Tool-Using Agents

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31 papers in the last four weeks, up 210% on the four weeks before. 0.3% of all new papers.

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Latest papers 199

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Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows
Jun 5, 2026M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen +2LLM Agent Orchestration LLM Agent Workflow Optimization
School of Computing and Information Systems Singapore Management University Singapore
Archi: Agentic Operations at the CMS Experiment
Jun 3, 2026Pietro Lugato, Luca Lavezzo, Jason Mohoney +16Retrieval-Augmented Generation Tool-Using Agents
Massachusetts Institute of Technology, Cambridge, MA, USA · CMS Collaboration, CERN, Geneva, Switzerland · University of Wisconsin-Madison, Madison, WI, USA +3
Uncertainty-Aware Clarification in LLM Agents with Information Gain
Jun 2, 2026Mengyi Deng, Zhiwei Li, Xin Li +4Clarification Question Generation Tool-Using Agents
Information Hub, HKUST(GZ) · Jilin University · et al.
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
Jun 1, 2026Hengrui Gu, Xiaotian Han, Kaixiong ZhouTool-Augmented Language Model Agents Tool-Using Agents
North Carolina State University · Case Western Reserve University
Ghost Tool Calls: Issue-Time Privacy for Speculative Agent Tools
Jun 1, 2026Bardia Mohammadi, Lars Klein, Akhil Arora +1Data Leakage LLM Agent Security
Max Planck Institute for Software Systems · EPFL · Aarhus University
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Jun 1, 2026Wenhao Wang, Peizhi Niu, Gongyi Zou +9LLM Agent Evaluation AI Agent Benchmarks
Multi-Agent Governance & Intelligence Crew (MAGIC), Shanghai Jiao Tong University, Shanghai, China · Department of Computer Science and Technology, Zhejiang University, Hangzhou, China · University of Illinois at Urbana-Champaign, Illinois, USA +2
Beyond One-shot: AI Agents for Learning in Field Experiments
Jun 1, 2026Junjie Luo, Ritu Agarwal, Gordon GaoTool-Using Agents Autonomous Experimentation
Johns Hopkins School of Medicine, Baltimore, MD, USA · Carey Business School, Johns Hopkins University, Baltimore, MD, USA
SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
May 31, 2026Yangbo Wei, Zhen Huang, Shaoqiang Lu +4Tool-Using Agents Agent Skill Learning
Shanghai Jiao Tong University, Shanghai, China · Eastern Institute of Technology, Ningbo, China · University of Science and Technology of China, Hefei, China +2
CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation
May 31, 2026Ruihui Hou, Ziyue Huai, Chennuo Zhang +5Tool-Using Agents Clinical Reasoning
East China University of Science and Technology, Shanghai, China · Zhongshan Hospital, Fudan University, Shanghai, China
Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
May 30, 2026Chishui Chen, Jiaye Lin, Te Sun +6LLM Agent Skill Learning Tool-Using Agents
Meituan · Shanghai Jiao Tong University · Nanjing University +2
A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems
May 28, 2026Titu Ranjan Sarker, Muhammed Jawaad Zulqernine, Ling Yue +3Multi-Agent LLM Systems Structural Mechanics
University of Texas at Arlington, Arlington, TX 76010, USA · Rensselaer Polytechnic Institute, Troy, NY 12180, USA · Institute for Predictive Performance Methodologies, Fort Worth, TX 76118, USA
Scaling Laws for Agent Harnesses via Effective Feedback Compute
May 28, 2026Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2Inference-Time Scaling Agent Harness Optimization
Harbin Institute of Technology
AIRGuard: Guarding Agent Actions with Runtime Authority Control
May 27, 2026Suliu Qin, Haomin Zhuang, Yujun Zhou +2AI Agent Security Tool-Using Agents
University of Liverpool · University of Notre Dame · 2Inria, France
Do Agents Know What They Can't Do? Evaluating Feasibility Awareness in Tool-Using Agents
May 27, 2026Liang Cheng, Mingsheng Cai, Jiuming Jiang +1LLM Agent Evaluation AI Agent Benchmarks
†University of Edinburgh
Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution
May 27, 2026Swanand RaoLLM Agent Orchestration Tool-Using Agents
Next Moca Global, Inc.
AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios
May 27, 2026Kou Shi, Ziao Zhang, Shiting Huang +7Tool-Use Evaluation AI Agent Benchmarks
University of Science and Technology of China · University of Toronto
EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents
May 27, 2026Yunqi Liu, Tong Niu, Zitong Wang +4AI Agent Evaluation Egocentric Video Understanding
