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Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective
🤖AI Summary
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.
Key Takeaways
- →LLM web agents still fall far short of human reliability on realistic, long-horizon web navigation tasks.
- →The proposed hierarchical framework evaluates agents across three layers: high-level planning, low-level execution, and replanning.
- →Structured PDDL plans produce more concise and goal-directed strategies compared to natural language plans.
- →Low-level execution remains the dominant bottleneck, not high-level reasoning capabilities.
- →Improving perceptual grounding and adaptive control is critical for achieving human-level agent reliability.
#llm#web-agents#hierarchical-planning#pddl#ai-research#navigation#grounding#execution#reasoning#arxiv
Read Original →via arXiv – CS AI
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