Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effective data design across continued pretraining (CPT) and post-training remains challenging. Medical sources vary substantially in structure, granularity, and information dens...
Guang-Hao Zhu, Ze-Yu Liu, Zhitian Hou et al.· 0 citations
Model merging efficiently combines specialized large language models (LLMs) without joint retraining, but can substantially alter expert routing in Mixture-of-Experts (MoE) models. Such \emph{routing drift} is often interpreted as routing failure, raising a fundamental question that remains unclear: \emph{does routing...
Yuan-Yi Wang, Yang-Gan Gu, Su Lu et al.· 0 citations
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.
Zhitian Hou, Yuhang Liu, Peng-Kai Wang et al.· 0 citations
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