Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents

The paper investigates how different natural language plan representations affect the performance of large language model (LLM) web agents. Using the PlanAhead framework, the authors automatically classify WebArena tasks into three difficulty levels and evaluate four plan types—sequential subgoals, narrative, pseudocode, and checklist—on hard tasks across multiple multimodal LLM agents. They introduce a decomposition of Success Rate into Achievement Rate and Solved-Task Consistency to better understand stochastic variability, finding that both plan formulation and the LLM used to generate the plan significantly influence agent robustness and success.

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• llms
• agents
• multimodal