TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search
Quick Answer
TreeSeeker introduces a framework for controlled trial-and-error in deep search, outperforming strong baselines on XBench-DeepSearch and BrowseComp benchmarks.
Quick Take
By organizing search as a branch-and-return process, it effectively manages exploration and exploitation, leading to better decision-making in complex web searches.
Key Points
- TreeSeeker organizes search as branch-and-return over tree-structured states.
- It uses textual UCB signals to balance exploration and exploitation.
- Experiments show consistent performance improvement over open-source baselines.
- TreeMem supports decision-making by retaining evidence and uncertainty cues.
- The framework addresses the challenge of navigating multiple plausible search directions.
Paper Resources
Source Excerpt
arXiv:2606. 11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge is deciding how to search when several directions look plausible but only some will later lead to reliable evidence. If an agent greedily follows the current best-looking direction, it may keep extending a weak continuation. If it explores without discipline, it may waste budget on disconnected trials.
We propose TreeSeeker, an inference-time framework for controlled trial-and-error in deep search. …
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