RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios

Jin Wang; Kaiwen Luo; Kun Wang; Liang Lin; Li Sun; Shilinlu Yan; Yalan Qin; Yaoqi Guo; Yibo Zhang; Yitian Chen

arxiv: 2601.10384 · v2 · submitted 2026-01-15 · 💻 cs.SD

RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios

Yibo Zhang , Liang Lin , Kaiwen Luo , Shilinlu Yan , Jin Wang , Yaoqi Guo , Yitian Chen , Yalan Qin

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Zhenhong Zhou Kun Wang Li Sun

This is my paper

Pith reviewed 2026-05-16 14:10 UTC · model grok-4.3

classification 💻 cs.SD

keywords audio large modelsrobustness evaluationacoustic ecologyreal-world noisereasoning tasksspeech enhancementperception gap

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The pith

Audio large models hold up on basic sound recognition but collapse on complex reasoning when real-world noises overlap speech.

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

The paper creates RSA-Bench to test audio large models by overlaying natural soundscapes such as pasture, extreme weather, classroom, and outdoor scenes onto clean speech at different strengths. It evaluates performance across six tasks from simple perception to advanced reasoning and reports three main findings: models keep relative strength on low-level recognition yet lose function on high-order reasoning; vocal-like sounds damage performance more than mechanical noise; and standard speech enhancement tends to increase errors through new distortions. A sympathetic reader would care because these results indicate current models are not yet reliable for deployment in authentic acoustic settings where multiple sound layers mix. The work uses high-fidelity natural superposition rather than artificial noise to expose these gaps.

Core claim

RSA-Bench demonstrates that audio large models exhibit a perception-cognition gap: they maintain resilience in low-level recognition tasks but undergo functional collapse in high-order reasoning under acoustic stress. Vocal-like interference such as background laughter proves more destructive than mechanical noise, and standard speech enhancement often worsens results by introducing semantic distortions that the models cannot ignore.

What carries the argument

RSA-Bench, which builds test samples by naturally superimposing environmental soundscapes from Pasture, Extreme Weather, Classroom, and Outdoors onto clean speech across interference levels and then measures six core tasks ranging from perception to reasoning.

If this is right

Models require training methods that specifically strengthen high-order reasoning under overlapping natural sounds rather than isolated noise.
Auditory attention mechanisms must be redesigned to better resist vocal-like interferences that currently dominate performance drops.
Denoising pipelines need reevaluation because the semantic artifacts they introduce degrade model outputs more than the original noise.
Real-world applications cannot rely on basic task accuracy to predict success on complex audio reasoning in mixed environments.
Benchmarking should shift from synthetic Gaussian noise to layered natural soundscapes to reflect actual deployment conditions.

Where Pith is reading between the lines

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

Benchmarks could extend to urban or multi-speaker scenes with even denser overlaps to test whether the perception-cognition gap widens further.
Integration with visual or contextual inputs might help models recover reasoning performance when audio alone fails under stress.
Developers may need to trade some clean-condition accuracy for greater robustness across the full range of real acoustic ecologies.
Field validation studies comparing simulated overlays to live recordings would clarify how well the benchmark generalizes beyond its constructed scenarios.

Load-bearing premise

The naturally superimposed environmental soundscapes accurately capture the multi-layered acoustic dynamics of real physical environments.

What would settle it

Demonstrating that models maintain consistent performance from low-level recognition through high-order reasoning when tested on actual field recordings of the same scene types would falsify the functional collapse claim.

read the original abstract

While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics -- or ``Acoustic Ecology'' -- that characterize authentic physical environments. To bridge this ecological gap, we introduce \textbf{RSA-Bench}, a comprehensive robustness benchmark designed to stress-test ALLMs through high-fidelity auditory scene simulations. Unlike traditional methods, we construct evaluation samples by naturally superimposing diverse environmental soundscapes -- spanning \textit{Pasture}, \textit{Extreme Weather}, \textit{Classroom}, and \textit{Outdoors} -- onto clean speech signals across a spectrum of interference intensities. By evaluating models on six core tasks ranging from fundamental perception to complex reasoning, our study unveils three macro-level insights: \textbf{(I) The Perception-Cognition Gap:} Models maintain relative resilience in low-level recognition but suffer a \textbf{functional collapse} in high-order reasoning tasks under stress; \textbf{(II) Scenario Sensitivity:} ``Vocal-like'' interference (e.g., background laughter) proves significantly more destructive than mechanical noise, challenging the model's auditory attention mechanisms; and \textbf{(III) The Denoising Paradox:} Standard speech enhancement often exacerbates performance degradation, as ALLMs prove highly sensitive to the semantic distortions introduced by denoising artifacts.

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.

Desk Editor's Note private letter to a colleague

RSA-Bench gives a practical way to test audio models with overlaid real soundscapes, but the mixing details and absent numbers leave the three main claims hard to judge yet.

read the letter

The paper's main move is to build test data by layering actual recordings from four scene types—Pasture, Extreme Weather, Classroom, Outdoors—onto clean speech at different strengths. It then runs models across six tasks that go from basic perception up to reasoning and reports three patterns: low-level recognition holds up better than high-level reasoning, vocal-like background sounds hurt more than mechanical ones, and standard denoising often makes results worse instead of better. That framing and the specific scene choices are the clearest new pieces compared with earlier noise-addition tests. The structure of the evaluation is straightforward and covers a useful range of difficulty, which is a step forward for anyone who needs benchmarks that feel closer to real deployment conditions like voice interfaces or surveillance audio. The soft spot is the construction of the interference itself. If the superposition is simple additive scaling without room impulse responses, distance effects, or source interactions, the resulting signals may miss frequency-dependent masking and phase details that matter in physical spaces. That gap could inflate the reported reasoning collapse or the denoising paradox. The abstract also gives no model names, scores, error bars, or statistical checks, so the size of the effects stays unclear. This is the kind of work that matters to groups building or stress-testing audio large models. A reader focused on robustness would find the task ladder and scene categories worth looking at. It deserves a serious referee because benchmarks shape what counts as progress, even if this version needs tighter documentation on the mixing pipeline and fuller results before the claims can be taken as settled.

