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arXiv Machine Learning By Junlin Fang, Chong Zhang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du

Toolcompass: Guiding Tool Trialing, Not Suppressing It

Read the original on arXiv Machine Learning →

ToolCompass is a post‑training framework that improves how large language model agents explore new tools by organizing tool‑call representations according to shared functions. It models each function class as a von Mises–Fisher distribution, reducing variation within a function while increasing separation between different functions, thereby guiding exploration toward functionally similar unseen tools. Experiments on AppWorld and FTRL show consistent gains, with up to a 10.71‑percentage‑point improvement in out‑of‑distribution task success over vanilla post‑training and outperforming competitive baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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