Record Omni floor-event screening results

PROJECT_CONTEXT.md

@@ -59,12 +59,28 @@ result.
 
 ### Measured screening result
 
+- The released EgoSpeak MiniROADA A+V checkpoint on Sessions 1--3 obtains
+  `74.124` 0.2-second mAP and `61.155` ten-horizon mean mAP. Raw wearer
+  onset/offset anticipation is harder (`24.491/38.024` mean AP). On provisional
+  LLM-assisted matched opportunities, ENTRY AP is `52.487` versus `32.731%`
+  prevalence, while INT/YIELD AP is `20.135`, indistinguishable from its
+  `20.438%` prevalence. Figure-7-style continuous wearer curves therefore
+  support dense forecasting, not semantic floor-control claims. Full curves,
+  counts, exclusions, and caveats are frozen in
+  [`results/easycom_egospeak_three_level_eval_2026-09-16/`](results/easycom_egospeak_three_level_eval_2026-09-16/README.md).
 - F1 Frame-set Attention uses fused G+A as one query over the latest half-open
   causal person set. On the existing three-seed, four-epoch test screen it
   obtained `81.77745 +/- 0.15638` 0.2-second mAP and
   `64.95099 +/- 0.09451` ten-horizon mean mAP. This is stable, but is not a
-  matched improvement over the historical no-dev pooled result; correct/wrong
-  visual controls remain missing.
+  matched improvement over the historical no-dev pooled result. Its three-seed
+  boundary response/yield AP is `28.423 +/- 0.480` / `48.641 +/- 0.391`; on
+  provisional reviewed opportunities its ENTRY/INT AP is
+  `41.561 +/- 0.581` / `22.991 +/- 0.202`. These values are respectively
+  `-4.461/-1.275` points below the historical pooled-V seed-0 result, so the
+  current F1 screen does not support the hoped-for semantic-selectivity gain.
+  A same-split three-seed pooled control and correct/wrong visual controls
+  remain missing. See the immutable
+  [`screening record`](results/easycom_omni_floor_screen_2026-09-16/README.md).
 
 ### Measured local-fusion screens
 

docs/easycom_opportunity_event_evaluation.md

@@ -57,3 +57,22 @@ disjoint Sessions 4--12 calibration reviews and out-of-fold predictions.
 Before manuscript reporting, freeze the 2.0-second ENTRY cap, 0.6-second YIELD
 cap, positive anchor-selection policy, and multiple-competitor handling using
 training-side data only.
+
+## Recorded Sessions 1--3 baseline
+
+The first threshold-free run gives ENTRY AP 52.487% (163 positive, 335 hard
+negative; 32.731% prevalence) and INT/YIELD AP 20.135% (28 positive, 109 hard
+negative; 20.438% prevalence). It excludes 81 ENTRY and 5 INT cases as invalid
+or post-outcome. These are provisional LLM-assisted labels, not human-reviewed
+confirmatory results. Dense, raw-boundary, and opportunity metrics and their
+Figure 7 interpretation are frozen together in the
+[`2026-09-16 result record`](../results/easycom_egospeak_three_level_eval_2026-09-16/README.md).
+
+The expanded Omni screen finds no opportunity-ranking gain from the current
+F1 Frame-set Attention model: its three-seed ENTRY/INT AP is
+41.561%/22.991%, below the historical pooled-V seed-0 values
+46.022%/24.266%. Because F1 and that pooled run use different train/dev model-
+selection protocols, this is a stable negative screen rather than a matched
+confirmatory comparison. Dense, boundary, per-seed, and provenance details are
+in the
+[`Omni floor screening record`](../results/easycom_omni_floor_screen_2026-09-16/README.md).

docs/omni_speaker_floor_experiment_plan_2026-08-30.md

@@ -159,6 +159,22 @@ adapter 实验。
 - addressed-to-wearer response recall 和 latency；
 - wearer 已经讲话时的 yield recall 和 latency。
 
