Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory

Autonomous web agents powered by large language models (LLMs) show strong potential for performing goal-oriented tasks such as information retrieval, report generation, and online transactions.These agents mark a key step toward practical embodied reasoning in open web environments.However, existing approaches remain limited in reasoning depth and efficiency: vanilla linear methods fail at multistep reasoning and lack effective backtracking, while other search strategies are coarse-grained and computationally costly.We introduce Branch-and-Browse, a fine-grained web agent framework that unifies structured reasoningacting, contextual memory, and efficient execution.It (i) employs explicit subtask management with tree-structured exploration for controllable multi-branch reasoning, (ii) bootstraps exploration through efficient web state replay with background reasoning, and (iii) leverages a page action memory to share explored actions within and across sessions.On the WebArena benchmark, Branch-and-Browse achieves a task success rate of 35.8% and reduces execution time by up to 40.4% relative to state-of-the-art methods.These results demonstrate that Branch-and-Browse is a reliable and efficient framework for LLM-based web agents.Code is available at https://github.com/ SymbioticLab/Branch-and-Browse.