pith. sign in

arxiv: 2606.07264 · v1 · pith:BIYHPIZJnew · submitted 2026-06-05 · 📡 eess.AS

VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track

Wenming Tu , Jian Gao , Yanru Huo , Yixuan Wang , Jing Peng , Bohan Li , Ziyang Ma , Tao Liu
show 4 more authors
Shuai Fan Kai Yu Xie Chen Zilong Zheng
This is my paper

Pith reviewed 2026-06-27 20:59 UTC · model grok-4.3

classification 📡 eess.AS
keywords audio reasoningmulti-modal evidencevisual clueslanguage modelsmodel votingcategory routingreasoning chainsrubrics evaluation
0
0 comments X

The pith

VISA strengthens audio reasoning by adding visual clues to language models under a tool-use approach.

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

The paper presents VISA as a way to handle audio reasoning tasks that require multi-step inference over changing and mixed sound signals. It does so by treating large audio language models as tools and supplementing them with auxiliary multi-modal evidence drawn from visuals. Three main parts make this work: extracting features from both audio and visual sources, using model voting with consistency checks for steadier outputs, and routing decisions by category to pick reasoning paths that match evaluation standards. A reader would care because many real audio problems involve overlapping sounds where extra visual context can fill gaps without requiring complex system coordination.

Core claim

Under the LALM as a Tool paradigm, VISA integrates multi-modal feature extraction for complementary audio and acoustic-visual clues, model-voting inference with consistency checking for stable predictions, and fine-grained category-aware routing to resolve disagreements and select rubric-aligned reasoning chains, resulting in second place on the Agent Track leaderboard with a 66.23 percent Rubrics score and the highest accuracy of 77.40 percent across listed systems.

What carries the argument

The LALM-as-a-tool paradigm that combines multi-modal feature extraction, model-voting inference with consistency checking, and fine-grained category-aware routing to incorporate visual evidence into audio reasoning.

If this is right

  • Visual clues help manage temporally dynamic and acoustically mixed signals by providing extra context.
  • Model voting with consistency checks produces more stable predictions than single-model approaches.
  • Category-aware routing resolves model disagreements and selects reasoning chains that fit evaluation criteria.
  • The overall setup achieves high performance on both accuracy and reasoning quality metrics without heavy orchestration.

Where Pith is reading between the lines

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

  • The same visual-strengthening method could be tested on other audio tasks that involve overlapping or changing sounds.
  • Category routing might apply to multi-step reasoning problems outside audio, such as in text or sensor data.
  • Real-world deployments could examine how well the system holds up when visual data is partial or low quality.

Load-bearing premise

That visual information supplies complementary clues which integrate effectively with audio models to improve reasoning stability and alignment with rubrics.

What would settle it

Disabling the visual feature extraction component and measuring whether Rubrics and accuracy scores drop below the reported levels or other listed systems.

Figures

Figures reproduced from arXiv: 2606.07264 by Bohan Li, Jian Gao, Jing Peng, Kai Yu, Shuai Fan, Tao Liu, Wenming Tu, Xie Chen, Yanru Huo, Yixuan Wang, Zilong Zheng, Ziyang Ma.

Figure 1
Figure 1. Figure 1: Overview of VISA. The system performs multi-modal feature extraction (audio descriptors, agentic SED, and VLM-based acoustic visual analysis), then conducts multi-model voting with consistency checking, and finally applies fine-grained category-aware routing to select the final CoT and answer. tails, temporal dynamics, and spatial clues. (2) Audio Rea￾soning Model Voting Inference queries multiple LALMs an… view at source ↗
read the original abstract

Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or captioning. We present VISA, our submission to the Interspeech 2026 Audio Reasoning Challenge (Agent Track), evaluated via the MMAR Rubrics for correctness and reasoning quality. Under a "LALM as a Tool" paradigm, VISA strengthens large audio language models with auxiliary multi-modal evidence while avoiding heavy orchestration. The system integrates three components: multi-modal feature extraction for complementary audio and acoustic-visual clues, model-voting inference with consistency checking for stable predictions, and fine-grained category-aware routing to resolve disagreements and select rubric-aligned reasoning chains. On the official Agent Track leaderboard, VISA ranks 2nd overall with a 66.23% Rubrics score. It also achieves 77.40% Accuracy, the highest among all systems listed across both the Single Model and Agent tracks.

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

2 major / 0 minor

Summary. The manuscript presents VISA, a system submitted to the Interspeech 2026 Audio Reasoning Challenge (Agent Track). It operates under an LALM-as-a-tool paradigm that augments large audio language models with auxiliary multi-modal evidence extracted from visuals. The architecture comprises three components: multi-modal feature extraction for complementary audio and acoustic-visual clues, model-voting inference with consistency checking, and fine-grained category-aware routing to resolve disagreements and produce rubric-aligned reasoning chains. On the official leaderboard VISA is reported to rank 2nd overall with a 66.23% Rubrics score and to achieve the highest accuracy (77.40%) across both Single Model and Agent tracks.

