The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
arXiv:2605.31404v1 Announce Type: cross Abstract: 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 disentangle
This research arrives as LLMs are increasingly integrated into navigation systems, making the optimization of their spatial reasoning capabilities a critical area of focus.
Understanding the linguistic inductive bias of LLMs for spatial reasoning will lead to more robust and efficient AI agents capable of complex navigation planning.
The explicit characterization of how linguistic structures and contextual features influence LLM behavior in spatial reasoning moves beyond neutral engineering choices, enabling more intentional design.
- · AI developers
- · Robotics companies
- · Logistics and autonomous vehicle sectors
- · Researchers in NLP and AI
- · Companies relying on naive LLM integration
- · Inefficient navigation systems
Improved performance and reliability of LLM-powered navigation systems and autonomous agents.
Acceleration of research into more natural and efficient human-AI interaction for spatial tasks.
Potentially enables new forms of embodied AI where agents learn spatial reasoning more intuitively from human-like instructions.
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Read at arXiv cs.AI