+首个 Sessions 1--3 EgoSpeak MiniROADA A+V 基线已把三层指标分开：dense 0.2 秒
+mAP/十-horizon mean mAP 为 `74.124/61.155`，raw wearer onset/offset mean AP 为
+`24.491/38.024`；在尚未人工复核的 LLM-assisted matched opportunities 上，ENTRY
+AP 为 `52.487`（prevalence `32.731%`），INT/YIELD AP 为 `20.135`
+（prevalence `20.438%`）。因此 EgoSpeak Figure 7 式连续 wearer score 只能作为 dense
+forecasting 的定性解释，不能代替 boundary 或 semantic opportunity 评估。完整 horizon
+曲线和限制见
+[`three-level baseline record`](../results/easycom_egospeak_three_level_eval_2026-09-16/README.md)。
+
+当前 F1 current-frame person-set screen 的三种子结果同样没有支持 semantic floor
+selectivity：ENTRY/INT opportunity AP 为 `41.561 +/- 0.581` / `22.991 +/- 0.202`，
+低于历史 pooled-V seed-0 的 `46.022/24.266`；dense mean mAP 也为
+`64.951 +/- 0.095`，没有超过历史 pooled-V 的 `65.400`。由于两者 train/dev
+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
+control。详见 [`Omni floor screening record`](../results/easycom_omni_floor_screen_2026-09-16/README.md)。
+
 ### P3：第一优先方法——匿名逐人时序旁路
 
 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。

manuscript/main/main.tex

@@ -328,6 +328,9 @@ configuration, trainable parameter counts, and inference latency.}
 
 We first retain dense future-activity evaluation for direct comparison with
 EgoSpeak, reporting AP at the 0.2--2.0~s horizons and their mean mAP.
+As a threshold-free diagnostic, we also report wearer OFF$\rightarrow$ON and
+ON$\rightarrow$OFF anticipation AP; these raw boundaries are timing proxies,
+not semantic EOT or INT labels.
 
 For dense-only models, behavioral evaluation is opportunity-conditioned and
 uses the model's original wearer-activity forecasts rather than treating them
@@ -403,7 +406,8 @@ verify identical targets, causal validity, and evaluation.}
 \begin{table}[t]
 \caption{Confirmatory EasyCom results. Entry and INT false-positive rates use
 the opportunity-conditioned activity score at matched recall $R=0.8$; E/I AP
-is the corresponding event-balanced opportunity AP.}
+is the corresponding opportunity AP. $\dagger$ denotes provisional
+LLM-assisted labels pending human review.}
 \label{tab:main}
 \centering
 \small
@@ -413,7 +417,7 @@ is the corresponding event-balanced opportunity AP.}
 Method & 0.2-s mAP $\uparrow$ & Mean mAP $\uparrow$
 & Entry FPR@$R=.8$ $\downarrow$ & INT FPR@$R=.8$ $\downarrow$ & E/I AP $\uparrow$\\
 \hline
-EgoSpeak reproduction       & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\
+EgoSpeak MiniROADA A+V      & 74.12 & 61.16 & \todo{--} & \todo{--} & $52.49/20.13^\dagger$\\
 Omni-3B frozen $G$          & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\
 Omni-3B matched PEFT        & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\
 $G+$local audio             & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\
@@ -425,6 +429,11 @@ Omni-7B scaling control     & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \t
 \end{table}
 
 \textbf{Dense forecasting.}
+The EgoSpeak baseline reaches 74.12\% 0.2-s mAP and 61.16\% mean mAP; its raw
+wearer-onset/offset mean APs are 24.49/38.02\%. On provisional reviewed
+opportunities, ENTRY AP is 52.49\% versus 32.73\% prevalence, whereas INT AP is
+20.13\% versus 20.44\%; continuous wearer-score plots therefore do not alone
+establish semantic floor-yield selectivity.
 The proposed local AV adapter improves 0.2-s mAP from
 \todo{$X$ to $Y$} over the strongest matched baseline and improves
 mean mAP by \todo{$Z$ points}.

results/README.md

@@ -6,6 +6,8 @@ readers to the right comparison.
 
