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New TraceAV-Bench highlights OmniLLM struggles with long audio-visual reasoning

By PulseAugur Editorial · [1 sources] ·

A new benchmark, TraceAV-Bench, has been introduced to evaluate multi-hop reasoning capabilities in OmniLLMs over long audio-visual videos. The benchmark includes 2,200 questions across 578 videos, totaling over 339 hours, and assesses multimodal hallucination robustness. Current leading models, including Gemini 3.1 Pro and Ming-Flash-Omni-2.0, show significant limitations, with the best-performing model achieving only 68.29% accuracy, indicating a substantial gap in long-form audio-visual reasoning. AI

IMPACT Highlights limitations in current OmniLLMs for complex, long-form audio-visual tasks, guiding future research and development.

RANK_REASON The cluster describes a new academic benchmark for evaluating AI models, detailed in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

New TraceAV-Bench highlights OmniLLM struggles with long audio-visual reasoning

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The cluster describes a new academic benchmark for evaluating AI models, detailed in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hengyi Feng, Hao Liang, Mingrui Chen, Bohan Zeng, Meiyi Qiang, Zhengyang Zhao, Zimo Meng, Zeang Sheng, Wentao Zhang ·

    TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos

    arXiv:2605.07593v2 Announce Type: replace Abstract: Real-world audio-visual understanding requires chaining evidence that is sparse, temporally dispersed, and split across the visual and auditory streams, whereas existing benchmarks largely fail to evaluate this capability. They …

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