# Internal ICASSP-style review loop ## Review 1: reject in original method framing **Main concerns** 1. The novelty claim was too broad. EgoSpeak already defines egocentric when-to-speak prediction, while MuVAP, AV-CONV, and 3M-Transformer cover adjacent multiparty audiovisual structure. 2. The learned visual method did not support the intended speaker-grounding claim. Pooled vision gave only `+0.291` mean-mAP points over `G+A`, and the current-frame person-set screen did not exceed the historical pooled row. 3. Dense mAP, raw endpoint AP, and semantic floor events were conflated. 4. Planned tail, context, transfer, and behavior experiments occupied too much of a four-page argument despite being unmeasured. **Revision** The paper was reframed from a successful visual adapter to a causal global--local evidence audit. The claim is now that recent audio and global context are complementary, while generic visual access remains insufficient. All semantic event numbers and planned cross-backbone claims were removed. ## Review 2: weak reject after reframing **Strengths** - Clear task asymmetry: visible interlocutors condition prediction of an invisible wearer. - Strict causal cutoff and fixed 5-Hz / ten-horizon contract. - A coherent positive result for `G+A`, especially wearer-offset anticipation. - An unusually informative negative result: visible-activity labels help, but current generic visual features do not recover that signal. - Oracle controls distinguish visibility, timeliness, aggregation, and identity continuity. **Remaining concerns** 1. The deployable main comparison is single seed; only the oracle has three seeds. 2. Sessions 1--3 have been inspected repeatedly, so the current matrix is not a clean confirmatory test. 3. `G+A` changes history, feature access, and trainable capacity relative to `A` or `G`; a matched wider/temporal control is missing. 4. Raw onset/offset AP is not a one-event-one-vote behavioral metric. 5. Checkpoint hashes, common valid-pair counts, and original predictions are not all archived on this host. **Revision** The abstract and results now identify single-seed versus three-seed evidence, avoid significance language, separate endpoint diagnostics from events, and state the capacity/history confound. The main paper no longer claims reduced false entry, semantic yield, speaker identity, or real-time throughput. ## Review 3: current verdict **Score: 5/10, borderline, structurally ready but not yet confirmatory.** The manuscript is substantially clearer and more defensible than a paper that claims a successful visual grounding method. Its strongest publishable insight is the controlled mismatch between oracle information value and learned visual access. However, writing quality cannot replace the missing confirmatory evidence. Calling the current artifact "accepted-paper ready" would overstate the science. ## Minimum gates for a credible accept recommendation 1. Re-run `A`, `G`, `G+A`, and the selected visual condition for seeds 0/1/2 under one frozen recipe and common valid pairs; archive predictions, checkpoint/config hashes, per-session values, and trainable parameter counts. 2. Add a parameter- and history-matched control. The central `G+A` gain must remain larger than seed variance and the matched-capacity alternative. 3. Run the corrected competing-risk evaluator with training-side calibration, manually audit real timelines, and report matched-recall false-entry and false-stop rates only if denominators and invalid counts agree. 4. If visual grounding remains a contribution, replace generic pooling with a causal active-speaker/floor estimator and require correct-time evidence to beat delayed, removed, and capacity-matched controls. Otherwise retain the honest audio-local paper and remove visual-method language. 5. Rebuild with the official 2027 kit, keep technical content within four pages, put only references on an optional fifth page, and retain the IEEE AI-assisted drafting disclosure. Passing gates 1--3 would support a solid ICASSP empirical paper. Passing gate 4 with a meaningful matched-recall behavior gain would support the stronger method paper originally envisioned.