Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory
Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury

TL;DR
Branch-and-Browse is a novel web agent framework that combines structured reasoning, memory, and efficient execution to improve multi-step web exploration tasks with higher success rates and reduced time.
Contribution
It introduces a tree-structured reasoning and action memory framework for controllable and efficient web exploration by LLM-based agents.
Findings
Achieves 35.8% success rate on WebArena benchmark.
Reduces execution time by up to 40.4% compared to state-of-the-art.
Demonstrates improved reasoning depth and efficiency in web tasks.
Abstract
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 multi-step 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 reasoning-acting, 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)…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Web Data Mining and Analysis · Topic Modeling
