15 citations · 25 across the 20 of their papers we have counts for
9 papers · 1 filter
From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
Juncheng Wu, Hardy Chen, Haoqin Tu +6
Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is primarily limited by a lack of vi…
ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
Juncheng Wu, Letian Zhang, Yuhan Wang +9
Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has already been curated and hand…
Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows
Hardy Chen, Nancy Lau, Haoqin Tu +8
Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a pu…
More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
Chengzhi Liu, Zhongxing Xu, Qingyue Wei +5
Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as multimodal math reasoning. Howe…
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains
Juncheng Wu, Sheng Liu, Haoqin Tu +5
Recent advances in reasoning-enhanced Large Language Models such as OpenAI-o1/3 and DeepSeek-R1 have significantly improved performance on complex tasks. However, the quality and t…
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
Juncheng Wu, Wenlong Deng, Xingxuan Li +12
Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasonin…
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data
Zijun Wang, Haoqin Tu, Yuhan Wang +6
This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles…
m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Test-time scaling has emerged as a powerful technique for enhancing the reasoning capabilities of large language models. However, its effectiveness in medical reasoning remains unc…
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Yunfei Xie, Juncheng Wu, Haoqin Tu +6
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest…