Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design. Significant barriers exist to extracting actionable knowledge from prior experimental data to inform new interventions. We study whether tool-augmented agentic AI can automatically learn from experimental data to generate new interventions in subsequent experiments. Through two-stage field experiments in healthcare prescription messaging (693,139 patient visits), we compare a Human + Chatbot method (Stage 1: behavioral experts with conversational AI co-designing 13 message variants, 444,691 patient visits) against a Tool-Augmented Agentic AI method (Stage 2: AI autonomously extracting principles from Stage 1 data to generate 17 new variants, 248,448 patient visits). The Agentic AI method, equipped with analytical tools, structured Data-Information-Knowledge-Wisdom (DIKW) reasoning agents, and transparent evidence chains, produces superior interventions: the best AI-generated message achieved a 69.8% CTR (+6.5 percentage points over baseline). Critically, our results suggest that the value comes from domain-specific experimental data, not from general reasoning ability: frontier LLMs operating without experimental data failed to predict which interventions would succeed. The field experiments also revealed that general-purpose behavioral theories used for intervention design do not extend uniformly to specific healthcare contexts, motivating an agentic AI approach to theory audits at field-experiment scale. Our research shows that tool-augmented AI can learn from experimental data and generate improved domain-relevant interventions, transforming behavioral experimentation from one-shot evaluation into a scalable system for cumulative design learning.
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
University of New South Wales（新南威尔士大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；CSIRO Energy Centre（联邦科学与工业研究组织能源中心）
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Sun Yat-sen University（中山大学）；Xiaomi Corporation（小米公司）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）
Automatic Curriculum Design for Zero-Shot Human-AI Coordination
Department of AI Convergence, Gwangju Institute of Science and Technology（人工智能融合系，全州科学技术研究院）；School of Integrated Technology, Gwangju Institute of Science and Technology（整合技术学院，全州科学技术研究院）
Camera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation
School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）
Camera Artist通过引入专门的 cinematography shot agent，增强了镜头间叙事连贯性和电影语言表达，提升了视频叙事的连贯性和表现力。
MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning
MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）；The University of Sydney（悉尼大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；SenseTime Research（商汤科技研究院）
Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
Shanghai Jiao Tong University（上海交通大学）；Alibaba Group（阿里巴巴集团）；Fudan University（复旦大学）；UT Austin（德克萨斯大学奥斯汀分校）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；King’s College London（伦敦国王学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The University of Sydney（悉尼大学）；University College London（伦敦大学学院）
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Emory University（埃默里大学）；University of Washington（华盛顿大学）；UCSD（加利福尼亚大学圣迭戈分校）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；School of Medicine, Emory University（埃默里大学医学院）
AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
Department of Software \& Microelectronics Peking University Beijing, China；Department of Software \& Microelectronics Peking University Beijing, China hangli\；Department of Software \& Microelectronics Peking University Beijing, China zehua\；Department of Software \& Microelectronics Peking University Beijing, China xiaofan\；Economic Law School China University of Political Science；School of Computer Science Peking University Beijing, China
MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
University of Houston（休斯顿大学）；NEC Labs（NEC实验室）；University of Waterloo（滑铁卢大学）；University of Connecticut（康涅狄格大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Singapore Management University（新加坡管理大学）
Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation
Tsinghua University（清华大学）；Stevens Institute of Technology（史蒂文斯理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Texas A&M University（德克萨斯A&M大学）
SpatialNav: Leveraging Spatial Scene Graphs for Zero-Shot Vision-and-Language Navigation
Fudan University, Shanghai, China（复旦大学）；University of Southern California, Los Angeles, USA（南加州大学）；Australian Institute for Machine Learning, Adelaide University, Australia（澳大利亚机器学习研究所）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）
Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision
New York University（纽约大学）；NYU Shanghai（上海纽约大学）；University of Alberta（阿尔伯塔大学）；Princeton University（普林斯顿大学）；University of Waterloo（滑铁卢大学）；Verdent AI, Inc.（Verdent AI公司）
OEMA: Ontology-Enhanced Multi-Agent Collaboration Framework for Zero-Shot Clinical Named Entity Recognition
Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis；Mining , School of Computer Science \& Technology, Beijing Jiaotong University , Beijing , 100044 , China
