Fix opportunity competing-risk evaluation

PROJECT_CONTEXT.md

@@ -54,19 +54,23 @@ result.
 - An opportunity-conditioned dense-output evaluator aligns ENTRY positives and
   core negatives at non-wearer releases, aligns INT positives and hold-floor
   negatives at competing non-wearer onsets, and applies strict competing-risk
-  censoring. It uses only the original wearer probabilities, excludes wearer
+  censoring. ENTRY positives whose selected release is followed by a competing
-  backchannels from headline ENTRY FPR, and records unanchored/ambiguous cases.
+  other onset or intervening wearer activity are excluded, and ambiguous
+  multi-competitor INT cases are not assigned an arbitrary onset. It uses only
+  the original wearer probabilities, excludes wearer backchannels from
+  headline ENTRY FPR, and records unanchored/ambiguous cases.
 
 ### 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%`
+  LLM-assisted matched opportunities, the original v1 evaluator reported
-  prevalence, while INT/YIELD AP is `20.135`, indistinguishable from its
+  ENTRY/INT AP of `52.487/20.135`; those opportunity values are superseded
-  `20.438%` prevalence. Figure-7-style continuous wearer curves therefore
+  because v1 could cross a competing ENTRY onset and arbitrarily selected among
-  support dense forecasting, not semantic floor-control claims. Full curves,
+  multiple INT competitors. Figure-7-style continuous wearer curves support
-  counts, exclusions, and caveats are frozen in
+  dense forecasting, not semantic floor-control claims. The unaffected dense
+  curves and historical v1 opportunity record are 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
@@ -74,12 +78,11 @@ result.
   `64.95099 +/- 0.09451` ten-horizon mean mAP. This is stable, but is not a
   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
+  the superseded v1 opportunity evaluator reported ENTRY/INT AP of
-  `41.561 +/- 0.581` / `22.991 +/- 0.202`. These values are respectively
+  `41.561 +/- 0.581` / `22.991 +/- 0.202`; these values require a v2 rerun and
-  `-4.461/-1.275` points below the historical pooled-V seed-0 result, so the
+  cannot currently support a semantic-selectivity conclusion. A same-split
-  current F1 screen does not support the hoped-for semantic-selectivity gain.
+  three-seed pooled control and correct/wrong visual controls remain missing.
-  A same-split three-seed pooled control and correct/wrong visual controls
+  See the historical
-  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

@@ -10,6 +10,12 @@ head.
 
 A positive must have a reviewer-selected preceding non-wearer release. Its
 opportunity starts at that release and resolves at the reviewed wearer entry.
+If a new non-wearer onset occurs before or exactly at that entry, the release
+has already been resolved by a competing event and the positive is excluded.
+The same applies when wearer activity already occurred between the selected
+release and the reviewed onset: the selected release is then a stale,
+post-outcome anchor. Both cases are counted separately in
+`review_diagnostics.skipped`.
 Core hard negatives start at the same type of non-wearer release and resolve
 when the same speaker resumes (`ENTRY_OTHER_MIDTURN_PAUSE`) or a different
 non-wearer takes the next floor (`ENTRY_OTHER_TO_OTHER`). The score is the
@@ -27,10 +33,10 @@ the maximum `1 - p_wearer` over 0.2--0.6 seconds, censored strictly at that
 non-wearer's offset. Reviewed `INT_YIELD` items are positive; other
 backchannels, failed takeovers, and clear wearer-hold cases are hard negatives.
 
-Current INT reviews do not select one competing onset. The loader therefore
+Current INT reviews do not select one competing onset. A unique supplied other
-uses the latest supplied other interval that crosses the wearer offset within
+interval that crosses the wearer offset within the annotation tolerance is
-the annotation tolerance. It records how many intervals were eligible so
+used. Cases with multiple eligible intervals are excluded and counted rather
-multi-competitor cases can be audited before the protocol is frozen.
+than being assigned a deterministic latest-onset anchor.
 
