Diagnosing and Mitigating Perception-Decision Misalignment in Omni-LLMs via Modality Subspace Activation
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Computer Science > Machine Learning
Title:Diagnosing and Mitigating Perception-Decision Misalignment in Omni-LLMs via Modality Subspace Activation
Abstract:Omni-Large Language Models (Omni-LLMs) power complex multi-modal reasoning in applications like World Action Models and autonomous agents. However, their strong performance often masks a profound Perceptual-Decision Misalignment (PDM), where decisions remain unfaithful to multi-modal perceptions. To diagnose this, we formalize Causal Modality Sensitivity (CMS), operationalized via a dual-lens framework: Answer Retention Rate (ARR) at the macro behavioral level, and Logit Angular Discrepancy (LAD) to track microscopic distribution shifts. We also curate CausalMSBench, a diagnostic dataset isolating language priors. Benchmarking reveals that popular Omni-LLMs exhibit critically low CMS, showing negligible distribution shifts even when key modalities are removed. To rectify this, we propose Modality Subspace Activation (MSA), a training-free inference-time framework that uses Singular Value Decomposition (SVD) to estimate modal activation strengths. MSA dynamically balances modal projections in the last hidden state, effectively restoring CMS across benchmarks.
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2608.14655 [cs.LG] |
| (or arXiv:2608.14655v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.14655
arXiv-issued DOI via DataCite (pending registration)
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