@inproceedings{mao-etal-2026-scaling,
title = "When Scaling Fails: Mitigating Audio Perception Decay of {LALM}s via Multi-Step Perception-Aware Reasoning",
author = "Mao, Ruixiang and
Ma, Xiangnan and
Chen, Dan and
Zhu, Ziming and
Ge, Yuan and
Zhao, Haishu and
Hao, AoKai and
Huo, Yifu and
Yang, Qing and
Chang, Kaiyan and
Liu, Xiaoqian and
Wang, Chenglong and
Shan, Weiqiao and
He, Qiaozhi and
Li, Changliang and
Yu, Zhengtao and
Xiao, Tong and
Zhu, JingBo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.495/",
pages = "11370--11393",
ISBN = "979-8-89176-459-0",
abstract = "Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to training for direct answering. To investigate it, we introduce **CAFE**, an evaluation framework designed to quantify audio reasoning errors. Evaluation results reveal that LALMs encounter a critical bottleneck: **Audio Perception Decay**, which compromises reasoning performance. To address it, we propose **MPAR$^2$**, a strategy that encourages a step-by-step reasoning process with rich perceptual awareness. With reinforcement learning, MPAR$^2$ improves performance on CAFE from 31.74{\%} to 63.51{\%} and effectively mitigates audio perception decay, concurrently enhancing reasoning ability to achieve a strong 74.59{\%} accuracy on the MMAU benchmark. Further analysis shows that MPAR$^2$ optimizes audio attention and enhances reasoning token budget efficiency."
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<abstract>Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to training for direct answering. To investigate it, we introduce **CAFE**, an evaluation framework designed to quantify audio reasoning errors. Evaluation results reveal that LALMs encounter a critical bottleneck: **Audio Perception Decay**, which compromises reasoning performance. To address it, we propose **MPAR²**, a strategy that encourages a step-by-step reasoning process with rich perceptual awareness. With reinforcement learning, MPAR² improves performance on CAFE from 31.74% to 63.51% and effectively mitigates audio perception decay, concurrently enhancing reasoning ability to achieve a strong 74.59% accuracy on the MMAU benchmark. Further analysis shows that MPAR² optimizes audio attention and enhances reasoning token budget efficiency.</abstract>
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%0 Conference Proceedings
%T When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning
%A Mao, Ruixiang
%A Ma, Xiangnan
%A Chen, Dan
%A Zhu, Ziming
%A Ge, Yuan
%A Zhao, Haishu
%A Hao, AoKai
%A Huo, Yifu
%A Yang, Qing
%A Chang, Kaiyan
%A Liu, Xiaoqian
%A Wang, Chenglong
%A Shan, Weiqiao
%A He, Qiaozhi
%A Li, Changliang
%A Yu, Zhengtao
%A Xiao, Tong
%A Zhu, JingBo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F mao-etal-2026-scaling
%X Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to training for direct answering. To investigate it, we introduce **CAFE**, an evaluation framework designed to quantify audio reasoning errors. Evaluation results reveal that LALMs encounter a critical bottleneck: **Audio Perception Decay**, which compromises reasoning performance. To address it, we propose **MPAR²**, a strategy that encourages a step-by-step reasoning process with rich perceptual awareness. With reinforcement learning, MPAR² improves performance on CAFE from 31.74% to 63.51% and effectively mitigates audio perception decay, concurrently enhancing reasoning ability to achieve a strong 74.59% accuracy on the MMAU benchmark. Further analysis shows that MPAR² optimizes audio attention and enhances reasoning token budget efficiency.
%U https://aclanthology.org/2026.emnlp-main.495/
%P 11370-11393
Markdown (Informal)
[When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning](https://aclanthology.org/2026.emnlp-main.495/) (Mao et al., EMNLP 2026)
ACL
- Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Haishu Zhao, AoKai Hao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Weiqiao Shan, Qiaozhi He, Changliang Li, Zhengtao Yu, Tong Xiao, and JingBo Zhu. 2026. When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11370–11393, Budapest, Hungary. Association for Computational Linguistics.