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English(EN) TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos

新的TraceAV-Bench凸显OmniLLM在长时音视频推理方面的挣扎

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的基准测试TraceAV-Bench已被推出，用于评估OmniLLMs在长时音视频上的多跳推理能力。该基准测试包含578个视频中的2200个问题，总时长超过339小时，并评估了多模态幻觉鲁棒性。包括Gemini 3.1 Pro和Ming-Flash-Omni-2.0在内的当前领先模型显示出显著的局限性，表现最好的模型准确率仅为68.29%，表明在长时音视频推理方面存在巨大差距。 AI

影响 凸显了当前OmniLLM在复杂、长时音视频任务中的局限性，为未来的研究和开发指明方向。

排序理由 该集群描述了一个用于评估AI模型的新学术基准测试，详情见arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的TraceAV-Bench凸显OmniLLM在长时音视频推理方面的挣扎

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该集群描述了一个用于评估AI模型的新学术基准测试，详情见arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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：对长时音视频进行多跳轨迹推理的基准测试

    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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