Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
Understanding how multimodal large language models use different modalities is important for reliable reasoning. We employ Partial Information Decomposition (PID) as a decision-level lens and introduce Sensory PID, a conditional formulation that conditions on language and separates unique, redundant, and synergistic contributions from video and audio. Applied to omni-modal models, Sensory PID reveals a sensory synergy bottleneck: even on audio-visual fusion tasks, decisions remain dominated by modality-unique information, with stronger reliance on vision. Modality-shuffling interventions support this asymmetry, while layer-wise analysis reveals a visual-first computation pattern and instr...
| Authors | Wanlong Fang, Tianle Zhang, Wen Tao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 May 2026 |
| Last updated at source | 8 August 2026 |
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