WebNavigator: Global Web Navigation via Interaction Graph Retrieval
This work solves the problem of inefficient web navigation for AI agents by overcoming a key bottleneck, with significant performance gains in complex environments.
The paper tackles the problem of autonomous web navigation by addressing Topological Blindness, where agents lack global topological structure, and introduces WebNavigator, which reframes navigation as deterministic retrieval and pathfinding, achieving a 72.9% success rate on WebArena multi-site tasks, more than doubling enterprise-level agent performance.
Despite significant advances in autonomous web navigation, current methods remain far from human-level performance in complex web environments. We argue that this limitation stems from Topological Blindness, where agents are forced to explore via trial-and-error without access to the global topological structure of the environment. To overcome this limitation, we introduce WebNavigator, which reframes web navigation from probabilistic exploration into deterministic retrieval and pathfinding. WebNavigator constructs Interaction Graphs via zero-token cost heuristic exploration offline and implements a Retrieve-Reason-Teleport workflow for global navigation online. WebNavigator achieves state-of-the-art performance on WebArena and OnlineMind2Web. On WebArena multi-site tasks, WebNavigator achieves a 72.9\% success rate, more than doubling the performance of enterprise-level agents. This work reveals that Topological Blindness, rather than model reasoning capabilities alone, is an underestimated bottleneck in autonomous web navigation.