Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
Diagnosing Search Behavior and Failure Modes in Long-Horizon Search Agents
TL;DR
Long-horizon search agents often struggle to correlate search effort with answer quality. This study identifies key failure modes in these agents, emphasizing the importance of effective evidence utilization over mere retrieval volume.
✦ Why It Matters
Focus on improving evidence utilization in search agents to enhance answer quality immediately.
Key Takeaways
- Prioritize quality of retrieved evidence over quantity in search queries.
- Implement stronger query formulation techniques to reduce redundancy.
- Monitor cumulative retrieval recall to assess answer accuracy.
- Develop stopping criteria based on sufficient evidence retrieval.
How It Works
The study evaluates search agents by analyzing their behavior in two stages: evidence retrieval and evidence utilization. By separating these stages, it identifies specific failure modes, allowing for targeted improvements in agent design.