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LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering
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 s
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