Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling. To address this limitation, we propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs that expose behavioral discrepancies among candidate solutions. Through multi-turn interaction with code execution environments, the verifier iteratively refines the candidate input generator and produces targeted counterexamples rather than blindly sampling inputs. We train the verifier to acquire this discriminative input generation capability via a scalable pipeline combining large-scale data synthesis, rejection fine-tuning, and agentic reinforcement learning. Extensive experiments across five competitive programming benchmarks demonstrate consistent improvements over strong execution-based baselines, achieving up to +10-15% absolute gains in Best@K accuracy. Further analysis reveals clear test-time scaling behavior and highlights the verifier's broader potential beyond reranking.
AgentV-RL: Scaling Reward Modeling with Agentic Verifier
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Huazhong University of Science and Technology（华中科技大学）；The University of Hong Kong（香港大学）；ByteDance Seed（字节跳动种子）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Shanghai Key Laboratory of Multimodal Embodied AI（上海多模态具身人工智能重点实验室）
Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
Infmedix, Co., Ltd.（Infmedix公司）；Department of Transdisciplinary Studies, Seoul National University（首尔国立大学跨学科研究系）；Healthcare AI Research Institute, Seoul National University Hospital（首尔国立大学医院医疗人工智能研究所）；Department of Medicine, Seoul National University College of Medicine（首尔国立大学医学院医学系）；Department of Transdisciplinary Medicine, Seoul National University Hospital（首尔国立大学医院跨学科医学系）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院放射科及哈佛医学院）
Meta Superintelligence Labs（Meta超级智能实验室）；University of Washington（华盛顿大学）；New York University（纽约大学）；Google DeepMind（谷歌DeepMind）；Carnegie Mellon University（卡内基梅隆大学）；Princeton University（普林斯顿大学）
Agentic Framework for Political Biography Extraction
Department of Politics and Public Administration, The University of Hong Kong（政治与公共行政系，香港大学）；School of International Studies, Peking University（国际关系学院，北京大学）；Department of Political Science, Columbia University（政治学系，哥伦比亚大学）
An Agentic AI System for Multi-Framework Communication Coding
Duke School of Medicine（杜克大学医学院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；MedStar Health National Center for Human Factors in Healthcare（MedStar Health国家医疗人因中心）；University of Pennsylvania（宾夕法尼亚大学）；Duke Cancer Institute（杜克癌症研究所）；Duke University（杜克大学）
How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
University of Michigan（密歇根大学）；Stanford University（斯坦福大学）；All Hands AI；Google Deepmind（谷歌DeepMind）；Microsoft AI（微软AI）；Massachusetts Institute of Technology（麻省理工学院）
