@inproceedings{kang-etal-2026-modality,
title = "Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning",
author = "Kang, Zhaolu and
Wu, Meixin and
Xue, Yu and
He, Yingjie and
Shi, Qiming and
Wei, Lei and
Wang, Yidi and
Xuan, Richeng and
Hu, Zhichao",
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.791/",
pages = "16715--16748",
ISBN = "979-8-89176-462-0",
abstract = "Omni-modal large language models are increasingly evaluated on clean text{--}vision{--}audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly. Built from 273 human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates 15 proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text{--}vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable."
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<abstract>Omni-modal large language models are increasingly evaluated on clean text–vision–audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly. Built from 273 human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates 15 proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text–vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable.</abstract>
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%0 Conference Proceedings
%T Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
%A Kang, Zhaolu
%A Wu, Meixin
%A Xue, Yu
%A He, Yingjie
%A Shi, Qiming
%A Wei, Lei
%A Wang, Yidi
%A Xuan, Richeng
%A Hu, Zhichao
%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 kang-etal-2026-modality
%X Omni-modal large language models are increasingly evaluated on clean text–vision–audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly. Built from 273 human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates 15 proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text–vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable.
%U https://aclanthology.org/2026.findings-emnlp.791/
%P 16715-16748
Markdown (Informal)
[Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning](https://aclanthology.org/2026.findings-emnlp.791/) (Kang et al., Findings 2026)
ACL
- Zhaolu Kang, Meixin Wu, Yu Xue, Yingjie He, Qiming Shi, Lei Wei, Yidi Wang, Richeng Xuan, and Zhichao Hu. 2026. Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16715–16748, Budapest, Hungary. Association for Computational Linguistics.