Ant Group
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
May 26, 2026Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj +2AI-Assisted Decision Making Causal Discovery
Bosch Global Software Technologies Company Limited, Vietnam · Robert Bosch GmbH, Germany · Computer Science Department, TU Darmstadt, Germany +2
Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance
May 24, 2026Chengrui Li, Rujing Li, Yitong Bai +1Multi-Agent Orchestration Tool-Using Agents
Columbia University
MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning
May 24, 2026Xuanye Zhang, Yongsen Zheng, Zhuqin Xu +5LLM Agent Security Tool-Using Agents
Nanyang Technological University, Singapore
Agent Manufacturing: Foundation-Model Agents as First-Class Industrial Entities
May 24, 2026Yilei ZhangManufacturing Tool-Using Agents
Department of Mechanical Engineering University of Canterbury Christchurch, New Zealand
How Many Tools Should an LLM Agent See? A Chance-Corrected Answer
May 23, 2026Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh +1Tool-Use Evaluation Tool-Augmented Language Model Agents
Meta Platforms
Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning
May 21, 2026Banghao Chi, Yining Xie, Mingyuan Wu +9Tool-Using Agents LLM Agent Training
University of Illinois Urbana-Champaign · Meta
TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks
May 21, 2026Zhaoyang Chu, Jiarui Hu, Xingyu Jiang +8Computer-Use Agent Benchmarks Terminal Agents
University College London · Nanjing University · Tencent
When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity
May 19, 2026Samuel Jacob Chacko, James Hugglestone, Chashi Mahiul Islam +1LLM Agent Evaluation LLM Agent Skill Learning
Florida State University, Tallahassee, Florida, USA
Formal Skill: Programmable Runtime Skills for Efficient and Accurate LLM Agents
May 19, 2026Xi Zhang, Meijun Gao, Yuntian Zhao +6Tool-Using Agents LLM Agents
Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
May 18, 2026Changkun OuTool-Using Agents Safe Bayesian Optimization
Agent Meltdowns: The Road to Hell Is Paved with Helpful Agents
May 18, 2026Rishi Jha, Harold Triedman, Arkaprabha Bhattacharya +1Agent Failure Analysis AI Agent Reliability
Department of Computer Science Cornell University
TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents
May 16, 2026Zhiqiang Liu, Wenhui Dong, Yilang Tan +3Tool-Use Evaluation AI Agent Benchmarks
Nanjing University · Huazhong University of Science and Technology · Southwest Jiaotong University +1
CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
May 15, 2026Haolin Chen, Deon Metelski, Leon Qi +30Healthcare Long-Horizon Agent Evaluation
Johns Hopkins Medicine · Wellstar Health System · Stanford University +16

Jun 5, 2026, cs.AIJ/K move · Enter open · S save

Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows

M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen, Cedric Lim+1

School of Computing and Information Systems Singapore Management University Singapore

LLM Agent Orchestration LLM Agent Workflow Optimization Tool-Using Agents Agentic RAG

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We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orchestration paradigm. Concretely, we compare (i) a DeclarativeAgent that reads three domain-specific skill files at inference time and decides its own control flow, (ii) an ImperativeAgent based on a programmatic state machine with explicit phases, and (iii) an unscaffolded baseline agent modeled after the

τ

-Knowledge benchmark agent. Our ImperativeAgent is motivated by externalised-control inference as in Recursive Language Models and graph-based orchestration frameworks. We formalise the three agents as policy classes within a decentralised partially-observable Markov decision process and analyse their information-theoretic and structural properties; we then test the predicted differences empirically on five language models and two retrieval regimes. Our results show that retrieval quality is a dominant bottleneck for AI agents: when evidence is incomplete or skewed, all agents degrade substantially, and skill files cannot recover lost performance. Under high-quality retrieval, however, declarative skills consistently improve accuracy on procedural tasks and reduce orchestration errors, while the imperative state machine's brittleness does not reliably improve task success or compliance.

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