OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning

Junming Lin; Yuxuan Wang; Zhenxin Lei; Yuxin Liu; Ruixun Liu; Yinsong Yan; Ling Wang; Minghao Han; Yunfei Chu; Shun Lei; Xueyao Zhang; Qize Yang; Jin Xu; Yiwu Zhong

arXiv · 2609.39490

OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning

Junming Lin·Yuxuan Wang·Zhenxin Lei·Yuxin Liu·Ruixun Liu·Yinsong Yan·Ling Wang·Minghao Han·Yunfei Chu·Shun Lei·Xueyao Zhang·Qize Yang·Jin Xu·Yiwu Zhong

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Abstract

Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficiently elicited. We address this gap with a benchmark, data engine, and learning method. First, we introduce OmniReasoningBench, a benchmark where both audio and visual evidence are indispensable. It comprises 1,150 multiple-choice and open-ended questions across two tasks, reasoning over video and reasoning beyond video. Second, we develop a data engine OmniQA. It automatically constructs evidence-grounded QA pairs that explicitly necessitate audio-visual joint reasoning, together with time-stamped clue chains that guide the annotation of thinking process. Besides our benchmark, this engine produces training data OmniReasoning-SFT-112K and OmniReasoning-RL-19K. Finally, we propose an on-policy self-distillation method Modality-Factored Self-Distillation (MFSD). It evaluates each sampled response under modality-specific clue contexts, disentangling the contributions of individual clues and their cross-modal interactions for token-level credit assignment. With our training data and learning method, our model OmniReasoning-30B-A3B achieves 50.0% on OmniVideoBench and 42.5% on OmniReasoningBench, improving the base model Qwen3-Omni-30B-A3B-Thinking by 12.8 and 9.3 percentage points, respectively. Moreover, it delivers substantial gains on general and long-video benchmarks, including Video-MME-v2. We hope our work offers a solid step for facilitating future research in omni-modal joint reasoning.

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Junming Lin, Yuxuan Wang, Zhenxin Lei, Yuxin Liu, Ruixun Liu, Yinsong Yan, Ling Wang, Minghao Han, Yunfei Chu, Shun Lei, Xueyao Zhang, Qize Yang, Jin Xu, Yiwu Zhong. 2026-09-30. OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning. https://arxiv.org/abs/2609.39490

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