Recent advances in large language models have enabled LLM-based agents to achieve strong performance on a variety of benchmarks. However, their performance in real-world deployments often that observed on benchmark settings, especially in complex and imperfect environments. This discrepancy largely arises because prevailing training and evaluation paradigms are typically built on idealized assumptions, overlooking the inherent stochasticity and noise present in real-world interactions. To bridge this gap, we introduce AgentNoiseBench, a framework for systematically evaluating the robustness of agentic models under noisy environments. We first conduct an in-depth analysis of biases and uncertainties in real-world scenarios and categorize environmental noise into two primary types: user-noise and tool-noise. Building on this analysis, we develop an automated pipeline that injects controllable noise into existing agent-centric benchmarks while preserving task solvability. Leveraging this pipeline, we perform extensive evaluations across a wide range of models with diverse architectures and parameter scales. Our results reveal consistent performance variations under different noise conditions, highlighting the sensitivity of current agentic models to realistic environmental perturbations.
Imaging-101: Benchmarking LLM Coding Agents on Scientific Computational Imaging
College of Future Technology and the National Biomedical Imaging Center, Peking University（北京大学未来技术学院和国家生物医学成像中心）；AI for Science Institute (AISI)（科学人工智能研究所）；University of Michigan（密歇根大学）；State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所声场声信息国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；School of Astronautics, Beihang University（北京航空航天大学宇航学院）；Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education（教育部航天器设计优化与动态仿真技术重点实验室）
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
University of Science and Technology of China（中国科学技术大学）；City University of Hong Kong（香港城市大学）；Northeastern University（东北大学）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）科技有限公司）
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
School of Computational Science \& Engineering Georgia Institute of Technology Atlanta GA USA；Scheller College of Business Georgia Institute of Technology Atlanta GA USA；Georgia Institute of Technology
SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University（北京大学）
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Huazhong University of Science and Technology（华中科技大学）；Institution for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与空间研究所）；Zhongguancun Laboratory（中关村实验室）
MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Gaotu Techedu Inc.（高途科技公司）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Science and Technology of China（中国科学技术大学）
CUB: Benchmarking Context Utilisation Techniques for Language Models
Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）；Seoul National University（首尔国立大学）；University of Copenhagen（哥本哈根大学）；Ewha Womans University（成均馆大学）
AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping
Department of Artificial Intelligence Yonsei University Seoul Republic of Korea（人工智能系 首尔国立庆尚大学 首尔 大韩民国）；ParamitaAI Seoul Republic of Korea（ParamitaAI 首尔 大韩民国）；Yonsei University（首尔国立庆尚大学）；ParamitaAI
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Division of Medical Image Computing, Germany Cancer Research Center（德国癌症研究中心医学图像计算部）；Medical Faculty, University of Heidelberg（海德堡大学医学院）；Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO)（海德堡放射肿瘤学研究所（HIRO），国家放射肿瘤学研究中心（NCRO））；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤科模式分析与学习组）；Faculty of Mathematics and Computer Science, University of Heidelberg（海德堡大学数学与计算机科学学院）；National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and the university medical center Heidelberg（国家肿瘤疾病中心（NCT），NCT海德堡，DKFZ与海德堡大学医学中心的合作机构）
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Rochester（罗切斯特大学）
