Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
Large language model agents must discover relevant tools from large libraries, e.g., thousands of tools, as new needs arise during task execution.
ProofPaper ↗
Key points
- To address this, we present BRIA (BRanch-Inspect-Adopt), a simple yet effective agent that temporarily isolates candidate investigations in separate branches.
- Each branch retrieves and executes tools to investigate its candidate; a task agent inspects the resulting execution evidence and adopts only useful results into its continuing trajectory.
- BRIA retrieves additional tools as new capability needs arise during task execution.
- We evaluate BRIA on PlanBench-XL and MCP-Atlas using a shared pool of 1897 tools, three language models, and static and adaptive retrieval baselines.
Sources (1)
- [1]Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool LibrariesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:56 PM
Large language model agents must discover relevant tools from large libraries, e.g., thousands of tools, as new needs arise during task execution.
To address this, we present BRIA (BRanch-Inspect-Adopt), a simple yet effective agent that temporarily isolates candidate investigations in separate branches.
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Before this
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