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AI CURATED HOT STORY · 10/15/2026

Plan-then-Retrieve Outperforms Retrieve-then-Plan in Multi-Tool Agent Tasks

1 reports archived1 independent sourcesupdated 10/15/2026, 08:00:00
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The study introduces the Plan-then-Retrieve (PtR) paradigm, using abstract tool stubs as queries to replace the traditional Retrieve-then-Plan (RtP) approach. Evaluation on ToolBench reveals that while RtP excels in single-tool tasks, PtR significantly outperforms it in multi-tool and cross-category complex scenarios. This performance crossover is robust across various retrieval budgets, registry sizes, and model families, suggesting complexity-adaptive routing can balance inference costs.

LATEST/The study introduces the Plan-then-Retrieve (PtR) paradigm, using abstract tool stubs as queries to replace the traditional Retrieve-then-Plan (RtP) approach. Evaluation on ToolBench reveals that while RtP excels in single-tool tasks, PtR significantly outperforms it in multi-tool and cross-category complex scenarios. This performance crossover is robust across various retrieval budgets, registry sizes, and model families, suggesting complexity-adaptive routing can balance inference costs.

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Total 1 reports · Latest first
  1. Apple Machine Learning ResearchT1·68 pts
    • The PtR paradigm shifts retrieval logic by having the LLM generate hypothetical function signatures (abstract tool stubs) before searching, rather than relying solely on user query embeddings.
    • A clear complexity-dependent crossover exists: RtP performs better on single-tool queries, while PtR dominates in multi-tool and cross-category scenarios.
    • Although PtR roughly doubles inference cost, its accuracy gains in complex tasks justify the expense, with potential for adaptive routing using low-cost signals from the pipeline.
    • The findings hold consistently across different model families (Gemini 2.5 Flash, gpt-oss-120b) and registry scales (1k to 47k APIs).
Plan-then-Retrieve Outperforms Retrieve-then-Plan in Multi-Tool Agent Tasks | Today for AI