A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.
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LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.