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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 16/10/2026, 02:33:24 UTC.

Briefings

A scale study compares Retrieve-then-Plan (RtP) and Plan-then-Retrieve (PtR) architectures for LLM agents discovering APIs. On ToolBench, RtP excels on single-tool queries while PtR dominates multi-tool scenarios. The study identifies a stub-hallucination failure mode where PtR invents non-existent API names, creating a trade-off between inference cost and retrieval accuracy that requires complexity-adaptive routing.

Key points

• RtP outperforms PtR on single-tool queries due to direct matching of proper nouns, whereas PtR outperforms RtP on multi-tool queries by decomposing complex intent into functional sub-needs.
• PtR incurs approximately double the inference cost compared to RtP, which motivates the need for complexity-adaptive routing strategies.

Context

This research investigates tool discovery architectures for LLM agents, specifically comparing two paradigms: Retrieve-then-Plan (RtP) and Plan-then-Retrieve (PtR). The findings inform practical deployment strategies for agent systems.

Limitations

Evidence is limited to a single scale study on ToolBench with 46,984 unique API endpoints. Results are specific to Gemini 2.5 Flash and gpt-oss-120b models; generalizability across other model families remains unverified. The study does not account for registry size variations beyond the tested range of 1k to 47k APIs, nor does it address the long-term stability of the stub-hallucination failure mode.

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