TreeFlex: Agents Navigating Their Own Search Trees

Dongzhuo Li, Fangzhou Cheng, Weiwei Li, Kevin Han, Haixu Ma +6

A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes its limited capacity and obscures relevant evidence, making focused reasoning increasingly difficult. We introduce TreeFlex, a framework in which the agent's own policy decides where to search. The agent records each intermediate state, with a short summary, as a node in a search tree maintained by the harness. The policy is a single language model that acts in two roles. In the Reflection role, it has a global view of the tree through these summaries and picks a node to resume, together with an instruction. In the Execution role, the harness restores the path that led to that node, and the model carries out the instruction with the usual task tools until it saves a new node or proposes a solution, which Reflection then approves or sends back to another node. Through these choices, the policy decides whether to deepen the current line, branch from or revisit an earlier state, verify a proposal, or stop, without a fixed traversal order or a score for each node. We train navigation, task actions, checkpointing,…