A Polanyi-Like Paradox in Large Language Models: The Gap Between Spatial Representation and Decision

Polanyi’s paradox—that we can know more than we can tell—motivates a question for spatial decision-making in large language models (LLMs): can spatial information recovered from their internal representations support more accurate spatial predictions than the models' final choices? We investigate this question in a route-choice task requiring the integration of separate travel experiences to select the next move yielding the shortest route to a destination. Using 115,362 task inputs, we trace how spatial information recovered from hidden states relates to model choices across layers, and use causal interventions to test how intermediate computations influence output preferences. The results show that spatial connectivity and distance are partially recoverable from hidden states, with more accurate recovery of start-centered distances than global pairwise distances under the respective probing setups. Route-comparison accuracy exhibits a sharp increase at intermediate layers, suggesting an emergence-like transition in the accessibility of goal-relevant spatial information, consistent with the integration of fragmented spatial experiences. Route preferences obtained from hidden states are more accurate than the final choices in every evaluated model, revealing a discrepancy between internally recoverable spatial information and behavior. Causal interventions suggest that opposing component effects and their downstream changes may contribute to output-preference instability across layers. Together, these findings reveal a Polanyi-like representation–decision gap: spatial information recoverable through supervised readouts is not fully reflected in the models’ choices. Our results distinguish the emergence of decodable spatial information from its reliable behavioral expression and provide a framework for diagnosing failures in LLM spatial decision-making.