 ## Provisional EgoSpeak run
 
@@ -58,21 +64,24 @@ 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
+## Superseded v1 Sessions 1--3 baseline
 
-The first threshold-free run gives ENTRY AP 52.487% (163 positive, 335 hard
+The first threshold-free run used the superseded v1 loader and reported ENTRY
-negative; 32.731% prevalence) and INT/YIELD AP 20.135% (28 positive, 109 hard
+AP 52.487% (163 positive, 335 hard negative; 32.731% prevalence) and INT/YIELD
-negative; 20.438% prevalence). It excludes 81 ENTRY and 5 INT cases as invalid
+AP 20.135% (28 positive, 109 hard negative; 20.438% prevalence). It excluded
-or post-outcome. These are provisional LLM-assisted labels, not human-reviewed
+81 ENTRY and 5 INT cases as invalid or post-outcome. These opportunity values
-confirmatory results. Dense, raw-boundary, and opportunity metrics and their
+must not be reported: v1 allowed a
-Figure 7 interpretation are frozen together in the
+selected ENTRY release to cross an intervening other onset and selected the
+latest onset in ambiguous multi-competitor INT cases. Dense and raw-boundary
+metrics are unaffected. Rerun the opportunity evaluation with protocol v2
+before updating the manuscript. The historical output is retained 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
+The expanded v1 Omni screen found 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
+selection protocols and used the superseded opportunity loader, only its dense
-confirmatory comparison. Dense, boundary, per-seed, and provenance details are
+and raw-boundary findings remain current. 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

@@ -168,12 +168,13 @@ forecasting 的定性解释，不能代替 boundary 或 semantic opportunity 评
 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
+当前 F1 current-frame person-set screen 的 dense mean mAP 为
-selectivity：ENTRY/INT opportunity AP 为 `41.561 +/- 0.581` / `22.991 +/- 0.202`，
+`64.951 +/- 0.095`，没有超过历史 pooled-V 的 `65.400`。原 v1 evaluator 给出的
-低于历史 pooled-V seed-0 的 `46.022/24.266`；dense mean mAP 也为
+ENTRY/INT opportunity AP（`41.561 +/- 0.581` / `22.991 +/- 0.202`）已失效：v1
-`64.951 +/- 0.095`，没有超过历史 pooled-V 的 `65.400`。由于两者 train/dev
+可能跨过 competing ENTRY onset，且会在多个 INT competitor 中任意选择 latest onset。
-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
+必须用 v2 重跑后才能判断 semantic floor selectivity；正式归因还必须补同协议三种子
-control。详见 [`Omni floor screening record`](../results/easycom_omni_floor_screen_2026-09-16/README.md)。
+pooled control。历史数值与勘误见
+[`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
 

evaluate_egospeak_opportunity_events.py

@@ -8,14 +8,18 @@ event head.
 
 ENTRY opportunities are anchored at a reviewed preceding non-wearer release.
 Positive opportunities resolve at the wearer entry; core hard negatives
-resolve when the same or a different non-wearer speaks next.  The score is the
+resolve when the same or a different non-wearer speaks next.  A positive is
-maximum wearer probability over available 0.2--2.0 s forecasts up to that
+excluded if another non-wearer onset competes before (or exactly at) its wearer
-competing resolution.
+entry, or if wearer activity already occurred after the selected release.  The
+score is the maximum wearer probability over available 0.2--2.0 s forecasts up
+to the uncontested resolution.
 
 YIELD opportunities are anchored at a non-wearer onset during wearer speech.
 The score is the maximum future wearer-inactivity probability over 0.2--0.6 s,
-censored at that non-wearer's offset.  Positive and hard-negative examples
+censored at that non-wearer's offset.  Reviews with multiple eligible
-therefore begin at the same kind of observed conversational opportunity.
+competing onsets are excluded until the annotation selects one.  Positive and
+hard-negative examples therefore begin at the same kind of unambiguous
+observed conversational opportunity.
 
