tasks": [
+        "timeline_subtask",
+        "transition_detection",
+        "next_action",
+        "object_relevance",
+        "caption_grounding",
+        "cross_modal_retrieval",
+        "modality_reconstruction",
+        "temporal_order",
+        "misalignment_detection"
+      ],
+      "counts": {
+        "direct": 0,
+        "proxy": 6,
+        "diagnostic": 3,
+        "total_links": 9
+      }
+    }
+  },
+  "tasks": {
+    "timeline_action": {
+      "name": "Timeline action recognition",
+      "family": "supervised",
+      "input": "all featurized modalities",
+      "output": "current action label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "A": "proxy"
+      },
+      "why": "Reads egocentric sensor state as the current human action; also provides a weak human-motion readout.",
+      "current_limit": "Chronological single-episode split creates unseen future action classes.",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.05,
+        "neural_mlp": 0.02631578947368421,
+        "better_baseline": "minimal"
+      }
+    },
+    "timeline_subtask": {
+      "name": "Timeline subtask recognition",
+      "family": "supervised",
+      "input": "all featurized modalities",
+      "output": "current subtask label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Segments egocentric task state and provides a first proxy for symbolic world/task state.",
+      "current_limit": "Single-episode ordering makes future subtasks hard to generalize.",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.04954121121178666,
+        "neural_mlp": 0.017518248175182476,
+        "better_baseline": "minimal"
+      }
+    },
+    "transition_detection": {
+      "name": "Action transition detection",
+      "family": "diagnostic",
+      "input": "all featurized modalities",
+      "output": "boundary vs steady state",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "diagnostic"
+      },
+      "why": "Localizes egocentric task boundaries and diagnoses temporal state changes.",
+      "current_limit": "Boundary class is sparse, so accuracy alone is misleading.",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.6551829268292684,
+        "neural_mlp": 0.6484848484848484,
+        "better_baseline": "minimal"
+      }
+    },
+    "next_action": {
+      "name": "Short-horizon next action",
+      "family": "supervised",
+      "input": "current multimodal window",
+      "output": "action 20 frames later",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Tests action intention/task-flow prediction from egocentric context.",
+      "current_limit": "Unseen future labels dominate the single-episode chronological test.",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.05925925925925927,
+        "neural_mlp": 0.023529411764705882,
+        "better_baseline": "minimal"
+      }
+    },
+    "hand_trajectory_forecast": {
+      "name": "Hand trajectory forecasting",
+      "family": "forecast",
+      "input": "current multimodal window",
+      "output": "future left/right hand 3D joints",
+      "primary_direction": "A",
+      "direction_roles": {
+        "A": "direct",
+        "C": "proxy"
+      },
+      "why": "Directly predicts human hand motion and supports hand-object interaction modeling.",
+      "current_limit": "Forecasting is window-level and not yet a full sequence or policy model.",
+      "metric": {
+        "key": "mpjpe",
+        "name": "MPJPE",
+        "direction": "lower",
+        "minimal": 0.8222644925117493,
+        "neural_mlp": 0.11163123697042465,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "contact_prediction": {
+      "name": "Body/object contact prediction",
+      "family": "supervised",
+      "input": "non-contact/non-caption features",
+      "output": "binary contact label",
+      "primary_direction": "A",
+      "direction_roles": {
+        "A": "direct",
+        "C": "proxy"
+      },
+      "why": "Targets physical interaction state, a core affordance and manipulation signal.",
+      "current_limit": "The public sample is degenerate for this target because one class dominates.",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 1.0,
+        "neural_mlp": 1.0,
+        "better_baseline": "tie"
+      }
+    },
+    "object_relevance": {
+      "name": "Relevant object set prediction",
+      "family": "supervised",
+      "input": "non-caption feature blocks",
