Add competing-risk evaluation and align ICASSP story

2026-09-10 11:12:02 +08:00
parent 0abefd6f77
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--- a/results/README.md
+++ b/results/README.md
@@ -6,6 +6,7 @@ readers to the right comparison.

 | Result record | Question answered |
 |---|---|
+| [easycom_oracle_pooled_wearer_audit_2026-09-10/](easycom_oracle_pooled_wearer_audit_2026-09-10/README.md) | Fixed-four-epoch three-seed oracle test results and the limited wearer gains of historical pooled AV |
 | [`easycom_open_stream_vs_causal_prefix_2026-08-19/`](easycom_open_stream_vs_causal_prefix_2026-08-19/README.md) | How prepared-media open-stream and causal-prefix features compare |
 | [`easycom_open_stream_8s_vs_20s_2026-08-23/`](easycom_open_stream_8s_vs_20s_2026-08-23/README.md) | Frozen open-stream context-length comparison |
 | [`easycom_sparse_supervision_vs_lora_2026-08-24/`](easycom_sparse_supervision_vs_lora_2026-08-24/README.md) | Sparse supervision versus LoRA comparison |
--- a/results/easycom_dense_last8_qvo_frozen_head_epoch3_2026-08-25/README.md
+++ b/results/easycom_dense_last8_qvo_frozen_head_epoch3_2026-08-25/README.md
@@ -0,0 +1,159 @@
+# EasyCom dense last-8 Thinker LoRA with a frozen head
+
+Recorded on **2026-08-25** from user-reported remote evaluation output. This is
+a single-run diagnostic result, not a multi-seed paper number.
+
+## Experiment contract
+
+- Model: Qwen2.5-Omni-3B Thinker.
+- Inputs: dense cached AV phases, 2 FPS video, raw-audio tower embeddings, and
+  an 8-second causal memory budget.
+- Prediction/label rate: 5 Hz with ten horizons from 0.2 to 2.0 seconds.
+- LoRA scope: `q_proj`, `v_proj`, and `o_proj` in Thinker layers 28--35 only.
+- LoRA configuration: rank 8, alpha 16, dropout 0.05, learning rate `5e-6`,
+  weight decay 0.01.
+- Head: the trained frozen 8-second head, loaded from
+  `checkpoints/easycom_open_stream_2fps_8s_head_seed0.pth` and kept frozen.
+- LoRA training: dense Sessions 4--11, global batch 16, five continuous epochs.
+- Development selection: Session 12, primary metric 0.2-second full-timeline
+  mAP and mean mAP as tie-breaker.
+- Formal evaluation: epoch 3 was first evaluated on official EasyCom test
+  Sessions 3, 2, and 1 after development selection. Epoch 1 was subsequently
+  evaluated as a diagnostic before fixing the one-epoch full-training recipe.
+- Selected checkpoint: epoch 3, step 11910.
+
+The selected remote checkpoint was:
+
+```text
+checkpoints/omni_turn_lora_dense_8s_last8_qvo_frozen_head_epoch5/
+  checkpoint-step-11910/
+```
+
+The diagnostic epoch-1 checkpoint was:
+
+```text
+checkpoints/omni_turn_lora_dense_8s_last8_qvo_frozen_head_epoch5/
+  checkpoint-step-3970/
+```
+
+## Development selection on Session 12
+
+All values are percentages.
+
+| Epoch | Step | 0.2s mAP | Mean mAP |
+|---:|---:|---:|---:|
+| 1 | 3970 | 70.80918 | 63.06487 |
+| 2 | 7940 | 70.96876 | **63.09318** |
+| 3 | 11910 | **70.99153** | 63.01294 |
+| 4 | 15880 | 70.87364 | 62.85465 |
+| 5 | 19850 | 70.83634 | 62.81744 |
+
+Epoch 3 was selected by the preregistered 0.2-second criterion. The five-run
+range is only 0.18235 points at 0.2 seconds and 0.27574 mean points, indicating
+that the restricted low-learning-rate LoRA is much more stable than the prior
+full-Thinker sparse-LoRA runs. Epochs 4--5 nevertheless show mild overfitting.
+
+## Official Sessions 1--3 results
+
+Epoch 1 and epoch 3 are close at 0.2 seconds, but epoch 1 preserves the longer
+horizons better and therefore has the higher mean mAP.
+
+| Checkpoint | 0.2s mAP | Mean mAP | 0.2s delta vs frozen | Mean delta vs frozen |
+|---|---:|---:|---:|---:|
+| Frozen dense control | **71.69695** | **58.16091** | -- | -- |
+| LoRA epoch 1 / step 3970 | 71.59585 | 58.01500 | -0.10110 | -0.14591 |
+| LoRA epoch 3 / step 11910 | 71.65000 | 57.88833 | -0.04695 | -0.27258 |
+
+Epoch 1 is only `0.05415` below epoch 3 at 0.2 seconds and is `0.12667`
+higher in mean mAP. Relative to epoch 3, it improves every horizon from 0.6
+through 2.0 seconds. This supports fixing one epoch and retraining once on all
+official training Sessions 4--12, without development checkpoint selection.
+
+### Epoch 1 / step 3970
+
+| Horizon | LoRA mAP | Frozen dense mAP | Delta | Background AP | Wearer AP | Others AP |
+|---:|---:|---:|---:|---:|---:|---:|
+| 0.2s | 71.59585 | 71.69695 | -0.10110 | 54.40604 | 76.39165 | 83.98987 |
+| 0.4s | 66.37987 | 66.46468 | -0.08481 | 47.56313 | 70.58803 | 80.98845 |
+| 0.6s | 61.91962 | 62.03503 | -0.11541 | 42.34205 | 64.79174 | 78.62507 |
+| 0.8s | 58.62537 | 58.77409 | -0.14872 | 39.04030 | 59.81340 | 77.02241 |
+| 1.0s | 56.30013 | 56.43606 | -0.13593 | 37.19759 | 55.95535 | 75.74744 |
+| 1.2s | 54.86526 | 55.00306 | -0.13780 | 36.31219 | 53.41217 | 74.87142 |
+| 1.4s | 53.59386 | 53.73114 | -0.13728 | 35.39635 | 51.13887 | 74.24635 |
+| 1.6s | 52.86682 | 53.04042 | -0.17360 | 35.19829 | 49.67869 | 73.72348 |
+| 1.8s | 52.23019 | 52.42454 | -0.19435 | 34.71486 | 48.59750 | 73.37822 |
+| 2.0s | 51.77300 | 52.00315 | -0.23015 | 34.68133 | 47.63528 | 73.00237 |
+| **Mean** | **58.01500** | **58.16091** | **-0.14591** | **39.68521** | **57.80027** | **76.55951** |
+
+### Epoch 3 / step 11910
+
+The fair control is the frozen Thinker evaluated from the same dense AV cache
+path with the same frozen head. `Delta` is selected LoRA minus that control.
+
+| Horizon | LoRA mAP | Frozen dense mAP | Delta | Background AP | Wearer AP | Others AP |
+|---:|---:|---:|---:|---:|---:|---:|
+| 0.2s | 71.65000 | 71.69695 | -0.04695 | 54.59245 | 76.13410 | 84.22345 |
+| 0.4s | 66.38669 | 66.46468 | -0.07799 | 47.57127 | 70.36283 | 81.22596 |
+| 0.6s | 61.87698 | 62.03503 | -0.15805 | 42.33591 | 64.49864 | 78.79640 |
+| 0.8s | 58.51356 | 58.77409 | -0.26053 | 38.97583 | 59.42986 | 77.13498 |
+| 1.0s | 56.13447 | 56.43606 | -0.30159 | 37.07515 | 55.55303 | 75.77523 |
+| 1.2s | 54.69993 | 55.00306 | -0.30313 | 36.20847 | 53.01781 | 74.87352 |
+| 1.4s | 53.41760 | 53.73114 | -0.31354 | 35.30111 | 50.71685 | 74.23484 |
+| 1.6s | 52.64243 | 53.04042 | -0.39799 | 35.00690 | 49.21422 | 73.70616 |
+| 1.8s | 52.00887 | 52.42454 | -0.41567 | 34.55009 | 48.10873 | 73.36779 |
+| 2.0s | 51.55275 | 52.00315 | -0.45040 | 34.55502 | 47.13447 | 72.96877 |
+| **Mean** | **57.88833** | **58.16091** | **-0.27258** | **39.61722** | **57.41705** | **76.63071** |
+
+The class-mean comparison is:
+
+| Class | LoRA mean AP | Frozen dense mean AP | Delta |
+|---|---:|---:|---:|
+| Background | 39.61722 | 39.68434 | -0.06712 |
+| Camera wearer speaking | 57.41705 | 58.04423 | **-0.62718** |
+| Other people speaking | 76.63071 | 76.75417 | -0.12346 |
+
+At 0.2 seconds specifically, background and other-person AP increase by
+`+0.10051` and `+0.09021`, but wearer AP falls by `-0.33157`; the net mAP is
+therefore `-0.04695`. The negative gap grows almost monotonically with horizon
+and reaches `-0.45040` at 2.0 seconds.
