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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.07293 |
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| _version_ | 1866912636456665088 |
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| author | He, Peize Wen, Zichen Wang, Yubo Wang, Yuxuan Liu, Xiaoqian Huang, Jiajie Lei, Zehui Gu, Zhuangcheng Jin, Xiangqi Yang, Jiabing Li, Kai Liu, Zhifei Li, Weijia Wang, Cunxiang He, Conghui Zhang, Linfeng |
| author_facet | He, Peize Wen, Zichen Wang, Yubo Wang, Yuxuan Liu, Xiaoqian Huang, Jiajie Lei, Zehui Gu, Zhuangcheng Jin, Xiangqi Yang, Jiabing Li, Kai Liu, Zhifei Li, Weijia Wang, Cunxiang He, Conghui Zhang, Linfeng |
| contents | Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention ($O(N^2)$) and with modeling long-range temporal dependencies. Existing audio benchmarks are built mostly from short clips and do not evaluate models in realistic long context settings. To address this gap, we introduce AudioMarathon, a benchmark designed to evaluate both understanding and inference efficiency on long-form audio. AudioMarathon provides a diverse set of tasks built upon three pillars: long-context audio inputs with durations ranging from 90.0 to 300.0 seconds, which correspond to encoded sequences of 2,250 to 7,500 audio tokens, respectively, full domain coverage across speech, sound, and music, and complex reasoning that requires multi-hop inference. We evaluate state-of-the-art LALMs and observe clear performance drops as audio length grows. We also study acceleration techniques and analyze the trade-offs of token pruning and KV cache eviction. The results show large gaps across current LALMs and highlight the need for better temporal reasoning and memory-efficient architectures. We believe AudioMarathon will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs He, Peize Wen, Zichen Wang, Yubo Wang, Yuxuan Liu, Xiaoqian Huang, Jiajie Lei, Zehui Gu, Zhuangcheng Jin, Xiangqi Yang, Jiabing Li, Kai Liu, Zhifei Li, Weijia Wang, Cunxiang He, Conghui Zhang, Linfeng Sound Artificial Intelligence Computation and Language Audio and Speech Processing Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention ($O(N^2)$) and with modeling long-range temporal dependencies. Existing audio benchmarks are built mostly from short clips and do not evaluate models in realistic long context settings. To address this gap, we introduce AudioMarathon, a benchmark designed to evaluate both understanding and inference efficiency on long-form audio. AudioMarathon provides a diverse set of tasks built upon three pillars: long-context audio inputs with durations ranging from 90.0 to 300.0 seconds, which correspond to encoded sequences of 2,250 to 7,500 audio tokens, respectively, full domain coverage across speech, sound, and music, and complex reasoning that requires multi-hop inference. We evaluate state-of-the-art LALMs and observe clear performance drops as audio length grows. We also study acceleration techniques and analyze the trade-offs of token pruning and KV cache eviction. The results show large gaps across current LALMs and highlight the need for better temporal reasoning and memory-efficient architectures. We believe AudioMarathon will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks. |
| title | AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.07293 |