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arxiv: 2606.09925 · v1 · pith:PDCNKYB4new · submitted 2026-06-07 · 💻 cs.SD

AudioProcessBench: Benchmark for Identifying Process Errors in Audio-Grounded Reasoning

Xiangyu Zhao , Junyu Yan , Yaling Shen , Zimu Wang , Yiwen Jiang , Stephanie Fong , Qingyang Xu , Jiahe Liu
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Dominic Dwyer Zongyuan Ge
This is my paper

Pith reviewed 2026-06-27 18:06 UTC · model grok-4.3

classification 💻 cs.SD
keywords audio reasoningprocess errorsbenchmarkslarge audio-language modelsverifierserror detectionmulti-modal reasoningstep-level evaluation
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The pith

AudioProcessBench supplies annotated reasoning traces from six models to test whether verifiers can spot step-level process errors in audio tasks and improve final answer selection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates AudioProcessBench to fill the gap in evaluating reasoning quality for large audio-language models that produce explicit step-by-step traces. It segments traces into discrete steps, labels each for binary correctness and specific error types, then measures verifier performance in three ways: spotting incorrect steps, detecting errors conditioned on audio-specific types, and aggregating multiple traces to pick better answers. A sympathetic reader cares because audio models increasingly rely on these traces for complex understanding, yet no prior testbed existed to check if the steps themselves are sound. If the benchmark works, it would show whether verification at the process level actually leads to more reliable audio reasoning outputs.

Core claim

AudioProcessBench contains diverse reasoning traces generated by six audio and omni language models, with each trace segmented into steps and annotated for binary correctness plus fine-grained error types, supporting evaluation under step correctness identification, error-type-conditioned detection, and chain-level aggregation to select or combine traces for the same question.

What carries the argument

AudioProcessBench benchmark, built from segmented and annotated reasoning traces, evaluated through three paradigms that separately test error detection, type-specific diagnosis, and answer improvement via verification.

If this is right

  • Models can be measured for their capacity to detect process errors during audio reasoning.
  • Differences in verifier performance across audio-specific error types can be diagnosed directly.
  • Process verification can be checked for whether it produces better answer selection than using raw traces alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The annotation method could be applied to build similar process benchmarks for other sensory modalities beyond audio.
  • Verifiers improved via this benchmark might be integrated into training loops to create more reliable audio-language models.
  • The error-type taxonomy may reveal systematic gaps that point to needed changes in how models generate audio reasoning steps.

Load-bearing premise

The reasoning traces produced by the six models represent real audio reasoning well enough, and the binary labels plus error-type annotations are accurate and consistent enough to act as ground truth.

What would settle it

If verifiers trained or tested on AudioProcessBench show no gain in selecting correct final answers compared with models that skip process verification, the claim that step-level error identification improves audio reasoning would not hold.

Figures

Figures reproduced from arXiv: 2606.09925 by Dominic Dwyer, Jiahe Liu, Junyu Yan, Qingyang Xu, Stephanie Fong, Xiangyu Zhao, Yaling Shen, Yiwen Jiang, Zimu Wang, Zongyuan Ge.

