Add competing-risk evaluation and align ICASSP story

manuscript/main/REVIEWER_CHECKLIST.md

@@ -2,13 +2,11 @@
 
 This is an internal execution checklist. It is not part of the submitted PDF.
 
-## Reviewer verdict on the current draft
+## Reviewer verdict and frozen paper shape
 
-The potentially publishable question is now model-agnostic: **do
-general-purpose omni representations expose the fine-grained evidence needed
-for 200 ms egocentric floor action, and can causal local evidence reduce false
-wearer entries at matched onset recall when frozen readout or PEFT is
-insufficient?**
+The potentially publishable question is: **can a general-purpose Omni readout
+be adapted into 200 ms, wearer-relative floor control by explicitly preserving
+short-window causal audio and anonymous visible-person evidence?**
 
 The contribution is not “the first audio-visual multiparty turn model.” VAP,
 EgoSpeak, SICNet, and MuVAP already occupy adjacent territory. The defensible
@@ -17,17 +15,25 @@ wearer, combined with a controlled diagnosis of global omni states and a
 behavior-aligned multi-other event protocol.
 
 Current Qwen2.5-Omni-3B results are exploratory motivation only. They neither
-prove a limitation shared by omni models nor establish track-consistent fusion
-as the final method. The planned initial backbone set is Qwen2.5-Omni-3B,
-Qwen2.5-Omni-7B, and MiniCPM-o 4.5.
+prove a limitation shared by Omni models nor establish track-consistent fusion
+as the final method. Qwen2.5-Omni-3B is primary; Qwen2.5-Omni-7B is an optional
+capacity control. MiniCPM-o and a cross-family sweep are outside the core paper.
 
-## P0 — Freeze the task before the backbone sweep
+The four-page paper has only three contributions: temporal--role adaptation
+mismatch, one causal local floor adapter, and causal behavioral evidence.
+LoRA, the oracle, and the event protocol are respectively a baseline,
+motivation, and validation mechanism.
+
+## P0 — Freeze the task before confirmatory adaptation runs
 
 - [ ] Define the exact 5 Hz causal cutoff and physical target interval for each
   of the ten 0.2 s horizons.
 - [ ] Freeze wearer-onset/yield and multi-other exchange definitions.
-- [ ] Add competing-risk rules: a candidate event ends at the first matched
-  wearer action, incompatible floor change, timeout, or invalid source boundary.
+- [x] Implement and unit-test competing-risk rules: a response candidate ends
+  before the next other onset and a yield candidate before the next other
+  offset; right-censored and invalid windows are counted explicitly.
+- [ ] Audit the competing-risk implementation on real timelines and freeze the
+  timeout/source-boundary convention before confirmatory reporting.
 - [ ] Specify overlap, minimum silence, maximum response delay, duplicate
   detections, source discontinuities, track visibility, offscreen speakers,
   and whether target-of-speech strata are diagnostic only.
@@ -41,9 +47,9 @@ Qwen2.5-Omni-7B, and MiniCPM-o 4.5.
 least 100 randomly selected events. Publish the number of unique other IDs and
 multi-other opportunity minutes before the main sweep.
 
-## P1 — Audit each backbone interface
+## P1 — Audit the primary backbone interface
 
-For Qwen2.5-Omni-3B, Qwen2.5-Omni-7B, and MiniCPM-o 4.5, record:
+For Qwen2.5-Omni-3B, and for 7B only if used as the scaling control, record:
 
 - [ ] exact model/code revision, processor, dtype, and hardware;
 - [ ] native audio/video sampling and streaming/chunk semantics;
@@ -57,9 +63,9 @@ a model cannot expose a comparable causal state, report that protocol as
 unsupported. Raw scores are not a pure architecture ranking; emphasize
 within-backbone deltas.
 
-## P2 — Establish the compact representation matrix
+## P2 — Establish the compact adaptation matrix
 
-Run three seeds per supported backbone with the same data, physical cutoffs,
+Run three seeds for the primary backbone with the same data, physical cutoffs,
 target, prediction-head capacity, optimizer exposure, and evaluator:
 
 1. Frozen global causal state.
@@ -67,41 +73,39 @@ target, prediction-head capacity, optimizer exposure, and evaluator:
 3. Global state plus recent local audio.
 4. Global state plus pooled local audio and vision.
 
-Use a common local encoder as the primary cross-backbone control. If native
-local encoders are tested, report them in a separate within-model panel. Do not
-mix common-encoder and native-encoder deltas in one claim.
+Add a parameter- and history-matched temporal local branch before attributing
+gain to audiovisual organization. Use the same local adapter for the optional
+7B scaling row.
 
 **Gate:** baseline standard deviations are known; the evaluator reproduces
 identical scores from saved predictions; parameter counts and training budgets
 are reported. A single failed LoRA recipe is evidence about that recipe, not
 proof that the model cannot adapt.
 
