most citedLungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning

Jiaxuan Lu, Ziyu Kong, Yemin Wang +10

The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on…

cs.AI2025

AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning

Jiayi Zhang, Yiran Peng, Fanqi Kong +12

Humans naturally adapt to diverse environments by learning underlying rules across worlds with different dynamics, observations, and reward structures. In contrast, existing agents…

cs.CV20251 cited

LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules

Cheng Yang, Hui Jin, Xinlei Yu +7

Diagnosing lung cancer typically involves physicians identifying lung nodules in Computed tomography (CT) scans and generating diagnostic reports based on their morphological featu…

cs.CV2025

Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks

Cheng Yang, Haiyuan Wan, Yiran Peng +8

Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reaso…

cs.CV2025

Small Lesions-aware Bidirectional Multimodal Multiscale Fusion Network for Lung Disease Classification

Jianxun Yu, Ruiquan Ge, Zhipeng Wang +6

The diagnosis of medical diseases faces challenges such as the misdiagnosis of small lesions. Deep learning, particularly multimodal approaches, has shown great potential in the fi…

cs.CV2025

Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling

Xinlei Yu, Chengming Xu, Zhangquan Chen +6

The dominant paradigm of monolithic scaling in Vision-Language Models (VLMs) is failing for understanding and reasoning in documents, yielding diminishing returns as it struggles w…