 Prediction arrays use the repository contract
 ``[target_time, class, future_horizon]``.  All scores are read diagonally from
@@ -178,17 +182,83 @@ def _candidate_region(
     return start, end
 
 
-def _int_competing_interval(
+def _entry_selected_option(
+    candidate: dict[str, Any],
+    review: dict[str, Any],
+    *,
+    selected_release_seconds: float,
+    uid: str,
+) -> dict[str, Any] | None:
+    evidence = candidate.get("temporal_evidence")
+    evidence = evidence if isinstance(evidence, dict) else {}
+    options = evidence.get("opportunity_options")
+    if not isinstance(options, list):
+        return None
+    selected_uid = review.get("selected_opportunity_uid")
+    matches: list[dict[str, Any]] = []
+    for index, option in enumerate(options):
+        if not isinstance(option, dict):
+            continue
+        option_seconds = _optional_seconds(
+            option.get("anchor_seconds"),
+            field=f"opportunity_options[{index}].anchor_seconds",
+            uid=uid,
+        )
+        if selected_uid is not None and option.get("uid") == selected_uid:
+            matches.append(option)
+        elif selected_uid is None and option_seconds is not None and math.isclose(
+            option_seconds, selected_release_seconds, abs_tol=1e-7
+        ):
+            matches.append(option)
+    if len(matches) > 1:
+        raise ValueError(f"{uid}: selected ENTRY opportunity is not unique")
+    return matches[0] if matches else None
+
+
+def _entry_competing_onsets(
+    candidate: dict[str, Any],
+    *,
+    selected_release_seconds: float,
+    behavior_anchor_seconds: float,
+    uid: str,
+) -> list[float]:
+    """Return other onsets that resolve a release before the wearer entry."""
+
+    evidence = candidate.get("temporal_evidence")
+    evidence = evidence if isinstance(evidence, dict) else {}
+    raw_intervals = evidence.get("gap_other_intervals")
+    if not isinstance(raw_intervals, list):
+        raw_intervals = []
+    onsets: set[float] = set()
+    for index, interval in enumerate(raw_intervals):
+        if not isinstance(interval, dict):
+            continue
+        onset = _optional_seconds(
+            interval.get("onset_seconds"),
+            field=f"gap_other_intervals[{index}].onset_seconds",
+            uid=uid,
+        )
+        if onset is None:
+            continue
+        if (
+            onset >= selected_release_seconds - 1e-7
+            and onset <= behavior_anchor_seconds + 1e-7
+        ):
+            onsets.add(onset)
+    return sorted(onsets)
+
+
+def _int_competing_intervals(
     candidate: dict[str, Any],
     *,
     behavior_anchor_seconds: float,
     uid: str,
-) -> tuple[float, float, int] | None:
+) -> list[tuple[float, float]]:
     evidence = candidate.get("temporal_evidence")
     evidence = evidence if isinstance(evidence, dict) else {}
     raw_intervals = evidence.get("nearby_other_intervals")
     if not isinstance(raw_intervals, list):
-        return None
+        return []
     eligible: list[tuple[float, float]] = []
     for index, interval in enumerate(raw_intervals):
         if not isinstance(interval, dict):
@@ -209,13 +279,7 @@ def _int_competing_interval(
             continue
         if onset < behavior_anchor_seconds and offset > onset:
             eligible.append((onset, offset))
-    if not eligible:
+    return sorted(set(eligible))
-        return None
-    # A semantic INT review does not currently select one competing onset.
-    # The latest interval crossing the wearer offset is the deterministic,
-    # most proximal causal candidate.  Multiplicity is surfaced for audit.
-    onset, offset = max(eligible, key=lambda item: (item[0], item[1]))
-    return onset, offset, len(eligible)
 