Significance. If the performance numbers are reproducible and the visual component is shown to be contributory, the result would indicate that auxiliary multi-modal evidence can improve reasoning quality on temporally dynamic, acoustically mixed signals under a lightweight tool-use paradigm. The emphasis on consistency checking and category-aware routing for stable, rubric-aligned outputs is a constructive design choice. However, the absence of any quantitative support for the necessity of the visual module substantially reduces the significance that can be attached to the reported leaderboard placement.

major comments (2)
  1. [Abstract] Abstract: the central performance claim (2nd place, 66.23% Rubrics score; highest accuracy 77.40%) is stated without any ablation results, implementation details, or quantitative description of how the multi-modal feature extraction component contributes relative to the voting and routing modules, rendering the claim that visual strengthening is responsible for the scores unevaluable from the given text.
  2. [Results / Experiments] No section or table presents an ablation that removes the visual extraction module (or compares against an audio-only LALM baseline on the same ARC test set); without this evidence the reported scores do not establish that the auxiliary multi-modal evidence is necessary or even contributory, as the gains could arise solely from the consistency-checking or category-aware routing steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on the VISA manuscript. We address the major concerns point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central performance claim (2nd place, 66.23% Rubrics score; highest accuracy 77.40%) is stated without any ablation results, implementation details, or quantitative description of how the multi-modal feature extraction component contributes relative to the voting and routing modules, rendering the claim that visual strengthening is responsible for the scores unevaluable from the given text.

    Authors: We agree that the abstract would be strengthened by briefly noting the distinct roles of the three components. In revision we will update the abstract to include one sentence describing how multi-modal feature extraction supplies complementary clues, consistency-checked voting stabilizes outputs, and category-aware routing produces rubric-aligned chains. This will make the performance claims more evaluable while preserving length constraints. revision: yes

  2. Referee: [Results / Experiments] No section or table presents an ablation that removes the visual extraction module (or compares against an audio-only LALM baseline on the same ARC test set); without this evidence the reported scores do not establish that the auxiliary multi-modal evidence is necessary or even contributory, as the gains could arise solely from the consistency-checking or category-aware routing steps.

    Authors: We acknowledge that the current manuscript lacks an explicit ablation isolating the visual module on the official test set. We will add a new subsection in the revised version that reports an ablation on a held-out validation split, comparing the full system against an audio-only LALM baseline that retains the voting and routing modules. Any limitations in extrapolating validation results to the test set will be stated explicitly. This directly addresses the concern about quantitative support for the visual component. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical leaderboard result against external benchmark

full rationale

The paper presents an engineering system (VISA) for the Interspeech 2026 ARC Agent Track and reports its ranking and scores (2nd place, 66.23% Rubrics, 77.40% Accuracy) directly from the official external leaderboard. No derivation chain, equations, fitted parameters, or first-principles results are described that could reduce to self-citations, internal definitions, or fitted inputs. The central claim is an empirical performance number measured outside the paper, satisfying the criterion for self-contained results against external benchmarks. No load-bearing self-citations or ansatzes appear in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; all technical details remain unspecified.

pith-pipeline@v0.9.1-grok · 5735 in / 1076 out tokens · 28904 ms · 2026-06-27T20:59:58.055804+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

31 extracted references · 24 canonical work pages · 9 internal anchors

  1. [1]

    LALM as a Tool

    Introduction Humans routinely infer events, social roles, intentions, and causal relations from speech, music, and ambient sound. Repli- cating this capability in machines is a core goal of multi-modal AI. Unlike conventional tasks such as ASR [1, 2], sound event detection [3, 4], and audio captioning [5] that mainly evalu- ate perception, audio reasoning...

    2026

  2. [2]

    VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track

    System Design As illustrated in Figure 1, VISA consists of three key compo- nents.(1) Multi-modal Feature Extractionsummarizes au- dio using acoustic descriptors and auxiliary visual clues (e.g., spectrogram analysis via a VLM) to capture fine-grained de- 1https://audio-reasoning-challenge.github.io/ arXiv:2606.07264v1 [eess.AS] 5 Jun 2026 Multi-modal Fea...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  3. [3]

    Rubrics” denotes the official CoT quality score, and “Acc

    Results We submitted VISA to the Interspeech 2026 Audio Reasoning Challenge (Agent Track) and conducted a comprehensive eval- uation on the MMAR benchmark in terms of both final pre- diction accuracy and CoT reasoning quality. Results show that VISA achieves state-of-the-art performance across key metrics, validating the effectiveness of its advanced syst...

    work page arXiv 2026

  4. [4]

    VISA enhances LALMs with visualized acoustic evidence and structured inference strategies, achieving strong accuracy while maintaining high-quality, rubric-aligned reasoning chains

    Conclusion We present VISA, a system for complex audio reasoning in the Interspeech 2026 Audio Reasoning Challenge (Agent Track). VISA enhances LALMs with visualized acoustic evidence and structured inference strategies, achieving strong accuracy while maintaining high-quality, rubric-aligned reasoning chains. The combination of multi-modal feature extrac...

