22 citations · 31 across the 5 of their papers we have counts for
5 papers
Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning
Kai Liu, Peijie Dong, Xinchen Xie +5
The rapid progress of reasoning and agentic large language models (LLMs) has increased the demand for long-context inference, but self-attention (SA) scales quadratically with cont…
Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
Kexin Ding, Mu Zhou, Akshay Chaudhari +2
The wide exploration of large language models (LLMs) raises the awareness of alignment between healthcare stakeholder preferences and model outputs. This alignment becomes a crucia…
MedForge: Building Medical Foundation Models Like Open Source Software Development
Zheling Tan, Kexin Ding, Jin Gao +4
Foundational models (FMs) have made significant strides in the healthcare domain. Yet the data silo challenge and privacy concern remain in healthcare systems, hindering safe medic…
Data-Centric Foundation Models in Computational Healthcare: A Survey
Yunkun Zhang, Jin Gao, Zheling Tan +5
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models,…
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao, Zhuowei Li, Di Liu +3
A major focus of clinical imaging workflow is disease diagnosis and management, leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has…