 | Result record | Question answered |
 |---|---|
+| [`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 |
+| [`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 |
 | [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 |

results/easycom_egospeak_three_level_eval_2026-09-16/README.md

@@ -0,0 +1,76 @@
+# EasyCom EgoSpeak three-level evaluation
+
+This record freezes the first Sessions 1--3 evaluation of the released
+EgoSpeak MiniROADA audio-visual checkpoint under three deliberately separate
+questions. The values below were produced on the remote experiment host; the
+aggregate console output was copied into this record on 2026-09-16. Generated
+prediction and per-event artifacts remain on that host and are not committed.
+
+## Protocol and split
+
+- Test split: EasyCom Sessions 1--3.
+- Model: released EgoSpeak MiniROADA audio-visual checkpoint.
+- Dense forecasting: framewise AP for background, wearer, and aggregate
+  others at ten horizons from 0.2 to 2.0 seconds.
+- Raw boundary anticipation: threshold-free AP for wearer `OFF->ON` and
+  `ON->OFF` changes. These are timing-derived diagnostics, not semantic EOT or
+  INT claims.
+- Opportunity-conditioned behavior: one causally censored activity-derived
+  score per reviewed ENTRY or INT/YIELD opportunity. Labels are provisional
+  two-pass LLM-assisted reviews and have not been human reviewed.
+- No test-set threshold is fitted for any AP reported here. Session 4 is needed
+  only for threshold-dependent operating points, which are intentionally not
+  summarized as headline findings in this record.
+
+## Findings
+
+Dense forecasting reaches **74.124% mAP at 0.2 s** and **61.155% mean mAP**
+over ten horizons. The wearer class alone reaches 63.750% AP at 0.2 s and
+53.493% averaged over horizons. Restricting evaluation to causally valid rows
+changes mean mAP by only +0.038 percentage points (61.193% versus 61.155%).
+
+Raw wearer onset and offset anticipation obtain **24.491%** and **38.024%**
+mean AP, respectively. These lower values do not contradict the dense result:
+dense AP rewards correct future state over sustained regions, whereas a raw
+boundary event is counted once and has many harder non-boundary comparisons.
+
+On the provisional opportunity labels, ENTRY AP is **52.487%** for 163
+positives and 335 hard negatives, versus 32.731% positive prevalence
+(+19.756 points; 1.60x prevalence). INT/YIELD AP is **20.135%** for 28 positives
+and 109 hard negatives, essentially equal to its 20.438% prevalence. This is
+consistent with the output contract: wearer probability can rank many entry
+opportunities, but `1-p(wearer)` does not encode whether an interruption should
+cause a yield. The ENTRY and INT loaders additionally exclude 81 and 5
+invalid/post-outcome cases; these exclusions require an audit before any final
+paper claim.
+
+## Relation to EgoSpeak Figure 7
+
+Figure 7 of EgoSpeak plots a continuous target/wearer-speaking confidence over
+ground-truth speaking regions. It therefore corresponds most directly to the
+dense **wearer AP**, not to raw onset/offset AP or semantic ENTRY/YIELD AP. A
+visually convincing dense curve can coexist with weak boundary or semantic
+selectivity. The figure caption does not identify one forecast horizon, and it
+uses the paper's Transformer variants rather than proving the aggregate result
+of this MiniROADA run.
+
+For our eventual qualitative figure, the continuous wearer curve can be kept,
+but it should also show the causal opportunity anchor, event boundary,
+competing-risk censor, and the single score assigned to each positive or hard
+negative.
+
+## Interpretation limits
+
+- The three metric layers answer different questions and their similar numeric
+  values must not be equated.
+- Aggregate `others` activity cannot identify a particular speaker, addressee,
+  semantic EOT, or interruption intent.
+- The opportunity result is provisional until label/exclusion audit and human
+  review; it is useful for baseline diagnosis, not a final confirmatory claim.
+- This is one released checkpoint, not a multi-seed uncertainty estimate.
+
+Machine-readable curves and counts are in [`summary.json`](summary.json) and
+the compact comparison is in [`metrics.csv`](metrics.csv). The opportunity
+protocol and reproduction command are documented in
+[`docs/easycom_opportunity_event_evaluation.md`](../../docs/easycom_opportunity_event_evaluation.md).
+

