mlsanityData to 7 Oct 2026

Agentic Tool Use in Large Language Models

arXiv 2604.00835 ↗First posted 1 Apr 2026cs.CLcited by 2

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Large language models are increasingly being deployed as autonomous agents yet their real world effectiveness depends on reliable tools for information retrieval, computation and external action. Existing studies remain fragmented across tasks, tool types, and training settings, lacking a unified view of how tool-use methods differ and evolve. This paper organizes the literature into three paradigms: prompting as plug-and-play, supervised tool learning and reward-driven tool policy learning, analyzes their methods, strengths and failure modes, reviews the evaluation landscape and highlights key challenges, aiming to address this fragmentation and provide a more structured evolutionary view of agentic tool use.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 137 of its 179 references that are in the map. First posted on arXiv 1 Apr 2026.

Builds on

137 of 179 references.

Closest ancestors · 22

Same direction, newest first

  1. 2025When2Call: When (not) to Call Tools26 Apr
  2. 2024NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls4 Sep
  3. 2024ToolACE: Winning the Points of LLM Function Calling2 Sep
  4. 2024WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models2 Jul
  5. 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets26 Jun
  6. 2024Octopus v2: On-device language model for super agent2 Apr
  7. 2024ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages16 Feb
  8. 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls6 Feb
  9. 2024RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning16 Jan
  10. 2024EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction11 Jan
  11. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use4 Oct
  12. 2023Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum27 Aug
10 more
  1. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  2. 2023ToolQA: A Dataset for LLM Question Answering with External Tools23 Jun
  3. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases8 Jun
  4. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction30 May
  5. 2023Large Language Models as Tool Makers26 May
  6. 2023Gorilla: Large Language Model Connected with Massive APIs24 May
  7. 2023Making Language Models Better Tool Learners with Execution Feedback22 May
  8. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings19 May
  9. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs14 Apr
  10. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 115

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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  8. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  9. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  10. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  11. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  12. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
38 more
  1. 2023Let's Verify Step by Stepcited by 3,598
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
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  8. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
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  10. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
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  12. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  13. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  14. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  15. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  16. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  17. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
  18. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  19. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
  20. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  21. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  22. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  23. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  24. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 915
  25. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  26. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  27. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  28. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
  29. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  30. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  31. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 551
  32. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  33. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  34. 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 527
  35. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 514
  36. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
  37. 2022DS-1000: A Natural and Reliable Benchmark for Data Science Code Generationcited by 442
  38. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 407

50 most cited of 115 All 115 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Co-Evolving Skill Generation and Policy Optimizationcited by 5
  2. 2026OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agentscited by 1

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  1. 2026ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
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  3. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users
  4. 2026The Remarkable Effectiveness of Providing AI Agents with Natural Language Tools: A Replication Study Validating NLT Performance Across 14 Models
  5. 2024Learning to Use Tools via Cooperative and Interactive Agents
  6. 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents
  7. 2025ToolRM: Towards Agentic Tool-Use Reward Modeling
  8. 2026Joint Optimization of Tool Creation and Use for Large Language Model Agents