+
+## Interpretation
+
+1. **Restricted LoRA nearly eliminates the short-horizon collapse but does not
+   create positive transfer.** The prior frozen-head initialized sparse LoRA
+   scored 71.08589 at 0.2 seconds and 56.19608 mean; the new recipe improves
+   those by `+0.56411` and `+1.69225`, respectively, but remains below the
+   frozen dense control.
+2. **The remaining negative transfer is horizon-dependent.** The 0.2-second
+   representation is essentially preserved, while longer anticipation is
+   progressively damaged. This is inconsistent with a simple global
+   calibration error.
+3. **Camera-wearer speech is the main failure mode.** Its average AP decreases
+   by 0.62718 points, much more than background or other-person speech.
+4. **More epochs are not justified.** Epoch 1 is already within 0.05415 points
+   of epoch 3 at 0.2 seconds and is 0.12667 points better in mean mAP on the
+   official test sessions. The remaining official-split experiment therefore
+   fixes one epoch and trains on Sessions 4--12 without checkpoint selection.
+5. **This checkpoint is an ablation, not a headline improvement.** It does not
+   beat frozen Omni, the 58.51937 open-stream frozen-8s result, or EgoSpeak GRU
+   A+V at 60.6 mean mAP.
+
+The next defensible post-training diagnostic is adapter scaling/interpolation
+on a clean development protocol. It should not be tuned from the already-seen
+Sessions 1--3 result. For publication, retrain the frozen head without the
+chosen development session; the reused frozen head was originally trained on
+the official training split and may already have seen Session 12.
+
+## Provenance and limitations
+
+- Source: user-reported remote `eval_omni_turn_lora.py` console tables.
+- Raw remote `metrics.json` archived here: no.
+- Strict causal-valid table supplied: no.
+- Exact remote git revision confirmed: no; the relevant implementation commits
+  include `7acab44`, `7fdda29`, and `aa4278e`.
+- Multi-seed: no.
+- Test-use caveat: epoch 1 was inspected after the epoch-3 test result; their
+  comparison is diagnostic rather than untouched test-set model selection.
+- Development caveat: the frozen head may have been trained with Session 12;
+  this is adequate for diagnosis but is not a clean publication dev split.
+- Publication status: negative-transfer ablation only.
--- a/results/easycom_dense_last8_qvo_frozen_head_epoch3_2026-08-25/summary.json
+++ b/results/easycom_dense_last8_qvo_frozen_head_epoch3_2026-08-25/summary.json
@@ -0,0 +1,93 @@
+{
+  "schema_version": 1,
+  "recorded_date": "2026-08-25",
+  "status": "single_run_negative_transfer_ablation",
+  "dataset": "EasyCom",
+  "prediction_step_seconds": 0.2,
+  "video_fps": 2,
+  "context_seconds": 8,
+  "train_sessions": [4, 5, 6, 7, 8, 9, 10, 11],
+  "development_sessions": [12],
+  "test_sessions": [3, 2, 1],
+  "configuration": {
+    "model": "Qwen2.5-Omni-3B Thinker",
+    "anchor_policy": "dense_5hz",
+    "lora_layers": [28, 29, 30, 31, 32, 33, 34, 35],
+    "lora_target_modules": ["q_proj", "v_proj", "o_proj"],
+    "lora_rank": 8,
+    "lora_alpha": 16,
+    "lora_dropout": 0.05,
+    "learning_rate": 0.000005,
+    "weight_decay": 0.01,
+    "head_frozen": true,
+    "epochs": 5,
+    "selected_epoch": 3,
+    "selected_step": 11910
+  },
+  "development_metrics_percent": [
+    {"epoch": 1, "step": 3970, "horizon_0.2_map": 70.80918, "mean_map": 63.06487},
+    {"epoch": 2, "step": 7940, "horizon_0.2_map": 70.96876, "mean_map": 63.09318},
+    {"epoch": 3, "step": 11910, "horizon_0.2_map": 70.99153, "mean_map": 63.01294},
+    {"epoch": 4, "step": 15880, "horizon_0.2_map": 70.87364, "mean_map": 62.85465},
+    {"epoch": 5, "step": 19850, "horizon_0.2_map": 70.83634, "mean_map": 62.81744}
+  ],
+  "epoch1_test_metrics_percent": {
+    "protocol": "egospeak_full_timeline",
+    "num_timesteps": 23048,
+    "step": 3970,
+    "horizon_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "horizon_map": [71.59585, 66.37987, 61.91962, 58.62537, 56.30013, 54.86526, 53.59386, 52.86682, 52.23019, 51.77300],
+    "mean_map": 58.01500,
+    "background_ap": [54.40604, 47.56313, 42.34205, 39.04030, 37.19759, 36.31219, 35.39635, 35.19829, 34.71486, 34.68133],
+    "camera_wearer_speaking_ap": [76.39165, 70.58803, 64.79174, 59.81340, 55.95535, 53.41217, 51.13887, 49.67869, 48.59750, 47.63528],
+    "other_people_speaking_ap": [83.98987, 80.98845, 78.62507, 77.02241, 75.74744, 74.87142, 74.24635, 73.72348, 73.37822, 73.00237]
+  },
+  "test_metrics_percent": {
+    "protocol": "egospeak_full_timeline",
+    "num_timesteps": 23048,
+    "horizon_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "horizon_map": [71.65000, 66.38669, 61.87698, 58.51356, 56.13447, 54.69993, 53.41760, 52.64243, 52.00887, 51.55275],
+    "mean_map": 57.88833,
+    "background_ap": [54.59245, 47.57127, 42.33591, 38.97583, 37.07515, 36.20847, 35.30111, 35.00690, 34.55009, 34.55502],
+    "camera_wearer_speaking_ap": [76.13410, 70.36283, 64.49864, 59.42986, 55.55303, 53.01781, 50.71685, 49.21422, 48.10873, 47.13447],
+    "other_people_speaking_ap": [84.22345, 81.22596, 78.79640, 77.13498, 75.77523, 74.87352, 74.23484, 73.70616, 73.36779, 72.96877]
+  },
+  "frozen_dense_control_percent": {
+    "horizon_map": [71.69695, 66.46468, 62.03503, 58.77409, 56.43606, 55.00306, 53.73114, 53.04042, 52.42454, 52.00315],
+    "mean_map": 58.16091,
+    "class_mean_ap": {
+      "background": 39.68434,
+      "camera_wearer_speaking": 58.04423,
+      "other_people_speaking": 76.75417
+    }
+  },
+  "selected_lora_minus_frozen_dense_percent_points": {
+    "horizon_map": [-0.04695, -0.07799, -0.15805, -0.26053, -0.30159, -0.30313, -0.31354, -0.39799, -0.41567, -0.45040],
+    "mean_map": -0.27258,
+    "class_mean_ap": {
+      "background": -0.06712,
+      "camera_wearer_speaking": -0.62718,
+      "other_people_speaking": -0.12346
+    }
+  },
+  "epoch1_lora_minus_frozen_dense_percent_points": {
+    "horizon_map": [-0.10110, -0.08481, -0.11541, -0.14872, -0.13593, -0.13780, -0.13728, -0.17360, -0.19435, -0.23015],
+    "mean_map": -0.14591,
+    "class_mean_ap": {
+      "background": 0.00087,
+      "camera_wearer_speaking": -0.24396,
+      "other_people_speaking": -0.19466
+    }
+  },
+  "epoch1_minus_epoch3_percent_points": {
+    "horizon_0.2_map": -0.05415,
+    "mean_map": 0.12667
+  },
+  "provenance": {
+    "source": "User-reported remote evaluator console tables",
+    "raw_metrics_json_archived": false,
+    "strict_causal_metrics_supplied": false,
+    "multi_seed": false,
+    "clean_development_head_split": false
+  }
+}
--- a/results/easycom_dense_last8_qvo_full_train_epoch1_2026-08-25/README.md
+++ b/results/easycom_dense_last8_qvo_full_train_epoch1_2026-08-25/README.md
@@ -0,0 +1,84 @@
+# EasyCom dense last-8 LoRA, full official train split, one epoch
+
+Recorded on **2026-08-25** from user-reported remote evaluator output. This is
+a single-seed diagnostic experiment.
