Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory
📰 ArXiv cs.AI
Learn how to build efficient web exploration agents using tree-structured reasoning and action memory, enabling controllable and autonomous goal-oriented tasks on the web
Action Steps
- Build a tree-structured reasoning model to represent the web environment and agent actions
- Implement an action memory mechanism to store and retrieve relevant information during exploration
- Configure the agent to use a branch-and-browse strategy for efficient navigation
- Test the agent's performance on goal-oriented tasks such as information retrieval and online transactions
- Apply the tree-structured reasoning and action memory to improve the agent's backtracking and multi-step reasoning capabilities
Who Needs to Know This
AI engineers and researchers working on autonomous web agents and embodied reasoning can benefit from this approach to improve the efficiency and controllability of their systems
Key Insight
💡 Tree-structured reasoning and action memory enable efficient and controllable web exploration for autonomous agents
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🤖 Efficient web exploration with tree-structured reasoning and action memory! 📊
Key Takeaways
Learn how to build efficient web exploration agents using tree-structured reasoning and action memory, enabling controllable and autonomous goal-oriented tasks on the web
Full Article
Title: Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory
Abstract:
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 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,
Abstract:
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 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,
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