Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
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Computer Science > Computation and Language
Title:Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
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.
| Comments: | EMNLP 2026 Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.29278 [cs.CL] |
| (or arXiv:2608.29278v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29278
arXiv-issued DOI via DataCite (pending registration)
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