+      "output": "multi-label object set",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "A": "proxy",
+        "D": "proxy"
+      },
+      "why": "Connects egocentric activity to manipulated objects and early object-centric state.",
+      "current_limit": "Object labels are language-derived and sparse in one episode.",
+      "metric": {
+        "key": "micro_f1",
+        "name": "micro-F1",
+        "direction": "higher",
+        "minimal": 0.18393030009680542,
+        "neural_mlp": 0.1797583081570997,
+        "better_baseline": "minimal"
+      }
+    },
+    "caption_grounding": {
+      "name": "Caption-to-window grounding",
+      "family": "retrieval",
+      "input": "caption objects/interaction query and candidate sensor windows",
+      "output": "matching time window",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Grounds language annotation into egocentric sensor time and task state.",
+      "current_limit": "Bag-of-objects language features are too weak for rich grounding.",
+      "metric": {
+        "key": "mrr",
+        "name": "MRR",
+        "direction": "higher",
+        "minimal": 0.017183946083791223,
+        "neural_mlp": 0.01781111161035397,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "cross_modal_retrieval": {
+      "name": "Cross-modal retrieval",
+      "family": "retrieval",
+      "input": "motion/IMU/camera query",
+      "output": "matching depth/video window",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "diagnostic",
+        "B": "proxy",
+        "D": "proxy"
+      },
+      "why": "Tests whether synchronized modalities identify the same 4D moment, a prerequisite for reconstruction and world modeling.",
+      "current_limit": "Retrieval proves alignment signal, not geometric reconstruction.",
+      "metric": {
+        "key": "mrr",
+        "name": "MRR",
+        "direction": "higher",
+        "minimal": 0.26335984006618296,
+        "neural_mlp": 0.1530070022204131,
+        "better_baseline": "minimal"
+      }
+    },
+    "modality_reconstruction": {
+      "name": "Modality reconstruction",
+      "family": "forecast",
+      "input": "motion/IMU/camera",
+      "output": "depth/video feature vector",
+      "primary_direction": "B",
+      "direction_roles": {
+        "B": "proxy",
+        "D": "proxy"
+      },
+      "why": "Predicts visual/depth state from non-target sensors as a weak reconstruction/world-model objective.",
+      "current_limit": "Feature-vector reconstruction is not pixel, depth-map, mesh, NeRF, or Gaussian reconstruction.",
+      "metric": {
+        "key": "r2",
+        "name": "R2",
+        "direction": "higher",
+        "minimal": -0.016022846771134747,
+        "neural_mlp": -0.010198171891414143,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "temporal_order": {
+      "name": "Temporal order verification",
+      "family": "diagnostic",
+      "input": "two adjacent windows",
+      "output": "correct vs reversed order",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "diagnostic",
+        "D": "diagnostic"
+      },
+      "why": "Checks whether features encode local time direction and task progression.",
+      "current_limit": "Only local adjacent ordering, not long-horizon causal modeling.",
+      "metric": {
+        "key": "f1",
+        "name": "F1",
+        "direction": "higher",
+        "minimal": 0.5487364620938628,
+        "neural_mlp": 0.8717948717948718,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "misalignment_detection": {
+      "name": "Cross-modal misalignment detection",
+      "family": "diagnostic",
+      "input": "motion plus visual/depth pair",
+      "output": "aligned vs shifted",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "diagnostic",
+        "B": "diagnostic",
+        "D": "diagnostic"
+      },
+      "why": "Detects temporal desynchronization, a key data-quality gate for multimodal reconstruction and world models.",
+      "current_limit": "Synthetic shifts diagnose alignment but do not solve calibration or mapping.",
+      "metric": {
+        "key": "f1",
+        "name": "F1",
+        "direction": "higher",
+        "minimal": 0.4865671641791045,
+        "neural_mlp": 0.7335243553008597,
+        "better_baseline": "neural_mlp"
+      }
+    }
+  }
+}

scripts/research_direction_taxonomy.py

@@ -0,0 +1,589 @@
+#!/usr/bin/env python3
+"""Organize the 12 Xperience-10M tasks into the four Ropedia research tracks.
+
+The script is intentionally deterministic: it reads the committed task metrics,
+adds a hand-audited taxonomy, and writes machine-readable artifacts used by the
+README, website, and Hugging Face pages.
+"""
+
+from __future__ import annotations
+
+import csv
+import html
+import json
+from collections import OrderedDict
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parents[1]
+RESULTS = ROOT / "results" / "episode_task_suite"
+OUT_DIR = RESULTS / "research_directions"
+DOCS_DATA = ROOT / "docs" / "data"
+CHARTS = ROOT / "docs" / "assets" / "charts"
+
+SUMMARY_REPORT = RESULTS / "summary_report.json"
+
+
+DIRECTIONS: OrderedDict[str, dict[str, Any]] = OrderedDict(
+    [
+        (
+            "A",
+            {
+                "id": "human_motion",
+                "name": "Human Modeling & Motion Understanding",
+                "focus": "Human/hand/body motion, deformation priors, human-object interaction, affordance modeling.",
+                "preferred_background": "Human pose/shape estimation, SMPL-style models, motion capture, or motion generation.",
+                "current_status": "partially implemented",
+                "current_readout": "The sample supports hand trajectory forecasting and contact/object probes, but it does not yet include a full body/shape model or multi-person priors.",
+                "next_steps": [
+                    "Add SMPL/SMPL-X or MANO-style body/hand parameter targets where available.",
+                    "Train sequence models over multi-episode motion trajectories instead of isolated windows.",
+                    "Evaluate affordance prediction on held-out objects and held-out episodes.",
+                ],
+            },
+        ),
+        (
+            "B",
+            {
+                "id": "reconstruction_rendering",
+                "name": "3D/4D Reconstruction & Neural Rendering",
+                "focus": "Multi-view dynamic scene reconstruction, NeRF/Gaussian Splatting, novel-view synthesis.",
+                "preferred_background": "3D reconstruction, neural rendering, camera calibration, and bundle adjustment.",
+                "current_status": "proxy tasks only",
+                "current_readout": "The current suite checks cross-modal alignment and depth/video reconstruction proxies; it does not yet train a renderer or reconstruct geometry.",
+                "next_steps": [
+                    "Use calibrated multi-view video plus SLAM pose to build per-episode camera trajectories.",
+                    "Add depth-supervised point clouds, TSDF, Gaussian Splatting, or NeRF baselines.",
+                    "Evaluate novel-view synthesis and temporal consistency across held-out views/time.",
+                ],
+            },
+        ),
+        (
+            "C",
+            {
+                "id": "egocentric_interaction",
+                "name": "Egocentric Vision & Interaction",
+                "focus": "Egocentric action and intention understanding, hand-object interaction, gaze/attention modeling, task structure modeling.",
+                "preferred_background": "Video understanding, action recognition, or egocentric vision.",
+                "current_status": "strongest implemented track",
+                "current_readout": "Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment.",
+                "next_steps": [
+                    "Move from single-episode chronological splits to held-out-episode splits.",
+                    "Add audio features and stronger multimodal backbones for action, intent, and grounding.",
+                    "Evaluate long-horizon task success prediction and action-conditioned generation.",
+                ],
+            },
+        ),
+        (
+            "D",
+            {
+                "id": "world_modeling",
+                "name": "Scene Reconstruction & World Modeling",
+                "focus": "Long-term consistent 3D/4D scene mapping, scene graphs, object- and space-centric representations, spatial reasoning.",
+                "preferred_background": "Large-scale mapping, semantic reconstruction, or agent world models.",
+                "current_status": "early proxy tasks",
+                "current_readout": "The current tasks probe temporal structure, object relevance, cross-modal retrieval, and modality prediction, but they do not yet build persistent maps or scene graphs.",
+                "next_steps": [
+                    "Convert windows into persistent object/scene-state nodes with timestamps and camera poses.",
+                    "Add map consistency, object permanence, and spatial relation prediction tasks.",
+                    "Train held-out-episode world models that predict future observations and task state.",
+                ],
+            },
+        ),
+    ]
+)
+
+
+TASK_TAXONOMY: OrderedDict[str, dict[str, Any]] = OrderedDict(
+    [
+        (
+            "timeline_action",
+            {
+                "name": "Timeline action recognition",
+                "family": "supervised",
+                "input": "all featurized modalities",
+                "output": "current action label",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "A": "proxy"},
+                "why": "Reads egocentric sensor state as the current human action; also provides a weak human-motion readout.",
+                "current_limit": "Chronological single-episode split creates unseen future action classes.",
+            },
+        ),
+        (
+            "timeline_subtask",
+            {
+                "name": "Timeline subtask recognition",
+                "family": "supervised",
+                "input": "all featurized modalities",
+                "output": "current subtask label",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "D": "proxy"},
+                "why": "Segments egocentric task state and provides a first proxy for symbolic world/task state.",
+                "current_limit": "Single-episode ordering makes future subtasks hard to generalize.",
+            },
+        ),
+        (
+            "transition_detection",
+            {
+                "name": "Action transition detection",
+                "family": "diagnostic",
+                "input": "all featurized modalities",
+                "output": "boundary vs steady state",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "D": "diagnostic"},
+                "why": "Localizes egocentric task boundaries and diagnoses temporal state changes.",
+                "current_limit": "Boundary class is sparse, so accuracy alone is misleading.",
+            },
+        ),
+        (
+            "next_action",
+            {
+                "name": "Short-horizon next action",
+                "family": "supervised",
+                "input": "current multimodal window",
+                "output": "action 20 frames later",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "D": "proxy"},
+                "why": "Tests action intention/task-flow prediction from egocentric context.",
+                "current_limit": "Unseen future labels dominate the single-episode chronological test.",
+            },
+        ),
+        (
+            "hand_trajectory_forecast",
+            {
+                "name": "Hand trajectory forecasting",
+                "family": "forecast",
+                "input": "current multimodal window",
+                "output": "future left/right hand 3D joints",
+                "primary_direction": "A",
+                "direction_roles": {"A": "direct", "C": "proxy"},
+                "why": "Directly predicts human hand motion and supports hand-object interaction modeling.",
+                "current_limit": "Forecasting is window-level and not yet a full sequence or policy model.",
+            },
+        ),
+        (
+            "contact_prediction",
+            {
+                "name": "Body/object contact prediction",
+                "family": "supervised",
+                "input": "non-contact/non-caption features",
+                "output": "binary contact label",
+                "primary_direction": "A",
+                "direction_roles": {"A": "direct", "C": "proxy"},
+                "why": "Targets physical interaction state, a core affordance and manipulation signal.",
+                "current_limit": "The public sample is degenerate for this target because one class dominates.",
+            },
+        ),
+        (
+            "object_relevance",
+            {
+                "name": "Relevant object set prediction",
+                "family": "supervised",
+                "input": "non-caption feature blocks",
+                "output": "multi-label object set",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "A": "proxy", "D": "proxy"},
+                "why": "Connects egocentric activity to manipulated objects and early object-centric state.",
+                "current_limit": "Object labels are language-derived and sparse in one episode.",
+            },
+        ),
+        (
+            "caption_grounding",
+            {
+                "name": "Caption-to-window grounding",
+                "family": "retrieval",
+                "input": "caption objects/interaction query and candidate sensor windows",
+                "output": "matching time window",
+                "primary_direction": "C",
+                "direction_roles": {"C": "direct", "D": "proxy"},
+                "why": "Grounds language annotation into egocentric sensor time and task state.",
+                "current_limit": "Bag-of-objects language features are too weak for rich grounding.",
+            },
+        ),
+        (
+            "cross_modal_retrieval",
+            {
+                "name": "Cross-modal retrieval",
+                "family": "retrieval",
+                "input": "motion/IMU/camera query",
+                "output": "matching depth/video window",
+                "primary_direction": "C",
+                "direction_roles": {"C": "diagnostic", "B": "proxy", "D": "proxy"},
+                "why": "Tests whether synchronized modalities identify the same 4D moment, a prerequisite for reconstruction and world modeling.",
+                "current_limit": "Retrieval proves alignment signal, not geometric reconstruction.",
+            },
+        ),
+        (
+            "modality_reconstruction",
+            {
+                "name": "Modality reconstruction",
+                "family": "forecast",
+                "input": "motion/IMU/camera",
+                "output": "depth/video feature vector",
+                "primary_direction": "B",
+                "direction_roles": {"B": "proxy", "D": "proxy"},
+                "why": "Predicts visual/depth state from non-target sensors as a weak reconstruction/world-model objective.",
+                "current_limit": "Feature-vector reconstruction is not pixel, depth-map, mesh, NeRF, or Gaussian reconstruction.",
+            },
+        ),
+        (
+            "temporal_order",
+            {
+                "name": "Temporal order verification",
+                "family": "diagnostic",
+                "input": "two adjacent windows",
+                "output": "correct vs reversed order",
+                "primary_direction": "C",
+                "direction_roles": {"C": "diagnostic", "D": "diagnostic"},
+                "why": "Checks whether features encode local time direction and task progression.",
+                "current_limit": "Only local adjacent ordering, not long-horizon causal modeling.",
+            },
+        ),
+        (
+            "misalignment_detection",
+            {
+                "name": "Cross-modal misalignment detection",
+                "family": "diagnostic",
+                "input": "motion plus visual/depth pair",
+                "output": "aligned vs shifted",
+                "primary_direction": "C",
+                "direction_roles": {"C": "diagnostic", "B": "diagnostic", "D": "diagnostic"},
+                "why": "Detects temporal desynchronization, a key data-quality gate for multimodal reconstruction and world models.",
+                "current_limit": "Synthetic shifts diagnose alignment but do not solve calibration or mapping.",
+            },
+        ),
+    ]
+)
+
+
+METRIC_SPECS = {
+    "timeline_action": ("macro_f1", "macro-F1", "higher"),
+    "timeline_subtask": ("macro_f1", "macro-F1", "higher"),
+    "transition_detection": ("macro_f1", "macro-F1", "higher"),
+    "next_action": ("macro_f1", "macro-F1", "higher"),
+    "hand_trajectory_forecast": ("mpjpe", "MPJPE", "lower"),
+    "contact_prediction": ("macro_f1", "macro-F1", "higher"),
+    "object_relevance": ("micro_f1", "micro-F1", "higher"),
+    "caption_grounding": ("mrr", "MRR", "higher"),
+    "cross_modal_retrieval": ("mrr", "MRR", "higher"),
+    "modality_reconstruction": ("r2", "R2", "higher"),
+    "temporal_order": ("f1", "F1", "higher"),
+    "misalignment_detection": ("f1", "F1", "higher"),
+}
+
+
+def load_summary() -> dict[str, Any]:
+    return json.loads(SUMMARY_REPORT.read_text(encoding="utf-8"))
+
+
+def metric_value(metrics: dict[str, Any] | None, task: str) -> float | None:
+    if not metrics:
+        return None
+    key = METRIC_SPECS[task][0]
+    value = metrics.get(key)
+    return float(value) if value is not None else None
+
+
+def choose_better(task: str, minimal: float | None, neural: float | None) -> str:
+    if minimal is None or neural is None:
+        return "unavailable"
+    _, _, direction = METRIC_SPECS[task]
+    delta = neural - minimal
+    if abs(delta) < 1e-9:
+        return "tie"
+    if direction == "lower":
+        return "neural_mlp" if delta < 0 else "minimal"
+    return "neural_mlp" if delta > 0 else "minimal"
+
+
+def fmt_metric(value: float | None) -> str:
+    if value is None:
+        return "n/a"
+    if abs(value) >= 10:
+        return f"{value:.3f}"
+    return f"{value:.4f}"
+
+
+def baseline_readout(label: str) -> str:
+    if label == "tie":
+        return "Both baselines are tied"
+    if label == "minimal":
+        return "Minimal baseline is stronger"
+    if label == "neural_mlp":
+        return "Neural MLP is stronger"
+    return "Baseline comparison is unavailable"
+
+
+def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
+    minimal_tasks = summary["tasks"]
+    neural_tasks = summary.get("neural_tasks", {})
+
+    task_records: OrderedDict[str, dict[str, Any]] = OrderedDict()
+    direction_counts = {
+        code: {"direct": 0, "proxy": 0, "diagnostic": 0, "total_links": 0}
+        for code in DIRECTIONS
+    }
+
+    for task, spec in TASK_TAXONOMY.items():
+        metric_key, metric_name, metric_direction = METRIC_SPECS[task]
+        minimal_metric = metric_value(minimal_tasks.get(task), task)
+        neural_metric = metric_value(neural_tasks.get(task), task)
+        better = choose_better(task, minimal_metric, neural_metric)
+
+        roles = spec["direction_roles"]
+        for direction_code, role in roles.items():
+            direction_counts[direction_code][role] += 1
+            direction_counts[direction_code]["total_links"] += 1
+
+        task_records[task] = {
+            **spec,
+            "metric": {
+                "key": metric_key,
+                "name": metric_name,
+                "direction": metric_direction,
+                "minimal": minimal_metric,
+                "neural_mlp": neural_metric,
+                "better_baseline": better,
+            },
+        }
+
+    direction_records = OrderedDict()
+    for code, info in DIRECTIONS.items():
+        linked_tasks = [
+            task
+            for task, spec in task_records.items()
+            if code in spec["direction_roles"]
+        ]
+        direction_records[code] = {
+            **info,
+            "tasks": linked_tasks,
+            "counts": direction_counts[code],
+        }
+
+    return {
+        "source": "results/episode_task_suite/summary_report.json",
+        "dataset_scope": {
+            "sample_episode_count": 1,
+            "num_frames": summary.get("num_frames"),
+            "num_windows": summary.get("num_windows"),
+            "feature_dim": summary.get("feature_dim"),
+            "warning": "Single public sample episode; this supports pipeline/task evidence, not cross-episode generalization claims.",
+        },
+        "baselines": {
+            "minimal": "Interpretable softmax, logistic, ridge, and retrieval heads over the 8,378-d window feature vector.",
+            "neural_mlp": "Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts.",
+        },
+        "directions": direction_records,
+        "tasks": task_records,
+    }
+
+
+def write_csv(taxonomy: dict[str, Any]) -> None:
+    path = OUT_DIR / "research_direction_task_map.csv"
