@inproceedings{zhao-etal-2026-audioprocessbench,
title = "{A}udio{P}rocess{B}ench: Benchmark for Identifying Process Errors in Audio-Grounded Reasoning",
author = "Zhao, Xiangyu and
Yan, Junyu and
Shen, Yaling and
Fong, Stephanie and
Wang, Zimu and
Jiang, Yiwen and
Xu, Qingyang and
Liu, Jiahe and
Dwyer, Dominic and
Ge, Zongyuan",
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.1686/",
pages = "35981--36003",
ISBN = "979-8-89176-462-0",
abstract = "Large audio-language models (LALMs) increasingly use explicit reasoning traces for complex audio understanding, yet the evaluation of reasoning quality remains underexplored. Although process-level benchmarks for process reward models (PRMs) have advanced reasoning evaluation in text and multi-modal domains, comparable evaluation for audio reasoning remains limited. In this paper, we present AudioProcessBench, a comprehensive benchmark for step-level process error identification in audio reasoning. AudioProcessBench contains diverse reasoning traces generated by 6 audio and omni language models. Each trace is segmented into discrete reasoning steps and annotated with binary step correctness and fine-grained error types. Our benchmark evaluates models under three complementary paradigms: (1) step correctness identification, (2) error-type-conditioned detection for diagnosing audio-specific verifier capacities, and (3) chain-level aggregation, where verifiers select or aggregate among multiple reasoning traces for the same question. This design enables a systematic analysis of whether current models can detect process errors, whether their weaknesses differ across audio-specific error types, and whether process verification translates into improved answer selection. AudioProcessBench provides a testbed for future research on audio reasoning verifiers, process reward models, and reliable omni-modal reasoning."
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<abstract>Large audio-language models (LALMs) increasingly use explicit reasoning traces for complex audio understanding, yet the evaluation of reasoning quality remains underexplored. Although process-level benchmarks for process reward models (PRMs) have advanced reasoning evaluation in text and multi-modal domains, comparable evaluation for audio reasoning remains limited. In this paper, we present AudioProcessBench, a comprehensive benchmark for step-level process error identification in audio reasoning. AudioProcessBench contains diverse reasoning traces generated by 6 audio and omni language models. Each trace is segmented into discrete reasoning steps and annotated with binary step correctness and fine-grained error types. Our benchmark evaluates models under three complementary paradigms: (1) step correctness identification, (2) error-type-conditioned detection for diagnosing audio-specific verifier capacities, and (3) chain-level aggregation, where verifiers select or aggregate among multiple reasoning traces for the same question. This design enables a systematic analysis of whether current models can detect process errors, whether their weaknesses differ across audio-specific error types, and whether process verification translates into improved answer selection. AudioProcessBench provides a testbed for future research on audio reasoning verifiers, process reward models, and reliable omni-modal reasoning.</abstract>
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%0 Conference Proceedings
%T AudioProcessBench: Benchmark for Identifying Process Errors in Audio-Grounded Reasoning
%A Zhao, Xiangyu
%A Yan, Junyu
%A Shen, Yaling
%A Fong, Stephanie
%A Wang, Zimu
%A Jiang, Yiwen
%A Xu, Qingyang
%A Liu, Jiahe
%A Dwyer, Dominic
%A Ge, Zongyuan
%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 zhao-etal-2026-audioprocessbench
%X Large audio-language models (LALMs) increasingly use explicit reasoning traces for complex audio understanding, yet the evaluation of reasoning quality remains underexplored. Although process-level benchmarks for process reward models (PRMs) have advanced reasoning evaluation in text and multi-modal domains, comparable evaluation for audio reasoning remains limited. In this paper, we present AudioProcessBench, a comprehensive benchmark for step-level process error identification in audio reasoning. AudioProcessBench contains diverse reasoning traces generated by 6 audio and omni language models. Each trace is segmented into discrete reasoning steps and annotated with binary step correctness and fine-grained error types. Our benchmark evaluates models under three complementary paradigms: (1) step correctness identification, (2) error-type-conditioned detection for diagnosing audio-specific verifier capacities, and (3) chain-level aggregation, where verifiers select or aggregate among multiple reasoning traces for the same question. This design enables a systematic analysis of whether current models can detect process errors, whether their weaknesses differ across audio-specific error types, and whether process verification translates into improved answer selection. AudioProcessBench provides a testbed for future research on audio reasoning verifiers, process reward models, and reliable omni-modal reasoning.
%U https://aclanthology.org/2026.findings-emnlp.1686/
%P 35981-36003
Markdown (Informal)
[AudioProcessBench: Benchmark for Identifying Process Errors in Audio-Grounded Reasoning](https://aclanthology.org/2026.findings-emnlp.1686/) (Zhao et al., Findings 2026)
ACL
- Xiangyu Zhao, Junyu Yan, Yaling Shen, Stephanie Fong, Zimu Wang, Yiwen Jiang, Qingyang Xu, Jiahe Liu, Dominic Dwyer, and Zongyuan Ge. 2026. AudioProcessBench: Benchmark for Identifying Process Errors in Audio-Grounded Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 35981–36003, Budapest, Hungary. Association for Computational Linguistics.