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New Benchmark and Agentic Framework Enhance Audio-Visual Reasoning in LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MOV-Bench, a new benchmark designed to evaluate multi-hop audio-visual reasoning capabilities in Omni-LLMs. This benchmark features 519 questions requiring complex reasoning over dispersed audio and visual evidence. To address the limitations of current models, the team also developed AOP-Agent, an agentic framework that enhances Omni-LLMs' active perception abilities without requiring additional training. Experiments show AOP-Agent significantly improves reasoning performance, especially on longer videos and more demanding questions. AI

IMPACT Introduces a new benchmark and framework to push the boundaries of multi-hop audio-visual reasoning in LLMs.

RANK_REASON This is a research paper introducing a new benchmark and an agentic framework for AI reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Benchmark and Agentic Framework Enhance Audio-Visual Reasoning in LLMs

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This is a research paper introducing a new benchmark and an agentic framework for AI reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ke Xu, Yuhao Wang, Ziyang Cheng, Hongcheng Liu, Yanfeng Wang, Yu Wang ·

    Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

    arXiv:2605.28192v1 Announce Type: new Abstract: Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams. Existing benchmarks provide limited investigation …

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