The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper introduces SearchWeaver, a framework that automatically optimizes search interfaces for language-model agents by revising corpus artifacts, executable tools, and usage instructions based on execution trajectories and task rewards. By tailoring the interface to each corpus, the method improves answer accuracy from 71.6% to 78.4% on BrowseComp-Plus and raises average nDCG@10 from 45.7 to 55.0 across four BRIGHT subsets. These results demonstrate the benefit of jointly optimizing interface components to enhance agentic search performance.
arXiv:2606. 14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.
arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Mo...
arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.