AI RESEARCH
STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks
arXiv CS.AI
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ArXi:2603.05294v2 Announce Type: replace Recent advances in large language models (LLMs) have enabled agentic systems for sequential decision-making. Such agents must perceive their environment, reason across multiple time steps, and take actions that optimize long-term objectives. However, existing web agents struggle on complex, long-horizon tasks due to limited in-context memory for tracking history, weak planning abilities, and greedy behaviors that lead to premature termination.