@inproceedings{ma-etal-2026-perception,
title = "Perception Is the Bottleneck for Large Audio Reasoning Models",
author = "Ma, Ziyang and
Xu, Ruiyang and
Tu, Wenming and
Xing, Zhenghao and
Wang, Yuxuan and
Chu, Yunfei and
Xu, Jin and
Yu, Kai and
Lin, Junyang and
Chng, Eng Siong and
Chen, Xie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1272/",
pages = "26908--26923",
ISBN = "979-8-89176-462-0",
abstract = "While Chain-of-Thought (CoT) reasoning has revolutionized text-based LLMs, its application to the audio modality reveals a counter-intuitive thinking paradox: forcing models to ``think'' via prompting or post-training often degrades performance compared to direct answering. In this work, we investigate the bottleneck of audio reasoning and reveal a critical insight: the primary constraint is not the model{'}s logical deduction capability, but its inability to accurately perceive acoustic details. We demonstrate that keeping perceptual cues effectively activates the LALM{'}s internal reasoning potential. Building on this finding, we propose \textbf{Audio-ReaPer}, a post-training framework and corresponding model designed to unlock \textbf{Rea}soning potential by explicitly fortifying \textbf{Per}ception. Our approach reforms the post-training paradigm through three key strategies: (1) utilizing agentic data generation pipeline to synthesize perception-enhanced data for SFT; (2) curating perception-focused data for RLVR; and (3) introducing a novel perceptual density reward during RL to stabilize the learning process and balance observation with deduction. Extensive experiments show that Audio-ReaPer establishes a new state-of-the-art on benchmarks such as MMAU, MMAU-Pro, and MMAR. Notably, our 7B model outperforms previous open-source models by significant margins and surpasses proprietary models like GPT-4o Audio and Gemini 2.5 Flash. The model and code will be open-source to facilitate further research for audio reasoning."
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<abstract>While Chain-of-Thought (CoT) reasoning has revolutionized text-based LLMs, its application to the audio modality reveals a counter-intuitive thinking paradox: forcing models to “think” via prompting or post-training often degrades performance compared to direct answering. In this work, we investigate the bottleneck of audio reasoning and reveal a critical insight: the primary constraint is not the model’s logical deduction capability, but its inability to accurately perceive acoustic details. We demonstrate that keeping perceptual cues effectively activates the LALM’s internal reasoning potential. Building on this finding, we propose Audio-ReaPer, a post-training framework and corresponding model designed to unlock Reasoning potential by explicitly fortifying Perception. Our approach reforms the post-training paradigm through three key strategies: (1) utilizing agentic data generation pipeline to synthesize perception-enhanced data for SFT; (2) curating perception-focused data for RLVR; and (3) introducing a novel perceptual density reward during RL to stabilize the learning process and balance observation with deduction. Extensive experiments show that Audio-ReaPer establishes a new state-of-the-art on benchmarks such as MMAU, MMAU-Pro, and MMAR. Notably, our 7B model outperforms previous open-source models by significant margins and surpasses proprietary models like GPT-4o Audio and Gemini 2.5 Flash. The model and code will be open-source to facilitate further research for audio reasoning.</abstract>
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%0 Conference Proceedings
%T Perception Is the Bottleneck for Large Audio Reasoning Models
%A Ma, Ziyang
%A Xu, Ruiyang
%A Tu, Wenming
%A Xing, Zhenghao
%A Wang, Yuxuan
%A Chu, Yunfei
%A Xu, Jin
%A Yu, Kai
%A Lin, Junyang
%A Chng, Eng Siong
%A Chen, Xie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ma-etal-2026-perception
%X While Chain-of-Thought (CoT) reasoning has revolutionized text-based LLMs, its application to the audio modality reveals a counter-intuitive thinking paradox: forcing models to “think” via prompting or post-training often degrades performance compared to direct answering. In this work, we investigate the bottleneck of audio reasoning and reveal a critical insight: the primary constraint is not the model’s logical deduction capability, but its inability to accurately perceive acoustic details. We demonstrate that keeping perceptual cues effectively activates the LALM’s internal reasoning potential. Building on this finding, we propose Audio-ReaPer, a post-training framework and corresponding model designed to unlock Reasoning potential by explicitly fortifying Perception. Our approach reforms the post-training paradigm through three key strategies: (1) utilizing agentic data generation pipeline to synthesize perception-enhanced data for SFT; (2) curating perception-focused data for RLVR; and (3) introducing a novel perceptual density reward during RL to stabilize the learning process and balance observation with deduction. Extensive experiments show that Audio-ReaPer establishes a new state-of-the-art on benchmarks such as MMAU, MMAU-Pro, and MMAR. Notably, our 7B model outperforms previous open-source models by significant margins and surpasses proprietary models like GPT-4o Audio and Gemini 2.5 Flash. The model and code will be open-source to facilitate further research for audio reasoning.
%U https://aclanthology.org/2026.findings-emnlp.1272/
%P 26908-26923
Markdown (Informal)
[Perception Is the Bottleneck for Large Audio Reasoning Models](https://aclanthology.org/2026.findings-emnlp.1272/) (Ma et al., Findings 2026)
ACL
- Ziyang Ma, Ruiyang Xu, Wenming Tu, Zhenghao Xing, Yuxuan Wang, Yunfei Chu, Jin Xu, Kai Yu, Junyang Lin, Eng Siong Chng, and Xie Chen. 2026. Perception Is the Bottleneck for Large Audio Reasoning Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 26908–26923, Budapest, Hungary. Association for Computational Linguistics.