Record Omni floor-event screening results
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@@ -72,8 +72,15 @@ result.
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causal person set. On the existing three-seed, four-epoch test screen it
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obtained `81.77745 +/- 0.15638` 0.2-second mAP and
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`64.95099 +/- 0.09451` ten-horizon mean mAP. This is stable, but is not a
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matched improvement over the historical no-dev pooled result; correct/wrong
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visual controls remain missing.
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matched improvement over the historical no-dev pooled result. Its three-seed
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boundary response/yield AP is `28.423 +/- 0.480` / `48.641 +/- 0.391`; on
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provisional reviewed opportunities its ENTRY/INT AP is
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`41.561 +/- 0.581` / `22.991 +/- 0.202`. These values are respectively
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`-4.461/-1.275` points below the historical pooled-V seed-0 result, so the
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current F1 screen does not support the hoped-for semantic-selectivity gain.
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A same-split three-seed pooled control and correct/wrong visual controls
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remain missing. See the immutable
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[`screening record`](results/easycom_omni_floor_screen_2026-09-16/README.md).
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### Measured local-fusion screens
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@@ -67,3 +67,12 @@ or post-outcome. These are provisional LLM-assisted labels, not human-reviewed
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confirmatory results. Dense, raw-boundary, and opportunity metrics and their
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Figure 7 interpretation are frozen together in the
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[`2026-09-16 result record`](../results/easycom_egospeak_three_level_eval_2026-09-16/README.md).
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The expanded Omni screen finds no opportunity-ranking gain from the current
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F1 Frame-set Attention model: its three-seed ENTRY/INT AP is
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41.561%/22.991%, below the historical pooled-V seed-0 values
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46.022%/24.266%. Because F1 and that pooled run use different train/dev model-
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selection protocols, this is a stable negative screen rather than a matched
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confirmatory comparison. Dense, boundary, per-seed, and provenance details are
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in the
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[`Omni floor screening record`](../results/easycom_omni_floor_screen_2026-09-16/README.md).
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@@ -168,6 +168,13 @@ forecasting 的定性解释,不能代替 boundary 或 semantic opportunity 评
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曲线和限制见
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[`three-level baseline record`](../results/easycom_egospeak_three_level_eval_2026-09-16/README.md)。
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当前 F1 current-frame person-set screen 的三种子结果同样没有支持 semantic floor
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selectivity:ENTRY/INT opportunity AP 为 `41.561 +/- 0.581` / `22.991 +/- 0.202`,
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低于历史 pooled-V seed-0 的 `46.022/24.266`;dense mean mAP 也为
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`64.951 +/- 0.095`,没有超过历史 pooled-V 的 `65.400`。由于两者 train/dev
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选择协议不同,这只能判为稳定的负向 screening,正式归因前必须补同协议三种子 pooled
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control。详见 [`Omni floor screening record`](../results/easycom_omni_floor_screen_2026-09-16/README.md)。
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### P3:第一优先方法——匿名逐人时序旁路
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先做易解释、能在 8×L20 上完成的旁路,而不是直接修改整个 Thinker。
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@@ -6,6 +6,7 @@ readers to the right comparison.
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| Result record | Question answered |
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|---|---|
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| [`easycom_omni_floor_screen_2026-09-16/`](easycom_omni_floor_screen_2026-09-16/README.md) | Whether G+A, pooled-V, or current-frame F1 improve raw boundaries and provisional semantic opportunities over EgoSpeak |
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| [`easycom_egospeak_three_level_eval_2026-09-16/`](easycom_egospeak_three_level_eval_2026-09-16/README.md) | How dense forecasting, raw boundary anticipation, and provisional opportunity-conditioned ENTRY/YIELD differ for the EgoSpeak baseline |
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| [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 |
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| [`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 |
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# EasyCom Omni floor-event screening
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This record freezes the first comparison of EgoSpeak, Omni `G+A`, Omni
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`G+A+pooled-V`, and the three-seed current-frame F1 Frame-set Attention screen
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on EasyCom Sessions 1--3. Values were produced on the remote experiment host
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and supplied by the user on 2026-09-16. Generated predictions and per-event
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CSVs remain on that host and are not committed here.
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## Evaluated layers
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1. Dense future activity uses causal-valid framewise AP/mAP over ten horizons.
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2. Raw wearer endpoint and aggregate-other-boundary AP are threshold-free
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timing diagnostics; they are not semantic EOT/INT labels.
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3. Opportunity-conditioned ENTRY and INT/YIELD AP assigns one causally
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censored activity-derived score to each provisional LLM-assisted reviewed
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opportunity.
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All opportunity runs use identical denominators: 163 ENTRY positives and 335
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hard negatives, with 81 invalid/post-outcome cases; and 28 INT positives and
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109 hard negatives, with 5 invalid/post-outcome cases. Positive prevalence is
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32.731% for ENTRY and 20.438% for INT.
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## Main results
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| Model | 0.2-s mAP | Mean mAP | Boundary response AP | Boundary yield AP | ENTRY AP | INT/YIELD AP |
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|---|---:|---:|---:|---:|---:|---:|
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| EgoSpeak | 74.204 | 61.193 | 29.989 | 41.078 | 52.487 | 20.135 |
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| Omni G+A, seed 0 | 81.723 | 65.109 | 28.840 | 47.873 | 43.886 | 21.535 |
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| Omni G+A+pooled-V, seed 0 | 82.027 | 65.400 | 29.601 | 48.344 | 46.022 | 24.266 |
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| F1, three-seed mean | 81.777 | 64.951 | 28.423 | 48.641 | 41.561 | 22.991 |
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F1 standard deviations are 0.156 points for 0.2-s mAP, 0.095 for mean mAP,
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0.480 for boundary response AP, 0.391 for boundary yield AP, 0.581 for ENTRY
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AP, and 0.202 for INT/YIELD AP.
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The raw endpoint diagnostic, available for the three seed-0 baselines, changes
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from EgoSpeak to G+A to G+A+pooled-V as follows: onset AP
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24.491/25.960/26.019 and offset AP 38.024/45.251/45.371. Thus most generic
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offset/yield-timing gain comes from G+A; pooled visual input adds almost
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nothing to raw transition timing.
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## Interpretation
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- G+A strongly improves dense forecasting and generic wearer-offset/yield
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timing over EgoSpeak, but not reviewed semantic opportunities. In particular,
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its INT AP remains close to prevalence and its ENTRY AP is lower than
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EgoSpeak's.
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- Adding pooled-V over G+A gives only +0.304/+0.291 points in 0.2-s/mean mAP,
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+0.059/+0.120 in raw onset/offset AP, and +0.761/+0.471 in boundary
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response/yield AP. The larger but still preliminary changes are +2.135 ENTRY
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AP and +2.732 INT AP.
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- F1 is stable across seeds but does not improve on the historical pooled-V
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run: its mean changes are -0.250/-0.449 points in 0.2-s/mean mAP, -1.178 in
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boundary response AP, +0.297 in boundary yield AP, -4.461 in ENTRY AP, and
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-1.275 in INT AP. The hoped-for pattern of unchanged dense/timing performance
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with improved semantic opportunity ranking is absent.
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- Current F1 attends to a current anonymous visual person set using G+A. It has
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no per-person temporal history, person-conditioned audio, or verified
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identity continuity. This screen therefore does not support promoting F1 as
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the paper's main floor-reasoning method.
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## Hard limitations
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- The opportunity labels are provisional two-pass LLM-assisted annotations,
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not human-reviewed confirmatory labels.
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- The old G+A and pooled-V runs are seed-0 fixed-four-epoch models trained on
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Sessions 4--12 without development selection. F1 trains on Sessions 4--11
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and uses Session 12 for checkpoint selection. Their comparison is historical,
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not a protocol-matched attribution.
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- F1 needs a three-seed pooled-V control trained and selected with exactly the
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same train/dev protocol before a confirmatory conclusion.
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- All matched-recall trigger/FPR values produced in these exploratory commands
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selected thresholds on Sessions 1--3 and are excluded from this record.
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- Aggregate `others` activity cannot establish speaker identity, addressee, or
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semantic interruption intent.
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Exact aggregate values are stored in [`summary.json`](summary.json) and
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[`metrics.csv`](metrics.csv). The earlier EgoSpeak-only record is
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[`easycom_egospeak_three_level_eval_2026-09-16`](../easycom_egospeak_three_level_eval_2026-09-16/README.md).
