# EasyCom EgoSpeak-style person-track GRU ablation

## Status and purpose

Implemented as
`--local-fusion causal-person-adapter-egospeak-gru`; GPU results are not yet
measured. This is a controlled temporal-stem ablation of
`causal-person-adapter-v2`, not a reproduction of EgoSpeak MiniROADA.

The comparison changes only how each anonymous person's causal visual history
is embedded before the existing GRU/set-attention path:

| Component | Native V2 | EgoSpeak-style ablation |
|---|---|---|
| track input | `LayerNorm -> Linear -> GELU` | `Linear -> LayerNorm -> ReLU -> Dropout` |
| temporal layer | one-layer shared causal GRU | the same one-layer shared causal GRU |
| GRU output | final hidden state | `ReLU(final hidden state)` |
| all downstream fusion | unchanged | unchanged |

Both modes use the same raw local visual features, causal track histories,
history length, G+A query, masked multi-person attention, direct local-audio
state, dynamic vector gate, zero-initialized residual output, pooled base
checkpoint, loss, split, optimizer and seed. The actual trainable parameter
count is printed and stored because moving LayerNorm across the projection
causes a small parameter-count difference. With the default 2048D visual input
and 256D person state, the EgoSpeak-style stem has 3,584 fewer affine
LayerNorm parameters than native V2; all Linear and GRU matrix shapes match.

## Relation to EgoSpeak

EgoSpeak MiniROADA applies
`Linear -> LayerNorm -> ReLU -> Dropout -> one-layer GRU -> ReLU` to one
**global 5 Hz RGB+audio sequence**. Its EasyCom configuration uses a 128-step
window, 2048D embedding and 1024D hidden state, followed by its own anticipation
expansion and shared classifier.

This repository uses a clean-room PyTorch implementation of only that temporal
stem and applies it independently to each anonymous **visual person track**.
It intentionally retains V2's 256D screen, two-second person history and
residual forecasting head so the experiment isolates temporal parameterization.
The correct name in results and the paper is **EgoSpeak-style per-track GRU**,
not “EgoSpeak reproduction.” The audited reference is
[`third_party/EgoSpeak/MiniROAD/model/rnn/rnn.py`](../third_party/EgoSpeak/MiniROAD/model/rnn/rnn.py).

## L20 smoke test

Use only training sessions and isolated output roots:

```bash
EPOCHS=1 \
WARMUP_EPOCHS=0 \
SESSIONS="4 5" \
DEV_SESSIONS=5 \
SCREEN_SESSIONS=5 \
CHECKPOINT_ROOT=checkpoints/easycom_egospeak_person_gru_smoke \
RESULT_ROOT=results/easycom_egospeak_person_gru_smoke \
CUDA_VISIBLE_DEVICES=0 \
bash run_easycom_causal_person_adapter_v2.sh all 0
```

This runs the matched pooled base, native V2 and EgoSpeak-style ablation. Check
that all three use the same split and base-checkpoint hash, both adapter modes
start from the pooled logits, and their residual-output/delta diagnostics become
nonzero after training.

## Three-seed development screen

If matched pooled/V2 checkpoints already exist, run only the new ablation:

```bash
SUMMARIZE=0 CUDA_VISIBLE_DEVICES=0 bash run_easycom_causal_person_adapter_v2.sh egospeak-gru 0
SUMMARIZE=0 CUDA_VISIBLE_DEVICES=1 bash run_easycom_causal_person_adapter_v2.sh egospeak-gru 1
SUMMARIZE=0 CUDA_VISIBLE_DEVICES=2 bash run_easycom_causal_person_adapter_v2.sh egospeak-gru 2
```

Otherwise, run `all` instead of `egospeak-gru` to create each matched pooled
checkpoint and both adapters. Do not run official Sessions 1--3 while selecting
the temporal stem.

After all jobs finish:

```bash
python summarize_local_baselines.py \
  --root results/easycom_causal_person_adapter_v2 \
  --dataset easycom \
  --protocol causal_valid_only \
  --paired-deltas
```

The relevant paired rows are `egospeak_gru - pooled` and
`egospeak_gru - person_adapter_v2`. Dense AP/mAP is only the architecture
screen; wearer event claims still wait for the frozen competing-risk evaluator.