results/easycom_egospeak_three_level_eval_2026-09-16/metrics.csv

@@ -0,0 +1,10 @@
+layer,metric,horizon_seconds,value_percent,num_positive,num_hard_negative,positive_prevalence_percent
+dense,dense_mAP,0.2,74.124,,,
+dense,dense_mean_mAP,mean,61.155,,,
+dense,wearer_AP,0.2,63.750,,,
+dense,wearer_mean_AP,mean,53.493,,,
+raw_boundary,wearer_onset_mean_AP,mean,24.491,,,
+raw_boundary,wearer_offset_mean_AP,mean,38.024,,,
+opportunity,ENTRY_AP,opportunity,52.4867,163,335,32.7309
+opportunity,INT_YIELD_AP,opportunity,20.1349,28,109,20.4380
+

results/easycom_egospeak_three_level_eval_2026-09-16/summary.json

@@ -0,0 +1,60 @@
+{
+  "recorded_at": "2026-09-16",
+  "source": "user-provided remote experiment output",
+  "dataset": "EasyCom",
+  "sessions": ["Session_1", "Session_2", "Session_3"],
+  "model": "EgoSpeak MiniROADA audio-visual released checkpoint",
+  "scale": "percent",
+  "dense_forecasting": {
+    "horizons_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "full_timeline": {
+      "map": [74.124, 68.291, 64.267, 61.091, 59.734, 58.774, 57.471, 57.064, 55.933, 54.802],
+      "background_ap": [67.957, 58.434, 52.256, 47.545, 46.106, 45.787, 43.552, 43.491, 42.212, 40.345],
+      "wearer_ap": [63.750, 59.595, 56.614, 54.163, 52.903, 51.471, 50.745, 49.721, 48.587, 47.376],
+      "others_ap": [90.666, 86.845, 83.930, 81.565, 80.192, 79.062, 78.116, 77.982, 76.998, 76.684],
+      "mean_map": 61.155,
+      "mean_wearer_ap": 53.493
+    },
+    "causal_valid_only": {
+      "map": [74.204, 68.349, 64.313, 61.128, 59.768, 58.805, 57.498, 57.090, 55.954, 54.821],
+      "background_ap": [68.189, 58.596, 52.373, 47.629, 46.172, 45.840, 43.587, 43.518, 42.227, 40.349],
+      "wearer_ap": [63.750, 59.595, 56.619, 54.172, 52.916, 51.488, 50.765, 49.739, 48.605, 47.393],
+      "others_ap": [90.673, 86.857, 83.947, 81.585, 80.216, 79.088, 78.143, 78.012, 77.031, 76.720],
+      "mean_map": 61.193,
+      "mean_wearer_ap": 53.504
+    }
+  },
+  "raw_boundary_anticipation": {
+    "horizons_seconds": [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0],
+    "wearer_onset_ap": [5.139, 9.878, 15.389, 21.093, 27.225, 30.997, 33.563, 34.046, 34.030, 33.550],
+    "wearer_offset_ap": [9.696, 18.846, 27.730, 34.971, 40.646, 45.112, 47.880, 50.444, 52.051, 52.862],
+    "mean_wearer_onset_ap": 24.491,
+    "mean_wearer_offset_ap": 38.024,
+    "timing_derived_eot_hold_mean_ap": 9.872,
+    "timing_derived_yield_mean_ap": 47.688
+  },
+  "opportunity_conditioned_behavior": {
+    "labels": "provisional two-pass LLM-assisted; not human reviewed",
+    "entry": {
+      "positive": 163,
+      "hard_negative": 335,
+      "invalid_or_post_outcome": 81,
+      "average_precision": 52.4867,
+      "positive_prevalence": 32.7309,
+      "ap_lift_points": 19.7558,
+      "ap_over_prevalence": 1.6036
+    },
+    "int_yield": {
+      "positive": 28,
+      "hard_negative": 109,
+      "invalid_or_post_outcome": 5,
+      "average_precision": 20.1349,
+      "positive_prevalence": 20.4380,
+      "ap_lift_points": -0.3031,
+      "ap_over_prevalence": 0.9852
+    }
+  },
+  "threshold_free": true,
+  "session4_required_for_reported_metrics": false
+}
+

