Xiaoxing Hu, Wenxi Yao, Chaoyuan Shi, Ziqi Ye, Xiaorong Guo, Peixuan Li, Zhuoran Sun, Ziyang Gong · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28444 · open-access PDF
Omni LLMs는 자연스럽게 중요한 오디오 및 시각적 증거를 찾고 오디오 시각적 추론을위한 모형을 통해 정보를 통합 할 수 있습니다. 다중 선택 질문에 주로 의존하는 기존 오디오 시각적 추론 벤치마크는 대개 응답의 정확성에만 기초하여 모델을 점수하여 모델이 증거에서 올바른 답에 대한 추론 체인을 진정으로 형성하는지 평가하는 것이 어렵습니다.
Machine translation (Meta M2M100); the English original follows.
Omni LLMs can naturally locate key audio and visual evidence and integrate information across modalities for audiovisual reasoning. Existing audio-visual reasoning benchmarks that mainly rely on multiple-choice questions usually score models solely based on answer correctness, making it difficult to assess whether a model truly forms a reasoning chain from evidence to the correct answer. In addition, textual or unimodal shortcuts may allow models to answer correctly without analyzing the necessary audio-visual evidence, further weakening the reliability of evaluation benchmarks. To address the
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