Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

Abstract:Tool retrieval is a critical bottleneck for LLM-based agents operating over large, heterogeneous API ecosystems. Existing approaches face an inherent trade-off: semantic retrievers are fast but suffer from the semantic-functional gap, while execution-based validation improves precision at the cost of prohibitive latency. We propose Lookahead-R, a planning-based framework that reformulates tool retrieval as a resource-constrained sequential decision-making problem. At its core, Lookahead-R introduces a lightweight execution-aware surrogate world model that jointly predicts tool execution success, latency cost, and semantic utility---without invoking real APIs. This world model drives a cost-sensitive, uncertainty-guided Monte Carlo Tree Search that navigates the tool space under strict budget constraints. Evaluated on the large-scale ToolBench benchmark, Lookahead-R achieves a superior accuracy-efficiency trade-off across all test scenarios. On the most challenging I3 split, it attains an NDCG@5 of 91.40\%, outperforming the state-of-the-art ToolGen (90.16\%) by 1.24\%. Ablation studies confirm that explicit latency modeling is the key discriminative signal for identifying high-quality tools under resource constraints.

10 pages, 3 figures, 3 tables. Published in ICMR 2026

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2609.35811 [cs.CL]

(or arXiv:2609.35811v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.35811 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3805622.3810632 Focus to learn more DOI(s) linking to related resources

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