results/easycom_omni_floor_screen_2026-09-16/README.md

@@ -0,0 +1,80 @@
+# EasyCom Omni floor-event screening
+
+This record freezes the first comparison of EgoSpeak, Omni `G+A`, Omni
+`G+A+pooled-V`, and the three-seed current-frame F1 Frame-set Attention screen
+on EasyCom Sessions 1--3. Values were produced on the remote experiment host
+and supplied by the user on 2026-09-16. Generated predictions and per-event
+CSVs remain on that host and are not committed here.
+
+## Evaluated layers
+
+1. Dense future activity uses causal-valid framewise AP/mAP over ten horizons.
+2. Raw wearer endpoint and aggregate-other-boundary AP are threshold-free
+   timing diagnostics; they are not semantic EOT/INT labels.
+3. Opportunity-conditioned ENTRY and INT/YIELD AP assigns one causally
+   censored activity-derived score to each provisional LLM-assisted reviewed
+   opportunity.
+
+All opportunity runs use identical denominators: 163 ENTRY positives and 335
+hard negatives, with 81 invalid/post-outcome cases; and 28 INT positives and
+109 hard negatives, with 5 invalid/post-outcome cases. Positive prevalence is
+32.731% for ENTRY and 20.438% for INT.
+
+## Main results
+
+| Model | 0.2-s mAP | Mean mAP | Boundary response AP | Boundary yield AP | ENTRY AP | INT/YIELD AP |
+|---|---:|---:|---:|---:|---:|---:|
+| EgoSpeak | 74.204 | 61.193 | 29.989 | 41.078 | 52.487 | 20.135 |
+| Omni G+A, seed 0 | 81.723 | 65.109 | 28.840 | 47.873 | 43.886 | 21.535 |
+| Omni G+A+pooled-V, seed 0 | 82.027 | 65.400 | 29.601 | 48.344 | 46.022 | 24.266 |
+| F1, three-seed mean | 81.777 | 64.951 | 28.423 | 48.641 | 41.561 | 22.991 |
+
+F1 standard deviations are 0.156 points for 0.2-s mAP, 0.095 for mean mAP,
+0.480 for boundary response AP, 0.391 for boundary yield AP, 0.581 for ENTRY
+AP, and 0.202 for INT/YIELD AP.
+
+The raw endpoint diagnostic, available for the three seed-0 baselines, changes
+from EgoSpeak to G+A to G+A+pooled-V as follows: onset AP
+24.491/25.960/26.019 and offset AP 38.024/45.251/45.371. Thus most generic
+offset/yield-timing gain comes from G+A; pooled visual input adds almost
+nothing to raw transition timing.
+
+## Interpretation
+
+- G+A strongly improves dense forecasting and generic wearer-offset/yield
+  timing over EgoSpeak, but not reviewed semantic opportunities. In particular,
+  its INT AP remains close to prevalence and its ENTRY AP is lower than
+  EgoSpeak's.
+- Adding pooled-V over G+A gives only +0.304/+0.291 points in 0.2-s/mean mAP,
+  +0.059/+0.120 in raw onset/offset AP, and +0.761/+0.471 in boundary
+  response/yield AP. The larger but still preliminary changes are +2.135 ENTRY
+  AP and +2.732 INT AP.
+- F1 is stable across seeds but does not improve on the historical pooled-V
+  run: its mean changes are -0.250/-0.449 points in 0.2-s/mean mAP, -1.178 in
+  boundary response AP, +0.297 in boundary yield AP, -4.461 in ENTRY AP, and
+  -1.275 in INT AP. The hoped-for pattern of unchanged dense/timing performance
+  with improved semantic opportunity ranking is absent.
+- Current F1 attends to a current anonymous visual person set using G+A. It has
+  no per-person temporal history, person-conditioned audio, or verified
+  identity continuity. This screen therefore does not support promoting F1 as
+  the paper's main floor-reasoning method.
+
+## Hard limitations
+
+- The opportunity labels are provisional two-pass LLM-assisted annotations,
+  not human-reviewed confirmatory labels.
+- The old G+A and pooled-V runs are seed-0 fixed-four-epoch models trained on
+  Sessions 4--12 without development selection. F1 trains on Sessions 4--11
+  and uses Session 12 for checkpoint selection. Their comparison is historical,
+  not a protocol-matched attribution.
+- F1 needs a three-seed pooled-V control trained and selected with exactly the
+  same train/dev protocol before a confirmatory conclusion.
+- All matched-recall trigger/FPR values produced in these exploratory commands
+  selected thresholds on Sessions 1--3 and are excluded from this record.
+- Aggregate `others` activity cannot establish speaker identity, addressee, or
+  semantic interruption intent.
+
+Exact aggregate values are stored in [`summary.json`](summary.json) and
+[`metrics.csv`](metrics.csv). The earlier EgoSpeak-only record is
+[`easycom_egospeak_three_level_eval_2026-09-16`](../easycom_egospeak_three_level_eval_2026-09-16/README.md).
+

