Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration
2 Pith papers cite this work. Polarity classification is still indexing.
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Exploratory experience produces more spatially organized and transition-preserving predictive representations in maze navigation for both agents and mice.
citing papers explorer
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Pre-trained Large Language Models Learn Hidden Markov Models In-context
Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
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Exploratory Experience Shapes the Geometry of Predictive Representations
Exploratory experience produces more spatially organized and transition-preserving predictive representations in maze navigation for both agents and mice.