Figure 1
Figure 1. Figure 1: The paradigm of AUDIOPROCESSBENCH. Left: the data construction pipeline of the benchmark; Right: the evaluation paradigm of the benchmark. acoustic attributes, or unsupported grounding to the audio signal (Sahoo et al., 2024; Cheng et al., 2026). As a result, it remains unclear whether exist￾ing process verifiers can detect and diagnose errors in audio-based reasoning traces. 2.2 Audio-Grounded Reasoning a… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of the first erroneous step position [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Error-type distribution of reasoning traces [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of error types across reasoning [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Self-critique gap of critic models on AU￾DIOPROCESSBENCH. Positive values indicate self￾advantage, while negative values indicate self-blindness. The marker size is proportional to the absolute gap mag￾nitude. higher-level verification abilities reflected in cross￾modal binding and reasoning errors, where Qwen3- Omni-30B-A3B, Step-Audio-R1, and Gemini-3- Flash substantially outperform earlier models. Error… view at source ↗
Figure 7
Figure 7. Figure 7: System prompt for segmenting a solution into discrete steps. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt template for step-level audio reasoning verification and error-type annotation (Part 1). [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt template for step-level audio reasoning verification and error-type annotation (Part 2). [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: System prompt for critic models. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Few-shot examples used for step-level audio reasoning verification. Example A shows an all-correct [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Few-shot example used for step-level audio reasoning verification. Example C shows a late perceptual [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces AudioProcessBench, a benchmark for identifying process errors in audio-grounded reasoning. It contains reasoning traces from six audio and omni-language models, segmented into steps and annotated with binary correctness labels plus fine-grained error types. The benchmark supports three evaluation paradigms: step-level correctness identification, error-type-conditioned detection, and chain-level aggregation to select or combine traces for improved answer selection.

Significance. If the annotations are reliable, the benchmark would fill a clear gap by enabling systematic diagnosis of verifier performance on audio-specific errors and testing whether process verification improves final outputs. This provides a concrete testbed for audio process reward models and omni-modal reasoning research.

major comments (1)
  1. [Abstract / benchmark construction] Abstract and benchmark construction section: the manuscript describes the three evaluation paradigms and states that traces are annotated with binary step correctness and fine-grained error types, yet supplies no annotation protocol, number of annotators, inter-annotator agreement statistics, or validation procedure for audio-specific categories such as acoustic misinterpretation or temporal misalignment. Because all three paradigms treat these labels as ground truth, the absence of reliability evidence is load-bearing for the central claim of diagnostic utility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on annotation reliability, which is critical for the benchmark's utility. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / benchmark construction] Abstract and benchmark construction section: the manuscript describes the three evaluation paradigms and states that traces are annotated with binary step correctness and fine-grained error types, yet supplies no annotation protocol, number of annotators, inter-annotator agreement statistics, or validation procedure for audio-specific categories such as acoustic misinterpretation or temporal misalignment. Because all three paradigms treat these labels as ground truth, the absence of reliability evidence is load-bearing for the central claim of diagnostic utility.

    Authors: We agree that the absence of annotation details weakens the claims. In the revised manuscript we will add a new subsection under benchmark construction that specifies: (1) the full annotation protocol (step segmentation rules, error taxonomy definitions, and audio-specific guidelines), (2) the number of annotators and their qualifications, (3) inter-annotator agreement statistics, and (4) the validation procedure used for categories such as acoustic misinterpretation and temporal misalignment, including example annotations. This will directly support the ground-truth status of the labels across all three evaluation paradigms. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction is self-contained

full rationale

This is a benchmark paper introducing AudioProcessBench with human-annotated reasoning traces from six models, evaluated under three paradigms (step correctness, error-type detection, chain aggregation). No equations, fitted parameters, or derivations are present that could reduce to inputs by construction. The central claims rest on the creation and use of the dataset itself rather than any self-referential prediction or uniqueness theorem. Annotation reliability is an external validity concern, not a circularity issue within the reported framework.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces a new evaluation benchmark without introducing new mathematical parameters, axioms beyond standard ML evaluation assumptions, or invented physical entities.

axioms (1)
  • domain assumption Human or expert annotations of step correctness and error types provide reliable ground truth for audio reasoning evaluation.
    The benchmark's utility rests on the assumption that the annotations accurately reflect true process errors.

pith-pipeline@v0.9.1-grok · 5771 in / 1259 out tokens · 20159 ms · 2026-06-27T18:06:47.809999+00:00 · methodology

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Reference graph

Works this paper leans on

17 extracted references · 3 canonical work pages · 1 internal anchor

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    DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

    Let’s verify step by step. InInternational Conference on Learning Representations, volume 2024, pages 39578–39601. Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingx- uan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, and 1 others. 2025. Deepseek-v3. 2: Pushing the frontier of open large language models.arXiv preprint arXiv:2512.02556...

