AI Agent

Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs

Yifan Wei, Xiaoyan Yu, Tengfei Pan, Angsheng Li, Li Du

机构 * State Key Laboratory of CCSE,School of Computer Science and Engineering,Beihang University（信息与通信系统国家重点实验室，计算机科学与工程学院，北京航空航天大学） ； Beijing Academy of Artificial Intelligence（北京人工智能研究院） ； Beijing Institute of Technology（北京理工大学） 纠错

专题命中 工具调用 ：agent(title)；分类 cs.CL

Patience is all you need! An agentic system for performing scientific literature review

David Brett, Anniek Myatt

专题命中 工具调用 ：agentic(title)；分类 cs.AI

Comments 10 pages, 5 figures

Agentic AI for Improving Precision in Identifying Contributions to Sustainable Development Goals

William A. Ingram, Bipasha Banerjee, Edward A. Fox

专题命中 工具调用 ：agentic(title)；分类 cs.AI

Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning

Jan-Hendrik Ewers, David Anderson, Douglas Thomson

专题命中 工具调用 ：planning(title)；分类 cs.LG

Comments Submitted to Machine Learning with Applications

SibylSat: Using SAT as an Oracle to Perform a Greedy Search on TOHTN Planning

Gaspard Quenard, Damier Pellier, Humbert Fiorino

专题命中 工具调用 ：planning(title)；分类 cs.AI

Journal ref ECAI 2024, Oct 2024, Santiago de Compostela, Spain. pp.4157 - 4164

A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph

Cheonsu Jeong

专题命中 工具调用 ：agent(title)；分类 cs.AI

Journal ref 2024 Knowledge Management Research

Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

Quentin Gallouédec, Edward Beeching, Clément Romac, Emmanuel Dellandréa

专题命中 工具调用 ：agent(title)；分类 cs.AI

Journal ref 38th Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET 2024)

AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study

Ze Shi Li, Nowshin Nawar Arony, Ahmed Musa Awon, Daniela Damian, Bowen Xu

专题命中 工具调用 ：tool use(title)；分类 cs.SE

Budget-Constrained Tool Learning with Planning

Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

专题命中 工具调用 ：planning(title)；分类 cs.AI

Comments Accepted for Findings of ACL 2024

Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use

Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin, Jan Dlabal, Wenlei Zhou, Enming Luo, Otilia Stretcu, Hao Xiong, Chun-Ta Lu, Howard Zhou, Ranjay Krishna, Ariel Fuxman, Tom Duerig

专题命中 工具调用 ：tool-use(title)；分类 cs.LG

Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance

Adrian Theuma, Ehsan Shareghi

专题命中 工具调用 ：tool use(title)；分类 cs.CL

Comments Accepted to EACL2024; code, model and dataset are available at https://raven-lm.github.io

Online search of unknown terrains using a dynamical system-based path planning approach

Karan Sridharan, Patrick McNamee, Zahra Nili Ahmadabadi, Jeffrey Hudack

专题命中 工具调用 ：planning(title)；分类 cs.AI

Journal ref J Intell Robot Syst 106, 21 (2022)

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters

Pierrick Pochelu, Serge G. Petiton, Bruno Conche

专题命中 工具调用 ：workflow(title)；分类 cs.LG

Journal ref ACM International Conference Proceeding Series 2022

Designing an Automatic Agent for Repeated Language based Persuasion Games

Maya Raifer, Guy Rotman, Reut Apel, Moshe Tennenholtz, Roi Reichart

专题命中 工具调用 ：agent(title)；分类 cs.CL

Comments Accepted for TACL in December 2021

Improving Confidence in the Estimation of Values and Norms

Luciano Cavalcante Siebert, Rijk Mercuur, Virginia Dignum, Jeroen van den Hoven, Catholijn Jonker

专题命中 工具调用 ：agent(abstract,comments)；autonomous agent(abstract)；分类 cs.AI；multi-agent(comments)

Comments 16 pages, 3 figures, pre-print for the International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2020

MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Training

Arkady Arkhangorodsky, Amittai Axelrod, Christopher Chu, Scot Fang, Yiqi Huang, Ajay Nagesh, Xing Shi, Boliang Zhang, Kevin Knight

专题命中 工具调用 ：agent(title)；分类 cs.CL

Comments 10 pages

Strategic API Analysis and Planning: APIS Technical Report

Jennifer Horkoff, Juho Lindman, Imed Hammouda, Eric Knauss

专题命中 工具调用 ：planning(title)；分类 cs.SE

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

Dhaval Adjodah, Dan Calacci, Yan Leng, Peter Krafft, Esteban Moro, Alex Pentland

专题命中 工具调用 ：agent(title)；分类 cs.AI

Comments This paper is obsolete

The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation

Simon M Lucas, Jialin Liu, Diego Perez-Liebana

专题命中 工具调用 ：agent(title)；分类 cs.AI

Comments 9 pages, 3 figures, 3 table. This is the final version of the article accepted by WCCI2018

Towards Real-Time Search Planning in Subsea Environments

James McMahon, Harun Yetkin, Artur Wolek, Zachary Waters, Dan Stilwell

专题命中 工具调用 ：planning(title)；分类 cs.AI

Comments 8 pages, 5 figures. Submitted to 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)

