#hierarchical-planning News & Analysis

4 articles tagged with #hierarchical-planning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · May 117/10

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Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

Researchers introduce HCL-GP, a machine learning approach that enables large language model agents to learn and reuse hierarchical task decompositions for improved performance on complex applications. The method achieves 98.2% accuracy on standard tasks and demonstrates significant improvements over static synthesis approaches, particularly benefiting open-source models through dynamic component reuse.

AINeutralarXiv – CS AI · May 126/10

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Zero-shot Imitation Learning by Latent Topology Mapping

Researchers introduce ZALT, an imitation learning method that enables AI agents to solve unseen tasks by identifying latent hub states in demonstrated trajectories and planning over abstract topology. The approach achieves 55% zero-shot success on complex maze tasks compared to 6% for existing baselines, addressing the challenge of adapting learned behaviors to new long-horizon goals without additional training.

AINeutralarXiv – CS AI · Mar 176/10

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Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

Researchers propose a hierarchical planning framework to analyze why LLM-based web agents fail at complex navigation tasks. The study reveals that while structured PDDL plans outperform natural language plans, low-level execution and perceptual grounding remain the primary bottlenecks rather than high-level reasoning.

AIBullisharXiv – CS AI · Mar 66/10

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

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.