Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents
TL;DR
Fetch-then-Explore introduces a novel approach for search agents by decoupling page selection from evidence extraction, allowing for more efficient information retrieval. This method significantly improves accuracy in open-web benchmarks compared to traditional browsing techniques.
✦ Why It Matters
Implement Fetch-then-Explore in your search agent to improve information retrieval accuracy today.
Key Takeaways
- Decouple page selection from evidence extraction for better efficiency.
- Utilize a persistent workspace to store selected pages.
- Enable agents to revisit pages for missed information.
- Test performance improvements on benchmarks like BrowseComp and WideSearch.
How It Works
Fetch-then-Explore allows search agents to select and store web pages in a persistent workspace, enabling them to extract information on demand. This decoupling means that agents can revisit pages as their understanding of the query evolves, leading to more accurate information retrieval.