Add transition-aware R1 and R2 objectives

README.md

@@ -4,6 +4,9 @@ This project tests whether **Qwen2.5-Omni-3B can replace both the handcrafted
 audio/visual feature extractors and the downstream RNN/Transformer used by
 EgoSpeak** for egocentric turn-taking anticipation.
 
+The persistent paper index and Chinese summaries are maintained in
+[literature_notes](literature_notes/README.md).
+
 The main experiment is deliberately simple:
 
 ```text
@@ -160,6 +163,115 @@ reducing background AP. The full protocol, per-seed summary, seed-0 horizon
 curves, and machine-readable data are archived in
 [`results/easycom_loss_structure_4ep_2026-08-26/`](results/easycom_loss_structure_4ep_2026-08-26/README.md).
 
+#### R1/R2 transition-aware head ablation
+
+The transition target for current state t and future horizon h is one exactly
+when the complete three-label activity row changes between t+h-1 and t+h.
+This includes onset, offset, speaker handover, and entering or leaving overlap.
+It never uses media or labels after the prediction target t+h.
+
+Both variants share the activity MLP trunk and add a small binary 10-horizon
+transition readout:
+
+- R1 supervised uses
+  L = L_activity + 0.2 L_transition,
+  where L_transition is BCEWithLogits with positive weight 3.
+- R2 expected reward uses
+  L = L_activity + 0.2 L_transition - 0.1 E[R_transition].
+  For transition probability p and target z, correctness reward is
+  z p + (1-z)(1-p). Its expectation is computed exactly, weighted with the
+  same positive weight 3, and normalized by total weight. There is no sampled
+  policy gradient, so this is the low-variance control rather than a claim of
+  interactive reinforcement learning.
+
+Run the fixed four-epoch, no-development-set comparison with the same schedule
+as the loss-structure ablation:
+
+~~~bash
+FEATURE_ROOT=/data1/yfzx/wangx/Datasets/easycom/omni_features/open_stream_2fps_8s_last4_stack
+TARGET_ROOT=/data1/yfzx/wangx/Datasets/easycom/easycom_target_frame
+
+for seed in 0 1 2
+do
+  CUDA_VISIBLE_DEVICES=0 \
+  OMP_NUM_THREADS=4 \
+  MKL_NUM_THREADS=4 \
+  OPENBLAS_NUM_THREADS=4 \
+  python train_turn_head.py \
+    --feature-root "$FEATURE_ROOT" \
+    --target-root "$TARGET_ROOT" \
+    --output "checkpoints/easycom-transition-r1-4ep-seed-$seed.pth" \
+    --sessions 4 5 6 7 8 9 10 11 12 \
+    --epochs 4 \
+    --batch-size 512 \
+    --learning-rate 7e-5 \
+    --weight-decay 5e-5 \
+    --warmup-epochs 1.6 \
+    --warmup-factor 0.3 \
+    --dropout 0.2 \
+    --loss lstr-mce \
+    --transition-objective r1-supervised \
+    --transition-loss-weight 0.2 \
+    --transition-pos-weight 3 \
+    --layer-fusion concat-mlp \
+    --expected-mode open-stream-replay \
+    --seed "$seed" \
+    --device cuda:0
+
+  CUDA_VISIBLE_DEVICES=0 \
+  OMP_NUM_THREADS=4 \
+  MKL_NUM_THREADS=4 \
+  OPENBLAS_NUM_THREADS=4 \
+  python train_turn_head.py \
+    --feature-root "$FEATURE_ROOT" \
+    --target-root "$TARGET_ROOT" \
+    --output "checkpoints/easycom-transition-r2-4ep-seed-$seed.pth" \
+    --sessions 4 5 6 7 8 9 10 11 12 \
+    --epochs 4 \
+    --batch-size 512 \
+    --learning-rate 7e-5 \
+    --weight-decay 5e-5 \
+    --warmup-epochs 1.6 \
+    --warmup-factor 0.3 \
+    --dropout 0.2 \
+    --loss lstr-mce \
+    --transition-objective r2-expected-reward \
+    --transition-loss-weight 0.2 \
+    --transition-pos-weight 3 \
+    --transition-reward-weight 0.1 \
+    --layer-fusion concat-mlp \
+    --expected-mode open-stream-replay \
+    --seed "$seed" \
+    --device cuda:0
+done
+~~~
+
+Evaluate all six checkpoints. In addition to the unchanged EgoSpeak activity
+metrics, inference writes transition_pred arrays and prints transition AP,
+precision, recall, and F1 at threshold 0.5:
+
+~~~bash
+for variant in r1 r2
+do
+  for seed in 0 1 2
+  do
+    CUDA_VISIBLE_DEVICES=0 python infer_turn_head.py \
+      --feature-root "$FEATURE_ROOT" \
+      --target-root "$TARGET_ROOT" \
+      --checkpoint "checkpoints/easycom-transition-$variant-4ep-seed-$seed.pth" \
+      --output-dir "results/easycom-transition-$variant-4ep-seed-$seed" \
+      --expected-mode open-stream-replay \
+      --batch-size 1024 \
+      --device cuda:0
+  done
+done
+~~~
+
+Compare R1 and R2 against the existing three-seed LSTR-MCE baseline under the
+same four-epoch budget. The primary paper outcome remains activity mAP; the
+transition metrics diagnose whether an activity improvement is genuinely
+associated with better boundary prediction.
+
 ## Current Ego4D context-length results
 
 The following single-seed snapshot was recorded on 2026-08-22. Both variants