Research Takeaways
This generated note summarizes what the current public Xperience-10M sample pipeline actually shows. It is built from committed metric artifacts, not from hand-edited score text.
Scope
- validated episodes: 1
- frames: 5,821
- aligned windows: 1,161
- current feature dimension: 8,546
- raw Xperience-10M data is not redistributed
- Audio from the sample MP4 stream is represented in the current feature vector
Takeaways
One episode can become a real benchmark contract
The public sample is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional feature contract.
| Metric | Value |
|---|---|
frames |
5,821 |
windows |
1,161 |
feature_dim |
8,546 |
Source: docs/data/summary_metrics.json.
Current scope: This benchmark defines the task contract; cross-episode generalization is evaluated in the multi-episode stage.
Chronological splits expose action-class shift
Earlier all-feature action classifiers reach high macro-F1 on their local split, but the 12-task chronological action/subtask heads are much harder because later held-out windows include unseen labels.
| Metric | Value |
|---|---|
all_feature_action_macro_f1 |
0.9829 |
suite_action_macro_f1 |
0.0500 |
suite_subtask_macro_f1 |
0.0506 |
unseen_action_test_classes |
4 |
Source: results/episode_task_suite/summary_report.json.
Current scope: This split is useful for studying label shift; broad action-recognition conclusions need held-out episodes.
Small neural heads help dynamic and temporal probes
The MLP heads substantially improve hand trajectory forecasting, temporal-order verification, and motion/visual synchronization.
| Metric | Value |
|---|---|
hand_mpjpe_minimal |
0.8647 |
hand_mpjpe_neural |
0.1079 |
hand_mpjpe_relative_improvement |
0.8753 |
temporal_order_f1_minimal |
0.5400 |
temporal_order_f1_neural |
0.8520 |
misalignment_f1_minimal |
0.5052 |
misalignment_f1_neural |
0.7153 |
Source: results/episode_task_suite/neural_mlp/*/metrics.json.
Current scope: These gains are measured within one episode and are candidates for held-out-episode testing.
Retrieval and reconstruction remain the harder multimodal problems
Ridge/cosine retrieval remains stronger than the neural projection on this sample, and cross-modal reconstruction still has negative R2.
| Metric | Value |
|---|---|
retrieval_mrr_minimal |
0.2693 |
retrieval_mrr_neural |
0.1300 |
retrieval_top5_minimal |
0.3678 |
reconstruction_r2_minimal |
-0.0153 |
reconstruction_r2_neural |
-0.0102 |
Source: results/episode_task_suite/cross_modal_retrieval/metrics.json.
Current scope: The current reconstruction task predicts feature vectors; depth, mesh, NeRF, and Gaussian-splatting outputs are future task variants.
Audio helps some tasks and hurts others on the public sample
Audio improves the primary metric on 6 of 12 tasks, while raw log-mel replacement improves over the current handcrafted block on 6 of 12 tasks. The largest current-audio gain appears in feature reconstruction, not in action classification.
| Metric | Value |
|---|---|
tasks_where_current_audio_improves |
6 |
mean_current_audio_delta |
0.0418 |
tasks_where_raw_replacement_improves |
6 |
mean_raw_replacement_delta_vs_current |
0.0936 |
reconstruction_current_audio_delta |
0.6524 |
object_relevance_current_audio_delta |
0.0102 |
Source: results/audio_ablation/audio_ablation_summary.json.
Current scope: This is a single-episode ablation over fixed ridge heads. It validates that audio is wired into the task suite and shows where it changes metrics; it does not prove cross-episode audio generalization.
The next scientific unit is held-out episodes, not more adjacent windows
The prepared Qwen3-Omni path now targets a selected 128-episode pilot; held-out metrics will be reported after staging, training, and evaluation complete.
| Metric | Value |
|---|---|
target_episodes |
128 |
selected_sessions |
128 |
valid_candidates |
12,102 |
Source: results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md.
Current scope: The selected-episode Qwen3-Omni fine-tune requires completed data staging and held-out evaluation.
How To Read These Results
- High single-episode scores are useful pipeline checks for the current task contracts.
- Low chronological action/subtask scores are informative because they expose later-label shift.
- Neural gains on trajectory/order/alignment make those tasks good candidates for the next fine-tuning stage.
- Audio ablation is task-specific: audio representation choices help some probes and hurt others.
- Retrieval and reconstruction remain the main multimodal representation challenges.
- The next credible model-quality result needs held-out episodes.