This experiment compares the matched pooled checkpoint with a residual branch containing a shared per-track GRU and a G+A query over current track states. The pooled path is copied exactly and frozen. The residual is multiplied by a learned scalar initialized at zero.

Protocol and provenance

Global features: open_stream_2fps_8s_last4_stack.
Local sidecars: local_qwen_1s_encoded_tf4576.
Training: EasyCom Sessions 4--11.
Development/model selection: Session 12.
Official test: Sessions 1--3.
Development seeds: 0, 1 and 2; four epochs.
Test seeds observed: seed 0 only.
Loss: lstr-mce; layer fusion: concat-mlp.
Person history: 16 observations at 2 FPS, with a 1-second maximum track gap.
Reported table protocol: causal_valid_only; all values are percentage points.
Checkpoints: checkpoints/easycom_track_gru_residual/ on the remote L20 machine.
Predictions: results/easycom_track_gru_residual/ for development and results/easycom_track_gru_residual_test_check/ for the one-seed test audit.

The test audit was run before the residual optimization defect described below was diagnosed. It is recorded for transparency and must not be used to tune a corrected architecture.

Session 12 development result

Run	0.2 s mAP	BG AP	wearer AP	others AP	10-horizon mean mAP
pooled seed 0	72.91519	58.39896	68.44729	91.89933	59.42976
track-GRU seed 0	72.91538	58.39965	68.44750	91.89900	59.42956
pooled seed 1	73.02189	57.73258	69.35309	91.97999	59.50838
track-GRU seed 1	73.02358	57.74019	69.35432	91.97624	59.50736
pooled seed 2	72.45010	57.26123	68.35503	91.73403	59.53730
track-GRU seed 2	72.47576	57.15675	68.59149	91.67904	59.57771

Three-seed paired track-GRU - pooled deltas, mean +/- sample standard deviation:

Metric	Delta (percentage points)
0.2 s mAP	+0.00918 +/- 0.01429
BG AP	-0.03206 +/- 0.06281
wearer AP	+0.07930 +/- 0.13610
others AP	-0.01969 +/- 0.03062
10-horizon mean mAP	+0.01306 +/- 0.02368

The development difference is effectively zero and is much smaller than the between-seed variation.

Sessions 1--3 test audit

Only seed 0 was evaluated after the matched checkpoints had already been selected on Session 12.

Run	0.2 s mAP	BG AP	wearer AP	others AP	10-horizon mean mAP
pooled seed 0	81.74161	68.57326	84.71790	91.93366	64.88810
track-GRU seed 0	81.74160	68.57367	84.71783	91.93330	64.88780
track-GRU - pooled	-0.00001	+0.00041	-0.00007	-0.00037	-0.00030

Under the EgoSpeak full-timeline printout, the corresponding ten-horizon means were 64.87493 for pooled and 64.87463 for track-GRU. The small difference from the table is due to the evaluation mask; protocols must not be mixed.

Gate diagnostic

The selected track checkpoints retained an almost closed residual gate:

Seed	Selected epoch	saved `tanh(gate)`	selection-time dev mean mAP
0	1	-0.00152618	59.45070
1	1	+0.00409810	59.52841
2	2	+0.01081575	59.59214

The checkpoint JSON contains history only through the epoch at which that best checkpoint was written, so it is not a complete log of later non-improving epochs. The values above nevertheless describe the models used for inference.

With logits

base_logits + tanh(gate) * delta(track_parameters)

the zero-initialized gate makes the first-step gradient of all track/delta parameters exactly zero. Only the scalar gate can move initially; subsequent branch gradients are scaled by its still-small value. The saved gates and the near-identical test predictions are therefore consistent with an optimization failure in the residual parameterization.

Interpretation and decision

The implemented zero-gated track-GRU produces no meaningful dense-AP gain.
This run does not establish that track continuity is uninformative, because the residual branch was almost closed throughout the selected models.
It also does not support event-level or visual-grounding claims: no corrected competing-risk event evaluation or wrong-track/time-shift control was run.
The appropriate next diagnostic is to remove the scalar gate, zero-initialize the last residual output layer, keep the pooled base frozen, and verify that residual-output and GRU gradients become nonzero during optimization.
Tune that corrected model on Session 12 only. Do not inspect Sessions 1--3 again until its architecture and stopping rule are frozen.

Machine-readable values are in summary.json and per-run rows are in metrics.csv.