EasyCom dense last-8 LoRA, full official train split, one epoch

Recorded on 2026-08-25 from user-reported remote evaluator output. This is a single-seed diagnostic experiment.

Experiment contract

Backbone: Qwen2.5-Omni-3B Thinker.
Input representation: dense cached AV phases, 2 FPS video, 5 Hz prediction, and 8 seconds of causal open-stream memory.
Head: pretrained frozen 8-second head from checkpoints/easycom_open_stream_2fps_8s_head_seed0.pth.
LoRA: q_proj, v_proj, and o_proj in Thinker layers 28--35, rank 8, alpha 16, dropout 0.05, learning rate 5e-6, and weight decay 0.01.
Training: all official EasyCom train Sessions 4--12, one fixed epoch, 4526 updates, global batch 16, and no development checkpoint selection.
Evaluation: official test Sessions 3, 2, and 1 using the EgoSpeak Table 2 full-timeline protocol.
Training runtime: 1475.936 seconds on eight GPUs, 3.067 updates/second, with 8.86 GiB as the maximum per-rank CUDA allocation.
Cached loss: 1.272857 before training and 1.269106 after training.

The evaluated checkpoint was:

checkpoints/
  omni_turn_lora_dense_8s_last8_qvo_frozen_head_train4to12_epoch1_ddp8/

Official Sessions 1--3 result

Horizon	LoRA mAP	Frozen dense mAP	Delta	Background AP	Wearer AP	Others AP
0.2s	71.62100	71.69695	-0.07595	54.41759	76.41473	84.03068
0.4s	66.38400	66.46468	-0.08068	47.56390	70.58441	81.00369
0.6s	61.93324	62.03503	-0.10179	42.35133	64.80023	78.64817
0.8s	58.64198	58.77409	-0.13211	39.04739	59.81664	77.06191
1.0s	56.30504	56.43606	-0.13102	37.17699	55.95400	75.78412
1.2s	54.87690	55.00306	-0.12616	36.30712	53.40678	74.91682
1.4s	53.60541	53.73114	-0.12573	35.40362	51.12561	74.28699
1.6s	52.89234	53.04042	-0.14808	35.22795	49.68491	73.76416
1.8s	52.26201	52.42454	-0.16253	34.76888	48.59595	73.42119
2.0s	51.82720	52.00315	-0.17595	34.76777	47.63587	73.07796
Mean	58.03491	58.16091	-0.12600	39.70325	57.80191	76.59957

Controlled comparisons

Checkpoint	Train sessions	Epoch	0.2s mAP	Mean mAP	Mean delta vs frozen
Frozen dense control	4--12 head	--	71.69695	58.16091	--
Restricted LoRA	4--12	1	71.62100	58.03491	-0.12600
Restricted LoRA	4--11	1	71.59585	58.01500	-0.14591
Restricted LoRA	4--11	3	71.65000	57.88833	-0.27258

Adding Session 12 to the one-epoch LoRA training improves 0.2-second mAP by 0.02515 and mean mAP by 0.01991 relative to the Sessions 4--11 epoch-1 checkpoint. All ten horizons improve, but only by 0.00413--0.05420 points. This is too small to explain or reverse the negative transfer.

Relative to the frozen dense control, background mean AP is unchanged (+0.01891), while camera-wearer and other-person mean AP remain lower by -0.24232 and -0.15460. The final mean gap is -0.12600, less than half the epoch-3 gap but still negative.

Interpretation

Using the complete official training split helps consistently but only marginally; withholding Session 12 was not the cause of the regression.
One epoch is preferable to three for this restricted adapter because it preserves longer-horizon representations better.
The adapter still does not improve the frozen Omni representation. This experiment should be reported as a negative-transfer ablation rather than the main result.
The next primary comparison should use a stronger frozen backbone, such as Qwen2.5-Omni-7B, without LoRA.

Provenance and limitations

Source: user-reported eval_omni_turn_lora.py console output.
Raw remote metrics.json archived here: no.
Strict causal-valid metrics supplied: no.
Multi-seed: no.
Test sessions had already been inspected in earlier LoRA diagnostics, so this is not an untouched test-set selection result.