Add offline sparse supervision control

README.md

@@ -904,6 +904,62 @@ Recommended experimental procedure:
 Class-balanced/focal loss, a learned turn-query token, and LoRA are later
 ablations. They should not replace the frozen-Omni linear/MLP-head baseline.
 
+### Offline frozen states with transition-sparse supervision
+
+This is the clean sampling-only control.  It does not reconstruct cached AV
+tokens and does not run the Thinker during training.  It loads the same dense
+5 Hz frozen states used by the standard head baseline, then retains only the
+`target_index` values listed by the existing transition-sparse cache
+manifests.  The representation, head architecture, optimizer, 50 epochs, loss,
+targets, and final dense test evaluator are otherwise unchanged.
+
+Set `FROZEN_FEATURE_ROOT` to the exact feature directory used by the matching
+dense-supervision baseline.  Do not compare sparse 8-second states with a
+dense 20-second baseline (or vice versa):
+
+```bash
+FROZEN_FEATURE_ROOT=/data1/yfzx/wangx/Datasets/easycom/omni_features/open_stream_2fps_20s_decord
+TARGET_ROOT=/data1/yfzx/wangx/Datasets/easycom/easycom_target_frame
+SPARSE_ROOT=/data1/yfzx/wangx/Datasets/easycom/omni_lora_cache_sparse_8s
+
+CUDA_VISIBLE_DEVICES=3 \
+python train_turn_head.py \
+  --feature-root "$FROZEN_FEATURE_ROOT" \
+  --target-root "$TARGET_ROOT" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_7" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_11" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_10" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_6" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_9" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_4" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_8" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_5" \
+  --sparse-anchor-cache-dir "$SPARSE_ROOT/Session_12" \
+  --output checkpoints/easycom_frozen_states_sparse_supervision_50ep.pth \
+  --epochs 50 \
+  --batch-size 512 \
+  --expected-mode any \
+  --device cuda:0
+```
+
+Evaluation remains dense 5 Hz on the official test sessions:
+
+```bash
+CUDA_VISIBLE_DEVICES=3 \
+python infer_turn_head.py \
+  --feature-root "$FROZEN_FEATURE_ROOT" \
+  --target-root "$TARGET_ROOT" \
+  --checkpoint checkpoints/easycom_frozen_states_sparse_supervision_50ep.pth \
+  --output-dir results/easycom_frozen_states_sparse_supervision_50ep \
+  --expected-mode any \
+  --device cuda:0
+```
+
+Compare this result against a head trained for the same 50 epochs on every
+dense 5 Hz anchor from the same `FROZEN_FEATURE_ROOT`.  A large drop isolates
+the problem to sparse supervision; a close result means sparse supervision is
+not sufficient to explain the LoRA regression.
+
 ## Thinker-LoRA sampling and cache policy
 
 This is an implemented second-stage experiment, not yet a reported benchmark