activity
20242026
most citedAgentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

7 citations · 11 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents

Jinge Wu, Hongjian Zhou, Mingde Zeng +8

Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness an…

cs.AI2025★ 1 cited

MedicalOS: An LLM Agent based Operating System for Digital Healthcare

Jared Zhu, Junde Wu

Decades' advances in digital health technologies, such as electronic health records, have largely streamlined routine clinical processes. Yet, most these systems are still hard to…

cs.CV2025★ 1 cited

MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning

Jiazhen Pan, Che Liu, Junde Wu +6

Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. A…

cs.LG2025★ 2 cited

RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction

Fenglin Liu, Jinge Wu, Hongjian Zhou +9

Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decisi…

cs.AI2025★ 7 cited

Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

Junde Wu, Jiayuan Zhu, Yuyuan Liu +2

We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents. Agentic Reasoning dynamically leverages we…

eess.IV2024

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation

Jiayuan Zhu, Junde Wu, Cheng Ouyang +2

Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which d…