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New framework boosts LLM web agent efficiency with tree-structured reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Branch-and-Browse, a new framework designed to enhance the capabilities of large language model (LLM)-powered web agents. This framework addresses limitations in reasoning depth and efficiency found in current approaches by employing a tree-structured exploration method for multi-branch reasoning and incorporating contextual memory. Branch-and-Browse also features efficient web state replay and a page action memory to share explored actions across sessions, leading to improved performance on benchmarks. AI

IMPACT Enhances LLM web agent efficiency and controllability for complex tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework for LLM-based web agents, including performance metrics on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework boosts LLM web agent efficiency with tree-structured reasoning

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The cluster contains an academic paper detailing a new framework for LLM-based web agents, including performance metrics on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury ·

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

    arXiv:2510.19838v2 Announce Type: replace 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 towar…

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