Add causal person fusion and MuVAP stage-3 baselines

docs/easycom_egospeak_style_person_gru.md

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+# 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.