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🧠 AI🟢 BullishImportance 6/10

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

arXiv – CS AI|ELita Lobo, Xu Chen, Jingjing Meng, Nan Xi, Yang Jiao, Chirag Agarwal, Yair Zick, Yan Gao|
🤖AI Summary

Researchers propose STRUCTUREDAGENT, a new AI framework that uses hierarchical planning with AND/OR trees to improve web agent performance on complex, long-horizon tasks. The system addresses limitations in current LLM-based agents through better memory tracking and structured planning approaches.

Key Takeaways
  • →STRUCTUREDAGENT introduces hierarchical planning with dynamic AND/OR trees for more efficient web task execution.
  • →The framework includes a structured memory module to better track candidate solutions and improve constraint satisfaction.
  • →Current web agents struggle with long-horizon tasks due to limited memory, weak planning, and premature termination issues.
  • →The system produces interpretable hierarchical plans that enable easier debugging and human intervention.
  • →Testing on WebVoyager, WebArena, and shopping benchmarks shows improved performance over standard LLM-based agents.
#ai-agents#llm#web-automation#hierarchical-planning#structured-memory#decision-making#research#arxiv
Read Original →via arXiv – CS AI
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