AI RESEARCH
LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering
arXiv CS.AI
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ArXi:2605.09384v1 Announce Type: cross The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VLMs of 2--4B parameters can run on resource-constrained hardware but lack the multi-step reasoning capacity needed for interpretable clinical decision. Existing knowledge distillation methods transfer answers without the reasoning process behind them.