Tool Use

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Tool use in AI studies how language and multimodal agents choose tools such as APIs, code interpreters, search systems, and robotic actions, then use their results to complete tasks. Common approaches train agents on synthetic or verified interaction traces, use reinforcement learning to improve multi-step planning and tool selection, and add runtime mechanisms for state tracking, verification, and recovery. Benchmarks test function calling, long workflows, multimodal and embodied tasks, and performance when tools or environments are unreliable. A central challenge is making tool use both selective and dependable: agents must know when a tool helps, execute calls correctly, and recover from failures without unnecessary calls or harmful side effects.

Latest papers

91 papers11 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Grounding LLM Agents in Business Processes: Process-Aware Knowledge Graphs for Reliable Multi-Step Service Invocation
Oct 27, 2026Saiful Khan, Afrah FareaEnterprise AI Agents Tool Discovery for LLM Agents
Scientific Computing, Science and Technology Facilities Council, RAL, OX11 0QX, UK. · Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, UK.
GRADUS: Discovering and Recovering Agent Lessons from the Environment's Latent Syllabus
Oct 20, 2026Qing Ping, Wei Yang, Panpan Xu +4LLM Agent Training LLM Agent Trajectory Synthesis
AWS AI · University of Southern California · BMW Group Research and Technology
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness
Oct 14, 2026Artin Tajdini, Manpreet Kaur, Peng QiLLM Post-Training Tool Use
University of Washington · Uniphore
TRAP: Tool-result Replay Attack for Probing Misbinding in LLM Agents
Oct 12, 2026Yutong Zhi, Jie Zhang, Xiuqi Lu +2Tool-Using Agents LLM Agent Safety
Tsinghua University · Shanghai AI Laboratory
RetryBench: Separating Retry Permission from Recovery Outcomes in GPT Tool Use
Oct 11, 2026You Liu, Yue Liu, Quanchao LuTool Use Tool Access Control for LLM Agents
RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution
Oct 10, 2026Daijin Hu, Xi WangTool Use
Beihang University
SchemaFill: Efficient LLM Tool Calling via Slot-Parallel Speculative Decoding
Oct 5, 2026Zhi-Kai Chen, Song-Yan Li, De-Chuan Zhan +1Speculative Decoding LLM Inference Acceleration
School of Artificial Intelligence, Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China · Nanjing University, China
Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine
Oct 5, 2026Aaron Fainman, Gabriela Kadlecová, Maciej Gryka +7LLM Fine-Tuning Synthetic Data Generation
Imperial College London · distil labs · Agon +1
Rethinking Multi-Image Re-Representation in Multi-Image Understanding
Sep 30, 2026Gengyuan Zhang, Xiao Han, Xinyu Xie +2Visual Representation Learning Multimodal CoT Reasoning
LMU Munich · MCML
VISTA: Internalizing Collective Visual Experience via On-Policy Distillation for Active Multimodal Agents
Sep 29, 2026Zheng Jiang, Houde Qian, Yiming Chen +5On-Policy Distillation Multimodal Agents
Tsinghua University
When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs
Sep 28, 2026Jiayi Li, Ruizhe LiLLM Auditing LLM Tool Use
University of the Chinese Academy of Sciences, China · School of Computer Science, University of Birmingham, UK
Almost Human, Except When It Matters: VoxParity and the Decisions a Voice Should Change
Sep 28, 2026Bhavik ManglaSpoken Dialogue Systems Audio Reasoning
Independent Research
ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments
Sep 27, 2026Shengbin Yue, Hongru Wang, Siyuan Wang +3LLM Agent Training Tool Use
Fudan University · University of Edinburgh · Chinese University of Hong Kong +3
A Wrong Turn Does Not Ruin the Journey: Deviation-Guided Skill Self-Evolution for LLM Agents
Sep 24, 2026Yichun Feng, Jiawei Wang, Haozhe SunLLM Agent Self-Improvement LLM Agent Skill Learning
University of Chinese Academy of Sciences · University of Science and Technology of China · Meituan
Toolcompass: Guiding Tool Trialing, Not Suppressing It
Sep 22, 2026Junlin Fang, Chong Zhang, Do Nguyen-Thanh +3Representation Learning Tool-Augmented Language Model Agents
College of Computing and Data Science, Nanyang Technological University · Zhejiang University · Australian Artificial Intelligence Institute, University of Technology Sydney +1
MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agents
Sep 17, 2026Pritish Mishra, Ishaan Kumar, Akshat Mandoli +1Voice Agent Evaluation Multilingual Language Model Evaluation
Smallest AI
MATCH: Model-Aware Tool Learning with Curriculum Scheduling and Hierarchically Gated Rewards
Sep 17, 2026Shihao Liu, Hao Yin, Lijun Liu +3Reward Modeling Curriculum RL
School of Cyber Security, University of the Chinese Academy of Sciences · Honor Device Co., Ltd · University of the Chinese Academy of Sciences
Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It
Sep 16, 2026Yipeng Liu, Yingqiang Zhang, Feifei Li +1LLM Inference Efficiency KV Caching
Tsinghua University, China · Alibaba Cloud Computing, China · Zhejiang University, China
Selection Is Retrieval, Abstention Is Not: On-Device Tool Routing over 70 Korean-English Actions
Sep 16, 2026Janghoon LeeTool-Augmented Language Model Agents On-Device Language Model Inference
Redrob
Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe
Sep 4, 2026Dain Kim, Eungi Cho, Kyumin Kim +2Tool-Augmented Language Model Agents LLM Tool Use
LG CNS
From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix
Sep 1, 2026Olga Tsymboi, Dmitrii Stoianov, Ramil Latypov +11LLM Serving Instruction Following
T-Tech
CoBRA: Learning Tool-Use Boundaries via Counterfactual Margins
Sep 1, 2026Wenhao Zou, Xianglong Liu, Wendong Bi +3Tool Use RL for Tool Use
WeChat, Tencent Inc, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Wuhan University, Wuhan, China
One Policy Is Enough: Single-Agent Reinforcement Learning Outperforms Tree Search for Chemistry Tool Learning
Aug 31, 2026Armin Dariani, Sifan Wu, Bang Liu +1LLM Tool Use Tool Use
1DIRO, Université de Montréal · 2Mila - Quebec Artificial Intelligence Institute · 3Innovation Campus Delaware, Air Liquide
A Pinch of SFT, A Dash of RL: When Reinforcement Learning Helps Long-Horizon Advertising Agents
Aug 29, 2026Aakash Kolekar, Sahika Genc, Bunyamin Sisman +6RL Fine-Tuning RL for Tool Use
Amazon Advertising · Amazon Web Services Agentic AI
AlgoWorlds: Benchmarking Tool Use for Global Optimization in Algorithmic Worlds
Aug 29, 2026Zixiang Xu, Jiaan Wang, Fandong MengAI Agent Benchmarks Global Optimization
Weixin AI, Tencent · University of Southern California
Below the Noise Floor: Bimodal Seed Collapse and Distinct Failure Modes in Small-Model Knowledge Distillation
Aug 27, 2026Dipto Sumit, Sakib Ul Haque, Farig SadequeKnowledge Distillation Language Model Distillation
Department of Computer Science and Engineering BRAC University, Bangladesh
Paint What You See: Benchmarking Dexterous Visual Tool Use in Multimodal Agents
Aug 26, 2026Shudong Liu, Dongyang Chen, Enci Zhang +3AI Agent Benchmarks Tool Use
Peking University · SenseTime Research · Tsinghua University
Tunable Tool-Call Rates in LLM Agents via Representation Steering
Aug 25, 2026Yuqi Chen, Vincent Siu, Yang Liu +2LLM Tool Use Tool Use
Department of Computer Science and Engineering, UC Santa Cruz · Department of Computer Science, UC Berkeley
From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use
Aug 25, 2026Rongfeng Guo, Yinxuan Huang, Yusen Wu +5Tool Use Sequential Decision Making
Huazhong University of Science and Technology · Knowin AI, Shenzhen, China · Fujian University of Technology +4
PROOF-Gen: From Optimized Data to Better Distillation
Aug 24, 2026Anh Ta, Junjie Zhu, Shahin ShayandehLLM Agent Training LLM Agent Trajectory Synthesis
Apple