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle, lineterminator="\n")
+        writer.writerow(
+            [
+                "direction",
+                "direction_name",
+                "task",
+                "task_name",
+                "family",
+                "relationship",
+                "primary_direction",
+                "metric_name",
+                "minimal_metric",
+                "neural_mlp_metric",
+                "better_baseline",
+                "why",
+                "current_limit",
+            ]
+        )
+        for task, spec in taxonomy["tasks"].items():
+            metric = spec["metric"]
+            for direction_code, relationship in spec["direction_roles"].items():
+                writer.writerow(
+                    [
+                        direction_code,
+                        taxonomy["directions"][direction_code]["name"],
+                        task,
+                        spec["name"],
+                        spec["family"],
+                        relationship,
+                        spec["primary_direction"],
+                        metric["name"],
+                        "" if metric["minimal"] is None else f"{metric['minimal']:.12g}",
+                        "" if metric["neural_mlp"] is None else f"{metric['neural_mlp']:.12g}",
+                        metric["better_baseline"],
+                        spec["why"],
+                        spec["current_limit"],
+                    ]
+                )
+
+
+def write_markdown(taxonomy: dict[str, Any]) -> None:
+    lines = [
+        "# Four-Direction Task Taxonomy",
+        "",
+        "This file is generated by `scripts/research_direction_taxonomy.py` from the committed 12-task metrics.",
+        "It maps the current Xperience-10M sample tasks to the four Ropedia research directions without claiming that a single episode solves any full direction.",
+        "",
+        "## Baseline Families",
+        "",
+        "| Baseline | Meaning |",
+        "| --- | --- |",
+        f"| Minimal | {taxonomy['baselines']['minimal']} |",
+        f"| Neural MLP | {taxonomy['baselines']['neural_mlp']} |",
+        "",
+        "## Direction Coverage",
+        "",
+        "| Direction | Current status | Direct | Proxy | Diagnostic | Current readout |",
+        "| --- | --- | ---: | ---: | ---: | --- |",
+    ]
+    for code, info in taxonomy["directions"].items():
+        counts = info["counts"]
+        lines.append(
+            f"| {code}. {info['name']} | {info['current_status']} | {counts['direct']} | {counts['proxy']} | {counts['diagnostic']} | {info['current_readout']} |"
+        )
+
+    lines.extend(
+        [
+            "",
+            "## Task Mapping With Two Baselines",
+            "",
+            "| Task | Primary direction | Related directions | Minimal | Neural MLP | Readout |",
+            "| --- | --- | --- | ---: | ---: | --- |",
+        ]
+    )
+    for task, spec in taxonomy["tasks"].items():
+        metric = spec["metric"]
+        related = ", ".join(
+            f"{code}:{role}" for code, role in spec["direction_roles"].items()
+        )
+        minimal = f"{fmt_metric(metric['minimal'])} {metric['name']}"
+        neural = f"{fmt_metric(metric['neural_mlp'])} {metric['name']}"
+        readout = f"{baseline_readout(metric['better_baseline'])}. {spec['current_limit']}"
+        lines.append(
+            f"| `{task}` | {spec['primary_direction']} | {related} | {minimal} | {neural} | {readout} |"
+        )
+
+    lines.extend(["", "## Next-Step Interpretation", ""])
+    for code, info in taxonomy["directions"].items():
+        lines.append(f"### {code}. {info['name']}")
+        lines.append("")
+        lines.append(info["current_readout"])
+        lines.append("")
+        for step in info["next_steps"]:
+            lines.append(f"- {step}")
+        lines.append("")
+
+    (OUT_DIR / "research_direction_summary.md").write_text(
+        "\n".join(lines).rstrip() + "\n", encoding="utf-8"
+    )
+
+
+def svg_text(x: int, y: int, text: str, size: int = 16, weight: int = 500, color: str = "#16213a") -> str:
+    return (
+        f'<text x="{x}" y="{y}" font-size="{size}" font-weight="{weight}" '
+        f'fill="{color}">{html.escape(text)}</text>'
+    )
+
+
+def write_svg(taxonomy: dict[str, Any]) -> None:
+    width = 1180
+    height = 700
+    margin = 58
+    card_w = 515
+    card_h = 220
+    colors = {"direct": "#1f6c9f", "proxy": "#2e7775", "diagnostic": "#956400"}
+    cards = []
+
+    for idx, (code, info) in enumerate(taxonomy["directions"].items()):
+        row = idx // 2
+        col = idx % 2
+        x = margin + col * (card_w + 34)
+        y = 130 + row * (card_h + 34)
+        counts = info["counts"]
+        total = max(1, counts["direct"] + counts["proxy"] + counts["diagnostic"])
+        bar_x = x + 24
+        bar_y = y + 132
+        bar_w = card_w - 48
+        cursor = bar_x
+        segments = []
+        for key in ("direct", "proxy", "diagnostic"):
+            seg_w = round(bar_w * counts[key] / total)
+            if counts[key] > 0:
+                segments.append(
+                    f'<rect x="{cursor}" y="{bar_y}" width="{seg_w}" height="16" rx="8" fill="{colors[key]}"/>'
+                )
+            cursor += seg_w
+
+        task_labels = ", ".join(info["tasks"][:5])
+        if len(info["tasks"]) > 5:
+            task_labels += f", +{len(info['tasks']) - 5}"
+
+        cards.append(
+            "\n".join(
+                [
+                    f'<rect x="{x}" y="{y}" width="{card_w}" height="{card_h}" rx="8" fill="#ffffff" stroke="#d9e1ea"/>',
+                    svg_text(x + 24, y + 42, f"{code}. {info['name']}", 21, 700),
+                    svg_text(x + 24, y + 75, info["current_status"], 15, 700, "#566273"),
+                    svg_text(x + 24, y + 108, f"Tasks: {task_labels}", 14, 500, "#30394a"),
+                    *segments,
+                    svg_text(x + 24, y + 174, f"Direct {counts['direct']}", 14, 700, colors["direct"]),
+                    svg_text(x + 150, y + 174, f"Proxy {counts['proxy']}", 14, 700, colors["proxy"]),
+                    svg_text(x + 270, y + 174, f"Diagnostic {counts['diagnostic']}", 14, 700, colors["diagnostic"]),
+                ]
+            )
+        )
+
+    legend = []
+    lx = margin
+    for key, label in (
+        ("direct", "Direct task"),
+        ("proxy", "Proxy / prerequisite"),
+        ("diagnostic", "Diagnostic probe"),
+    ):
+        legend.extend(
+            [
+                f'<rect x="{lx}" y="622" width="16" height="16" rx="4" fill="{colors[key]}"/>',
+                svg_text(lx + 24, 636, label, 14, 600, "#30394a"),
+            ]
+        )
+        lx += 200
+
+    svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-label="Xperience-10M task coverage across four research directions">
+  <rect width="100%" height="100%" fill="#f7f9fb"/>
+  {svg_text(margin, 64, "Xperience-10M 12-Task Suite: Four Research Directions", 30, 800)}
+  {svg_text(margin, 96, "One public sample episode, two baseline families, explicit direct/proxy/diagnostic coverage.", 16, 500, "#566273")}
+  {"".join(cards)}
+  {"".join(legend)}
+  {svg_text(margin, 670, "Generated from results/episode_task_suite/summary_report.json and scripts/research_direction_taxonomy.py", 13, 500, "#6d7787")}
+</svg>
+"""
+    (CHARTS / "research_direction_coverage.svg").write_text(svg, encoding="utf-8")
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    DOCS_DATA.mkdir(parents=True, exist_ok=True)
+    CHARTS.mkdir(parents=True, exist_ok=True)
+
+    taxonomy = build_taxonomy(load_summary())
+    json_text = json.dumps(taxonomy, indent=2, ensure_ascii=False)
+    (OUT_DIR / "research_direction_taxonomy.json").write_text(json_text + "\n", encoding="utf-8")
+    (DOCS_DATA / "research_directions.json").write_text(json_text + "\n", encoding="utf-8")
+    write_csv(taxonomy)
+    write_markdown(taxonomy)
+    write_svg(taxonomy)
+
+    print(f"Wrote {OUT_DIR / 'research_direction_taxonomy.json'}")
+    print(f"Wrote {OUT_DIR / 'research_direction_task_map.csv'}")
+    print(f"Wrote {OUT_DIR / 'research_direction_summary.md'}")
+    print(f"Wrote {DOCS_DATA / 'research_directions.json'}")
+    print(f"Wrote {CHARTS / 'research_direction_coverage.svg'}")
+
+
+if __name__ == "__main__":
+    main()