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arxiv: 2606.04323 · v1 · pith:AM4BYPOVnew · submitted 2026-06-03 · 💻 cs.CV

Answer Self-Consistency with Margin-Triggered Question Re-Arbitration for the CVPR 2026 VidLLMs Challenge

Tomoya Miyazawa , Hiroyasu Okuno This is my paper

Pith reviewed 2026-06-28 07:23 UTC · model grok-4.3

classification 💻 cs.CV
keywords self-consistencyvideo question answeringmultimodal reasoningtest-time reasoningvisual relational reasoningVidLLMs
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The pith

Multiple stochastic runs with answer-level self-consistency improve video question-answering accuracy over single-pass inference.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

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.

Core claim

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.

What carries the argument

Answer Self-Consistency (ASC), which counts identical answer choices across multiple independent stochastic runs of the same video-question pair.

If this is right

  • 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.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • 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.

Load-bearing premise

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.

What would settle it

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.

Figures

Figures reproduced from arXiv: 2606.04323 by Hiroyasu Okuno, Tomoya Miyazawa.

Figure 1
Figure 1. Figure 1: Overview of the ASC-MQRA pipeline. Multi [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Category distribution of validation and test examples. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

In this report, we present our solution for Track 2 of the CVPR 2026 VidLLMs Challenge. This track evaluates visual relational reasoning in videos, where models must infer relations that are not always explicitly visible. We propose Answer Self-Consistency with Margin-Triggered Question Re-Arbitration (ASC-MQRA), a training-free test-time reasoning framework built on a multimodal reasoning model. 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. We further study MQRA, a conditional re-arbitration module for low-margin examples where the first-stage vote distribution indicates uncertainty. Our vote-margin analysis shows that low-margin examples often retain the ground-truth answer among the top candidates, motivating MQRA to narrow the candidate set and re-watch the video only over the retained candidates. On validation, MQRA further improves over ASC, indicating that low-margin vote distributions can provide a useful uncertainty signal. On test, however, MQRA slightly degrades performance relative to ASC, suggesting that re-arbitration is sensitive to the size and category distribution of the triggered subset. Our final test submission therefore uses ASC without re-arbitration, achieving 72.73 average accuracy and 78.34 category-wise macro average accuracy on validation, and 81.16 average accuracy and 80.91 category-wise macro average accuracy on test. This report details our prompting strategy, implementation setup, ablation studies, and diagnostic analyses. The code is available at https://github.com/data-analytics-labo/ASC-MQRA

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

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.

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 (2)
  1. [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).
  2. [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.
minor comments (2)
  1. [Diagnostic Analyses] 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.
  2. [Results] Category-wise macro accuracy is reported alongside average accuracy; clarify whether the macro is over the same categories as the challenge or a subset.

Simulated Author's Rebuttal

2 responses · 0 unresolved

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.

read point-by-point responses

  1. Referee: [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).

    Authors: 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: yes

  2. Referee: [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.

    Authors: 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: yes

Circularity Check

0 steps flagged

No significant circularity; purely empirical competition report

full rationale

The manuscript is a technical report for a CVPR challenge track that describes an empirical test-time method (ASC-MQRA) consisting of repeated stochastic inference followed by vote aggregation and optional margin-based re-arbitration. All performance claims are backed by concrete accuracy numbers on held-out validation and test splits, with ablations and diagnostic vote-margin statistics. No equations, derivations, uniqueness theorems, or self-citations appear; the central results are direct measurements rather than reductions of fitted parameters or prior author work. The derivation chain is therefore empty and the report is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical report on a competition solution with no mathematical axioms, free parameters, or new invented entities.

pith-pipeline@v0.9.1-grok · 5843 in / 1086 out tokens · 27264 ms · 2026-06-28T07:23:26.492875+00:00 · methodology

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Reference graph

Works this paper leans on

13 extracted references · 3 canonical work pages · 1 internal anchor

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    8 Restate what the question is asking in your own words

    Understand the question correctly. 8 Restate what the question is asking in your own words. Be precise about what must be answered: a person/object, count, order, direction, spatial relation, viewpoint, cause/reason, outcome/result, identity, social relation, or physical state. Mention only the aspects that are relevant to this question. Do not add assump...

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    question_understanding

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