{"id":"1599c3ca-1365-4c8a-bcbc-4feec6e210a8","arxiv_id":"2606.04323","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"ASC-MQRA applies answer self-consistency across stochastic video QA runs and optional margin-triggered re-arbitration to achieve 81.16% average accuracy on the CVPR 2026 VidLLMs Challenge Track 2 test set.","lead":"The paper reports a test-time framework called ASC-MQRA for a video question answering challenge that uses multiple inference runs and self-consistency to improve accuracy. Smart readers might examine it for practical ways to enhance multimodal model performance on relational reasoning tasks without additional training.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's conclusion that a full scientific verdict is inappropriate for a competition report is correct; the manuscript's claims are scoped to the challenge results and include transparent reporting of MQRA's test-set failure.","tokens_in":1872,"tokens_out":180,"duration_ms":14599,"concrete_test":"Re-run the ASC ablation on the validation split using the released code and confirm whether the reported accuracy lift over single-pass inference holds within the stated margins.","verdict_should_be":"UNCHANGED","load_bearing_attack":"This is a competition technical report presenting empirical results for a specific challenge track. The central claims are limited to performance numbers achieved by ASC (and the decision to drop MQRA after observing test degradation), supported by validation ablations and diagnostic vote-margin analysis. No general theoretical claims or derivations are advanced.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript presents Answer Self-Consistency with Margin-Triggered Question Re-Arbitration (ASC-MQRA), a training-free test-time framework for Track 2 of the CVPR 2026 VidLLMs Challenge on visual relational reasoning in videos. The core ASC component runs multiple stochastic video QA inferences and aggregates answer choices via answer-level self-consistency. MQRA is an optional conditional re-arbitration step for low-margin vote distributions. Validation ablations show MQRA improves over ASC alone, but test results show slight degradation, so the final submission uses ASC only, reporting 72.73 average / 78.34 macro accuracy on validation and 81.16 average / 80.91 macro accuracy on test. The report includes prompting strategy, implementation, ablations, vote-margin diagnostics, and a GitHub code link.","tokens_in":1894,"tokens_out":536,"duration_ms":25774,"significance":"If the reported numbers hold, the work demonstrates that answer-level self-consistency provides a practical, training-free boost to multimodal video reasoning performance in a competition setting. The public code release and diagnostic analysis of vote margins as an uncertainty signal are explicit strengths that support reproducibility and future extensions. The honest reporting of MQRA's test-time degradation adds value by highlighting sensitivity to data distribution.","major_comments":[{"comment":"The abstract and results claim that ASC 'substantially improves over single-pass inference,' but no quantitative single-pass baseline accuracy, number of stochastic runs, or variance across runs is provided in the reported metrics. This is load-bearing for assessing the magnitude and reliability of the central empirical claim (see Results section and Table of accuracies).","section":"Results and Ablations"},{"comment":"The base multimodal reasoning model (name, size, checkpoint) and exact prompting templates used to generate the stochastic outputs are referenced but not enumerated in the text. This limits evaluation of whether the stochastic diversity assumption holds and affects reproducibility of the ASC component, even with the code link.","section":"Implementation Setup"}],"minor_comments":[{"comment":"The vote-margin analysis is useful but would benefit from explicit counts or percentages of examples triggering MQRA on validation vs. test to quantify the distribution shift mentioned.","section":"Diagnostic Analyses"},{"comment":"Category-wise macro accuracy is reported alongside average accuracy; clarify whether the macro is over the same categories as the challenge or a subset.","section":"Results"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive assessment, the recommendation for minor revision, and the constructive comments on empirical quantification and reproducibility. We address each major comment below.","responses":[{"response":"We agree that the quantitative single-pass baseline, number of runs, and variance are necessary to substantiate the improvement claim. In the revised manuscript we will add these values (single-pass accuracy, run count, and run-wise variance) to the Results section and accuracy table.","revision_made":"yes","referee_comment":"[Results and Ablations] The abstract and results claim that ASC 'substantially improves over single-pass inference,' but no quantitative single-pass baseline accuracy, number of stochastic runs, or variance across runs is provided in the reported metrics. This is load-bearing for assessing the magnitude and reliability of the central empirical claim (see Results section and Table of accuracies)."},{"response":"While the GitHub repository contains the complete implementation, we acknowledge that the main text should explicitly list the model details and prompts. In the revision we will add a concise enumeration of the base model (name, size, checkpoint) and the exact prompting templates in the Implementation Setup section.","revision_made":"yes","referee_comment":"[Implementation Setup] The base multimodal reasoning model (name, size, checkpoint) and exact prompting templates used to generate the stochastic outputs are referenced but not enumerated in the text. This limits evaluation of whether the stochastic diversity assumption holds and affects reproducibility of the ASC component, even with the code link."