Finite-resolution similarity functions force a universal tradeoff between identification and generalization, with a predicted 1/n collapse of multi-input capacity.
Understanding the limits of vision language models through the lens of the binding problem
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Bound by semanticity: universal laws governing the generalization-identification tradeoff
Finite-resolution similarity functions force a universal tradeoff between identification and generalization, with a predicted 1/n collapse of multi-input capacity.