Archive EasyCom sparse supervision results

README.md

@@ -1241,7 +1241,9 @@ sparse LoRA recipe:
 | Checkpoint | Full-timeline mean mAP | Strict causal mean mAP |
 |---|---:|---:|
 | Frozen Thinker dense-cache control | 58.16091 | 58.17146 |
+| Frozen states + sparse supervision, 50 epochs | 57.20379 | 57.21367 |
 | Sparse LoRA epoch1 | 56.52480 | 56.53450 |
+| Frozen-head initialized LoRA, 3 epochs | 56.19608 | 56.20535 |
 | Sparse LoRA weights-only epoch2 | 54.19850 | 54.20711 |
 
 Epoch2 is a weights-only continuation: adapter/head weights resume, but AdamW
@@ -1251,6 +1253,14 @@ regression is camera-wearer speaking. Do not continue the same recipe to a
 third run. The complete snapshot and per-class metrics are archived in
 [`results/easycom_omni_lora_sparse_epoch2_2026-08-24/`](results/easycom_omni_lora_sparse_epoch2_2026-08-24/).
 
+The frozen-state sparse-supervision and frozen-head initialized three-epoch
+LoRA tables are archived in
+[`results/easycom_sparse_supervision_vs_lora_2026-08-24/`](results/easycom_sparse_supervision_vs_lora_2026-08-24/README.md).
+At 0.2 seconds, sparse supervision alone changes mAP from `71.69695` to
+`71.69194` (effectively unchanged), while the three-epoch LoRA result is
+`71.08589`. This isolates the short-horizon regression primarily to Thinker
+updating rather than sparse target selection.
+
 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