The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
Researchers propose a framework to evaluate how linguistic structures and contextual features shape Large Language Model behavior in spatial reasoning tasks. The study reveals that topological information provides robust navigation planning, linguistic format effectiveness depends on model size, and semantic errors can critically undermine performance.
This research addresses a fundamental gap in understanding how LLMs process spatial information for navigation planning. Rather than treating text-based spatial representations as neutral engineering choices, the study systematically isolates linguistic structure from contextual cues through dual interventions, revealing previously hidden inductive biases that affect model performance.
The findings emerge from growing recognition that LLM capabilities extend beyond text prediction into reasoning domains. As navigation systems increasingly rely on language models to interpret spatial environments, understanding what linguistic patterns they learn becomes crucial. Prior work typically focused on task performance without examining why certain representation choices succeeded or failed, leaving developers working with incomplete knowledge about their systems.
The study's three-part characterization—topological integrity as foundational, linguistic format as task-dependent, and semantic accuracy as critical—provides actionable guidance for practitioners building LLM-based navigation systems. Organizations developing autonomous agents, robotic systems, or AI navigation tools can optimize performance by prioritizing topological information preservation while avoiding semantic inconsistencies that systematically degrade planning quality.
The framework's applicability extends beyond navigation to any spatial reasoning task requiring LLMs. As enterprises invest in AI systems handling complex spatial domains—supply chain logistics, urban planning, robotics—this research offers concrete principles for representation design. The public code release enables broader validation and refinement of these principles across diverse applications and model architectures.
- →Topological information provides robust foundation for LLM-based navigation planning across diverse tasks
- →Linguistic format effectiveness varies by model size and task complexity, requiring calibrated compression strategies
- →Semantic errors create systematic failures in planning, making semantic correctness more critical than format optimization
- →Text-based spatial representations require careful design beyond treating them as neutral engineering decisions
- →Framework methodology enables systematic evaluation of linguistic inductive biases in language models for reasoning tasks