    2026

  5. [5]

    Generative AI Use Disclosure During this work, we utilized LLMs to assist in several aspects of the writing and presentation process. The specific applica- tions of LLMs were as follows: 1.Grammar and Language Refinement:LLMs were em- ployed to proofread the manuscript for grammatical errors, spelling mistakes, and awkward phrasing. This use was in- tende...

  6. [6]

    Qwen3-ASR Technical Report

    X. Shi, X. Wang, Z. Guo, Y . Wang, P. Zhang, X. Zhang, Z. Guo, H. Hao, Y . Xi, B. Yanget al., “Qwen3-asr technical report,”arXiv preprint arXiv:2601.21337, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  7. [7]

    Vibevoice-asr technical report,

    Z. Peng, J. Yu, Y . Chang, Z. Wang, L. Dong, Y . Hao, Y . Tu, C. Yang, W. Wang, S. Xuet al., “Vibevoice-asr technical report,” arXiv preprint arXiv:2601.18184, 2026

    work page arXiv 2026

  8. [8]

    Fine-tune the pretrained atst model for sound event detection,

    N. Shao, X. Li, and X. Li, “Fine-tune the pretrained atst model for sound event detection,” inICASSP 2024-2024 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 911–915

    2024

  9. [9]

    Flexsed: Towards open-vocabulary sound event detection,

    J. Hai, H. Wang, W. Guo, and M. Elhilali, “Flexsed: Towards open-vocabulary sound event detection,” in2025 IEEE Work- shop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2025, pp. 1–5

    2025

  10. [10]

    Omni-captioner: Data pipeline, mod- els, and benchmark for omni detailed perception,

    Z. Ma, R. Xu, Z. Xing, Y . Chu, Y . Wang, J. He, J. Xu, P.-A. Heng, K. Yu, J. Linet al., “Omni-captioner: Data pipeline, mod- els, and benchmark for omni detailed perception,”arXiv preprint arXiv:2510.12720, 2025

    work page arXiv 2025

  11. [11]

    GPT-4o System Card

    A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radfordet al., “Gpt-4o system card,”arXiv preprint arXiv:2410.21276, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  12. [12]

    Qwen2.5-Omni Technical Report

    J. Xu, Z. Guo, J. He, H. Hu, T. He, S. Bai, K. Chen, J. Wang, Y . Fan, K. Danget al., “Qwen2. 5-omni technical report,”arXiv preprint arXiv:2503.20215, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  13. [13]

    Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models

    A. Goel, S. Ghosh, J. Kim, S. Kumar, Z. Kong, S.-g. Lee, C.- H. H. Yang, R. Duraiswami, D. Manocha, R. Valleet al., “Audio flamingo 3: Advancing audio intelligence with fully open large audio language models,”arXiv preprint arXiv:2507.08128, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  14. [14]

    Mmar: A challenging benchmark for deep reasoning in speech, audio, music, and their mix,

    Z. Ma, Y . Ma, Y . Zhu, C. Yang, Y .-W. Chao, R. Xu, W. Chen, Y . Chen, Z. Chen, J. Conget al., “Mmar: A challenging bench- mark for deep reasoning in speech, audio, music, and their mix,” arXiv preprint arXiv:2505.13032, 2025

    work page arXiv 2025

  15. [15]

    Mmau-pro: A challenging and comprehensive benchmark for holistic evaluation of audio general intelligence,

    S. Kumar, ˇS. Sedl ´aˇcek, V . Lokegaonkar, F. L ´opez, W. Yu, N. Anand, H. Ryu, L. Chen, M. Pli ˇcka, M. Hlav ´aˇceket al., “Mmau-pro: A challenging and comprehensive benchmark for holistic evaluation of audio general intelligence,”arXiv preprint arXiv:2508.13992, 2025

    work page arXiv 2025

  16. [16]

    Omnibench: Towards the future of uni- versal omni-language models,

    Y . Li, Y . Ma, G. Zhang, R. Yuan, K. Zhu, H. Guo, Y . Liang, J. Liu, Z. Wang, J. Yanget al., “Omnibench: Towards the future of uni- versal omni-language models,”arXiv preprint arXiv:2409.15272, 2024

    work page arXiv 2024

  17. [17]

    Qwen2-Audio Technical Report

    Y . Chu, J. Xu, Q. Yang, H. Wei, X. Wei, Z. Guo, Y . Leng, Y . Lv, J. He, J. Linet al., “Qwen2-audio technical report,”arXiv preprint arXiv:2407.10759, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  18. [18]

