OmniAgent: Audio-Guided Active Perception Agent for Omnimodal Audio-Video Understanding

Keda Tao, wenjiedu, Bohan Yu, Weiqiang Wang, Jian Liu, Huan Wang

OmniAgent: Audio-Guided Active Perception Agent for Omnimodal Audio-Video Understanding: 15 upvotes on Hugging Face Daily Papers, #16 of 31 papers on 2025-12-30. Day-by-day upvote history.

Omnimodal large language models have made significant strides in unifying audio and visual modalities; however, they often lack the fine-grained cross-modal understanding and have difficulty with multimodal alignment. To address these limitations, we introduce OmniAgent, a fully audio-guided active perception agent that dynamically orchestrates specialized tools to achieve more fine-grained audio-visual reasoning. Unlike previous works that rely on rigid, static workflows and dense frame-captioning, this paper demonstrates a paradigm shift from passive response generation to active multimodal inquiry. OmniAgent employs dynamic planning to autonomously orchestrate tool invocation on demand, strategically concentrating perceptual attention on task-relevant cues. Central to our approach is a novel coarse-to-fine audio-guided perception paradigm, which leverages audio cues to localize temporal events and guide subsequent reasoning. Extensive empirical evaluations on three audio-video understanding benchmarks demonstrate that OmniAgent achieves state-of-the-art performance, surpassing leading open-source and proprietary models by substantial margins of 10% - 20% accuracy.

Paper page on Hugging Face · arXiv

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