随着AI智能体越来越依赖外部工具，准确的工具检索对实现可靠行为至关重要。现有方法优化语义相关性，但在很大程度上忽略了执行过程中的工具可用性。为解决这一差距，我们提出RATED（Reliability-Aware Tool Retrieval via Execution Dynamics，基于执行动态的可靠性感知工具检索），这是一种结合鲁棒语义匹配与动态可靠性适配的检索框架。RATED采用经过对比微调的冻结双编码器进行语义匹配，以及轻量型上下文博弈模块，该模块基于实时执行反馈对候选工具进行重新排序。此设计可在无需昂贵模型重新训练的情况下快速适配运行时工具故障。我们引入EcoAgent基准，包含5172个真实世界工具和超过210000个查询-工具对，用于离线检索和动态可靠性评估。实验结果表明，在真实世界查询上，RATED相比ToolLLM API检索器实现了52.2%的相对MRR提升；在故障发生率为30%的严重API中断场景下，将故障选择率从86.2%降至2.9%，且在仅编码推理基础上产生的延迟开销可忽略，不足1毫秒。代码和数据集可在指定URL获取。
英文摘要：
As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Aware Tool Retrieval via Execution Dynamics), a retrieval framework that combines robust semantic matching with dynamic reliability adaptation. RATED employs a frozen dual-encoder with contrastive fine-tuning for semantic matching, and a lightweight contextual bandit module that re-ranks candidates based on real-time execution feedback. This design enables rapid adaptation to runtime tool failures without expensive model retraining. We introduce EcoAgent, a benchmark of 5,172 real-world tools and over 210,000 query-tool pairs for offline retrieval and dynamic reliability evaluation. Experimental results demonstrate that RATED achieves a 52.2% relative MRR improvement over the ToolLLM API retriever on real-world queries, and reduces fault selection rate from 86.2% to 2.9% under severe API outage scenarios with a 30% failure rate, while incurring negligible latency overhead of less than 1 ms over encode-only inference. The code and dataset are available at https://github.com/Elliotes/EcoAgent.
APEX-Searcher: Refining Credit Assignment with Subgoaling for Agentic Retrieval-Augmented Generation
University of Chinese Academy of Sciences（中国科学院大学）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部）；Wenge Technology Co., Ltd（Wenger科技有限公司）
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
University of North Dakota（北达科他大学）；Youngstown State University（青年州大学）；University of Toledo（托莱多大学）；University of Missouri（密苏里大学）；Tribhuvan University（特里布文大学）
ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation
Chemical Sciences and Engineering Division Argonne National Laboratory（阿贡国家实验室化学科学与工程部）；Computational Science Division Argonne National Laboratory（阿贡国家实验室计算科学部）
We consider information retrieval when the data, for instance multimedia, is coputationally expensive to fetch. Our approach uses "information filters" to considerably narrow the universe of possiblities before retrieval. We are especially interested in redundant information filters that save time over more general but more costly filters. Efficient retrieval requires that decision must be made about the necessity, order, and concurrent processing of proposed filters (an "execution plan"). We develop simple polynomial-time local criteria for optimal execution plans, and show that most forms of…
Group of Skills: Group-Structured Skill Retrieval for Agent Skill Libraries
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；Sun Yat-sen University（孙中山大学）；University of California, San Diego（加州大学圣地亚哥分校）；Taiyuan University of Technology（太原理工大学）
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
Tongji University（同济大学）；The University of Sydney（悉尼大学）；University of Science and Technology of China（中国科学技术大学）；Nankai University（南开大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；City University of Hong Kong（香港城市大学）；University of Surrey（萨里大学）
STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
University of Science；Technology of China \& State Key Laboratory of Cognitive Intelligence Hefei Anhui China；Technology of China \& State Key Laboratory of Cognitive Intelligence
Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap. Recent successes in deep learning enabled detection of objects belonging to many classes greatly outperforming traditional computer vision techniques. However, deep learning solutions capable of executing retrieval queries are still not available. We propose a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution. Specifically, we use YOLOv2 and OpenCog.…
PrecogUI: Proactive GUI Agents via Pre-cognitive Simulation and Experience Retrieval
University of Chinese Academy of Sciences（中国科学院大学）；The Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Loop Area Institute（深圳河套学院）
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Griffith University（格里菲斯大学）；Jiangsu University（江苏大学）；University of Southern Queensland（南方昆士兰大学）；Peking University（北京大学）；Great Bay University（大湾大学）；Nanjing University（南京大学）；Macquarie University（麦觉瑞大学）；Southern University of Science and Technology（南方科学与技术大学）
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
iASYS Technology Solutions Pvt. Ltd.（iASYS技术解决方案私人有限公司）；SVPM’s College of Engineering（SVPM工程学院）；School of Data and Sciences（数据科学学院）；Brac University（拉克斯大学）
