STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks

Abstract:Existing LLM-based web agents struggle on complex, long-horizon tasks due to limited in-context memory, weak planning abilities, and greedy behaviors that lead to premature termination. To address these challenges, we propose \SA{}, a hierarchical planning framework that interleaves planning and execution via dynamic $\ANDOR$ trees. The framework separates structural planning from LLM-based reasoning, enabling principled error recovery through node repair, systematic exploration of alternatives via OR nodes, and modular plans that can facilitate human intervention. On WebArena (630 tasks), \SA{} achieves a $\sim$53\% success rate vs.\ $\sim$46\% for AgentOccam, and on complex multi-constraint Amazon shopping tasks, gains reach 10\% over the strongest baseline.

Artificial Intelligence (cs.AI)

arXiv:2603.05294 [cs.AI]

(or arXiv:2603.05294v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.05294 Focus to learn more arXiv-issued DOI via DataCite

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