Taming Visual Neglect: A Variational Information Bottleneck Framework for Adaptive Attention in Multimodal In-Context Learning
Large vision-language models exhibit strong in-context learning (ICL) capabilities, yet when and why visual context helps multimodal ICL remains poorly understood. Empirical studies show a puzzling dichotomy: models sometimes effectively leverage visual demonstrations, yet often neglect them entirely. We propose VIB-ICL, an information-theoretic framework that resolves this dichotomy through the Information Bottleneck principle. We introduce the Cross-Modal Information Gain (CMIG), which quantifies the additional mutual information that visual context provides about the target beyond textual context. We derive a generalization bound showing that multimodal ICL's excess risk over text-only...
| Authors | Kaito Tanaka, Yuji Nishimura, Keisuke Matsuda et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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