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The paper analytically studies Dense Associative Memories trained on codewords of random expander LDPC codes, identifying a memorization-to-generalization transition reminiscent of grokking. It introduces a thresholding learning rule that achieves sample-efficient learning with only O(log n) training samples and enables the emergence of latent variable representations.