Nested Browser-Use Learning for Agentic Information Seeking
Analysis
This paper addresses the limitations of current information-seeking agents, which primarily rely on API-level snippet retrieval and URL fetching, by introducing a novel framework called NestBrowse. This framework enables agents to interact with the full browser, unlocking access to richer information available through real browsing. The key innovation is a nested structure that decouples interaction control from page exploration, simplifying agentic reasoning while enabling effective deep-web information acquisition. The paper's significance lies in its potential to improve the performance of information-seeking agents on complex tasks.
Key Takeaways
- •Proposes NestBrowse, a new framework for agentic information seeking.
- •NestBrowse enables full browser interaction for richer information access.
- •The nested structure simplifies agentic reasoning and facilitates deep-web information acquisition.
- •Empirical results demonstrate benefits on challenging deep IS benchmarks.
“NestBrowse introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure.”