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New study compares LLM agent tool discovery architectures

A new study published on arXiv explores two architectures for tool discovery in LLM agents: Retrieve-then-Plan (RtP) and Plan-then-Retrieve (PtR). The research found that RtP is more effective for single-tool queries, while PtR excels with multi-tool queries, with a complexity-dependent crossover point. This finding holds across various retrieval budgets, registry sizes, and model families like Gemini 2.5 Flash and GPT-OSS 120B. The study also identified a 'stub-hallucination' failure mode in PtR and noted that PtR roughly doubles inference costs, making complexity-adaptive routing a promising direction. AI

IMPACT This research offers insights into optimizing LLM agent performance by adapting tool discovery strategies based on query complexity.

RANK_REASON The cluster contains a research paper detailing a study on LLM agent architectures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New study compares LLM agent tool discovery architectures

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The cluster contains a research paper detailing a study on LLM agent architectures. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Michael Xu ·

    Plan or Retrieve First? A Scale Study of Tool Discovery Architectures

    Tool-augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt. The dominant approach, which we term Retrieve-then-Plan (RtP), embeds the user query and retr…