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Cited by 18Natural Language Processing TechniquesTopic ModelingSpeech and dialogue systems

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LLM Agents Making Agent Tools

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Wölflein, Georg, Ferber, Dyke, Truhn, Daniel, Arandjelovic, Ognjen, Kather, Jakob Nikolas·2025·Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2025.acl-long.1266
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Georg Wölflein, Dyke Ferber, Daniel Truhn, Ognjen Arandjelovic, Jakob Nikolas Kather. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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  • Agentic techniques have been shown to be effective at external tool selection.

    “Agentic techniques have been shown to be effective at external tool selection [8, 15, 37, 40].”

    From Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification (2025) · cites this paper as [37]

    The Introduction reports success by LLM agents on specific tasks, and the Abstract reports that TOOLMAKER successfully creates tools from external code. But the Introduction distinguishes tool creation from learning to use appropriate existing tools; the paper does not demonstrate effective external tool selection. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Metadata

Authors
Wölflein, G., Ferber, D., Truhn, D., Arandjelovic, O., & Kather, J. N.
Affiliations
  • University Hospital Heidelberg (DE)
  • Universitätsklinikum Aachen (DE)
ORCID
Venue
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2025
Pages
26092-26130
ISSN
0736-587X (ISSN-L)
DOI
10.18653/v1/2025.acl-long.1266
OpenAlex
W4412889943
Cited by
16
References
0
Citations by year
2026: 14 · 2025: 2
FWCI
15.25
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • St Andrews Research Repository (St Andrews Research Repository) (submittedVersion)
Funders
  • National Institute for Health and Care Research (NIHR203331)
  • Department of Health and Social Care
  • European Commission (101096312, HORIZON2020, 101057091, 101017453)
  • Deutscher Akademischer Austauschdienst (57616814)
  • Bundesministerium für Bildung und Forschung (01EO2101, 01VSF21048, 57616814, 01KD2104C, 031L0312A)
  • Deutsche Krebshilfe (70115166)
  • Gemeinsame Bundesausschuss (01VSF21048)
  • National Institutes of Health
  • HORIZON EUROPE Framework Programme (101057091, 101017453)
  • Leeds Biomedical Research Centre (NIHR203331)
  • National Cancer Institute (R01CA263318)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
LLM agents, self-improving agents

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