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Yanfang Ye

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MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

This work introduces MEDGPT-OSS, an open-weight, 20B-parameter generalist vision-language model designed to facilitate open research in clinical AI, which successfully outperforms larger open medical models on out-of-distribution multimodal reasoning and complex text-only clinical tasks.

Kai Zhang, Zheng-Qing Yuan, Cheng Peng et al. · 0 citations
Book Open access Aug 2026

MedJudge: Medical Multimodal Reward Modeling

This work introduces MedJudge, a multimodal medical reward modeling method that supports interpretable, evidence-grounded, and clinically-aligned decision evaluation, and proposes UMLS-based Concept Overlap (UCO) to evaluate explanation quality, measuring concept-level alignment with clinician expectations.

Yun-Hong He, Kai Zhang, Jiarong Qian et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Dr. Claw: An AI Scientist Workspace for Vibe Research

Dr. Claw is presented, an open-source workspace that wraps existing coding-agent executors in a controllable and auditable human-in-the-loop workflow rather than introducing another autonomous agent.

D. Song, Han-Rong Zhang, Dawei Liu et al. · 1 citation
Jul 2026

PreDiff-LM: Pretrained Discrete Masked Diffusion Language Modeling with Hybrid Attention

PreDiff-LM preserves causal attention within the observed prompt while allowing full bidirectional attention within the masked target, position hybrid attention as a complementary mechanism for adapting pretrained causal backbones, while making explicit the remaining quality and inference-efficiency gaps to optimized A...

Zhengtao Yao, Run-Hao Li, Xu-Peng Chen et al. · 0 citations
Book Open access Aug 2026

MedJudge: Medical Multimodal Reward Modeling

This work introduces MedJudge, a multimodal medical reward modeling method that supports interpretable, evidence-grounded, and clinically-aligned decision evaluation, and proposes UMLS-based Concept Overlap (UCO) to evaluate explanation quality, measuring concept-level alignment with clinician expectations.

Yunhong He, Kai Zhang, Jiarong Qian et al. · 0 citations

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