LLMs show strong spatial generalization to unseen maps in shortest-path tasks but fail length scaling due to recursive instability, with data coverage setting hard limits.
In language, how- ever, understanding remains fragmented: studies have pointed to a variety of factors (e.g., from model-side (Kazemnejad et al., 2023; Petty et al.,
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Generalization in LLM Problem Solving: The Case of the Shortest Path
LLMs show strong spatial generalization to unseen maps in shortest-path tasks but fail length scaling due to recursive instability, with data coverage setting hard limits.