Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories. However, parallel execution comes at a significant computational cost: when different teams independently reason about similar sub-problems or execute analogous steps, they repeatedly perform substantial overlapping computation. To address these limitations, in this paper, we propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks that enables selective cross-team information reuse while controlling context growth. LTS introduces a global memory bank accessible to all teams and a lightweight controller that decides whether intermediate agent steps should be added to memory or not. The controller is trained using stepwise reinforcement learning with usage-aware credit assignment, allowing it to identify information that is globally useful across parallel executions. Experiments on the AssistantBench and GAIA benchmarks show that LTS significantly reduces overall runtime while matching or improving task performance compared to memory-free parallel baselines, demonstrating that learned memory admission is an effective strategy for improving the efficiency of parallel agentic systems. Project page: https://joefioresi718.github.io/LTS_webpage/
Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation
Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory（太平洋西北国家实验室物理与计算科学部门）；Department of Chemical Engineering, University of Washington（华盛顿大学化学工程系）
DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows
School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）；School of Foreign Languages, East China Normal University（华东师范大学外国语言学系）；School of Foreign Studies, Shanghai University（上海大学外国语言学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程学院）
SIPO: Selective-Inference Policy Optimization for Tree-Structured Agentic RL
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The University of Hong Kong（香港大学）；Peking University（北京大学）；Mininglamp Technology（明略科技）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education（计算力网络与信息安全教育部重点实验室；齐鲁工业大学（山东省科学院）山东省计算中心）；Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（计算力互联网与服务计算重点实验室，山东省计算机科学基础研究中心）；Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science
AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，地点，国家）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家）；University of Delaware, Newark, DE, USA（特拉华大学）
FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院计算技术研究所）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；University of Science and Technology Beijing（北京科技大学）；The University of Queensland, Brisbane, Australia（昆士兰大学）
Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization
Nanyang Technological University（南洋理工大学）；National Superior College for Engineers, Beihang University（北京航空航天大学高等工程学院）；Bitslab；The Chinese University of Hong Kong（香港中文大学）
Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Springbrand（斯普林布兰德公司）；Northwestern University（西北大学）；Rutgers University（罗格斯大学）；Arizona State University（亚利桑那州立大学）；University of California San Diego（加州大学圣迭戈分校）；Carnegie Mellon University（卡内基梅隆大学）；MBZUAI（Mohamed bin Zayed 人工智能大学）；Microsoft AI（微软人工智能部门）
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；School of Cyber Science and Technology, Sun Yat-sen University Shenzhen Campus, Shenzhen, China（中山大学深圳校区信息科学与技术学院）；State Key Laboratory of Internet Architecture, Tsinghua University, Beijing, China（清华大学互联网体系结构国家重点实验室）
Redefining Elderly Care with Agentic AI: Challenges and Opportunities
Engineering Requirement Unit, College of Engineering, United Arab Emirates University（阿联酋大学工程学院）；Department of Computer Science, Munster Technological University（穆斯特技术大学计算机科学系）；Division of Computing Science and Mathematics, University of Stirling（斯特林大学计算科学与数学系）
Agentic-J: An AI Agent for Biological Microscopy Image Analysis
University of Tartu（塔尔图大学）；Ruhr University Bochum（波鸿鲁尔大学）；University of Amsterdam（阿姆斯特丹大学）；University of Notre Dame（圣母大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
East China Normal University（华东师范大学）；Tongji University（同济大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Shenzhen Loop Area Institute（深圳河套学院）
Trade-offs in Decentralized Agentic AI Discovery Across the Compute Continuum
Department of Computer Science（计算机科学系）；University of Pisa（比萨大学）；Institute of Information Science and Technologies（信息科学与技术研究所）；National Research Council of Italy（意大利国家研究理事会）；Institute of Informatics and Telematics（信息学与电信研究所）
