剑、盾与阿喀琉斯之踵：大型语言模型在导航规划中空间推理的语言归纳偏置特征化
The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
AI总结：
提出双干预框架，通过表示干预和上下文干预分离语言结构与上下文线索，揭示LLM在导航规划中语言归纳偏置的规律：拓扑信息是稳健规划的支柱，语言格式是双刃剑，语义信息是致命弱点。
摘要：
基于大型语言模型（LLM）的导航系统通常构建显式空间表示（如拓扑图、语义栅格图）并将其转换为文本描述作为LLM的输入。然而，此类基于文本的空间表示的语言结构及其包含的上下文特征（如拓扑、几何）的选择通常被视为中性的工程决策，而非塑造LLM行为的关键因素。为填补这一空白，我们提出一个双干预框架，将语言结构与不同的上下文线索分离，以评估LLM在导航规划中的语言归纳偏置。在该框架中，表示干预改变语言格式和语言压缩程度，阐明语言表示何时支持或抑制导航规划。上下文干预结合上下文特征组合与冲突探测，明确澄清LLM在处理不同上下文线索时的偏好和弱点。跨多种空间推理任务和多个模型规模的实验揭示了一致模式：拓扑信息是坚固的盾牌和稳健规划的支柱；语言格式是双刃剑，其效果取决于模型大小、任务需求和压缩程度；语义信息是致命的阿喀琉斯之踵——错误的语义线索会系统性地破坏规划过程。总体而言，我们的研究表明，基于LLM的导航中有效的文本空间表示应保持拓扑完整性，根据模型能力校准表示压缩，并确保语义正确性，而非简单采用单一表示。我们的代码公开于https://github.com/jonesdong150/LLM-Navigation-Inductive-Bias。
英文摘要：
Large Language Model (LLM)-based navigation systems commonly construct explicit spatial representations (e.g., topological graphs, semantic raster maps) and translate them into textual descriptions as LLMs' inputs. However, the linguistic structures of such text-based spatial representations and the choices of contextual features (e.g., topology, geometry) they contain are often treated as neutral engineering decisions rather than key factors that shape LLMs' behavior. To fill the gap, we propose a dual-interventional framework that disentangles linguistic structures from different contextual cues to evaluate the linguistic inductive bias of LLMs for navigation planning. In the framework, representation intervention varies the linguistic format and the degree of linguistic compression, clarifying when linguistic representations support or inhibit navigation planning. Context intervention, combined with contextual feature combination and conflict probing, explicitly clarifies the preferences and weaknesses of LLMs when processing different contextual cues. Experiments across diverse spatial reasoning tasks and multiple model scales reveal a consistent pattern: topological information is a sturdy shield and the backbone of robust planning; linguistic format is a double-edged sword whose effect depends on model size, task demands, and the compression level; and semantic information is a fatal Achilles' heel -- incorrect semantic cues can systematically derail the planning process. Overall, our study shows that effective text-based spatial representations in LLM-based navigation should preserve topological integrity, calibrate representational compression to model capacity, and ensure semantic correctness, rather than simply adopting a single representation. Our code is publicly available at https://github.com/jonesdong150/LLM-Navigation-Inductive-Bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
