Publications (36)
Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems
Fangzhen Lin, Ziyi Shou, Chengcai Chen
For a natural language problem that requires some non-trivial reasoning to solve, there are at least two ways to do it using a large language model (LLM). One is to ask it to solve…
VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning
Haozhe Wang, Chao Qu, Zuming Huang +3
Recently, slow-thinking systems like GPT-o1 and DeepSeek-R1 have demonstrated great potential in solving challenging problems through explicit reflection. They significantly outper…
From Illusion to Intention: Visual Rationale Learning for Vision-Language Reasoning
Changpeng Wang, Haozhe Wang, Xi Chen +6
Recent advances in vision-language reasoning underscore the importance of thinking with images, where models actively ground their reasoning in visual evidence. Yet, prevailing fra…
Computing Universal Plans for Partially Observable Multi-Agent Routing Using Answer Set Programming
Fengming Zhu, Fangzhen Lin
Multi-agent routing problems have gained significant attention recently due to their wide range of industrial applications, ranging from logistics warehouse automation to indoor se…
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Haozhe Wang, Weijia Feng, Jinpeng Yu +8
Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending…
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
Che Liu, Haozhe Wang, Jiazhen Pan +6
Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasonin…