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20232026
most citedBoundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning

29 citations · 67 across the 48 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025

Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs

Feng Hong, Geng Yu, Yushi Ye +5

Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plague…

cs.CL2025

An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

Weike Zhao, Chaoyi Wu, Yanjie Fan +10

Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exc…

cs.CL2025

AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation

Xiechi Zhang, Zetian Ouyang, Linlin Wang +6

With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, t…

cs.CL2025

MedS: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision

Shuyang Jiang, Yusheng Liao, Zhe Chen +3

Medical language models face critical barriers to real-world clinical reasoning applications. However, mainstream efforts, which fall short in task coverage, lack fine-grained supe…

cs.CL20241 cited

CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios

Zetian Ouyang, Yishuai Qiu, Linlin Wang +4

With the proliferation of Large Language Models (LLMs) in diverse domains, there is a particular need for unified evaluation standards in clinical medical scenarios, where models n…

cs.CL20241 cited

Towards Evaluating and Building Versatile Large Language Models for Medicine

Chaoyi Wu, Pengcheng Qiu, Jinxin Liu +5

In this study, we present MedS-Bench, a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. Unlike existing benchmark…