{"type":"ScholarlyArticle","title":"What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks","authors":[{"name":"Chengrui Huang","slug":"chengrui-huang","given":"Chengrui","family":"Huang","affiliation":""},{"name":"Zhengliang Shi","slug":"zhengliang-shi","given":"Zhengliang","family":"Shi","affiliation":""},{"name":"Yuntao Wen","slug":"yuntao-wen","given":"Yuntao","family":"Wen","affiliation":""},{"name":"Xiuying Chen","slug":"xiuying-chen","given":"Xiuying","family":"Chen","affiliation":""},{"name":"Peng Han","slug":"peng-han","given":"Peng","family":"Han","affiliation":""},{"name":"Shen Gao","slug":"shen-gao","given":"Shen","family":"Gao","affiliation":""},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":""}],"abstract":"Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. \r\nMany existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research.","doi":"10.24963/ijcai.2026/16","url":"https://ijcai.org/proceedings/2026/16","pdf":"https://ijcai.org/proceedings/2026/0016.pdf","markdown":"https://ijcai.org/proceedings/2026/16.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/16","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":16,"pages":{"first":136,"last":144},"keywords":["Agent-based and Multi-agent Systems: Applications","Agent-based and Multi-agent Systems: Agent communication","Data Mining: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}