New work from Meta AI on agents that manage their own search.

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A ReAct agent keeps every failed attempt in one context, making useful evidence harder to find.

To improve on this, they propose TreeFlex, a framework in which the agent’s own policy decides where to search.

In TreeFlex, the agent saves each intermediate state as a node in a search tree. The same model then reads short summaries of all nodes and decides where to continue, whether to branch, when to check a proposed answer, and when to stop.

On a deep-research benchmark, a 4B model reaches 48.8% versus 41.5% for flat ReAct, at the same context limit.

Same 9B base model, both trained with RL. 54.9% on SWE-bench Verified with TreeFlex, versus 43.3% with FoldAgent.

https://arxiv.org/abs/2610.20334

Chat with Paper: https://academy.dair.ai/papers/treeflex-agents-navigating-their-own-search-trees-2610.20334