results/easycom_omni_floor_screen_2026-09-16/metrics.csv

@@ -0,0 +1,32 @@
+model,seeds,metric,value_percent,sample_std_percent
+egospeak,1,dense_0.2s_mAP,74.204,
+egospeak,1,dense_mean_mAP,61.193,
+egospeak,1,raw_onset_mean_AP,24.491,
+egospeak,1,raw_offset_mean_AP,38.024,
+egospeak,1,boundary_response_mean_AP,29.989,
+egospeak,1,boundary_yield_mean_AP,41.078,
+egospeak,1,ENTRY_opportunity_AP,52.4867,
+egospeak,1,INT_YIELD_opportunity_AP,20.1349,
+omni_ga,1,dense_0.2s_mAP,81.72257,
+omni_ga,1,dense_mean_mAP,65.10895,
+omni_ga,1,raw_onset_mean_AP,25.960,
+omni_ga,1,raw_offset_mean_AP,45.251,
+omni_ga,1,boundary_response_mean_AP,28.840,
+omni_ga,1,boundary_yield_mean_AP,47.873,
+omni_ga,1,ENTRY_opportunity_AP,43.8864,
+omni_ga,1,INT_YIELD_opportunity_AP,21.5345,
+omni_gav,1,dense_0.2s_mAP,82.02661,
+omni_gav,1,dense_mean_mAP,65.39978,
+omni_gav,1,raw_onset_mean_AP,26.019,
+omni_gav,1,raw_offset_mean_AP,45.371,
+omni_gav,1,boundary_response_mean_AP,29.601,
+omni_gav,1,boundary_yield_mean_AP,48.344,
+omni_gav,1,ENTRY_opportunity_AP,46.0215,
+omni_gav,1,INT_YIELD_opportunity_AP,24.2662,
+f1,3,dense_0.2s_mAP,81.77745,0.15638
+f1,3,dense_mean_mAP,64.95099,0.09451
+f1,3,boundary_response_mean_AP,28.4227,0.4796
+f1,3,boundary_yield_mean_AP,48.6407,0.3914
+f1,3,ENTRY_opportunity_AP,41.5607,0.5805
+f1,3,INT_YIELD_opportunity_AP,22.9911,0.2015
+

results/easycom_omni_floor_screen_2026-09-16/summary.json

@@ -0,0 +1,85 @@
+{
+  "recorded_at": "2026-09-16",
+  "source": "user-provided remote experiment output",
+  "dataset": "EasyCom",
+  "test_sessions": ["Session_1", "Session_2", "Session_3"],
+  "scale": "percent",
+  "opportunity_counts": {
+    "entry": {"positive": 163, "hard_negative": 335, "invalid_or_post_outcome": 81, "prevalence": 32.7309},
+    "int_yield": {"positive": 28, "hard_negative": 109, "invalid_or_post_outcome": 5, "prevalence": 20.4380}
+  },
+  "models": {
+    "egospeak": {
+      "dense_0.2s_map": 74.204,
+      "dense_mean_map": 61.193,
+      "raw_onset_mean_ap": 24.491,
+      "raw_offset_mean_ap": 38.024,
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+  },
+  "labels": "provisional two-pass LLM-assisted; not human reviewed",
+  "threshold_free_metrics_only": true,
+  "matched_pooled_control_available": false
+}
+