Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory
arXiv:2510.19838v2 Announce Type: replace-cross 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 Bra
The rapid advancement of large language models is driving the need for more efficient and robust autonomous web exploration agents to perform complex, goal-oriented tasks.
Improved autonomous web agents can significantly enhance productivity, automate complex online workflows, and transform how businesses and individuals interact with the internet.
Existing limitations in agent reasoning depth, efficiency, and backtracking capabilities are being addressed, leading to more reliable and powerful AI agents.
- · AI software developers
- · Businesses with complex online operations
- · Digital service providers
- · Consumers of automated services
- · Tasks requiring manual web navigation
- · Inefficient web-based service providers
- · Legacy automation tools
More sophisticated and reliable AI agents will emerge, capable of handling multi-step online tasks.
Automation will expand into more complex white-collar tasks, potentially leading to significant workforce displacement or re-skilling requirements.
The development of highly autonomous web agents could lead to new forms of digital commerce and entirely new service sectors, alongside increased cybersecurity challenges related to agent impersonation and misuse.
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