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

2026-03-05

AI Summary

This work addresses the limitations of existing large language model (LLM) agents in complex, long-horizon web tasks, where constrained context memory, inadequate planning capabilities, and greedy action selection often lead to premature failure. To overcome these challenges, we propose STRUCTUREDAGENT, a novel framework that integrates a dynamic AND/OR tree with a structured memory mechanism to enable interpretable, online hierarchical planning. The AND/OR tree facilitates efficient search and maintenance of candidate solutions, while the structured memory enhances long-term reasoning and fault tolerance. Extensive experiments on WebVoyager, WebArena, and a custom shopping benchmark demonstrate that our approach significantly outperforms standard LLM agents, achieving substantial improvements in both constraint satisfaction and overall task success rates.

Technology Category

• Multiagent Systems: Multiagent Planning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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. To address these challenges, we propose STRUCTUREDAGENT, a hierarchical planning framework with two core components: (1) an online hierarchical planner that uses dynamic AND/OR trees for efficient search and (2) a structured memory module that tracks and maintains candidate solutions to improve constraint satisfaction in information-seeking tasks. The framework also produces interpretable hierarchical plans, enabling easier debugging and facilitating human intervention when needed. Our results on WebVoyager, WebArena, and custom shopping benchmarks show that STRUCTUREDAGENT improves performance on long-horizon web-browsing tasks compared to standard LLM-based agents.

Problem

• long-horizon web tasks
• LLM-based agents
• planning
• in-context memory
• greedy behavior

Innovation

• hierarchical planning
• AND/OR trees
• structured memory
• long-horizon tasks
• web agents