    SALMONN: Towards generic hearing abilities for large language models,

    C. Tang, W. Yu, G. Sun, X. Chen, T. Tan, W. Li, L. Lu, Z. Ma, and C. Zhang, “SALMONN: Towards generic hearing abilities for large language models,”Proc. ICLR, 2024

    2024

  19. [19]

    Audio-reasoner: Improving reasoning capability in large audio language models,

    Z. Xie, M. Lin, Z. Liu, P. Wu, S. Yan, and C. Miao, “Audio- reasoner: Improving reasoning capability in large audio language models,”arXiv preprint arXiv:2503.02318, 2025

    work page arXiv 2025

  20. [20]

    Audio-thinker: Guiding audio language model when and how to think via reinforcement learning,

    S. Wu, C. Li, W. Wang, H. Zhang, H. Wang, M. Yu, and D. Yu, “Audio-thinker: Guiding audio language model when and how to think via reinforcement learning,”arXiv preprint arXiv:2508.08039, 2025

    work page arXiv 2025

  21. [21]

    Audiogenie-reasoner: A training-free multi-agent framework for coarse-to-fine audio deep reasoning,

    Y . Rong, C. Li, D. Yu, and L. Liu, “Audiogenie-reasoner: A training-free multi-agent framework for coarse-to-fine audio deep reasoning,”arXiv preprint arXiv:2509.16971, 2025

    work page arXiv 2025

  22. [22]

    Audiotoolagent: An agentic framework for audio-language models,

    G. Wijngaard, E. Formisano, and M. Dumontier, “Audiotoolagent: An agentic framework for audio-language models,”arXiv preprint arXiv:2510.02995, 2025

    work page arXiv 2025

  23. [23]

    Sar-lm: Symbolic au- dio reasoning with large language models,

    T. Taheri, Y . Ma, and E. Benetos, “Sar-lm: Symbolic au- dio reasoning with large language models,”arXiv preprint arXiv:2511.06483, 2025

    work page arXiv 2025

  24. [24]

    Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel

    F. Tian, X. T. Zhang, Y . Zhang, H. Zhang, Y . Li, D. Liu, Y . Deng, D. Wu, J. Chen, L. Zhaoet al., “Step-audio-r1 technical report,” arXiv preprint arXiv:2511.15848, 2025

    work page arXiv 2025

  25. [25]

    Qwen3-Omni Technical Report

    J. Xu, Z. Guo, H. Hu, Y . Chu, X. Wang, J. He, Y . Wang, X. Shi, T. He, X. Zhuet al., “Qwen3-omni technical report,”arXiv preprint arXiv:2509.17765, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  26. [26]

    The interspeech 2026 audio reasoning challenge: Evaluating reasoning process quality for audio reasoning models and agents,

    Z. Ma, R. Xu, Y . Ma, C.-H. H. Yang, B. Li, J. Kim, J. Xu, J. Li, C. Busso, K. Yuet al., “The interspeech 2026 audio reasoning challenge: Evaluating reasoning process quality for audio reason- ing models and agents,”arXiv preprint arXiv:2602.14224, 2026

    work page arXiv 2026

  27. [27]

    Gemini 2.5 flash,

    Google, “Gemini 2.5 flash,” https://cloud.google.com/vertex-ai/ generative-ai/docs/models/gemini/2-5-flash, 2025

    2025

  28. [28]

    Audio-cot: Exploring chain-of-thought reasoning in large audio language model,

    Z. Ma, Z. Chen, Y . Wang, E. S. Chng, and X. Chen, “Audio- CoT: Exploring chain-of-thought reasoning in large audio lan- guage model,”arXiv preprint arXiv:2501.07246, 2025

    work page arXiv 2025

  29. [29]

    Reinforce- ment learning outperforms supervised fine-tuning: A case study on audio question answering,

    G. Li, J. Liu, H. Dinkel, Y . Niu, J. Zhang, and J. Luan, “Reinforce- ment learning outperforms supervised fine-tuning: A case study on audio question answering,”arXiv preprint arXiv:2503.11197, 2025

    work page arXiv 2025

  30. [30]

    Qwen3-VL Technical Report

    S. Bai, Y . Cai, R. Chen, K. Chen, X. Chen, Z. Cheng, L. Deng, W. Ding, C. Gao, C. Geet al., “Qwen3-vl technical report,”arXiv preprint arXiv:2511.21631, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  31. [31]

    GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

    A. Zeng, X. Lv, Q. Zheng, Z. Hou, B. Chen, C. Xie, C. Wang, D. Yin, H. Zeng, J. Zhanget al., “Glm-4.5: Agentic, rea- soning, and coding (arc) foundation models,”arXiv preprint arXiv:2508.06471, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025