Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medicine, developing clinical guidelines critically depends on such deep evidence integration. However, existing benchmarks fail to evaluate this capability in realistic workflows requiring multi-step evidence integration and expert-level judgment. To address this gap, we introduce MedProbeBench, the first benchmark leveraging high-quality clinical guidelines as expert-level references. Medical guidelines, with their rigorous standards in neutrality and verifiability, represent the pinnacle of medical expertise and pose substantial challenges for deep research agents. For evaluation, we propose MedProbe-Eval, a comprehensive evaluation framework featuring: (1) Holistic Rubrics with 1,200+ task-adaptive rubric criteria for comprehensive quality assessment, and (2) Fine-grained Evidence Verification for rigorous validation of evidence precision, grounded in 5,130+ atomic claims. Evaluation of 17 LLMs and deep research agents reveals critical gaps in evidence integration and guideline generation, underscoring the substantial distance between current capabilities and expert-level clinical guideline development. Project: https://github.com/uni-medical/MedProbeBench
@article{arxiv.2604.18418,
title = {MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline},
author = {Jiyao Liu and Jianghan Shen and Sida Song and Tianbin Li and Xiaojia Liu and Rongbin Li and Ziyan Huang and Jiashi Lin and Junzhi Ning and Changkai Ji and Siqi Luo and Wenjie Li and Chenglong Ma and Ming Hu and Jing Xiong and Jin Ye and Bin Fu and Ningsheng Xu and Yirong Chen and Lei Jin and Hong Chen and Junjun He},
journal= {arXiv preprint arXiv:2604.18418},
year = {2026}
}