Evaluation Protocol
This file defines how the public Xperience-10M sample episode is turned into benchmark-style tasks, how the baselines are evaluated, and what the reported metrics are allowed to mean.
Protocol At A Glance
| Item | Current protocol |
|---|---|
| Source scope | 1 public Xperience-10M sample episode |
| Frames | 5,821 |
| Sliding windows | 1,161 windows, 20 frames each, stride 5 frames |
| Current feature vector | 8,546 dimensions |
| Split | chronological 70/30 train/test by time |
| Baselines | minimal interpretable heads plus compact neural MLP heads |
| Audio | AAC stream extracted from the sample MP4 and included in the current baseline vector |
| Raw data | not redistributed |
Data Unit
The basic unit is a 20-frame aligned window built from one synchronized
public episode. Feature blocks are documented in
results/episode_task_suite/feature_manifest.json; the committed window
table is results/episode_task_suite/windows.csv.
Split Policy
The current suite uses single_episode_chronological: The split preserves time order so future episode segments are not mixed randomly into the train set. It is still one episode; cross-episode generalization is evaluated in the multi-episode stage.
This makes some classification metrics intentionally harsh: later test
segments can contain action or subtask labels not present in the train
segment. Those cases are recorded in the task metrics as unseen_test_classes.
Feature And Head Policy
- Input contract: 8,546-dimensional current feature vector.
- Source manifest:
results/episode_task_suite/feature_manifest.json. - Normalization: Scalers are fit on train windows only for the baseline heads.
- Audio status: AAC audio is extracted from the sample MP4 stream and included in the current feature vector.
Minimal heads are used first because they make task contracts debuggable. Neural MLP heads reuse the same windows, splits, and feature tensors; they are not foundation models.
Task Contracts
| Task | Family | Unit | Input -> target | Primary metric | Minimal | Neural |
|---|---|---|---|---|---|---|
| timeline_action | supervised classification | single window | current 20-frame all-feature window -> current action label | macro_f1 (higher better) | 0.0500 | 0.0148 |
| timeline_subtask | supervised classification | single window | current 20-frame all-feature window -> current subtask label | macro_f1 (higher better) | 0.0506 | 0.0281 |
| transition_detection | temporal diagnostic | single window | current 20-frame all-feature window -> action boundary versus steady | macro_f1 (higher better) | 0.6118 | 0.5862 |
| next_action | short-horizon prediction | single window | current 20-frame all-feature window at time t -> action label at t + 20 frames | macro_f1 (higher better) | 0.0593 | 0.0419 |
| hand_trajectory_forecast | trajectory regression | single window | current all-feature window -> future left/right hand 3D joints for 10 frames | mpjpe (lower better) | 0.8647 | 0.1079 |
| contact_prediction | binary classification | single window | non-contact and non-caption feature blocks -> any body contact | macro_f1 (higher better) | 1.0000 | 1.0000 |
| object_relevance | multi-label classification | single window | non-caption feature blocks -> current relevant object set | micro_f1 (higher better) | 0.1803 | 0.1679 |
| caption_grounding | retrieval | caption query | caption object/interaction query plus candidate sensor windows -> matching time window | mrr (higher better) | 0.0160 | 0.0168 |
| cross_modal_retrieval | retrieval | sensor query | motion, IMU, and camera query features -> matching depth/video window | top5_accuracy (higher better) | 0.3678 | 0.1983 |
| modality_reconstruction | cross-modal regression | single window | motion, IMU, and camera features -> depth/video feature vector | r2 (higher better) | -0.0153 | -0.0102 |
| temporal_order | pairwise diagnostic | adjacent window pair | two adjacent windows -> correct versus reversed order | f1 (higher better) | 0.5400 | 0.8520 |
| misalignment_detection | pairwise diagnostic | paired modality window | motion side plus visual/depth side -> aligned versus shifted by 8 windows | f1 (higher better) | 0.5052 | 0.7153 |
Leakage Controls
- Use chronological train/test splits instead of random window shuffling.
- Fit scalers and learned projections on train windows only.
- Keep future labels, future mocap, contact labels, object labels, and caption labels on the target side unless a task explicitly treats language as the query.
- For cross-modal tasks, split query-side and candidate-side feature blocks before training and ranking.
- Report unseen test classes when the chronological split exposes labels absent from the train segment.
Current Limitations
- Cross-episode generalization is evaluated in the later multi-episode stage.
- Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.
- Qwen3-Omni setup artifacts are preparation artifacts until the 32-episode held-out pilot runs.
- Full audio-visual representation learning still needs multi-episode training, but the current baseline vector now includes an extracted AAC audio feature block.
Scale-Up Gate
The full Qwen3-Omni fine-tuning pilot requires all of the following before reporting held-out model metrics:
- at least 32 valid Xperience-10M episodes
- held-out episode split with no train/test episode leakage
- manifest, training metadata, progress logs, metrics, predictions, and run report
- held-out evaluation on test episodes rather than train windows
Current status: prepared but data-gated. Read
results/omni_finetune/DATA_ACCESS_STATUS.md and
results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md before interpreting any
Qwen3-Omni artifact.
Machine-Readable Copy
The JSON mirror is docs/data/evaluation_protocol.json.