Revealing the Barriers of Language Agents in Planning (2025)

Abstract: 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.

## introduction planning is the process of determining the sequence of actions needed to achieve a goal. it involves goal decomposition, constraint consideration, and foresight for simulating and predicting outcomes. in the development of artificial intelligence, this capability is considered the "holy grail" for achiev- task plan direct prompting task plan parametric memory updating task plan episodic memory updating fine-tuned insights ing or even surpassing human intelligence @xcite . however, the path to achieving autonomous planning is a long journey. researchers have long focused on building custom systems tailored to specific tasks @xcite @xcite . while these systems could deliver precise solutions through rigorous problem solvers, the extensive effort required for task-specific design prevents them from achieving universal problem-solving capabilities or general intelligence. the advent of language agents @xcite @xcite , which are powered by large language models (llms; openai (2022, 2023a); g team et al. (2023); @xcite ; @xcite ), changes the landscape. thanks to the flexibility of natural language, llm-based language agents have shown strong potential to generalize to various planning tasks without relying on traditional curated, task-specific solvers written in domain-specific languages like planning domain definition language (pddl). however, despite these language agents demonstrating impressive capabilities across various tasks @xcite @xcite , their performance in planning remains disappointing and is viewed as mere "approximate retrieval" @xcite rather than engaging in genuine reasoning. specifically, even the most capable model, openai o1 (openai, 2024), which claims to surpass human phd-level accuracy on several reasoning tasks, achieves only 15.6% in a realworld travel planning benchmark, travelplanner (see figure 2 ), far below human-level planning abilities. to uncover the fundamental reasons behind the weak performance, we seek to answer the first research question in this paper: rq1: why do current language agents struggle with planning? in order to enhance language agents' performance in planning tasks, numerous strategies have been proposed recently, which can be categorized into three main branches, as shown in figure 1 : episodic memory updating through prompt optimization @xcite @xcite , parametric memory updating through model training @xcite @xcite , and translating queries into formal planning languages, followed by resolution using external solvers @xcite . although these strategies have shown performance improvements across various tasks, their underlying mechanisms remain largely opaque. moreover, these strategies still fall short of human-level intelligence @xcite , particularly in complex real-world tasks @xcite @xcite . therefore, based on the findings from rq1, this paper seeks to answer the research questions, rq2: what happens during memory updating for language agents and rq3: what hinders these strategies from achieving high-level planning abilities? specifically, we focus on language agents' vanilla planning as well as planning following memory updating, which reflect the internal planning capabilities of language agents rather than the translation ability. in this paper, we delve into the two main components of planning: constraints and questions, which serve as the foundational elements for planning tasks. constraints refer to the rules that agents must adhere to when generating a plan, while questions represent the goals that drive the planning process. understanding how agents handle these elements is crucial for improving their performance in complex planning tasks. using permutation feature importance @xcite to analyze the feature attribution of constraints and questions, our investigation reveals several key findings: 1) language agents show a limited understanding of constraints, and the influence of the question weakens as the planning horizon increases. ## why do current language agents struggle with planning? ## conclusion this paper utilizes permutation feature importance to investigate why current language agents struggle with planning tasks. our findings show that constraints play only a minor role in agent planning, indicating that agents are not effectively considering constraints during planning. additionally, the question's influence diminishes as the planning horizon extends, causing agents to lose focus on the goal and resulting in failed plans. furthermore, we examine the effects of episodic and parametric memory updating on agent performance. while both strategies improve the impact of constraints and questions in planning, they only mitigate the underlying issues rather than fully resolve them. we hope this paper provides valuable insights and sparks future research to address language agents' key challenges in planning, ultimately moving closer to achieving human-level intelligence.