The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an agent-centric benchmarking paradigm that moves beyond static datasets by introducing a dynamic protocol in which autonomous agents iteratively generate, validate, and solve problems. Within this protocol, a teacher agent generates candidate problems, an orchestrator agent rigorously verifies their validity and guards against adversarial attacks, and a student agent attempts to solve the validated problems. An invalid problem is revised by the teacher agent until it passes validation. If the student correctly solves the problem, the orchestrator prompts the teacher to generate more challenging variants. Consequently, the benchmark scales in difficulty automatically as more capable agents are substituted into any role, enabling progressive evaluation of large language models without manually curated datasets. Adopting text anomaly detection as our primary evaluation format, which demands cross-sentence logical inference and resists pattern-matching shortcuts, we demonstrate that this protocol systematically exposes corner-case reasoning errors that conventional benchmarks fail to reveal. We further advocate evaluating systems along several complementary axes including cross-model pairwise performance and progress between the initial and orchestrator-finalized problems. By shifting the focus from fixed datasets to dynamic protocols, our approach offers a sustainable direction for evaluating ever-evolving language models and introduces a research agenda centered on the co-evolution of agent-centric benchmarks.
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
Institute of Exact and Technological Sciences（精确与技术科学研究所）；Federal University of Viçosa（弗雷塔斯联邦大学）；Department of Informatics -- School of Engineering（信息学院 -- 工程学院）；University of Minho（明霍大学）；University of São Paulo（圣保罗大学）
Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection: An Empirical Study and Minimalist Baseline
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；Pengcheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Chongqing University（重庆大学）
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
Groupe de Recherche Interdisciplinaire en Cybersécurité (GRIC)（跨学科网络安全研究组（GRIC））；Department of Computer Science, Université de Sherbrooke（谢布鲁克大学计算机科学系）；Department of Management School, Université de Sherbrooke（谢布鲁克大学管理学院）
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Professorship of Autonomous Vehicle Systems, Technical University of Munich, Munich, Germany（自动驾驶车辆系统教授职位，慕尼黑技术大学，德国慕尼黑）；Bayerische Motoren Werke AG, Munich, Germany（巴伐利亚发动机有限公司，德国慕尼黑）；Data Analytics and Machine Learning Group, Technical University of Munich, Munich, Germany（数据分析与机器学习小组，慕尼黑技术大学，德国慕尼黑）
Dive into Time-Series Anomaly Detection: A Decade Review
Inria（法国国家信息与自动化研究所）；DI ENS（巴黎高等师范学院数字研究所）；PSL（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；The Ohio State University（俄亥俄州立大学）；Université Paris Cité（巴黎西岱大学）；IUF（法国大学研究院）
KokushiMD-10: Benchmark for Evaluating Large Language Models on Ten Japanese National Healthcare Licensing Examinations
Kyoto University（京都大学）；The Hong Kong University of Science and Technology（香港科技大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；The University of Tokyo（东京大学）
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
Renmin University of China（中国人民大学）；Independent Researcher（独立研究者）；Microsoft（微软）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Cornell University（康奈尔大学）
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Hong Kong University of Science and Technology（香港科技大学）；Apple（苹果公司）；Peking University（北京大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Science and Technology of China（中国科学技术大学）
Towards Robust Time Series Learning via Capacity-Centric Modulation
The Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；Central South University（中南大学）；The Hong Kong University of Science and Technology（香港科技大学）
