Dataset: EasyCom with EgoSpeak's official split.
Train sessions: 7, 11, 10, 6, 9, 4, 8, 5, and 12.
Test sessions: 3, 2, and 1 (n=23048 full-timeline rows).
Backbone: Qwen2.5-Omni-3B Thinker with LoRA plus the 10 x 3 turn head.
Context: 8 seconds; 2 FPS video; predictions and labels at 5 Hz.
Training sampling: transition-sparse anchors (ordinary 1 Hz plus dense 5 Hz anchors near target transitions).
Evaluation: dense 5 Hz AV cache on every test timestamp.
Continuation: adapter and head weights loaded from the epoch1 checkpoint, but optimizer moments and the learning-rate scheduler were reinitialized. Therefore, "epoch2" here means a weights-only second training run, not an exact optimizer-state resume.
Reported checkpoint directory: checkpoints/omni_turn_lora_easycom_sparse_8s_epoch2_ddp4/.

Summary

All values are percentages.

Checkpoint	Full-timeline mean mAP	Strict causal mean mAP
Frozen Thinker dense-cache control	58.16091	58.17146
Sparse LoRA epoch1	56.52480	56.53450
Sparse LoRA weights-only epoch2	54.19850	54.20711
Epoch2 minus epoch1	-2.32630	-2.32739
Epoch2 minus frozen control	-3.96241	-3.96435

The second run is materially worse than both epoch1 and the frozen control. This is not consistent with normal evaluator noise.

Per-horizon results

Horizon	Full mAP	Strict mAP	Background AP	Wearer AP	Others AP
0.2 s	65.55214	65.55385	49.03788	65.76312	81.85542
0.4 s	60.96517	60.96812	43.06922	61.14251	78.68378
0.6 s	56.79766	56.80301	39.37427	55.10528	75.91344
0.8 s	54.11741	54.12457	36.94546	51.56720	73.83955
1.0 s	52.09538	52.10400	35.40706	48.38835	72.49075
1.2 s	51.35516	51.36504	35.30874	46.86261	71.89412
1.4 s	50.99774	51.00914	35.29891	45.48818	72.20613
1.6 s	50.47830	50.49067	34.98109	44.74277	71.71105
1.8 s	50.00999	50.02296	34.64341	43.86161	71.52496
2.0 s	49.61605	49.62970	34.26290	43.14791	71.43735

The AP columns above are from the full-timeline protocol. Complete AP values for both protocols are stored in metrics.csv, and summary.json stores the machine-readable aggregate result.

Interpretation and decision

The largest class-specific regression is camera-wearer speaking. Relative to epoch1, wearer AP falls from 68.77118 to 65.76312 at 0.2 seconds and from 47.53875 to 43.14791 at 2.0 seconds. Background and other-speaker AP decline less at the long horizon. The pattern indicates negative transfer in the wearer-speaking representation rather than a uniform metric offset.

Likely contributors are sparse-train/dense-evaluation distribution mismatch, too much LoRA capacity for EasyCom, and restarting the learning-rate schedule during weights-only continuation. Do not run a third continuation epoch with the same recipe. Keep the frozen 8-second result as the main representation baseline and next test the parameter-matched frozen overlap-working-memory ablation.

Provenance

Source: user-reported eval_omni_turn_lora.py console output.
Raw remote metrics.json archived here: no; values were transcribed.
Multi-seed: no.
Publication status: diagnostic negative result only.