Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
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3 Pith papers cite this work, alongside 834 external citations. Polarity classification is still indexing.
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Pith papers citing it
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A geometric phase transition produces crystalline hippocampal coding in food-caching birds that yields over 100-fold higher location memory capacity than the mist-like coding in non-caching birds.
LLMs represent semantic relations geometrically via embedding distance and direction; a linear Polar Probe decodes these structures from middle-layer activations and generalizes to new entities.
citing papers explorer
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Social-spatial dependencies for learning visual navigation
Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
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Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity
A geometric phase transition produces crystalline hippocampal coding in food-caching birds that yields over 100-fold higher location memory capacity than the mist-like coding in non-caching birds.
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Polar probe linearly decodes semantic structures from LLMs
LLMs represent semantic relations geometrically via embedding distance and direction; a linear Polar Probe decodes these structures from middle-layer activations and generalizes to new entities.