-## P3 — Select local fusion only after P2
+## P3 — Train the no-ID local floor adapter
 
-If local evidence gives a reliable event-level gain, compare candidates in
-increasing complexity:
+The primary method is a short-window causal encoder over recent mixed audio and
+per-frame anonymous visible-person sets. It uses no participant-ID embedding or
+cross-time track state. Compare it with:
 
-1. Pooled local audio/vision.
-2. Parameter-matched temporal branch.
-3. Per-frame person set with no cross-time identity.
-4. Shared track-wise temporal encoder.
-5. Person–audio cross-attention.
-6. Optional per-person active-speaker auxiliary loss, only after selecting a
-   person-aware activity-only model.
+1. Recent local audio only.
+2. Global/full-frame visual pooling.
+3. A parameter- and history-matched temporal branch.
+4. Time-shifted local vision.
+5. Removed local vision.
+6. A same-rate control if the local path runs faster than 5 Hz.
 
 Keep the dense EgoSpeak wearer/others target as the primary task. Target of
-speech is analysis-only and never model input. Describe only the train-side
-winner as the final method; move rejected candidates to ablations.
+speech is analysis-only and never model input. Use ``dual-rate'' only if the
+local input/update rate differs explicitly and defeats the same-rate control.
 
-For a track-aware candidate, temporary IDs route hidden state only: no ID
-embedding, cross-recording identity, or fixed seating slot. Unit-test variable
-person count, empty sets, padding, slot permutation, global ID renaming,
+Unit-test variable person count, empty sets, padding, person-set permutation,
 source/gap resets, and strict causal cutoffs.
 
-## P4 — Run visual counterfactuals conditionally
+## P4 — Consider track continuity only after the no-ID method wins
 
-Only if a track-aware candidate is selected, evaluate one frozen checkpoint
-with:
+Only if a track-aware candidate then beats the no-ID method on training-side
+data, evaluate one frozen checkpoint with:
 
 - correct person tracks;
 - clip-consistent global renaming, which must be invariant;
@@ -121,11 +125,14 @@ retrain test-time counterfactuals.
 - [ ] Operating points at wearer-onset recall 0.7, 0.8, and 0.9; headline 0.8.
 - [ ] False wearer entries per multi-other opportunity minute, onset latency,
   wearer-yield recall/latency, and original ten-horizon AP/mAP.
-- [ ] Error strata: backbone, visibility, number of visible people, overlap,
-  offscreen activity, track age/fragmentation, and transition proxy.
+- [ ] Error strata: visibility, number of visible people, overlap, offscreen
+  activity, and transition proxy. Add track age/fragmentation only if a
+  track-aware method is selected.
 - [ ] Ego4D AVD coverage audit on the exact EgoSpeak split before claiming the
   same multi-other analysis.
-- [ ] Provided-track and online-tracker conditions named separately.
+- [ ] Name the causal box source and its limitations. Add separate
+  provided-track/online-tracker rows only if tracking enters the selected
+  method.
 
 Qualitative cases follow a frozen selection rule and include the largest paired
 improvement, a median case, a failure, and one preserved true wearer onset.
@@ -134,23 +141,20 @@ Examples explain rates; they never replace them.
 ## Paper assembly order
 
 1. Freeze and audit the event protocol.
-2. Implement and causally audit all backbone readouts.
-3. Complete the compact frozen/PEFT/local matrix.
-4. Decide whether the paper can support a cross-family or only model-specific
-   representation claim.
-5. Select a local fusion candidate using training-side event metrics.
-6. Run conditional visual/track counterfactuals.
-7. Produce the cross-backbone event table and paired intervals.
-8. Draw one compact final system figure; remove rejected methods from the main
+2. Audit the Qwen2.5-Omni-3B readout and matched PEFT baseline.
+3. Complete the frozen/PEFT/audio/matched-temporal/local-AV matrix.
+4. Run correct/time-shift/remove-vision controls and the optional 7B scaling row.
+5. Consider track continuity only if the no-ID method already passes.
+6. Produce one confirmatory event table and one mechanism-ablation table.
+7. Draw one compact final system figure; remove rejected methods from the main
    narrative.
-9. Replace every visible TODO and verify the ICASSP 2027 kit/page limit.
+8. Replace every visible TODO and verify the ICASSP 2027 kit/page limit.
 
 ## Stop/go decisions
 
-- **General claim:** at least two backbone families show the same frozen/PEFT
-  weakness and a reliable local-evidence improvement.
-- **Model-specific claim:** the effect appears only within Qwen; say so and do
-  not generalize to all omni models.
+- **Claim boundary:** with Qwen-only evidence, describe a Qwen2.5-Omni case
+  study and the tested adaptation mechanism; do not generalize to all Omni
+  models.
 - **Audio-local reframe:** local audio improves but all visual variants fail;
   claim a fine-grained acoustic readout bottleneck, not social visual reasoning.
 - **Track-aware method:** correct continuity beats pooled vision, temporal