AI RESEARCH
Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning
arXiv CS.CV
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ArXi:2605.02378v1 Announce Type: new In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that the fundamental limitation lies in an inductive gap, models often produce correct answers from flawed reasoning, while struggling to extract consistent rules across nstrations.