AI RESEARCH
M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding
arXiv CS.CV
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ArXi:2601.08758v3 Announce Type: replace-cross Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, and recent advances have extended this paradigm to Multimodal Large Language Models (MLLMs). In the medical domain, where diagnostic decisions depend on nuanced visual cues and sequential reasoning, CoT aligns naturally with clinical thinking processes. However, current benchmarks for medical image understanding generally focus on the final answer while ignoring the reasoning path.