# Tool Use in Language Models: Research Digest

> 10 foundational papers with 11,129 combined citations, plus the latest arXiv preprints.

- **Canonical:** https://agentflare.org/scholar/tool-use-in-language-models-research-digest.html
- **Updated:** 2026-10-30
- **Category:** scholar
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## Key data

- **Field:** tool use and function calling in language models
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-30

10 foundational papers with 11,129 combined citations, plus the latest arXiv preprints.

_…full analysis and the complete dataset are available to agents for $0.02 — fetch `/scholar/tool-use-in-language-models-research-digest.data.json` (HTTP 402)._

## Sources

1. [Toolformer: Language models can teach themselves to use tools](https://proceedings.neurips.cc/paper_files/paper/2023/hash/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html)
2. [Nexusraven: a commercially-permissive language model for function calling](https://openreview.net/forum?id=5lcPe6DqfI)
3. [Hammer: Robust function-calling for on-device language models via function masking](https://arxiv.org/abs/2410.04587)
4. [Meta-tool: Unleash open-world function calling capabilities of general-purpose large language models](https://aclanthology.org/2025.acl-long.1481/)
5. [Robust function-calling for on-device language model via function masking](https://proceedings.iclr.cc/paper_files/paper/2025/hash/d45e0bfb5a39477d56b55c0824200008-Abstract-Conference.html)
6. [Looped Language Models Improve Compositional Tool Calling](https://arxiv.org/abs/2608.18171)
7. [Toolllm: Facilitating large language models to master 16000+ real-world apis](https://proceedings.iclr.cc/paper_files/paper/2024/hash/28e50ee5b72e90b50e7196fde8ea260e-Abstract-Conference.html)
8. [Alignment for efficient tool calling of large language models](https://aclanthology.org/2025.emnlp-main.898/)
9. [Beyond function calling: Benchmarking tool-using agents under tool-environment unreliability](https://arxiv.org/abs/2606.25819)
10. [The Bitter Lesson of Tool Calling](https://arxiv.org/abs/2608.06370)
11. [Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis](https://arxiv.org/abs/2610.37648v1)
12. [Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use](https://arxiv.org/abs/2610.37533v1)
13. [PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers to Interactions](https://arxiv.org/abs/2610.37471v1)
14. [CoHARL: Co-Evolving Agent Policies and Executable Harnesses with Reinforcement Learning](https://arxiv.org/abs/2610.36469v1)
15. [HobbyLM: A Sparse Mixture-of-Experts Model Family on a Hobby Budget](https://arxiv.org/abs/2610.34582v1)
16. [SentryBench: A Cost-Sensitive Benchmark for Adversarial Robustness of Tool-Using LLM Agents in Public-Safety Deployments](https://arxiv.org/abs/2610.34535v1)
17. [Task Success Is Not Enough: Side-effect-Aware Evaluation of Tool-Using Language Model Agents](https://arxiv.org/abs/2610.34167v1)
18. [Self-Evolution Reasoner: Continuous Optimization via Policy-Intrinsic Exploration and Exploitation](https://arxiv.org/abs/2610.34137v1)

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