3 papers
cs.CL2026
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
Yuancheng Yang, Lin Yang, Xu Wang +2
As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is growing accordingly. We argue that…
cs.CL2026
MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
Baorong Shi, Bo Cui, Boyuan Jiang +17
We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIA…
cs.CV2025
BaseReward: A Strong Baseline for Multimodal Reward Model
Yi-Fan Zhang, Haihua Yang, Huanyu Zhang +11
The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for…