+
+## Experiment contract
+
+- Backbone: Qwen2.5-Omni-3B Thinker.
+- Input representation: dense cached AV phases, 2 FPS video, 5 Hz prediction,
+  and 8 seconds of causal open-stream memory.
+- Head: pretrained frozen 8-second head from
+  `checkpoints/easycom_open_stream_2fps_8s_head_seed0.pth`.
+- LoRA: `q_proj`, `v_proj`, and `o_proj` in Thinker layers 28--35, rank 8,
+  alpha 16, dropout 0.05, learning rate `5e-6`, and weight decay 0.01.
+- Training: all official EasyCom train Sessions 4--12, one fixed epoch, 4526
+  updates, global batch 16, and no development checkpoint selection.
+- Evaluation: official test Sessions 3, 2, and 1 using the EgoSpeak Table 2
+  full-timeline protocol.
+- Training runtime: 1475.936 seconds on eight GPUs, 3.067 updates/second, with
+  8.86 GiB as the maximum per-rank CUDA allocation.
+- Cached loss: 1.272857 before training and 1.269106 after training.
+
+The evaluated checkpoint was:
+
+```text
+checkpoints/
+  omni_turn_lora_dense_8s_last8_qvo_frozen_head_train4to12_epoch1_ddp8/
+```
+
+## Official Sessions 1--3 result
+
+| Horizon | LoRA mAP | Frozen dense mAP | Delta | Background AP | Wearer AP | Others AP |
+|---:|---:|---:|---:|---:|---:|---:|
+| 0.2s | 71.62100 | 71.69695 | -0.07595 | 54.41759 | 76.41473 | 84.03068 |
+| 0.4s | 66.38400 | 66.46468 | -0.08068 | 47.56390 | 70.58441 | 81.00369 |
+| 0.6s | 61.93324 | 62.03503 | -0.10179 | 42.35133 | 64.80023 | 78.64817 |
+| 0.8s | 58.64198 | 58.77409 | -0.13211 | 39.04739 | 59.81664 | 77.06191 |
+| 1.0s | 56.30504 | 56.43606 | -0.13102 | 37.17699 | 55.95400 | 75.78412 |
+| 1.2s | 54.87690 | 55.00306 | -0.12616 | 36.30712 | 53.40678 | 74.91682 |
+| 1.4s | 53.60541 | 53.73114 | -0.12573 | 35.40362 | 51.12561 | 74.28699 |
+| 1.6s | 52.89234 | 53.04042 | -0.14808 | 35.22795 | 49.68491 | 73.76416 |
+| 1.8s | 52.26201 | 52.42454 | -0.16253 | 34.76888 | 48.59595 | 73.42119 |
+| 2.0s | 51.82720 | 52.00315 | -0.17595 | 34.76777 | 47.63587 | 73.07796 |
+| **Mean** | **58.03491** | **58.16091** | **-0.12600** | **39.70325** | **57.80191** | **76.59957** |
+
+## Controlled comparisons
+
+| Checkpoint | Train sessions | Epoch | 0.2s mAP | Mean mAP | Mean delta vs frozen |
+|---|---|---:|---:|---:|---:|
+| Frozen dense control | 4--12 head | -- | **71.69695** | **58.16091** | -- |
+| Restricted LoRA | 4--12 | 1 | 71.62100 | 58.03491 | -0.12600 |
+| Restricted LoRA | 4--11 | 1 | 71.59585 | 58.01500 | -0.14591 |
+| Restricted LoRA | 4--11 | 3 | 71.65000 | 57.88833 | -0.27258 |
+
+Adding Session 12 to the one-epoch LoRA training improves 0.2-second mAP by
+`0.02515` and mean mAP by `0.01991` relative to the Sessions 4--11 epoch-1
+checkpoint. All ten horizons improve, but only by `0.00413--0.05420` points.
+This is too small to explain or reverse the negative transfer.
+
+Relative to the frozen dense control, background mean AP is unchanged
+(`+0.01891`), while camera-wearer and other-person mean AP remain lower by
+`-0.24232` and `-0.15460`. The final mean gap is `-0.12600`, less than half the
+epoch-3 gap but still negative.
+
+## Interpretation
+
+1. Using the complete official training split helps consistently but only
+   marginally; withholding Session 12 was not the cause of the regression.
+2. One epoch is preferable to three for this restricted adapter because it
+   preserves longer-horizon representations better.
+3. The adapter still does not improve the frozen Omni representation. This
+   experiment should be reported as a negative-transfer ablation rather than
+   the main result.
+4. The next primary comparison should use a stronger frozen backbone, such as
+   Qwen2.5-Omni-7B, without LoRA.
+
+## Provenance and limitations
+
+- Source: user-reported `eval_omni_turn_lora.py` console output.
+- Raw remote `metrics.json` archived here: no.
+- Strict causal-valid metrics supplied: no.
+- Multi-seed: no.
+- Test sessions had already been inspected in earlier LoRA diagnostics, so
+  this is not an untouched test-set selection result.
