Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents
arXiv:2605.29927v1 Announce Type: cross 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 representation remains unexplored. To address this, we introduce PlanAhead, a static planner-executor framework that evaluates the impact of plan representation in agent performance. We first automatically categorize WebArena tasks into 3 difficulty levels, enabli
The rapid advancement of large language models (LLMs) has highlighted their current limitations in complex, multi-step tasks, making improved planning a critical next frontier.
Enhanced planning capabilities for LLM-based web agents can significantly improve their reliability and broaden their applicability across various industries by overcoming current failure modes.
This research introduces a novel framework for evaluating and improving planning representations in LLM agents, potentially leading to more robust and autonomous systems.
- · AI developers
- · Automation software vendors
- · Industries relying on complex digital workflows
- · Companies with inefficient digital processes
- · Manual data entry roles
Improved performance and reliability of LLM-based agents in web-based tasks.
Accelerated development and deployment of genuinely autonomous AI agents for a wider range of white-collar tasks.
Significant productivity gains across sectors as AI agents handle increasingly complex and nuanced digital operations.
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Read at arXiv cs.LG