Add frozen Thinker cache-path control

README.md

@@ -1072,6 +1072,38 @@ and `valid/`, and writes the aggregate metrics to `metrics.json`.  Add
 Treat Sessions 1, 2, and 3 as the official test set: inspect their metrics only
 after the training recipe and hyperparameters have been fixed.
 
+To isolate whether a score change comes from LoRA or from the dense-cache
+reconstruction path, run the same evaluator without `--adapter-checkpoint` and
+load the original frozen 8-second head:
+
+```bash
+FROZEN_HEAD=checkpoints/easycom_open_stream_2fps_8s_head_seed0.pth
+
+CUDA_VISIBLE_DEVICES=0,1,2 \
+OMP_NUM_THREADS=1 \
+MKL_NUM_THREADS=1 \
+OPENBLAS_NUM_THREADS=1 \
+PYTORCH_ALLOC_CONF=expandable_segments:True \
+TORCH_NCCL_ASYNC_ERROR_HANDLING=1 \
+torchrun --standalone --nproc_per_node=3 eval_omni_turn_lora.py \
+  --model-id /data1/yfzx/wangx/Models/Qwen2.5-Omni-3B \
+  --cache-dir /data1/yfzx/wangx/Datasets/easycom/omni_lora_cache_dense_8s/Session_3 \
+  --cache-dir /data1/yfzx/wangx/Datasets/easycom/omni_lora_cache_dense_8s/Session_2 \
+  --cache-dir /data1/yfzx/wangx/Datasets/easycom/omni_lora_cache_dense_8s/Session_1 \
+  --head-checkpoint "${FROZEN_HEAD}" \
+  --output-dir results/easycom_frozen_8s_dense_cache_control \
+  --batch-size 2 \
+  --block-cache-size 32 \
+  --dtype bfloat16 \
+  --flash-attention-2 \
+  --ddp-timeout-minutes 60
+```
+
+This control should approach the previously reported frozen 8-second score of
+58.51937 full-timeline mean mAP.  A material mismatch indicates a representation
+difference between dense-cache reconstruction and the earlier open-stream
+feature files; a close match isolates the regression to LoRA/head training.
+
 For a speed-only alternative, `train_omni_turn_lora.py` supports a two-GPU
 producer-consumer benchmark.  The producer GPU runs the frozen AV towers and
 places current-tail embeddings in a bounded CPU queue while the consumer GPU