@inproceedings{wang-etal-2026-listen,
title = "Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding",
author = "Wang, Jieyi and
Niu, Yazhe and
Xu, Dexuan and
Wei, Zhongyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1776/",
pages = "35653--35671",
ISBN = "979-8-89176-395-1",
abstract = "Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors, especially in noisy and multi-speaker environments. We argue that reliable audio reasoning requires first grounding model{'}s perception in structured auditory scenes. Motivated by Auditory Scene Analysis, we introduce **PAQA**, a large-scale dataset for **Perception-Aware Question Answering** covering over 300 categories. PAQA adopts a hierarchical decoupling strategy that separates speech from environmental sounds and distinguishes among multiple speakers, providing explicit perceptual supervision for audio reasoning. Building on this, we propose **HyPeR**, a two-stage **Hybrid Perception-Reasoning** framework for perception-grounded audio understanding. In Stage I, the model is fine-tuned on PAQA for cold start to improve perception of acoustic attributes in complex auditory scenes. In Stage II, we further refine its internal reasoning via **Group Relative Policy Optimization (GRPO)**. To support deliberation under acoustic ambiguity, we introduce **PAUSE tokens** for latent computation and a **Perceptual Consistency Reward** to align reasoning rationales with the underlying audio evidence. Extensive ablation studies isolate the effects of the perception-attention mechanism, self-correction module, and pause-based reasoning strategy. Experiments on multiple benchmarks show that HyPeR consistently improves over the base model, including on MMAU-mini (+13.1{\%}), MMAR (+25.5{\%}), and PAQA (+28.2{\%}), while achieving performance comparable to much larger models. Additional analyses of inference latency and computational overhead show that these gains come with acceptable efficiency trade-offs. Overall, our results demonstrate the effectiveness of hybrid perception-grounded reasoning for robust audio understanding."
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<abstract>Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors, especially in noisy and multi-speaker environments. We argue that reliable audio reasoning requires first grounding model’s perception in structured auditory scenes. Motivated by Auditory Scene Analysis, we introduce **PAQA**, a large-scale dataset for **Perception-Aware Question Answering** covering over 300 categories. PAQA adopts a hierarchical decoupling strategy that separates speech from environmental sounds and distinguishes among multiple speakers, providing explicit perceptual supervision for audio reasoning. Building on this, we propose **HyPeR**, a two-stage **Hybrid Perception-Reasoning** framework for perception-grounded audio understanding. In Stage I, the model is fine-tuned on PAQA for cold start to improve perception of acoustic attributes in complex auditory scenes. In Stage II, we further refine its internal reasoning via **Group Relative Policy Optimization (GRPO)**. To support deliberation under acoustic ambiguity, we introduce **PAUSE tokens** for latent computation and a **Perceptual Consistency Reward** to align reasoning rationales with the underlying audio evidence. Extensive ablation studies isolate the effects of the perception-attention mechanism, self-correction module, and pause-based reasoning strategy. Experiments on multiple benchmarks show that HyPeR consistently improves over the base model, including on MMAU-mini (+13.1%), MMAR (+25.5%), and PAQA (+28.2%), while achieving performance comparable to much larger models. Additional analyses of inference latency and computational overhead show that these gains come with acceptable efficiency trade-offs. Overall, our results demonstrate the effectiveness of hybrid perception-grounded reasoning for robust audio understanding.</abstract>
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%0 Conference Proceedings
%T Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding
%A Wang, Jieyi
%A Niu, Yazhe
%A Xu, Dexuan
%A Wei, Zhongyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-listen
%X Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors, especially in noisy and multi-speaker environments. We argue that reliable audio reasoning requires first grounding model’s perception in structured auditory scenes. Motivated by Auditory Scene Analysis, we introduce **PAQA**, a large-scale dataset for **Perception-Aware Question Answering** covering over 300 categories. PAQA adopts a hierarchical decoupling strategy that separates speech from environmental sounds and distinguishes among multiple speakers, providing explicit perceptual supervision for audio reasoning. Building on this, we propose **HyPeR**, a two-stage **Hybrid Perception-Reasoning** framework for perception-grounded audio understanding. In Stage I, the model is fine-tuned on PAQA for cold start to improve perception of acoustic attributes in complex auditory scenes. In Stage II, we further refine its internal reasoning via **Group Relative Policy Optimization (GRPO)**. To support deliberation under acoustic ambiguity, we introduce **PAUSE tokens** for latent computation and a **Perceptual Consistency Reward** to align reasoning rationales with the underlying audio evidence. Extensive ablation studies isolate the effects of the perception-attention mechanism, self-correction module, and pause-based reasoning strategy. Experiments on multiple benchmarks show that HyPeR consistently improves over the base model, including on MMAU-mini (+13.1%), MMAR (+25.5%), and PAQA (+28.2%), while achieving performance comparable to much larger models. Additional analyses of inference latency and computational overhead show that these gains come with acceptable efficiency trade-offs. Overall, our results demonstrate the effectiveness of hybrid perception-grounded reasoning for robust audio understanding.
%U https://aclanthology.org/2026.findings-acl.1776/
%P 35653-35671
Markdown (Informal)
[Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding](https://aclanthology.org/2026.findings-acl.1776/) (Wang et al., Findings 2026)
ACL