We study budget-constrained tool-augmented agents, where a large language model must solve multi-step tasks by invoking external tools under a strict monetary budget. We formalize this setting as sequential decision making in context space with priced and stochastic tool executions, making direct planning intractable due to massive state-action spaces, high variance of outcomes and prohibitive exploration cost. To address these challenges, we propose INTENT, an inference-time planning framework that leverages an intention-aware hierarchical world model to anticipate future tool usage, risk-calibrated cost, and guide decisions online. Across cost-augmented StableToolBench, INTENT strictly enforces hard budget feasibility while substantially improving task success over baselines, and remains robust under dynamic market shifts such as tool price changes and varying budgets.
Verification and validation of agentic behavior have been suggested as important research priorities in efforts to reduce risks associated with the creation of general artificial intelligence (Russell et al 2015). In this paper we question the appropriateness of using language of certainty with respect to efforts to manage that risk. We begin by establishing a very general formalism to characterize agentic behavior and to describe standards of acceptable behavior. We show that determination of whether an agent meets any particular standard is not computable. We discuss the extent of the burden…
A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids
Philips University（菲利普斯大学）；University of Cyprus（塞浦路斯大学）；CYENS Centre of Excellence（CYENS卓越中心）；Japan Advanced Institute of Science and Technology（日本先端科学技术大学院大学）；Doshisha University（同志社大学）
From Intention to Text: AI-Supported Goal Setting in Academic Writing
Department of Media Technology and Interaction Design（媒体技术与交互设计系）；KTH Royal Institute of Technology（皇家理工学院）；Department of Digital Learning（数字学习系）；Digital Futures（数字未来）
Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
Florida International University（佛罗里达国际大学）；Arizona State University（亚利桑那州立大学）；NEC Laboratories America（美国 NEC 实验室）；Singapore Management University（新加坡管理大学）
SPO++: Stream-Aligned Policy Optimization for Asynchronous Agentic RL
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Duke University（杜克大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai University of Finance and Economics（上海财经大学）；Technische Universität München（慕尼黑技术大学）；Southwestern University of Finance and Economics（西南财经大学）
Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI
Electrical and Computer Engineering Department, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Natural Sciences, Pasadena City College（帕萨迪纳城市学院自然科学系）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives
École nationale des chartes – PSL（国立文献学校（巴黎文理研究大学））；EPITA（EPITA 工程师学院）；EPITA Research Laboratory（EPITA 研究实验室）；Geneva Graduate Institute（日内瓦高等国际关系及发展学院）
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
TU Darmstadt（达姆施塔特工业大学）；Honda Research Institute Europe（本田欧洲研究所）；Columbia University（哥伦比亚大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；University of Würzburg（维尔茨堡大学）；Hessian.AI（黑森人工智能中心）
GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure
Tencent（腾讯）；School of Integrated Circuits, Peking University（北京大学集成电路学院）；Southwest Jiaotong University（西南交通大学）；Institute of Electronic Design Automation, Peking University（北京大学电子设计自动化研究所）；Beijing Advanced Innovation Center for Integrated Circuits（北京集成电路高精尖创新中心）
An Information Theoretic Perspective on Agentic System Design
Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；Department of Statistics, Stanford University（斯坦福大学统计学系）；Wu Tsai Neurosciences Institute, Stanford University（斯坦福大学吴泰教授神经科学研究所）
Agentic neural architecture search for clinical prediction from electronic health records: Cross-hospital knowledge transfer and computational efficiency
University of Florida（佛罗里达大学）；University of Arizona（亚利桑那大学）；University of Houston（休斯顿大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
