arXiv cs.AI· Alejandra Zambrano, Sara Vera Marjanovic, Imene Kerboua, Xing Han L\`u, Leila Kosseim·· 3 小时前AI 评分39
规划表示方式是否影响 LLM Web Agent 表现？PlanAhead 实证研究
Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents
AI 导读
arXiv 论文提出 PlanAhead 静态规划-执行框架，实证研究不同自然语言规划表示对 LLM Web Agent 性能的影响。
正文 · 原文
Abstract:Despite recent advances, LLM-based web agents still struggle with limited exploration, omission of critical steps, and sensitivity to task constraints. Prior work suggests that many of these failures stem from weaknesses in planning, yet the impact of alternative natural language plan representations remains unexplored. To address this, we introduce PlanAhead, a static planner-executor framework that evaluates the impact of plan representation on agent performance. We first automatically categorize WebArena tasks into 3 difficulty levels, enabling consistent difficulty grading without human annotation. Then we systematically evaluate 4 different plan representations on the tasks categorized as hard: sequential subgoals, narrative, pseudocode, and checklist; across different families of multimodal LLM-powered agents(OpenAI, Alibaba, and Google). To account for stochastic variability, we introduce a novel decomposition of agent Success Rate (SR) into 2 interpretable evaluation metrics: Achievement Rate (AR) and Solved-Task Consistency (STC). Our results show that both, the plan formulation and the underlying LLM generating the plan, significantly influence web-agent robustness and task success.
| Comments: | Camera Ready version for REALM at EMNLP. Extended appendix, extended discussion section and change of main figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.29927 [cs.CL] |
| (or arXiv:2605.29927v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29927 arXiv-issued DOI via DataCite |
Submission history
From: Alejandra Zambrano [view email]
[v1]
Thu, 28 May 2026 13:39:49 UTC (2,466 KB)
[v2]
Tue, 27 Oct 2026 10:44:13 UTC (3,439 KB)
来源：arXiv cs.AI · arxiv.org