When One Modality Sabotages the Others: A Diagnostic Lens on Multimodal Reasoning
This work addresses the problem of understanding and auditing multimodal reasoning failures for researchers and practitioners, though it is incremental as it builds on existing diagnostic and evaluation methods.
The paper tackles the opacity of multimodal large language models (MLLMs) by introducing 'modality sabotage', a diagnostic failure mode where a high-confidence unimodal error misleads fused predictions, and proposes a lightweight, model-agnostic evaluation layer to analyze these dynamics, revealing systematic reliability profiles in a case study on multimodal emotion recognition benchmarks.
Despite rapid growth in multimodal large language models (MLLMs), their reasoning traces remain opaque: it is often unclear which modality drives a prediction, how conflicts are resolved, or when one stream dominates. In this paper, we introduce modality sabotage, a diagnostic failure mode in which a high-confidence unimodal error overrides other evidence and misleads the fused result. To analyze such dynamics, we propose a lightweight, model-agnostic evaluation layer that treats each modality as an agent, producing candidate labels and a brief self-assessment used for auditing. A simple fusion mechanism aggregates these outputs, exposing contributors (modalities supporting correct outcomes) and saboteurs (modalities that mislead). Applying our diagnostic layer in a case study on multimodal emotion recognition benchmarks with foundation models revealed systematic reliability profiles, providing insight into whether failures may arise from dataset artifacts or model limitations. More broadly, our framework offers a diagnostic scaffold for multimodal reasoning, supporting principled auditing of fusion dynamics and informing possible interventions.