--- a/results/easycom_dense_last8_qvo_full_train_epoch1_2026-08-25/summary.json
+++ b/results/easycom_dense_last8_qvo_full_train_epoch1_2026-08-25/summary.json
@@ -0,0 +1,76 @@
+{
+  "schema_version": 1,
+  "recorded_date": "2026-08-25",
+  "status": "single_run_negative_transfer_ablation",
+  "dataset": "EasyCom",
+  "model": "Qwen2.5-Omni-3B Thinker",
+  "prediction_step_seconds": 0.2,
+  "video_fps": 2,
+  "context_seconds": 8,
+  "train_sessions": [7, 11, 10, 6, 9, 4, 8, 5, 12],
+  "development_sessions": [],
+  "test_sessions": [3, 2, 1],
+  "configuration": {
+    "anchor_policy": "dense_5hz",
+    "lora_layers": [28, 29, 30, 31, 32, 33, 34, 35],
+    "lora_target_modules": ["q_proj", "v_proj", "o_proj"],
+    "lora_rank": 8,
+    "lora_alpha": 16,
+    "lora_dropout": 0.05,
+    "learning_rate": 0.000005,
+    "weight_decay": 0.01,
+    "head_frozen": true,
+    "epochs": 1,
+    "updates": 4526,
+    "global_batch_size": 16,
+    "num_gpus": 8,
+    "training_seconds": 1475.936,
+    "updates_per_second": 3.067,
+    "peak_cuda_allocated_gib_max_across_ranks": 8.86,
+    "initial_cached_loss": 1.272857,
+    "final_cached_loss": 1.269106
+  },
+  "test_metrics_percent": {
+    "protocol": "egospeak_full_timeline",
+    "num_timesteps": 23048,
+    "horizon_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "horizon_map": [71.62100, 66.38400, 61.93324, 58.64198, 56.30504, 54.87690, 53.60541, 52.89234, 52.26201, 51.82720],
+    "mean_map": 58.03491,
+    "background_ap": [54.41759, 47.56390, 42.35133, 39.04739, 37.17699, 36.30712, 35.40362, 35.22795, 34.76888, 34.76777],
+    "camera_wearer_speaking_ap": [76.41473, 70.58441, 64.80023, 59.81664, 55.95400, 53.40678, 51.12561, 49.68491, 48.59595, 47.63587],
+    "other_people_speaking_ap": [84.03068, 81.00369, 78.64817, 77.06191, 75.78412, 74.91682, 74.28699, 73.76416, 73.42119, 73.07796],
+    "class_mean_ap": {
+      "background": 39.70325,
+      "camera_wearer_speaking": 57.80191,
+      "other_people_speaking": 76.59957
+    }
+  },
+  "frozen_dense_control_percent": {
+    "horizon_map": [71.69695, 66.46468, 62.03503, 58.77409, 56.43606, 55.00306, 53.73114, 53.04042, 52.42454, 52.00315],
+    "mean_map": 58.16091,
+    "class_mean_ap": {
+      "background": 39.68434,
+      "camera_wearer_speaking": 58.04423,
+      "other_people_speaking": 76.75417
+    }
+  },
+  "lora_minus_frozen_dense_percent_points": {
+    "horizon_map": [-0.07595, -0.08068, -0.10179, -0.13211, -0.13102, -0.12616, -0.12573, -0.14808, -0.16253, -0.17595],
+    "mean_map": -0.12600,
+    "class_mean_ap": {
+      "background": 0.01891,
+      "camera_wearer_speaking": -0.24232,
+      "other_people_speaking": -0.15460
+    }
+  },
+  "full_train_epoch1_minus_train4to11_epoch1_percent_points": {
+    "horizon_map": [0.02515, 0.00413, 0.01362, 0.01661, 0.00491, 0.01164, 0.01155, 0.02552, 0.03182, 0.05420],
+    "mean_map": 0.01991
+  },
+  "provenance": {
+    "source": "User-reported remote evaluator console table",
+    "raw_metrics_json_archived": false,
+    "strict_causal_metrics_supplied": false,
+    "multi_seed": false
+  }
+}
--- a/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/README.md
+++ b/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/README.md
@@ -0,0 +1,121 @@
+# EasyCom last-4 concat-MLP epoch-budget sweep
+
+Recorded on **2026-08-26** from three user-reported remote runs. This is an
+exploratory single-seed capacity/overfitting diagnostic, not a clean
+test-independent checkpoint-selection result.
+
+## Shared experiment contract
+
+- Dataset: EasyCom.
+- Backbone: frozen Qwen2.5-Omni-3B Thinker.
+- Features: final four query states from 8-second open-stream replay, 2 FPS
+  video, and 5 Hz prediction timestamps.
+- Fusion: concatenate `[4, 2048]` to 8192 dimensions, then
+  `LayerNorm -> Linear(8192, 2048) -> GELU` before the unchanged `10 x 3`
+  anticipation MLP.
+- Training examples: dense 5 Hz anchors from the official training sessions.
+- Evaluation: EgoSpeak full-timeline AP/mAP on Sessions 3, 2, and 1.
+- The three runs differ in total training budget: 4, 12, or 50 epochs.
+
+## Main result
+
+| Total epochs | Final train loss | 0.2s mAP | Mean mAP | Delta mean from 4 epochs |
+|---:|---:|---:|---:|---:|
+| **4** | 0.806973 | **73.42020** | **60.87144** | -- |
+| 12 | 0.661751 | 72.44507 | 57.98809 | -2.88335 |
+| 50 | **0.300277** | 66.54645 | 53.03515 | -7.83629 |
+
+Training loss decreases monotonically while test mAP collapses. This is strong
+evidence that the 16.8-million-parameter concat fusion overfits EasyCom's small,
+highly correlated session-level training set when trained for conventional
+50-epoch head budgets.
+
+## Per-horizon mAP
+
+| Horizon | 4 epochs | 12 epochs | 50 epochs | 4ep minus 12ep | 4ep minus 50ep |
+|---:|---:|---:|---:|---:|---:|
+| 0.2s | 73.42020 | 72.44507 | 66.54645 | +0.97513 | +6.87375 |
+| 0.4s | 68.60449 | 66.95546 | 60.89720 | +1.64903 | +7.70729 |
+| 0.6s | 64.61782 | 62.31847 | 56.41553 | +2.29935 | +8.20229 |
+| 0.8s | 61.53765 | 58.54713 | 53.45517 | +2.99052 | +8.08248 |
+| 1.0s | 59.41820 | 56.08389 | 51.56044 | +3.33431 | +7.85776 |
+| 1.2s | 58.10069 | 54.58857 | 50.11650 | +3.51212 | +7.98419 |
+| 1.4s | 57.24684 | 53.57918 | 48.95956 | +3.66766 | +8.28728 |
+| 1.6s | 56.19807 | 52.66949 | 48.10094 | +3.52858 | +8.09713 |
+| 1.8s | 55.18182 | 51.73658 | 47.49916 | +3.44524 | +7.68266 |
+| 2.0s | 54.38864 | 50.95706 | 46.80053 | +3.43158 | +7.58811 |
+| **Mean** | **60.87144** | **57.98809** | **53.03515** | **+2.88335** | **+7.83629** |
+
+Longer horizons deteriorate earlier and more strongly than 0.2 seconds. The
+12-epoch model loses `0.97513` at 0.2 seconds but more than 3.3 points from
+1.0 through 2.0 seconds. The model is therefore not merely becoming globally
+miscalibrated; it increasingly memorizes short-term training correlations that
+do not support future-speaker anticipation.
+
+## Average class AP over ten horizons
+
+| Epoch budget | Background | Camera wearer | Other people |
+|---:|---:|---:|---:|
+| 4 | **42.12966** | **62.08079** | **78.40387** |
+| 12 | 40.15120 | 56.70353 | 77.10954 |
+| 50 | 34.28629 | 51.12830 | 73.69085 |
+| 4ep minus 50ep | +7.84337 | **+10.95249** | +4.71302 |
+
+The camera-wearer class is most sensitive to overfitting. This matches the
+project's earlier LoRA diagnosis: wearer prediction repeatedly suffers first
+when representation/head capacity is increased without enough independent
+conversation sessions.
+
+## Comparison with the lower-capacity readouts
+
+| Readout | Training budget | 0.2s mAP | Mean mAP |
+|---|---:|---:|---:|
+| Single final-layer query | prior run | 71.99768 | 58.51937 |
+| Last-4 learned scalar mixture | 25 epochs | **73.45085** | 60.53423 |
+| Last-4 concat-MLP | 4 epochs | 73.42020 | **60.87144** |
+
+Relative to scalar mixture, the 4-epoch concat-MLP is essentially tied at
+0.2 seconds (`-0.03065`) and improves mean mAP by `+0.33721`. Its additional
+gain is concentrated around 1.2--1.6 seconds, peaking at `+0.80974` at 1.4
+seconds. The higher-capacity fusion therefore helps longer-term anticipation,
+but does not improve the primary 0.2-second score beyond the scalar mixture.
+
+## Validity caveats
+
+1. The three commands wrote to the same misleadingly named checkpoint and
+   result paths containing `25ep`. Later runs therefore overwrite earlier
+   artifacts unless they were copied elsewhere. The 4-epoch model must be
+   rerun with a unique path if it was not preserved.
+2. Each total-epoch setting creates a different cosine schedule. These are
+   training-budget comparisons, not checkpoints sampled from one identical
+   optimization trajectory.
+3. The supplied excerpts do not contain complete optimizer/scheduler configs.
+   Confirm them from the remote checkpoint JSON before a paper table.
+4. The 4-epoch budget was identified after evaluating Sessions 1--3. It is a
+   diagnostic result and cannot be presented as test-independent model
+   selection.
+5. Only full-timeline metrics were supplied; strict causal-valid metrics and
+   remote `metrics.json` are not archived here.
+6. All values are single-seed results.
+
+## Clean follow-up protocol
+
+- Use Sessions 4--11 for training and Session 12 for model selection.
+- Save every epoch from one fixed 12-epoch schedule and select using a declared
+  development metric (0.2-second mAP for responsiveness, or mean mAP for Table
+  2 overall performance).
+- Freeze the selected epoch count before retraining on Sessions 4--12 and
+  evaluating Sessions 1--3 once.
+- Compare single-layer, fixed last-4 mean, scalar mixture, and concat-MLP with
+  identical seeds and schedules.
+- For the concat model, test a smaller bottleneck or low-rank fusion before
+  adding stronger generic regularization; this preserves the cross-layer idea
+  while matching EasyCom's effective sample size.
+
+Machine-readable values are stored in [`summary.json`](summary.json) and
+[`metrics.csv`](metrics.csv). A cleaned transcription of the supplied console
+tables is in [`console_transcription.txt`](console_transcription.txt).
+
+The data audit, recent-work comparison, paper framing, and prioritized next
+experiments are documented in
+[`reports/omni_turn_research_direction_2026-08-26.md`](../../reports/omni_turn_research_direction_2026-08-26.md).
--- a/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/console_transcription.txt
+++ b/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/console_transcription.txt
@@ -0,0 +1,41 @@
+4 epochs
+final train loss: 0.806973
+0.2s  mAP=73.42020  background=56.72497  wearer=77.87909  others=85.65653
+0.4s  mAP=68.60449  background=49.88131  wearer=73.26704  others=82.66511
+0.6s  mAP=64.61782  background=44.66684  wearer=68.57660  others=80.61002
+0.8s  mAP=61.53765  background=41.31402  wearer=64.21804  others=79.08089
+1.0s  mAP=59.41820  background=39.34993  wearer=61.35721  others=77.54745
+1.2s  mAP=58.10069  background=38.91573  wearer=58.45949  others=76.92686
+1.4s  mAP=57.24684  background=38.68165  wearer=57.00022  others=76.05865
+1.6s  mAP=56.19807  background=37.71307  wearer=55.21953  others=75.66162
+1.8s  mAP=55.18182  background=36.99897  wearer=53.45848  others=75.08801
+2.0s  mAP=54.38864  background=37.05013  wearer=51.37220  others=74.74359
+mean mAP=60.87144
+
+12 epochs
+final train loss: 0.661751
+0.2s  mAP=72.44507  background=55.62292  wearer=76.67027  others=85.04201
+0.4s  mAP=66.95546  background=48.28782  wearer=70.71200  others=81.86658
+0.6s  mAP=62.31847  background=42.64250  wearer=64.97839  others=79.33452
+0.8s  mAP=58.54713  background=38.96725  wearer=59.03811  others=77.63603
+1.0s  mAP=56.08389  background=37.02472  wearer=55.22792  others=75.99903
+1.2s  mAP=54.58857  background=36.78301  wearer=51.71494  others=75.26775
+1.4s  mAP=53.57918  background=36.51419  wearer=49.39741  others=74.82594
+1.6s  mAP=52.66949  background=35.89852  wearer=48.01650  others=74.09344
+1.8s  mAP=51.73658  background=35.09050  wearer=46.39426  others=73.72498
+2.0s  mAP=50.95706  background=34.68059  wearer=44.88548  others=73.30511
+mean mAP=57.98809
+
+50 epochs
+final train loss: 0.300277
+0.2s  mAP=66.54645  background=46.77405  wearer=71.62111  others=81.24419
+0.4s  mAP=60.89720  background=39.76093  wearer=64.88690  others=78.04378
+0.6s  mAP=56.41553  background=35.39797  wearer=58.39212  others=75.45650
+0.8s  mAP=53.45517  background=33.13157  wearer=53.17203  others=74.06192
+1.0s  mAP=51.56044  background=32.04965  wearer=49.57623  others=73.05543
+1.2s  mAP=50.11650  background=31.56647  wearer=46.40684  others=72.37618
+1.4s  mAP=48.95956  background=31.27035  wearer=44.07047  others=71.53785
+1.6s  mAP=48.10094  background=31.17011  wearer=42.20836  others=70.92435
+1.8s  mAP=47.49916  background=31.06982  wearer=41.03746  others=70.39020
+2.0s  mAP=46.80053  background=30.67198  wearer=39.91152  others=69.81809
+mean mAP=53.03515
--- a/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/metrics.csv
+++ b/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/metrics.csv
@@ -0,0 +1,31 @@
+epochs,horizon_seconds,map,background_ap,camera_wearer_ap,other_people_ap,train_loss
+4,0.2,73.42020,56.72497,77.87909,85.65653,0.806973
+4,0.4,68.60449,49.88131,73.26704,82.66511,0.806973
+4,0.6,64.61782,44.66684,68.57660,80.61002,0.806973
+4,0.8,61.53765,41.31402,64.21804,79.08089,0.806973
+4,1.0,59.41820,39.34993,61.35721,77.54745,0.806973
+4,1.2,58.10069,38.91573,58.45949,76.92686,0.806973
+4,1.4,57.24684,38.68165,57.00022,76.05865,0.806973
+4,1.6,56.19807,37.71307,55.21953,75.66162,0.806973
+4,1.8,55.18182,36.99897,53.45848,75.08801,0.806973
+4,2.0,54.38864,37.05013,51.37220,74.74359,0.806973
+12,0.2,72.44507,55.62292,76.67027,85.04201,0.661751
+12,0.4,66.95546,48.28782,70.71200,81.86658,0.661751
+12,0.6,62.31847,42.64250,64.97839,79.33452,0.661751
+12,0.8,58.54713,38.96725,59.03811,77.63603,0.661751
+12,1.0,56.08389,37.02472,55.22792,75.99903,0.661751
+12,1.2,54.58857,36.78301,51.71494,75.26775,0.661751
+12,1.4,53.57918,36.51419,49.39741,74.82594,0.661751
+12,1.6,52.66949,35.89852,48.01650,74.09344,0.661751
+12,1.8,51.73658,35.09050,46.39426,73.72498,0.661751
+12,2.0,50.95706,34.68059,44.88548,73.30511,0.661751
+50,0.2,66.54645,46.77405,71.62111,81.24419,0.300277
+50,0.4,60.89720,39.76093,64.88690,78.04378,0.300277
+50,0.6,56.41553,35.39797,58.39212,75.45650,0.300277
+50,0.8,53.45517,33.13157,53.17203,74.06192,0.300277
+50,1.0,51.56044,32.04965,49.57623,73.05543,0.300277
+50,1.2,50.11650,31.56647,46.40684,72.37618,0.300277
+50,1.4,48.95956,31.27035,44.07047,71.53785,0.300277
+50,1.6,48.10094,31.17011,42.20836,70.92435,0.300277
+50,1.8,47.49916,31.06982,41.03746,70.39020,0.300277
+50,2.0,46.80053,30.67198,39.91152,69.81809,0.300277
--- a/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/summary.json
+++ b/results/easycom_frozen_3b_8s_last4_concat_mlp_epoch_budget_2026-08-26/summary.json
@@ -0,0 +1,73 @@
+{
+  "schema_version": 1,
+  "recorded_date": "2026-08-26",
+  "status": "single_seed_test_inspected_diagnostic",
+  "dataset": "EasyCom",
+  "model": {
+    "backbone": "Qwen2.5-Omni-3B",
+    "thinker_frozen": true,
+    "memory_seconds": 8,
+    "streaming_mode": "open-stream-replay",
+    "video_fps": 2,
+    "prediction_hz": 5,
+    "readout": "last_4_query_concat_mlp",
+    "fusion_parameters_approx": 16795648
+  },
+  "runs": [
+    {
+      "epochs": 4,
+      "final_train_loss": 0.806973,
+      "horizon_0.2_map": 73.42020,
+      "mean_map": 60.87144,
+      "mean_class_ap": {
+        "background": 42.12966,
+        "camera_wearer": 62.08079,
+        "other_people": 78.40387
+      }
+    },
+    {
+      "epochs": 12,
+      "final_train_loss": 0.661751,
+      "horizon_0.2_map": 72.44507,
+      "mean_map": 57.98809,
+      "mean_class_ap": {
+        "background": 40.15120,
+        "camera_wearer": 56.70353,
+        "other_people": 77.10954
+      }
+    },
+    {
+      "epochs": 50,
+      "final_train_loss": 0.300277,
+      "horizon_0.2_map": 66.54645,
+      "mean_map": 53.03515,
+      "mean_class_ap": {
+        "background": 34.28629,
+        "camera_wearer": 51.12830,
+        "other_people": 73.69085
+      }
+    }
+  ],
+  "comparisons_percent": {
+    "epoch4_minus_epoch12_mean_map": 2.88335,
+    "epoch4_minus_epoch50_mean_map": 7.83629,
+    "epoch4_minus_scalar_mixture_mean_map": 0.33721,
+    "epoch4_minus_scalar_mixture_horizon_0.2_map": -0.03065,
+    "epoch4_minus_single_final_layer_mean_map": 2.35207,
+    "epoch4_minus_single_final_layer_horizon_0.2_map": 1.42252
+  },
+  "validity": {
+    "test_sessions": [3, 2, 1],
+    "test_was_used_to_compare_epoch_budgets": true,
+    "same_output_path_was_reused": true,
+    "cosine_schedule_changes_with_total_epochs": true,
+    "strict_causal_valid_reported": false,
+    "multi_seed": false
+  },
+  "provenance": {
+    "source": "user-reported remote console tables",
+    "shared_remote_checkpoint": "checkpoints/easycom_frozen_3b_8s_last4_concat_mlp_25ep.pth",
+    "shared_remote_result_dir": "results/easycom_frozen_3b_8s_last4_concat_mlp_25ep",
+    "complete_remote_checkpoint_json_archived": false
+  }
+}
--- a/results/easycom_oracle_pooled_wearer_audit_2026-09-10/README.md
+++ b/results/easycom_oracle_pooled_wearer_audit_2026-09-10/README.md
@@ -0,0 +1,71 @@
+# Pooled AV 与 oracle 的 wearer 增益审计
+
+日期：2026-09-10。来源：用户粘贴的远程三种子控制台结果，以及已有的
+[pooled/encoder-only 记录](../easycom_encoder_only_1s_4ep_2026-08-28/README.md)。
+本记录没有重新运行训练或读取远程 checkpoint/predictions。
+
+## 协议与来源
+
+新 oracle：Sessions 4--12 全训练，固定 4 epochs，保存末轮，无 dev checkpoint
+selection；Sessions 1--3 测试；seeds 0/1/2；`causal_valid_only`。
+命令入口：`bash run_easycom_oracle_information.sh final 0 1 2`。
+预测根目录：`results/easycom_oracle_information_4ep_train4_12_fixed4_test/`；
+checkpoint 按 runner 默认应为
+`checkpoints/easycom_oracle_information_4ep_train4_12_fixed4/<condition>_seed<seed>.pth`，
+但实际环境覆盖值、checkpoint hash 和运行代码 revision 未随日志提供，尚未核验。
+运行契约见 [oracle 手册](../../docs/easycom_oracle_information.md)。
+
+`metrics.csv` 保存用户提供的全部 18 行数值，单位均为 AP/mAP 百分点。
+BG/wearer/others AP 三列仅对应 0.2 秒；mean mAP 是三个类别、十个 horizon 的平均，
+不能当成十 horizon wearer AP。
+
+## 当前观测
+
+三种子均值差值（百分点）：
+
+| 比较 | 0.2s mAP | 0.2s wearer AP | 十 horizon mean mAP |
+|---|---:|---:|---:|
+| aggregate-visible − audio | +4.82609 | +0.57206 | +1.77462 |
+| delayed − audio | +0.08185 | +0.00647 | +0.16443 |
+| visibility-only − audio | +0.07598 | −0.06329 | +0.04018 |
+| per-person − shuffled-id | +0.34857 | +0.03092 | +0.20013 |
+| aggregate-visible − per-person | +0.42975 | +0.22818 | +0.29128 |
+
+aggregate 的 0.2s 宏平均增益主要来自 BG（+10.45109）和 others（+3.45514），
+wearer 增益为 +0.57206。三种子方向一致，但 seed 方差不能替代跨 session 泛化的
+置信区间，也不能据此声称事件级改善。
+
+## 与历史 pooled AV 的关系
+
+历史 seed-0 `causal_valid_only`：G+A wearer AP 85.06554，G+A+V 85.08583，
+局部视觉的 0.2s wearer 增量仅 +0.02029；mean mAP 增量 +0.29083。
+历史完整曲线采用另一协议 `egospeak_full_timeline`：
+
+- G+A+V − G+A：十 horizon wearer AP 平均 +0.06078，mean mAP +0.29073。
+- A+V − A：十 horizon wearer AP 平均 −0.99993；0.2s wearer AP −1.40150。
+
+因此普通 pooled V 尚无稳定改善 wearer 的证据；encoder-only A+V 还出现 wearer
+退步。历史单种子与新 oracle 三种子不是一组完全匹配实验，不能用它们直接计算
+oracle-gap recovery 或判定两个方法的统计显著差异。
+
+## 可以和不可以得出的结论
+
+1. 已支持：在当前实验设置下，真实的可见 others 活动标注具有互补预测价值；
+   aggregate 条件优于 visibility-only，收益不能仅由人物可见性控制解释。
+2. 及时性有证据：per-person 优于同结构 delayed 条件；aggregate 与 delayed 同时
+   改变了聚合方式和时间，不能单独作为纯时间干预对照。
+3. 身份连续性：per-person 对 shuffled-id 有小幅同向增益，不能继续称为完全持平或
+   断言 identity 无用；但 wearer 增量仅 +0.03，且 aggregate 更好，不支持其必要性。
+4. 尚未证明：增益来自真实 RGB 可恢复的信息，或 pooling 是失败原因。Oracle 使用
+   说话标注，可能提供当前视觉/音频前端根本无法可靠估计的信号；信息缺失与访问困难
+   目前也不能区分。
+5. 尚未证明：更少抢话、addressee 理解或更好的 entry/yield。短期活动延续也能改善
+   BG/others AP，需要直接分析已有 wearer predictions 的事件表现。
+6. Oracle 是特权信息参照，不是数学性能上界；learned latent 可以携带额外线索而超过它。
+
+## 对后续研究的含义
+
+优先验证从真实因果 AV 估计的局部活动是否对 wearer 提供增量，保留简单 aggregate
+对照；在证据出现前，不预设 track、pooling 修复或层间注入必然有效。
+新 oracle 尚缺十 horizon wearer AP 曲线与事件结果，不能仅凭 mean mAP 批准强论文主张。
+Sessions 1--3 已被查看；后续选择不能依赖这次 test 表，使用训练侧验证或预先固定配置。
--- a/results/easycom_oracle_pooled_wearer_audit_2026-09-10/metrics.csv
+++ b/results/easycom_oracle_pooled_wearer_audit_2026-09-10/metrics.csv
@@ -0,0 +1,19 @@
+condition,seed,map_0p2,bg_ap_0p2,wearer_ap_0p2,others_ap_0p2,mean_map
+aggregate-visible,0,86.70353,78.68879,85.97697,95.44481,66.97086
+aggregate-visible,1,86.60725,78.67625,85.68389,95.46161,66.95553
+aggregate-visible,2,86.77584,79.03497,85.84636,95.44621,66.92697
+audio,0,81.82018,68.32374,85.13784,91.99897,65.11495
+audio,1,81.91804,68.45640,85.31319,91.98453,65.23095
+audio,2,81.87011,68.26659,85.34002,92.00373,65.18361
+delayed,0,81.98017,68.44804,85.42083,92.07165,65.36472
+delayed,1,81.85499,68.35216,85.15280,92.06000,65.37526
+delayed,2,82.01874,68.81110,85.23682,92.00832,65.28282
+per-person,0,86.47493,78.23009,85.85070,95.34399,66.79960
+per-person,1,86.14132,77.84909,85.42206,95.15281,66.64966
+per-person,2,86.18113,77.91935,85.54992,95.07412,66.53026
+shuffled-id,0,86.01447,77.05511,85.83326,95.15503,66.54441
+shuffled-id,1,85.85603,77.03031,85.34384,95.19396,66.47007
+shuffled-id,2,85.88116,77.04115,85.55283,95.04951,66.36466
+visibility-only,0,81.99974,68.56855,85.35703,92.07364,65.23904
+visibility-only,1,81.83181,68.36851,85.07484,92.05209,65.24955
+visibility-only,2,82.00473,68.85219,85.16930,91.99268,65.16145
--- a/results/easycom_roma_frozen_last4_2026-08-27/README.md
+++ b/results/easycom_roma_frozen_last4_2026-08-27/README.md
@@ -0,0 +1,123 @@
+# EasyCom frozen-ROMA last-four-layer readout study
+
+Recorded on **2026-08-27** from three user-reported remote evaluations. These
+are single-seed diagnostic results on official EasyCom test Sessions 1--3, not
+test-independent paper numbers.