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    Step-audio-r1 technical report.arXiv preprint arXiv:2511.15848. Weiyun Wang, Zhangwei Gao, Lianjie Chen, Zhe Chen, Jinguo Zhu, Xiangyu Zhao, Yangzhou Liu, Yue Cao, Shenglong Ye, Xizhou Zhu, and 1 others. 2025. Visu- alprm: An effective process reward model for multi- modal reasoning.arXiv preprint arXiv:2503.10291. 10 Xumeng Wen, Zihan Liu, Shun Zheng, Sh...

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    Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin

    Reward models in deep reinforcement learn- ing: A survey.arXiv preprint arXiv:2506.15421. Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025. Processbench: Iden- tifying process errors in mathematical reasoning. In Proceedings of the 63rd Annual Meeting of the As- sociation for Comp...

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  4. [4]

    Do NOT paraphrase, summarize, translate, reorder, or insert any word, character, or punctuation

    PRESERVE THE ORIGINAL TEXT EXACTLY. Do NOT paraphrase, summarize, translate, reorder, or insert any word, character, or punctuation. Each output step must be a verbatim contiguous substring of the input reasoning trace

  5. [5]

    The concatenation of all steps (joined by single space or newline) must reconstruct the original reasoning trace (allowing only inter-step whitespace)

  6. [6]

    Step boundaries are decided by natural reasoning flow: one perceptual claim, one inference, one knowledge lookup, one conclusion, etc

  7. [7]

    steps": [

    Don’t over-split (every step does real work) or under-split (a step spans multiple distinct reasoning moves). GRANULARITY GUIDANCE: - Aim for 5–12 steps for most reasoning traces; up to 20 only for genuinely very long reasoning (>4000 chars). - Numbered bullets (1., 2., 3.) and bullet markers (-, *) are NOT automatic step boundaries — group consecutive bu...

  8. [8]

    Label the step as exactly one of: ‘correct’, ‘existence_error’, ‘temporal_error’, ‘acoustic_attribute_error’, ‘semantic_error’, ‘cross_modal_binding_error’, ‘reasoning_error’

  9. [9]

    Justify the label in a 2–4 sentence ‘analysis’ citing specific audio evidence or logical structure

  10. [10]

    step_id”: <int>, “step_text

    After labeling all steps, populate ‘first_error_step’ and ‘final_answer_correct’. You must label every step exactly once, in the order provided. Do not skip, merge, split, re-order, or invent steps. The set of ‘step_id’ values in your output must equal the set of ‘step_id’ values in <solver_segmented_steps> – no more, no less. # 2. Step-correctness criter...

  11. [11]

    Listen to the entire audio clip end-to-end at least once

  12. [12]

    Read the question and ground-truth answer to understand the task framing, without using the ground truth to retro-justify step labels

  13. [13]

    Read the pre-segmented steps in order to understand the chain’s trajectory and endpoints

  14. [14]

    For each step: re-listen to relevant audio spans, decide whether the step contains an objectively incorrect and load-bearing claim, apply the precedence rule, determine whether the step is a fresh error or propagation, and write the analysis

  15. [15]

    Populate ‘first_error_step’ with the smallest non-correct step id, or null if all steps are correct

  16. [16]

    Set ‘final_answer_correct’ by comparing the solver’s effective answer to the ground truth semantically

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    step_id": <int>,

    Output the complete JSON object and nothing else. Begin now. Figure 9: Prompt template for step-level audio reasoning verification and error-type annotation (Part 2). 17 You are an expert audio reasoning critic. Your task is to analyze problem-solving steps and provide structured assessments in JSON format. For each step, emit a numerical ‘score‘∈[-1, +1]...