 
 def load_opportunity_events(
@@ -278,6 +342,38 @@ def load_opportunity_events(
                 if selected_release is None:
                     skipped["entry_positive_without_selected_release"] += 1
                     continue
+                selected_option = _entry_selected_option(
+                    candidate,
+                    review,
+                    selected_release_seconds=selected_release,
+                    uid=uid,
+                )
+                if (
+                    selected_option is not None
+                    and selected_option.get(
+                        "wearer_active_between_release_and_onset"
+                    ) is True
+                ):
+                    skipped["entry_positive_post_outcome_wearer_activity"] += 1
+                    continue
+                competing_onsets = _entry_competing_onsets(
+                    candidate,
+                    selected_release_seconds=selected_release,
+                    behavior_anchor_seconds=behavior,
+                    uid=uid,
+                )
+                option_reports_competitor = (
+                    selected_option is not None
+                    and selected_option.get(
+                        "other_onset_between_release_and_onset"
+                    ) is True
+                )
+                if option_reports_competitor and not competing_onsets:
+                    skipped["entry_positive_competing_onset_unresolved"] += 1
+                    continue
+                if competing_onsets:
+                    skipped["entry_positive_competed_before_behavior"] += 1
+                    continue
                 anchor = selected_release
                 resolution = behavior
                 censor = behavior
@@ -298,17 +394,21 @@ def load_opportunity_events(
             competing_intervals = 0
         else:
             if positive:
-                interval = _int_competing_interval(
+                intervals = _int_competing_intervals(
                     candidate,
                     behavior_anchor_seconds=behavior,
                     uid=uid,
                 )
-                if interval is None:
+                if not intervals:
                     skipped["int_positive_without_crossing_other"] += 1
                     continue
-                anchor, censor, competing_intervals = interval
+                if len(intervals) > 1:
+                    skipped["int_positive_ambiguous_competing_others"] += 1
+                    continue
+                anchor, censor = intervals[0]
+                competing_intervals = 1
                 resolution = behavior
-                basis = "latest_other_onset_crossing_wearer_offset"
+                basis = "unique_other_onset_crossing_wearer_offset"
             elif subtype_value in INT_HARD_NEGATIVES:
                 if source in INT_MINED_SOURCES:
                     anchor, censor = _candidate_region(candidate, uid=uid)
@@ -316,17 +416,21 @@ def load_opportunity_events(
                     competing_intervals = 1
                     basis = "mined_competing_other_onset"
                 else:
-                    interval = _int_competing_interval(
+                    intervals = _int_competing_intervals(
                         candidate,
                         behavior_anchor_seconds=behavior,
                         uid=uid,
                     )
-                    if interval is None:
+                    if not intervals:
                         skipped["int_hn_without_competing_other"] += 1
                         continue
-                    anchor, censor, competing_intervals = interval
+                    if len(intervals) > 1:
+                        skipped["int_hn_ambiguous_competing_others"] += 1
+                        continue
+                    anchor, censor = intervals[0]
+                    competing_intervals = 1
                     resolution = behavior
-                    basis = "latest_other_onset_crossing_non_yield_offset"
+                    basis = "unique_other_onset_crossing_non_yield_offset"
             else:
                 skipped["int_unsupported_hard_negative"] += 1
                 continue
@@ -731,8 +835,8 @@ def main() -> None:
         else calibration_diagnostics["num_provisional_llm"]
     )
     payload = {
-        "schema_version": 1,
+        "schema_version": 2,
-        "protocol": "egospeak-opportunity-conditioned-floor-actions-v1",
+        "protocol": "egospeak-opportunity-conditioned-floor-actions-v2",
         "semantic_scope": (
             "LLM-assisted opportunity labels with dense wearer-activity scores; "
             "this is provisional and no human review is implied."
@@ -757,6 +861,13 @@ def main() -> None:
         "yield_max_seconds": yield_max,
         "entry_headline_hard_negatives": sorted(ENTRY_CORE_HARD_NEGATIVES),
         "entry_backchannel_policy": "separate diagnostic; excluded from headline",
+        "entry_competing_risk_policy": (
+            "exclude selected-release positives with an other onset at or before "
+            "the wearer entry, or with intervening wearer activity"
+        ),
+        "int_multiple_competitor_policy": (
+            "exclude until one competing onset is selected by review"
+        ),
         "matched_recalls": recalls,
         "review_diagnostics": review_diagnostics,
         "calibration_review_diagnostics": calibration_diagnostics,

