Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective
Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hierarchical planning framework to analyze web agents across three layers (i.e., high-level planning, low-level execution, and replanning), enabling process-based evaluation of reasoning, grounding, and recovery. Our experiments show that structured Planning Domain Definition Language (PDDL) plans produce more concise and goal-directed strategies than natural language (NL) plans, but low-level execution remains the do...
| Authors | Mohamed Aghzal, Gregory J. Stein, Ziyu Yao |
|---|---|
| Primary category | cs.AI |
| Published | 15 March 2026 |
| Last updated at source | 27 April 2026 |
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