Oct 27, 2026J/K move · Enter open · S save

Grounding LLM Agents in Business Processes: Process-Aware Knowledge Graphs for Reliable Multi-Step Service Invocation

Saiful Khan, Afrah Farea

Scientific Computing, Science and Technology Facilities Council, RAL, OX11 0QX, UK. · Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, UK.

Enterprise AI Agents Tool Discovery for LLM Agents LLM Agent Reliability Tool Use Agentic Retrieval

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Large language model (LLM) agents that call enterprise APIs must select the correct endpoints, follow process order, use valid payload keys, and choose coded values from predefined lists. However, current retrieval-augmented service discovery treats endpoints independently and therefore misses process and schema constraints. We address this limitation by integrating Open Data Protocol (OData) metadata and Business Process Model and Notation (BPMN) reference processes in a typed property graph. A hybrid retriever identifies candidate endpoints using dense embeddings and follows process edges to retrieve subsequent endpoints. We evaluate the method on 42 enterprise services with process-chain ground truth. For queries with low lexical overlap between the query and downstream entities, it improves Recall@3 by 5.7 points (n=74), with consistent gains across embedding models and cut-offs. It matches dense retrieval on other queries and provides no improvement on OpenAPI services without a process layer, indicating that the gains arise from process structure. We also evaluate an LLM agent on 81 multi-step queries. It completes 38.7% of tasks, compared with 10.3% for a chunk-based baseline. In an elicitation probe, providing valid code lists eliminates invented codes but leaves invalid keys in 46% of write runs. Full typed context reduces invalid calls to 3% (3 of 101 runs), with no invented codes. These results show that exposing process and schema constraints during retrieval improves the reliability of agent-driven API calls.