manuscript/main/main.tex

@@ -406,8 +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 opportunity AP. $\dagger$ denotes provisional
+is the corresponding opportunity AP. Event results will be populated only
-LLM-assisted labels pending human review.}
+after protocol-v2 reruns and human adjudication.}
 \label{tab:main}
 \centering
 \small
@@ -417,7 +417,7 @@ LLM-assisted labels pending human review.}
 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 MiniROADA A+V      & 74.12 & 61.16 & \todo{--} & \todo{--} & $52.49/20.13^\dagger$\\
+EgoSpeak MiniROADA A+V      & 74.12 & 61.16 & \todo{--} & \todo{--} & \todo{--/--}\\
 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{--/--}\\
@@ -430,10 +430,9 @@ Omni-7B scaling control     & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \t
 
 \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
+wearer-onset/offset mean APs are 24.49/38.02\%. Opportunity-conditioned values
-opportunities, ENTRY AP is 52.49\% versus 32.73\% prevalence, whereas INT AP is
+remain pending a protocol-v2 rerun and human review; continuous wearer-score
-20.13\% versus 20.44\%; continuous wearer-score plots therefore do not alone
+plots do not alone establish semantic floor-yield selectivity.
-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/easycom_egospeak_three_level_eval_2026-09-16/README.md

@@ -1,5 +1,11 @@
 # EasyCom EgoSpeak three-level evaluation
 
+> **Protocol erratum.** The dense and raw-boundary results in this record remain
+> valid. The opportunity-conditioned ENTRY/INT values were produced by the
+> superseded v1 loader, which could cross an intervening ENTRY competitor and
+> selected the latest onset in ambiguous multi-competitor INT cases. Do not cite
+> those opportunity values; rerun protocol v2 and create a new result record.
+
 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
@@ -15,7 +21,7 @@ prediction and per-event artifacts remain on that host and are not committed.
 - 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
+- Opportunity-conditioned behavior: historical v1 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
@@ -34,15 +40,16 @@ 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
+The superseded v1 run reported ENTRY AP of **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
+and 109 hard negatives, essentially equal to its 20.438% prevalence. These
-consistent with the output contract: wearer probability can rank many entry
+values are retained only for provenance and must be replaced by a v2 rerun. The
-opportunities, but `1-p(wearer)` does not encode whether an interruption should
+original pattern was consistent with the output contract: wearer probability
-cause a yield. The ENTRY and INT loaders additionally exclude 81 and 5
+can rank many entry opportunities, but `1-p(wearer)` does not encode whether an
-invalid/post-outcome cases; these exclusions require an audit before any final
+interruption should cause a yield. The ENTRY and INT loaders additionally
-paper claim.
+exclude 81 and 5 invalid/post-outcome cases; these exclusions require an audit
+before any final paper claim.
 