Referee Report

2 major / 1 minor

Summary. The paper introduces RSA-Bench, a robustness benchmark for Audio Large Models (ALMs) that constructs test samples by superimposing environmental soundscapes (Pasture, Extreme Weather, Classroom, Outdoors) onto clean speech at varying intensities. It evaluates models across six tasks spanning low-level perception to high-order reasoning and reports three insights: relative resilience in perception but functional collapse in reasoning under stress; greater destructiveness of vocal-like interference compared to mechanical noise; and that standard speech enhancement often worsens degradation due to semantic artifacts.

Significance. If the benchmark's acoustic simulations prove ecologically valid, the results would be significant for the field by quantifying a perception-cognition gap in ALMs and exposing limitations of current denoising pipelines, thereby providing concrete directions for improving model robustness in complex real-world audio environments.

major comments (2)

[Methods (sample construction)] Methods section (sample construction): the description of 'naturally superimposing' soundscapes onto clean speech provides no details on whether the procedure uses simple additive scaling, convolution with measured room impulse responses, distance-dependent attenuation, or coherent multi-source masking. This is load-bearing for the central claims, as the reported functional collapse and scenario sensitivity could be artifacts of simplified mixing rather than genuine robustness failures.
[Results] Results section: the three macro-level insights are stated without any quantitative performance metrics, error bars, specific model names, or statistical tests (e.g., no tables or figures showing accuracy drops across intensities or interference types). This prevents verification of the magnitude of the 'functional collapse' or the significance of vocal-like interference effects.

minor comments (1)

[Abstract] Abstract: the acronym 'ALLMs' is used once but the paper otherwise refers to 'ALMs'; consistent terminology would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for highlighting these important points regarding the clarity of our methods and the presentation of results. We believe these revisions will strengthen the paper and address the concerns raised. We respond to each major comment below.

read point-by-point responses

Referee: [Methods (sample construction)] Methods section (sample construction): the description of 'naturally superimposing' soundscapes onto clean speech provides no details on whether the procedure uses simple additive scaling, convolution with measured room impulse responses, distance-dependent attenuation, or coherent multi-source masking. This is load-bearing for the central claims, as the reported functional collapse and scenario sensitivity could be artifacts of simplified mixing rather than genuine robustness failures.

Authors: We agree that the Methods section requires greater precision on the mixing procedure. The superposition was performed using simple additive scaling of the soundscape amplitude to target specific SNR levels (0 dB down to -20 dB in 5 dB steps), with no room impulse responses, distance attenuation, or coherent masking applied. This design choice isolates the impact of interference type and intensity. We will revise the Methods section to explicitly document the SNR computation, scaling formula, and absence of spatial processing, thereby confirming that the observed perception-cognition gap and scenario sensitivity reflect genuine model limitations rather than mixing artifacts. revision: yes
Referee: [Results] Results section: the three macro-level insights are stated without any quantitative performance metrics, error bars, specific model names, or statistical tests (e.g., no tables or figures showing accuracy drops across intensities or interference types). This prevents verification of the magnitude of the 'functional collapse' or the significance of vocal-like interference effects.

Authors: We acknowledge that the main text of the Results section presents the three insights at a high level without embedding the supporting numbers. While the paper contains figures that plot accuracy trends, we agree these must be complemented by explicit quantitative statements. We will revise the Results section to include a summary table of accuracy scores for each evaluated model (e.g., Whisper, AudioLM, Wav2Vec2) across tasks and interference conditions, report standard deviations as error bars, name the models explicitly in the text, and add paired statistical tests (e.g., Wilcoxon signed-rank) for the vocal-like versus mechanical interference contrast. This will allow direct verification of the functional collapse magnitude and the differential harm of vocal-like noise. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with external model evaluations

full rationale

The paper presents RSA-Bench as a dataset construction and evaluation protocol for audio large models. Central claims (perception-cognition gap, scenario sensitivity, denoising paradox) are derived from performance measurements on models evaluated against the constructed samples. No equations, fitted parameters, self-citations, or ansatzes appear in the derivation chain; the benchmark is self-contained against external model outputs rather than reducing any prediction to its own inputs by construction. This is the expected non-finding for an empirical comparison study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the chosen soundscape overlays faithfully represent real-world acoustic ecology; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Naturally superimposed environmental soundscapes spanning Pasture, Extreme Weather, Classroom, and Outdoors capture the intricate multi-layered acoustic dynamics of authentic physical environments.
This premise underpins the claim that RSA-Bench closes the ecological gap with prior synthetic-noise evaluations.

pith-pipeline@v0.9.0 · 5582 in / 1302 out tokens · 60009 ms · 2026-05-16T14:10:12.360462+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

we construct evaluation samples by naturally superimposing diverse environmental soundscapes... via RMS-based energy alignment... x[n] = clip(s[n] + Σ λk · w̃k[n], -1, 1)
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat recovery theorem unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Perception-Cognition Gap: Models maintain relative resilience in low-level recognition but suffer a functional collapse in high-order reasoning tasks

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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.