Let Agents Design How They Search: Automatic Interface Optimization for Agentic Search
Agentic search depends on the interface through which a language-model agent accesses and explores a corpus.
ProofPaper ↗
Key points
- We introduce SearchWeaver, a framework for automatic search interface optimization guided by downstream search performance.
- A proposer uses execution trajectories and task rewards to jointly revise corpus artifacts, executable tools, and usage instructions.
- With GPT-5.4-nano, the optimized interfaces improve answer accuracy from 71.6% to 78.4% on BrowseComp-Plus and average nDCG@10 from 45.7 to 55.0 across four BRIGHT subsets over Direct Corpus Interaction.
- These findings highlight the value of jointly optimizing corpus artifacts, executable tools, and usage instructions to develop effective search interfaces.
Sources (1)
- [1]Let Agents Design How They Search: Automatic Interface Optimization for Agentic SearcharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:56 AM
Agentic search depends on the interface through which a language-model agent accesses and explores a corpus.
We introduce SearchWeaver, a framework for automatic search interface optimization guided by downstream search performance.
Extractive summary: sentences quoted from the sources.
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