 ## Relation to EgoSpeak Figure 7
 
@@ -73,4 +80,3 @@ 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/summary.json

@@ -1,6 +1,8 @@
 {
   "recorded_at": "2026-09-16",
   "source": "user-provided remote experiment output",
+  "opportunity_protocol_status": "superseded_v1_rerun_required",
+  "opportunity_protocol_erratum": "Dense and raw-boundary metrics remain valid; opportunity metrics must be rerun with egospeak-opportunity-conditioned-floor-actions-v2.",
   "dataset": "EasyCom",
   "sessions": ["Session_1", "Session_2", "Session_3"],
   "model": "EgoSpeak MiniROADA audio-visual released checkpoint",
@@ -57,4 +59,3 @@
   "threshold_free": true,
   "session4_required_for_reported_metrics": false
 }
-

results/easycom_omni_floor_screen_2026-09-16/README.md

@@ -1,5 +1,12 @@
 # EasyCom Omni floor-event screening
 
+> **Protocol erratum.** Dense and raw-boundary metrics remain valid. All
+> opportunity-conditioned ENTRY/INT values below used the superseded v1 loader,
+> which could cross an intervening ENTRY competitor and selected the latest
+> onset in ambiguous multi-competitor INT cases. Retain them for provenance
+> only; do not use them for model selection or manuscript claims before a v2
+> rerun.
+
 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
@@ -11,9 +18,8 @@ CSVs remain on that host and are not committed here.
 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
+3. Opportunity-conditioned ENTRY and INT/YIELD AP is a historical v1 screen
-   censored activity-derived score to each provisional LLM-assisted reviewed
+   that requires rerunning with the corrected v2 competing-risk protocol.
-   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
@@ -39,7 +45,7 @@ from EgoSpeak to G+A to G+A+pooled-V as follows: onset AP
 offset/yield-timing gain comes from G+A; pooled visual input adds almost
 nothing to raw transition timing.
 
-## Interpretation
+## Historical v1 interpretation
 
 - G+A strongly improves dense forecasting and generic wearer-offset/yield
   timing over EgoSpeak, but not reviewed semantic opportunities. In particular,
@@ -77,4 +83,3 @@ nothing to raw transition timing.
 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/summary.json

@@ -1,6 +1,8 @@
 {
   "recorded_at": "2026-09-16",
   "source": "user-provided remote experiment output",
+  "opportunity_protocol_status": "superseded_v1_rerun_required",
+  "opportunity_protocol_erratum": "Dense and raw-boundary metrics remain valid; opportunity metrics must be rerun with egospeak-opportunity-conditioned-floor-actions-v2.",
   "dataset": "EasyCom",
   "test_sessions": ["Session_1", "Session_2", "Session_3"],
   "scale": "percent",
@@ -82,4 +84,3 @@
   "threshold_free_metrics_only": true,
   "matched_pooled_control_available": false
 }
-

test_evaluate_egospeak_opportunity_events.py

@@ -30,7 +30,10 @@ def reviewed_row(
     region_start: float,
     region_end: float,
     selected_release: float | None = None,
+    selected_release_uid: str | None = None,
     nearby: list[dict[str, object]] | None = None,
+    opportunity_options: list[dict[str, object]] | None = None,
+    gap_other_intervals: list[dict[str, object]] | None = None,
     candidate_source: str | None = None,
 ) -> dict[str, object]:
     event_type = None
@@ -45,6 +48,7 @@ def reviewed_row(
             "human_verdict": verdict,
             "human_event_type": event_type,
             "human_subtype": subtype,
+            "selected_opportunity_uid": selected_release_uid,
             "selected_opportunity_seconds": selected_release,
             "boundary_correction_seconds": 0,
         },
@@ -59,6 +63,8 @@ def reviewed_row(
                 "behavior_anchor_seconds": anchor,
                 "wearer_offset_seconds": anchor,
                 "nearby_other_intervals": nearby or [],
+                "opportunity_options": opportunity_options or [],
+                "gap_other_intervals": gap_other_intervals or [],
             },
         },
     }
@@ -119,7 +125,7 @@ class OpportunityEventTests(unittest.TestCase):
             diagnostics["skipped"]["entry_backchannel_separate_diagnostic"], 1
         )
 