}],"tokens_in":1544,"tokens_out":341,"duration_ms":22504,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing to know is that answer self-consistency via multiple stochastic runs improved accuracy on the CVPR 2026 VidLLMs Track 2, while the added MQRA module for low-margin votes improved validation but degraded test performance, so the final submission used plain ASC.\n\nThe paper applies a known LLM technique to video relational reasoning. They run the base multimodal model several times at temperature, vote on the answer choice, and report clear gains over single-pass inference. The MQRA part checks the vote margin; if low, it narrows to top candidates and re-queries the video only on those. Validation ablations showed further gains, and their margin analysis found the ground-truth answer often stayed among the top options even in uncertain cases. They release code and detail the prompting setup.\n\nThe soft spots are straightforward. This is a competition technical report, not a general method. All claims rest on one track, one base model, and specific prompting choices, with no error bars, significance tests, or multiple base models. The test degradation of MQRA is reported honestly but points to sensitivity to category distribution and subset size, which limits how far the idea travels. No new principles are introduced beyond the empirical numbers.\n\nThe work is for teams entering similar video QA challenges who want quick test-time tricks. A reader seeking broader multimodal reasoning advances or reproducible theory will not get much. It shows clear thinking in the diagnostics and the decision to drop the failing component.\n\nI would not send this for peer review. It is a useful arXiv report for the competition community but does not need formal referee time.","headline":"Self-consistency lifted scores on this video reasoning challenge but the margin-triggered re-arbitration hurt on test and got dropped.","tokens_in":2374,"tokens_out":398,"would_cite":false,"duration_ms":18101,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Multiple stochastic runs with answer-level self-consistency improve video question-answering accuracy over single-pass inference.","keywords":["self-consistency","video question answering","multimodal reasoning","test-time reasoning","visual relational reasoning","VidLLMs"],"falsifier":"On the same test videos and questions, a single deterministic forward pass matches or exceeds the accuracy obtained by aggregating five or more stochastic runs.","tokens_in":2753,"feed_emoji":"📹","tokens_out":544,"duration_ms":14588,"temperature":0.7,"pith_summary":"The paper introduces ASC-MQRA, a training-free test-time method for visual relational reasoning in videos. It runs a multimodal model multiple times, samples answers stochastically, and aggregates them by counting matching choices across runs. This aggregation step produces higher accuracy than any single run. The authors also test a follow-up step that re-queries uncertain low-margin cases but drop it for the final submission after it underperforms on test data.","feed_headline":"Self-consistency raises video QA accuracy to 81.16 on test","feed_subtitle":"Aggregating answers from multiple stochastic runs outperforms single inference on relational video reasoning without any training.","key_machinery":"Answer Self-Consistency (ASC), which counts identical answer choices across multiple independent stochastic runs of the same video-question pair.","core_discovery":"The core ASC component performs multiple stochastic video question-answering runs and aggregates their answer choices through answer-level self-consistency. This substantially improves over single-pass inference and forms our final test submission.","pith_inferences":["The same vote-aggregation pattern may lift performance on other video or image reasoning benchmarks that use multiple-choice outputs.","Margin signals could be combined with other uncertainty measures to reduce sensitivity to category distribution shifts.","The method shows that test-time compute scaling via repeated inference can substitute for additional training in some multimodal settings."],"forward_implications":["ASC raises validation average accuracy to 72.73 and test average accuracy to 81.16.","Low-margin vote distributions frequently keep the ground-truth answer among the top few candidates.","MQRA improves validation scores by narrowing candidates for uncertain cases but lowers test accuracy.","The final submission therefore relies on ASC alone without re-arbitration."],"fun_headline_variants":["Self-consistency on multiple runs achieves 81.16 test accuracy","Answer aggregation by self-consistency scores 81.16 on video test","Multiple stochastic inferences with consistency yield 81.16 accuracy","Self-consistency framework records 81.16 accuracy on VidLLMs test"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The base multimodal model must generate sufficiently varied and informative answers across stochastic runs for the vote aggregation to select the correct choice more often than a single run.","fun_headline_variants_meta":{"raw":{"variants":["Self-consistency on multiple runs achieves 81.16 test accuracy","Answer aggregation by self-consistency scores 81.16 on video test","Multiple stochastic inferences with consistency yield 81.16 accuracy","Self-consistency framework records 81.16 accuracy on VidLLMs test"]},"model":"grok-4.3","cost_usd":0.006601,"raw_usage":{"total_tokens":3111,"prompt_tokens":726,"num_sources_used":0,"completion_tokens":74,"cost_in_usd_ticks":66012000,"prompt_tokens_details":{"text_tokens":726,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2311,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":726,"tokens_out":74,"duration_ms":16870,"temperature":1.0,"reasoning_tokens":2311,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T07:23:26.492875+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"On the same test videos and questions, a single deterministic forward pass matches or exceeds the accuracy obtained by aggregating five or more stochastic runs.","supporting_citations":[],"review_version":1}