From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation

Abstract:Large language model (LLM) agents increasingly face tasks from unseen domains and execution contexts, making zero-shot tool selection essential for identifying tools that can satisfy task requirements. Existing dominant methods mainly rely on relevance ranking, but semantic matching alone cannot ensure that a tool is functionally compatible or remains applicable in a new context. We preliminarily identify a \emph{transfer-validity gap}, where semantically relevant tools may still fail to satisfy functional or contextual requirements after transfer. To address this gap, we propose \methodname{}, a progressive validation framework that complements semantic relevance with endpoint-grounded functional compatibility and task-conditioned contextual evidence. Across cross-domain and cross-dataset transfer, \methodname{} consistently improves capability ranking, with larger gains under functional or contextual ambiguity. Ablation results further confirm the complementary contributions of functional and contextual validation.

Artificial Intelligence (cs.AI)

arXiv:2610.20388 [cs.AI]

(or arXiv:2610.20388v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.20388 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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