-    def test_loader_selects_latest_crossing_other_for_int(self) -> None:
+    def test_loader_excludes_ambiguous_crossing_others_for_int(self) -> None:
         nearby = [
             {
                 "onset_seconds": 1.0,
@@ -145,11 +151,91 @@ class OpportunityEventTests(unittest.TestCase):
             )
         ])
         self.addCleanup(temporary.cleanup)
-        events, _ = load_opportunity_events([path], ["Session_4"])
+        events, diagnostics = load_opportunity_events([path], ["Session_4"])
-        self.assertEqual(len(events), 1)
+        self.assertEqual(events, [])
-        self.assertEqual(events[0].opportunity_anchor_seconds, 2.0)
+        self.assertEqual(
-        self.assertEqual(events[0].censor_seconds, 3.8)
+            diagnostics["skipped"]["int_positive_ambiguous_competing_others"],
-        self.assertEqual(events[0].competing_intervals, 2)
+            1,
+        )
+
+    def test_loader_excludes_entry_positive_at_competing_other_onset(self) -> None:
+        rows = [
+            reviewed_row(
+                "entry-competed",
+                family="entry",
+                verdict="POSITIVE",
+                subtype=None,
+                anchor=1.6,
+                region_start=0.0,
+                region_end=3.0,
+                selected_release=1.0,
+                selected_release_uid="release",
+                opportunity_options=[
+                    {
+                        "uid": "release",
+                        "anchor_seconds": 1.0,
+                        "wearer_active_between_release_and_onset": False,
+                        "other_onset_between_release_and_onset": True,
+                    }
+                ],
+                gap_other_intervals=[
+                    {"uid": "competitor", "onset_seconds": 1.4}
+                ],
+            ),
+            reviewed_row(
+                "entry-simultaneous",
+                family="entry",
+                verdict="POSITIVE",
+                subtype=None,
+                anchor=4.0,
+                region_start=3.0,
+                region_end=5.0,
+                selected_release=3.2,
+                gap_other_intervals=[
+                    {"uid": "competitor", "onset_seconds": 4.0}
+                ],
+            ),
+        ]
+        temporary, path = self.write_reviews(rows)
+        self.addCleanup(temporary.cleanup)
+        events, diagnostics = load_opportunity_events([path], ["Session_4"])
+        self.assertEqual(events, [])
+        self.assertEqual(
+            diagnostics["skipped"]["entry_positive_competed_before_behavior"],
+            2,
+        )
+
+    def test_loader_excludes_entry_positive_after_prior_wearer_activity(self) -> None:
+        temporary, path = self.write_reviews([
+            reviewed_row(
+                "entry-post-outcome",
+                family="entry",
+                verdict="POSITIVE",
+                subtype=None,
+                anchor=2.0,
+                region_start=0.0,
+                region_end=3.0,
+                selected_release=1.0,
+                selected_release_uid="release",
+                opportunity_options=[
+                    {
+                        "uid": "release",
+                        "anchor_seconds": 1.0,
+                        "wearer_active_between_release_and_onset": True,
+                        "other_onset_between_release_and_onset": False,
+                    }
+                ],
+            )
+        ])
+        self.addCleanup(temporary.cleanup)
+        events, diagnostics = load_opportunity_events([path], ["Session_4"])
+        self.assertEqual(events, [])
+        self.assertEqual(
+            diagnostics["skipped"][
+                "entry_positive_post_outcome_wearer_activity"
+            ],
+            1,
+        )
 
     def test_entry_hn_requires_matching_independently_mined_source(self) -> None:
         rows = [
@@ -385,7 +471,7 @@ class OpportunityEventTests(unittest.TestCase):
         payload = json.loads(output.read_text(encoding="utf-8"))
         self.assertEqual(
             payload["protocol"],
-            "egospeak-opportunity-conditioned-floor-actions-v1",
+            "egospeak-opportunity-conditioned-floor-actions-v2",
         )
         self.assertTrue((root / "summary_metrics.csv").is_file())
         self.assertTrue((root / "summary_events.csv").is_file())