43 citations · 150 across the 26 of their papers we have counts for
7 papers · 1 filter
MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual Information
Jiaxi Li, Yucheng Shi, Xiao Huang +2
Tree search has become as a representative framework for test-time reasoning with large language models (LLMs), exemplified by methods such as Tree-of-Thought and Monte Carlo Tree…
GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models
Zhangyu Wang, Nemin Wu, Qian Cao +8
The widespread adoption of AI models, especially foundation models (FMs), has made a profound impact on numerous domains. However, it also raises significant ethical concerns, incl…
Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
Shuang Zhou, Wenya Xie, Jiaxi Li +16
As large language models (LLMs) become increasingly integrated into clinical decision-making, ensuring transparent and trustworthy reasoning is essential. However, existing evaluat…
MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment
Yucheng Shi, Wenhao Yu, Zaitang Li +5
Recently, there has been a surge of vision-based GUI agents designed to automate everyday mobile and web tasks. These agents interpret raw GUI screenshots and autonomously decide w…
RadFabric: Agentic AI System with Reasoning Capability for Radiology
Wenting Chen, Yi Dong, Zhaojun Ding +14
Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic conditions, but current automated systems face limitations in pathology coverage, diagnostic accuracy, and…
Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data
Yucheng Shi, Quanzheng Li, Jin Sun +2
Large Multimodal Models (LMMs), or Vision-Language Models (VLMs), have shown impressive capabilities in a wide range of visual tasks. However, they often struggle with fine-grained…
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?
Yucheng Shi, Tianze Yang, Canyu Chen +4
Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented G…