+
+## Shared experiment contract
+
+- Dataset: EasyCom; EgoSpeak full-timeline evaluator on Sessions 1--3.
+- Backbone checkpoint: ROMA, based on Qwen2.5-Omni-7B.
+- Backbone use: frozen representation extraction; no end-to-end ROMA update.
+- Streaming path: this project's 8-second Qwen-style open-stream replay, 2 FPS
+  video, one prediction every 200 ms.
+- Saved state: final four query states, shape `[T, 4, 3584]`.
+- Prediction target: ten future horizons from 0.2 to 2.0 seconds, three
+  EgoSpeak AP classes.
+
+ROMA's released model is trained with one-second aligned streaming units, but
+these features were extracted with this project's matched Qwen open-stream
+protocol. The result therefore tests the **frozen ROMA representation under
+our protocol**; it does not reproduce ROMA's own streaming training recipe.
+
+## Main result
+
+| Readout | Epochs | Approx. fusion parameters | 0.2s mAP | Mean mAP |
+|---|---:|---:|---:|---:|
+| Last-4 concat-MLP | 1 | 51.4M | 68.27145 | 58.89219 |
+| Last-4 concat-MLP | 4 | 51.4M | 68.65486 | 56.52425 |
+| Last-4 fixed mean | 4 | 0 | **69.21863** | **58.90402** |
+
+The fixed uniform mean is the strongest ROMA readout. It is effectively tied
+with the one-epoch concat model in mean mAP (`+0.01183`) while gaining
+`+0.94718` at 0.2 seconds. The 51.4M-parameter fusion is unnecessary here.
+
+## Per-horizon mAP
+
+| Horizon | Concat 1ep | Concat 4ep | Fixed mean 4ep |
+|---:|---:|---:|---:|
+| 0.2s | 68.27145 | 68.65486 | **69.21863** |
+| 0.4s | 65.29126 | 64.32941 | **65.67454** |
+| 0.6s | 62.15965 | 60.41845 | **62.52327** |
+| 0.8s | **59.95476** | 57.35198 | 59.92499 |
+| 1.0s | **58.25407** | 55.17851 | 58.14859 |
+| 1.2s | **56.83849** | 53.70716 | 56.61984 |
+| 1.4s | 55.65764 | 52.59370 | **55.68495** |
+| 1.6s | **54.91836** | 51.83772 | 54.52641 |
+| 1.8s | **54.20725** | 50.70519 | 53.67463 |
+| 2.0s | **53.36895** | 50.46558 | 53.04431 |
+| **Mean** | 58.89219 | 56.52425 | **58.90402** |
+
+## Mean class AP over ten horizons
+
+| Readout | Background | Camera wearer | Other people |
+|---|---:|---:|---:|
+| Concat 1ep | 42.17735 | **57.38537** | 77.11384 |
+| Concat 4ep | 40.19574 | 52.75190 | 76.62513 |
+| Fixed mean 4ep | **42.19246** | 57.26979 | **77.24980** |
+
+Extending concat training from one to four epochs raises 0.2-second mAP by
+only `+0.38341` but reduces mean mAP by `-2.36794`. Camera-wearer mean AP drops
+by `-4.63347`, much more than background (`-1.98161`) or other people
+(`-0.48871`). This is the same overfitting pattern observed with the 3B
+concat-MLP, amplified by ROMA's wider 3584-dimensional state.
+
+Replacing concat-MLP with a fixed mean at four epochs restores `+4.51789`
+camera-wearer AP and `+2.37977` mean mAP. This isolates readout capacity as the
+proximate cause of the four-epoch collapse.
+
+## Comparison with the strongest frozen Qwen2.5-Omni-3B controls
+
+| Frozen representation/readout | 0.2s mAP | Mean mAP |
+|---|---:|---:|
+| Qwen-3B final-layer query, 8s | 71.99768 | 58.51937 |
+| Qwen-3B last-4 scalar mixture | **73.45085** | 60.53423 |
+| Qwen-3B last-4 concat-MLP, 4ep | 73.42020 | **60.87144** |
+| ROMA last-4 fixed mean, 4ep | 69.21863 | 58.90402 |
+| ROMA last-4 concat-MLP, 4ep | 68.65486 | 56.52425 |
+
+The same concat design and epoch count gives a strong diagnostic: ROMA is
+`-4.76534` at 0.2 seconds and `-4.34719` in mean mAP relative to Qwen-3B.
+It is not a parameter-matched attribution test, because the 3584-dimensional
+ROMA fusion has 51.4M parameters versus about 16.8M for Qwen-3B. Together with
+the fixed-mean result, it shows that ROMA's proactive streaming adaptation is
+not automatically useful for 5 Hz egocentric future-speaker anticipation, but
+does not yet isolate checkpoint adaptation from width and protocol.
+
+The fixed-mean ROMA result is not directly architecture-matched to the 3B
+scalar-mixture/concat models. An exact Qwen-3B fixed-mean four-epoch control is
+still required. A second clean attribution test is ROMA versus its original
+Qwen2.5-Omni-7B base using the same last-four stack and fixed-mean head.
+
+## Interpretation and decision
+
+1. **Do not continue tuning the ROMA concat head.** Its capacity is much too
+   high for nine EasyCom training conversations.
+2. **Do not conclude that ROMA itself is universally worse.** The experiment
+   changes checkpoint family and uses a protocol different from ROMA's native
+   one-second training units.
+3. **The practical EasyCom backbone remains frozen Qwen2.5-Omni-3B.** Its
+   last-four readouts are stronger, cheaper, and already validated throughout
+   the current pipeline.
+4. **The next method should change readout structure and supervision.** The
+   strongest hypothesis is a low-capacity semantic query state fused with
+   recent 200 ms payload tokens, followed by a structured future-activity
+   projection rather than ten independent horizon classifiers.
+
+## Validity caveats
+
+- Only user-reported full-timeline metrics were available; strict
+  causal-valid tables and remote `metrics.json` files were not archived.
+- Results are single-seed and Sessions 1--3 were inspected repeatedly.
+- Exact optimizer settings and the remote checkpoint filenames for all three
+  runs were not supplied here.
+- The train split is assumed to follow the project's Sessions 4--12 contract;
+  verify each remote command before using the values in a paper table.
+- ROMA's official one-second streaming-unit path was not reproduced.
+
+Machine-readable values are in [`summary.json`](summary.json), and a formatted
+workbook with per-horizon metrics and comparison deltas is in
+[`metrics.xlsx`](metrics.xlsx).
+
+The literature-backed diagnosis and proposed ICASSP research plan are in
+[`reports/roma_negative_transfer_and_future_floor_projection_2026-08-27.md`](../../reports/roma_negative_transfer_and_future_floor_projection_2026-08-27.md).