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@@ -0,0 +1,32 @@
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model,seeds,metric,value_percent,sample_std_percent
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egospeak,1,dense_0.2s_mAP,74.204,
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egospeak,1,dense_mean_mAP,61.193,
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egospeak,1,raw_onset_mean_AP,24.491,
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egospeak,1,raw_offset_mean_AP,38.024,
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egospeak,1,boundary_response_mean_AP,29.989,
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egospeak,1,boundary_yield_mean_AP,41.078,
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egospeak,1,ENTRY_opportunity_AP,52.4867,
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egospeak,1,INT_YIELD_opportunity_AP,20.1349,
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omni_ga,1,dense_0.2s_mAP,81.72257,
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omni_ga,1,dense_mean_mAP,65.10895,
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omni_ga,1,raw_onset_mean_AP,25.960,
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omni_ga,1,raw_offset_mean_AP,45.251,
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omni_ga,1,boundary_response_mean_AP,28.840,
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omni_ga,1,boundary_yield_mean_AP,47.873,
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omni_ga,1,ENTRY_opportunity_AP,43.8864,
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omni_ga,1,INT_YIELD_opportunity_AP,21.5345,
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omni_gav,1,dense_0.2s_mAP,82.02661,
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omni_gav,1,dense_mean_mAP,65.39978,
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omni_gav,1,raw_onset_mean_AP,26.019,
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omni_gav,1,raw_offset_mean_AP,45.371,
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omni_gav,1,boundary_response_mean_AP,29.601,
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omni_gav,1,boundary_yield_mean_AP,48.344,
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omni_gav,1,ENTRY_opportunity_AP,46.0215,
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omni_gav,1,INT_YIELD_opportunity_AP,24.2662,
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f1,3,dense_0.2s_mAP,81.77745,0.15638
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f1,3,dense_mean_mAP,64.95099,0.09451
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f1,3,boundary_response_mean_AP,28.4227,0.4796
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f1,3,boundary_yield_mean_AP,48.6407,0.3914
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f1,3,ENTRY_opportunity_AP,41.5607,0.5805
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f1,3,INT_YIELD_opportunity_AP,22.9911,0.2015
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{
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"recorded_at": "2026-09-16",
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"source": "user-provided remote experiment output",
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"dataset": "EasyCom",
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"test_sessions": ["Session_1", "Session_2", "Session_3"],
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"scale": "percent",
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"opportunity_counts": {
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"entry": {"positive": 163, "hard_negative": 335, "invalid_or_post_outcome": 81, "prevalence": 32.7309},
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"int_yield": {"positive": 28, "hard_negative": 109, "invalid_or_post_outcome": 5, "prevalence": 20.4380}
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},
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"models": {
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"egospeak": {
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"dense_0.2s_map": 74.204,
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"dense_mean_map": 61.193,
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"raw_onset_mean_ap": 24.491,
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"raw_offset_mean_ap": 38.024,
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"boundary_response_mean_ap": 29.989,
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"boundary_yield_mean_ap": 41.078,
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"entry_opportunity_ap": 52.4867,
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"int_yield_opportunity_ap": 20.1349
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},
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"omni_ga_seed0": {
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"dense_0.2s_map": 81.72257,
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"dense_mean_map": 65.10895,
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"raw_onset_mean_ap": 25.960,
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"raw_offset_mean_ap": 45.251,
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"boundary_response_mean_ap": 28.840,
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"boundary_yield_mean_ap": 47.873,
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"entry_opportunity_ap": 43.8864,
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"int_yield_opportunity_ap": 21.5345
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},
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"omni_gav_seed0": {
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"dense_0.2s_map": 82.02661,
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"dense_mean_map": 65.39978,
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"raw_onset_mean_ap": 26.019,
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"raw_offset_mean_ap": 45.371,
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"boundary_response_mean_ap": 29.601,
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"boundary_yield_mean_ap": 48.344,
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"entry_opportunity_ap": 46.0215,
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"int_yield_opportunity_ap": 24.2662
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},
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"f1_seed0": {
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"dense_0.2s_map": 81.87817,
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"dense_mean_map": 65.03482,
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"boundary_response_mean_ap": 28.244,
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"boundary_yield_mean_ap": 48.566,
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"entry_opportunity_ap": 41.9125,
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"int_yield_opportunity_ap": 22.8024
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},
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"f1_seed1": {
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"dense_0.2s_map": 81.59730,
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"dense_mean_map": 64.96959,
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"boundary_response_mean_ap": 28.966,
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"boundary_yield_mean_ap": 48.292,
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"entry_opportunity_ap": 41.8788,
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"int_yield_opportunity_ap": 22.9676
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},
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"f1_seed2": {
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"dense_0.2s_map": 81.85689,
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"dense_mean_map": 64.84857,
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"boundary_response_mean_ap": 28.058,
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"boundary_yield_mean_ap": 49.064,
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"entry_opportunity_ap": 40.8907,
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"int_yield_opportunity_ap": 23.2033
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},
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"f1_three_seed": {
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"dense_0.2s_map_mean": 81.77745,
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"dense_0.2s_map_sample_std": 0.15638,
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"dense_mean_map_mean": 64.95099,
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"dense_mean_map_sample_std": 0.09451,
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"boundary_response_mean_ap_mean": 28.4227,
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"boundary_response_mean_ap_sample_std": 0.4796,
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"boundary_yield_mean_ap_mean": 48.6407,
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"boundary_yield_mean_ap_sample_std": 0.3914,
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"entry_opportunity_ap_mean": 41.5607,
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"entry_opportunity_ap_sample_std": 0.5805,
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"int_yield_opportunity_ap_mean": 22.9911,
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"int_yield_opportunity_ap_sample_std": 0.2015
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}
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},
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"labels": "provisional two-pass LLM-assisted; not human reviewed",
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"threshold_free_metrics_only": true,
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"matched_pooled_control_available": false
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}
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