Path planning with Inventory-driven Jump-Point-Search

Davide Aversa, Sebastian Sardina, Stavros Vassos

专题命中 工具调用 ：planning(title)；分类 cs.AI

Journal ref In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), pp. 2-8, 2015

Neurohex: A Deep Q-learning Hex Agent

Kenny Young, Ryan Hayward, Gautham Vasan

专题命中 工具调用 ：agent(title)；分类 cs.AI

An Agent based Approach towards Metadata Extraction, Modelling and Information Retrieval over the Web

Zeeshan Ahmed, Detlef Gerhard

专题命中 工具调用 ：agent(title)；分类 cs.AI

Comments In the proceedings of First International Workshop on Cultural Heritage on the Semantic Web in conjunction with the 6th International Semantic Web Conference and the 2nd Asian Semantic Web Conference 2007, (ISWC + ASWC 2007), P 117, 12-15 November 2007

IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software Development

IACDM：交互对抗收敛开发方法论——面向AI辅助软件开发的结构化框架

Jasmine Moreira

专题命中 工具调用 ：agent(abstract)；tool use(abstract)；分类 cs.AI、cs.SE

AI总结 本文提出IACDM方法论，通过外部验证代理解决AI生成应用中的验证缺口问题，强调通过层次语义分析、知识管理及系统对抗批评提升软件开发质量。

Comments 37 pages, 7 tables. Technical Foundation Document. v3 adds a pre-registered experiment testing lens non-redundancy over 12 projects, and withdraws the retrospective analysis of v1-v2 (defective instrument). Data: https://doi.org/10.5281/zenodo.21908908 Repo: https://github.com/jasminemoreira/Versus

Automated Design Optimization via Strategic Search with Large Language Models

通过大语言模型的战略搜索实现自动化设计优化

Anthony Carreon, Vansh Sharma, Venkat Raman

专题命中 工具调用 ：agent(abstract)；planning(abstract)；分类 cs.AI、cs.LG

AI总结 本研究提出AUTO框架，利用大语言模型的战略搜索实现GPU代码优化，提升搜索效率并降低成本。

Comments 16 pages, 4 tables, 8 figures, preprint

Recovering Wasted Compute in Autoresearch Agents

恢复自动研究智能体中浪费的计算资源

Au Kwok Chun, Abhigyan Acherjee, Amrutha Rao, Zaiqian Chen, Kazem Meidani, C. Bayan Bruss, Micah Goldblum

机构 * Columbia University（哥伦比亚大学） ； Georgetown University（乔治城大学） ； Capital One（第一资本金融公司） 纠错

专题命中 工具调用 ：agent(abstract)；agentic(abstract)；分类 cs.AI、cs.LG

AI总结 本文针对自动研究智能体应用于表格数据集时的四类计算资源浪费问题，提出针对性干预措施，仅通过优化智能体设计即可大幅提升其性能。

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

DOCSCHISEL：面向大语言模型智能体的自适应工具文档优化框架

You Lu, Kun Zhang, Bihuan Chen, Xin Peng

专题命中 工具调用 ：agent(abstract)；tool-use(abstract)；分类 cs.AI、cs.LG

AI总结 该研究针对LLM智能体工具文档的异质性与泛化问题，提出DocsChisel自适应优化框架，经实验较原始文档及EasyTool、DRAFT基线大幅提升任务成功率，且开销有限。

Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation

有特权但有偏差：PI条件化教师如何破坏自蒸馏

Sarthak Harne, Chinmay Karkar, Yash Pandya, Ahmed Awadallah, Akshay Nambi

机构 * Microsoft Research（微软研究院） 纠错

专题命中 工具调用 ：tool use(abstract)；agentic(abstract)；分类 cs.AI、cs.LG

AI总结 该研究发现，以特权信息为条件的自蒸馏在低难度任务上有效，但在困难任务上会因特权信息偏差导致模型推理能力下降，其优化信号与任务成功脱钩。

Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents

搜索、检查、获取：利用布尔检索构建深度研究智能体

Shuai Wang, Haodong Chen, Yu Yin, Shengyao Zhuang, Bevan Koopman, Guido Zuccon

专题命中 工具调用 ：agent(abstract)；workflow(abstract)；分类 cs.AI、cs.CL

AI总结 本研究针对现有深度研究智能体的缺陷，提出基于BQL的SIEVE接口，在三个问答数据集上实现更高准确率且token用量显著减少，验证了BQL过滤的有效性。

Comments added statistical test, restructure appendix etc

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

自进化推荐系统：基于LLM代理的端到端自主模型优化

Haochen Wang, Yi Wu, Daryl Chang, Li Wei, Lukasz Heldt

机构 * Google Inc（谷歌公司） 纠错

专题命中 工具调用 ：agent(abstract)；workflow(abstract)；分类 cs.AI、cs.LG

AI总结 本文提出了一种基于LLM代理的自进化推荐系统，通过端到端自动化流程自主优化模型，提升开发效率和性能。

Comments RecSys 2026