AI RESEARCH
Improving Medical VQA through Trajectory-Aware Process Supervision
arXiv CS.LG
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ArXi:2605.04064v1 Announce Type: new Reasoning capabilities are crucial for reliable medical visual question answering (VQA); however, existing datasets rarely include reasoning explanations. We address this by generating reasoning trajectories for six medical VQA benchmarks using the COMCTS algorithm with open-source vision-language models, with an LLM serving as the verification judge. Building on these generated datasets, we propose a two-stage