activity
20242026
most citedTool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models

2 citations · 3 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CV2026

CA-OPD: Confidence-Aware On-Policy Distillation for Structured Visual Prediction

Menghao Li, Linjie Mu, Yin Wang +6

Autoregressive vision language models unify heterogeneous perception tasks but are highly susceptible to compounding errors. On-policy distillation (OPD) bridges the training-infer…

cs.CL2026

Human-AI Co-reasoning for Clinical Diagnosis with Evidence-Integrated Language Agent

Zhongzhen Huang, Yan Ling, Hong Chen +7

We present PULSE, a medical reasoning agent that combines a domain-tuned large language model with scientific literature retrieval to support diagnostic decision-making in complex…

cs.CL2026

CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation

Yannian Gu, Zhongzhen Huang, Linjie Mu +3

Multimodal large language models (MLLMs) demonstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and textual data…

cs.CV2026

CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers

Yannian Gu, Xizhuo Zhang, Linjie Mu +4

Recent advances in Large Vision-Language Models (LVLMs) have shown strong potential for multi-modal radiological reasoning, particularly in tasks like diagnostic visual question an…

cs.AI2026

EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories

Linjie Mu, Zhongzhen Huang, Yannian Gu +3

World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains like medicine remains challeng…

cs.AI2025

MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph

Linjie Mu, Yannian Gu, Zhongzhen Huang +3

Large language models with reasoning capabilities have demonstrated impressive performance across a wide range of domains. In clinical applications, a transparent, step-by-step rea…

cs.AI2025

CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment

Yakun Zhu, Zhongzhen Huang, Qianhan Feng +5

Medical care follows complex clinical pathways that extend beyond isolated physician-patient encounters, emphasizing decision-making and transitions between different stages. Curre…

cs.AI2025★ 1 cited

Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting

Yongrui Yu, Zhongzhen Huang, Linjie Mu +2

In clinical practice, radiology reporting is an essential yet complex, time-intensive, and error-prone task, particularly for 3D medical images. Existing automated approaches based…

cs.CL2025

Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios

Zhongzhen Huang, Linjie Mu, Yakun Zhu +3

Effective clinical decision-making depends on iterative, multimodal reasoning across diverse sources of evidence. The recent emergence of multimodal reasoning models has significan…

cs.CL2025

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models

Yakun Zhu, Zhongzhen Huang, Linjie Mu +6

The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, includin…

cs.CV2025

MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression

Linjie Mu, Zhongzhen Huang, Shengqian Qin +3

Large vision-language models (LVLMs) have shown great promise in medical applications, particularly in visual question answering (MedVQA) and diagnosis from medical images. However…

cs.CL2024★ 2 cited

Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models

Zhongzhen Huang, Kui Xue, Yongqi Fan +5

Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortc…