Revealing the Barriers of Language Agents in Planning
Authors: name: Jian Xie, name: Kexun Zhang, name: Jiangjie Chen, name: Siyu Yuan, name: Kai Zhang, name: Yikai Zhang, name: Lei Li, name: Yanghua Xiao

Autonomous planning has been an ongoing pursuit since the inception of
artificial intelligence. Based on curated problem solvers, early planning
agents could deliver precise solutions for specific tasks but lacked
generalization. The emergence of large language models (LLMs) and their
powerful reasoning capabilities has reignited interest in autonomous planning
by automatically generating reasonable solutions for given tasks. However,
prior research and our experiments show that current language agents still lack
human-level planning abilities. Even the state-of-the-art reasoning model,
OpenAI o1, achieves only 15.6% on one of the complex real-world planning
benchmarks. This highlights a critical question: What hinders language agents
from achieving human-level planning? Although existing studies have highlighted
weak performance in agent planning, the deeper underlying issues and the
mechanisms and limitations of the strategies proposed to address them remain
insufficiently understood. In this work, we apply the feature attribution study
and identify two key factors that hinder agent planning: the limited role of
constraints and the diminishing influence of questions. We also find that
although current strategies help mitigate these challenges, they do not fully
resolve them, indicating that agents still have a long way to go before
reaching human-level intelligence.