--- a/results/easycom_roma_frozen_last4_2026-08-27/metrics.xlsx
+++ b/results/easycom_roma_frozen_last4_2026-08-27/metrics.xlsx
--- a/results/easycom_roma_frozen_last4_2026-08-27/summary.json
+++ b/results/easycom_roma_frozen_last4_2026-08-27/summary.json
@@ -0,0 +1,121 @@
+{
+  "schema_version": 1,
+  "recorded_date": "2026-08-27",
+  "status": "single_seed_user_reported_test_inspected_diagnostic",
+  "dataset": "EasyCom",
+  "evaluation": {
+    "protocol": "EgoSpeak full timeline",
+    "test_sessions": [1, 2, 3],
+    "prediction_hz": 5,
+    "future_horizons_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "classes": ["background", "camera_wearer_speaking", "other_people_speaking"],
+    "n": 23048
+  },
+  "representation": {
+    "checkpoint_family": "ROMA based on Qwen2.5-Omni-7B",
+    "thinker_frozen": true,
+    "streaming_mode": "open-stream-replay",
+    "memory_seconds": 8,
+    "video_fps": 2,
+    "state": "last_four_query_states",
+    "state_shape": [4, 3584],
+    "native_roma_one_second_streaming_recipe_reproduced": false
+  },
+  "runs": [
+    {
+      "id": "roma_last4_concat_mlp_1ep",
+      "layer_fusion": "concat-mlp",
+      "epochs": 1,
+      "fusion_parameters_approx": 51412480,
+      "reported_mean_map": 58.89219,
+      "mean_class_ap": {
+        "background": 42.17735,
+        "camera_wearer_speaking": 57.38537,
+        "other_people_speaking": 77.11384
+      },
+      "per_horizon": [
+        {"seconds": 0.2, "background": 52.51021, "camera_wearer_speaking": 70.83688, "other_people_speaking": 81.46725, "map": 68.27145},
+        {"seconds": 0.4, "background": 48.71387, "camera_wearer_speaking": 67.22686, "other_people_speaking": 79.93305, "map": 65.29126},
+        {"seconds": 0.6, "background": 45.13005, "camera_wearer_speaking": 62.85818, "other_people_speaking": 78.49071, "map": 62.15965},
+        {"seconds": 0.8, "background": 42.40835, "camera_wearer_speaking": 59.85171, "other_people_speaking": 77.60421, "map": 59.95476},
+        {"seconds": 1.0, "background": 41.04191, "camera_wearer_speaking": 56.82736, "other_people_speaking": 76.89293, "map": 58.25407},
+        {"seconds": 1.2, "background": 39.74672, "camera_wearer_speaking": 54.55271, "other_people_speaking": 76.21605, "map": 56.83849},
+        {"seconds": 1.4, "background": 38.80110, "camera_wearer_speaking": 52.54133, "other_people_speaking": 75.63049, "map": 55.65764},
+        {"seconds": 1.6, "background": 38.10108, "camera_wearer_speaking": 51.26856, "other_people_speaking": 75.38544, "map": 54.91836},
+        {"seconds": 1.8, "background": 37.64821, "camera_wearer_speaking": 49.94810, "other_people_speaking": 75.02545, "map": 54.20725},
+        {"seconds": 2.0, "background": 37.67202, "camera_wearer_speaking": 47.94197, "other_people_speaking": 74.49285, "map": 53.36895}
+      ]
+    },
+    {
+      "id": "roma_last4_concat_mlp_4ep",
+      "layer_fusion": "concat-mlp",
+      "epochs": 4,
+      "fusion_parameters_approx": 51412480,
+      "reported_mean_map": 56.52425,
+      "mean_class_ap": {
+        "background": 40.19574,
+        "camera_wearer_speaking": 52.75190,
+        "other_people_speaking": 76.62513
+      },
+      "per_horizon": [
+        {"seconds": 0.2, "background": 52.12125, "camera_wearer_speaking": 71.51813, "other_people_speaking": 82.32518, "map": 68.65486},
+        {"seconds": 0.4, "background": 47.23227, "camera_wearer_speaking": 65.34609, "other_people_speaking": 80.40987, "map": 64.32941},
+        {"seconds": 0.6, "background": 42.97315, "camera_wearer_speaking": 59.62350, "other_people_speaking": 78.65869, "map": 60.41845},
+        {"seconds": 0.8, "background": 39.98155, "camera_wearer_speaking": 54.81910, "other_people_speaking": 77.25528, "map": 57.35198},
+        {"seconds": 1.0, "background": 38.30512, "camera_wearer_speaking": 50.99168, "other_people_speaking": 76.23874, "map": 55.17851},
+        {"seconds": 1.2, "background": 37.25808, "camera_wearer_speaking": 48.42603, "other_people_speaking": 75.43737, "map": 53.70716},
+        {"seconds": 1.4, "background": 36.84681, "camera_wearer_speaking": 46.09807, "other_people_speaking": 74.83621, "map": 52.59370},
+        {"seconds": 1.6, "background": 36.30368, "camera_wearer_speaking": 44.70198, "other_people_speaking": 74.50749, "map": 51.83772},
+        {"seconds": 1.8, "background": 35.37777, "camera_wearer_speaking": 43.20047, "other_people_speaking": 73.53734, "map": 50.70519},
+        {"seconds": 2.0, "background": 35.55772, "camera_wearer_speaking": 42.79393, "other_people_speaking": 73.04510, "map": 50.46558}
+      ]
+    },
+    {
+      "id": "roma_last4_fixed_mean_4ep",
+      "layer_fusion": "fixed-mean",
+      "epochs": 4,
+      "fusion_parameters_approx": 0,
+      "reported_mean_map": 58.90402,
+      "mean_class_ap": {
+        "background": 42.19246,
+        "camera_wearer_speaking": 57.26979,
+        "other_people_speaking": 77.24980
+      },
+      "per_horizon": [
+        {"seconds": 0.2, "background": 53.55709, "camera_wearer_speaking": 72.09031, "other_people_speaking": 82.00848, "map": 69.21863},
+        {"seconds": 0.4, "background": 49.15182, "camera_wearer_speaking": 67.68575, "other_people_speaking": 80.18605, "map": 65.67454},
+        {"seconds": 0.6, "background": 45.55752, "camera_wearer_speaking": 63.19862, "other_people_speaking": 78.81368, "map": 62.52327},
+        {"seconds": 0.8, "background": 42.39609, "camera_wearer_speaking": 59.81524, "other_people_speaking": 77.56363, "map": 59.92499},
+        {"seconds": 1.0, "background": 41.02210, "camera_wearer_speaking": 56.50674, "other_people_speaking": 76.91692, "map": 58.14859},
+        {"seconds": 1.2, "background": 39.45131, "camera_wearer_speaking": 53.93851, "other_people_speaking": 76.46970, "map": 56.61984},
+        {"seconds": 1.4, "background": 38.78924, "camera_wearer_speaking": 52.30126, "other_people_speaking": 75.96435, "map": 55.68495},
+        {"seconds": 1.6, "background": 38.08597, "camera_wearer_speaking": 50.22392, "other_people_speaking": 75.26934, "map": 54.52641},
+        {"seconds": 1.8, "background": 37.22956, "camera_wearer_speaking": 49.02115, "other_people_speaking": 74.77319, "map": 53.67463},
+        {"seconds": 2.0, "background": 36.68386, "camera_wearer_speaking": 47.91641, "other_people_speaking": 74.53267, "map": 53.04431}
+      ]
+    }
+  ],
+  "comparisons_percent": {
+    "concat_4ep_minus_concat_1ep_horizon_0.2_map": 0.38341,
+    "concat_4ep_minus_concat_1ep_mean_map": -2.36794,
+    "fixed_mean_4ep_minus_concat_4ep_horizon_0.2_map": 0.56377,
+    "fixed_mean_4ep_minus_concat_4ep_mean_map": 2.37977,
+    "fixed_mean_4ep_minus_concat_1ep_horizon_0.2_map": 0.94718,
+    "fixed_mean_4ep_minus_concat_1ep_mean_map": 0.01183,
+    "roma_concat_4ep_minus_qwen3b_concat_4ep_horizon_0.2_map": -4.76534,
+    "roma_concat_4ep_minus_qwen3b_concat_4ep_mean_map": -4.34719
+  },
+  "validity": {
+    "single_seed": true,
+    "test_sessions_repeatedly_inspected": true,
+    "strict_causal_valid_reported": false,
+    "remote_metrics_json_archived": false,
+    "exact_training_commands_archived": false,
+    "train_sessions_assumed_from_project_protocol": [4, 5, 6, 7, 8, 9, 10, 11, 12]
+  },
+  "provenance": {
+    "source": "user-reported remote console tables",
+    "recorded_by": "Codex",
+    "values_are_percent": true
+  }
+}