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arxiv: 2510.00626 · v3 · submitted 2025-10-01 · 💻 cs.SD · cs.CL

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models

Chen-An Li , Tzu-Han Lin , Hung-yi Lee This is my paper

Pith reviewed 2026-05-18 11:05 UTC · model grok-4.3

classification 💻 cs.SD cs.CL
keywords large audio-language modelsirrelevant audiotext reasoningmodel robustnesscross-modal interferenceprediction volatilitysilence impact
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The pith

Even non-informative audio including silence reduces accuracy and raises volatility on text reasoning tasks in large audio-language models.

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

The paper examines what happens when large audio-language models receive audio that carries no useful information for a text-only reasoning problem. It reports consistent drops in accuracy together with greater instability in the model outputs. The amount of interference grows when the audio lasts longer, plays louder, or when the model samples at higher temperatures. Silence turns out to be as disruptive as synthetic noise or environmental sounds. Larger models suffer less but still show the same pattern, and simple prompting fails to remove the effect while self-consistency helps at extra compute cost.

Core claim

Appending irrelevant audio to text inputs causes large audio-language models to produce less accurate and more variable answers on standard text reasoning benchmarks, with the degree of degradation increasing with audio duration, amplitude, and decoding temperature, and with silence proving comparably harmful to synthetic noise or environmental sounds.

What carries the argument

Cross-modal interference triggered by non-informative audio channels, quantified through accuracy loss and output volatility that scale with duration, amplitude, and sampling temperature.

If this is right

  • Larger models display greater resistance to the interference but retain measurable vulnerabilities.
  • Prompt-based instructions produce only marginal improvement in stability.
  • Self-consistency decoding reduces volatility at the expense of higher inference cost.
  • The observed interference constitutes a general robustness limitation across the tested systems.

Where Pith is reading between the lines

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

  • Architectures that more cleanly separate or gate modalities could reduce this form of cross-talk without extra decoding steps.
  • The same pattern may appear in other multimodal systems whenever an unneeded input channel is present.
  • Real-world recordings with natural background sound would provide a direct test of whether the controlled findings generalize.

Load-bearing premise

The chosen text benchmarks stay purely text-based reasoning problems even after audio is added, without any hidden signal or training artifact that would let the model treat the audio as relevant.

What would settle it

Measuring whether accuracy and volatility return to the no-audio baseline when the audio input is replaced by a zeroed waveform or entirely omitted while keeping every other model component fixed.

read the original abstract

Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio, such as silence, synthetic noise, and environmental sounds, affects text reasoning tasks where audio is unnecessary. Across three text-based benchmarks, we find that even non-informative audio reduces accuracy and increases prediction volatility; the severity of interference scales with longer durations, higher amplitudes, and elevated decoding temperatures. Silence, often assumed neutral, destabilizes outputs as strongly as synthetic noise. While larger models show greater resilience, vulnerabilities persist across all evaluated systems. We further test mitigation strategies and find that prompting shows limited effectiveness, whereas self-consistency improves stability at the cost of increased computation. Our results reveal cross-modal interference as a key robustness challenge and highlight the need for efficient fusion strategies that preserve reasoning performance in the presence of irrelevant inputs.

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 / 2 minor

Summary. The manuscript investigates the effects of appending irrelevant audio (silence, synthetic noise, environmental sounds) to text reasoning tasks in large audio-language models. Across three text-based benchmarks, it reports that non-informative audio reduces accuracy and increases prediction volatility, with interference severity scaling with longer durations, higher amplitudes, and elevated decoding temperatures. Larger models show greater resilience, but vulnerabilities persist; prompting offers limited mitigation while self-consistency improves stability at higher computational cost.

Significance. If the central empirical findings hold after addressing controls and statistical reporting, the work would be significant for highlighting cross-modal interference as a robustness issue in LALMs, challenging the neutrality of silence and non-informative inputs. It offers practical observations on scaling behaviors and mitigation trade-offs that could guide fusion mechanism design, though the current lack of error bars and baseline verification limits immediate impact.

major comments (2)
  1. [Experimental Methodology] The description of audio input construction, audio encoder state for zeroed/silent inputs, prompt formatting, and explicit text-only baseline controls is insufficient to confirm that the three benchmarks remain strictly text-only reasoning tasks. Without these details, accuracy drops and volatility increases could arise from architectural side-effects, tokenization changes, or attention modulation rather than cross-modal interference (see skeptic concern on unintended signals).
  2. [Results] The reported accuracy reductions and volatility increases lack error bars, statistical significance tests, confidence intervals, or details on data exclusion rules. This makes it difficult to evaluate the reliability of the directional effects and scaling claims across the three benchmarks and model sizes.
minor comments (2)
  1. [Methods] Define 'prediction volatility' explicitly and describe its exact computation (e.g., variance over multiple runs or entropy).
  2. [Experimental Setup] Expand the model and benchmark details (specific LALMs, exact datasets, audio generation parameters) to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and outline the revisions planned to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Experimental Methodology] The description of audio input construction, audio encoder state for zeroed/silent inputs, prompt formatting, and explicit text-only baseline controls is insufficient to confirm that the three benchmarks remain strictly text-only reasoning tasks. Without these details, accuracy drops and volatility increases could arise from architectural side-effects, tokenization changes, or attention modulation rather than cross-modal interference (see skeptic concern on unintended signals).

    Authors: We agree that additional methodological detail is required to rule out alternative explanations. In the revised manuscript we will expand the relevant section to describe audio input construction in full, specify the audio encoder state for silent and zeroed inputs, document prompt formatting exactly, and present explicit text-only baseline controls. These additions will confirm that the benchmarks were kept strictly text-based and that observed effects are attributable to cross-modal interference rather than tokenization or attention artifacts. revision: yes

  2. Referee: [Results] The reported accuracy reductions and volatility increases lack error bars, statistical significance tests, confidence intervals, or details on data exclusion rules. This makes it difficult to evaluate the reliability of the directional effects and scaling claims across the three benchmarks and model sizes.

    Authors: We acknowledge that the current results section would benefit from statistical reporting. In revision we will add error bars from repeated runs where available, report confidence intervals, conduct appropriate significance tests for accuracy and volatility differences, and specify data exclusion rules. These changes will strengthen evaluation of the directional and scaling effects across benchmarks and model sizes. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical measurement study

full rationale

The paper is an empirical investigation that appends irrelevant audio to text benchmarks and measures resulting accuracy drops and volatility increases. No equations, derivations, fitted parameters, or self-citation chains are present in the provided text or abstract. Claims rest on direct experimental observations across models and conditions rather than any reduction of outputs to inputs by construction. This matches the default case of a self-contained empirical study against external benchmarks, warranting a score of 0 with no circular steps identified.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical robustness study. It introduces no free parameters, no new mathematical axioms, and no invented entities. It rests on the domain assumption that appended audio is processed by the model even when the task is defined as text-only.

axioms (1)
  • domain assumption LALMs integrate audio input into their internal representations even for tasks where audio carries no task-relevant information.
    This premise is required for the observed interference to be interpreted as cross-modal leakage rather than task misunderstanding.

pith-pipeline@v0.9.0 · 5690 in / 1291 out tokens · 34615 ms · 2026-05-18T11:05:25.965362+00:00 · methodology

discussion (0)

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Forward citations

Cited by 2 Pith papers

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  1. All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

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    Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.

  2. A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook

    cs.SD 2026-05 unverdicted novelty 5.0

    A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.

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