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University College London
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Random splits in urban representation learning benchmarks inflate performance and alter model rankings, making cross-city generalization claims unreliable.
Conversational navigation powered by LLMs risks becoming a manipulative dialogue, but can be made trustworthy by grounding it in verifiable pathfinding algorithms.
