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arXiv · 2606.07643

AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs

Yaoting Wang·Ziyi Zhang·Wenming Tu·Shaoxuan Xu·Wenjie Du·Cheng Liang·Weijun Wang·Yuanchao Li·Guangyao Li·Hao Fei·Yuanchun Li·Henghui Ding·Yunxin Liu
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Abstract

Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language. However, their audio-visual intelligence (AVI) remains insufficiently evaluated due to the lack of systematic and comprehensive benchmarks. We introduce AVI-Bench, a cognitively inspired benchmark that evaluates Omni-MLLMs across three stages, perception, understanding, and reasoning, through cross-modal tasks requiring joint audio-visual interpretation. This design enables fine-grained diagnosis of model capabilities and failure modes. To further assess robustness beyond familiar domains, we propose AVI-Bench-PriSe, an extension that probes models' primitive audio-visual sensation using unfamiliar, low-semantic stimuli, testing generalization beyond common training distributions. Extensive experiments on both open-source and closed-source models reveal substantial limitations in current Omni-MLLMs. Based on these findings, we present a four-level AVI taxonomy. Overall, AVI-Bench provides a principled evaluation framework to guide the development of more robust and generalizable AVI. Project website: https://fudancvl.github.io/AVI-Bench/

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Yaoting Wang, Ziyi Zhang, Wenming Tu, Shaoxuan Xu, Wenjie Du, Cheng Liang, Weijun Wang, Yuanchao Li, Guangyao Li, Hao Fei, Yuanchun Li, Henghui Ding, Yunxin Liu. 2026-06-01. AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs. https://arxiv.org/abs/2606.07643

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