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This work introduces InfiMed2, a family of 4B and 27B generalist medical multimodal foundation models built around stage-aware data design, and curates a 55.68B-token corpus that combines broad clinical knowledge with context-rich biomedical visual evidence through source-specific processing.
SMAT (Simple MAT) is introduced, which jointly optimizes expert loss and expected loss at simulated merged parameters generated by sampling scaling coefficients, masks, and additive noise, and improves the mean score across five merging methods.
MedPIC-Bench makes conditional rule application measurable and highlights the limitations of static medication-safety accuracy for assessing patient-specific reliability among medical-specific LLMs, whose average CF performance trails that of general LLMs.
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