STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks
cs.AI
Submitted: 2026-03-05
Updated: 2026-09-19
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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.
Terminology
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, a hierarchical planning framework that interleaves planning and execution via dynamic 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), achieves a about 53% success rate vs. about 46% for AgentOccam, and on complex multi-constraint Amazon shopping tasks, gains reach 10% over the strongest baseline.
Sources
- Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
- LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
- Reflexion: Language Agents with Verbal Reinforcement Learning
- WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration
- Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
- WebArena: A Realistic Web Environment for Building Autonomous Agents
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