Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation

arXiv:2605.06207v2 Announce Type: replace-cross Abstract: Most discrete visual tokenizers rely on a default design: every position in the sequence shares the same codebook. Such a constant-codebook design can consume the finite-data entropy budget very quickly. We observe that the per-position empirical conditional entropy decays so quickly along the sequence that, after a few positions, the conditional distribution becomes essentially deterministic. On ImageNet with $K=16384$, this happens after only about 2 out of 256 positions, when most prefixes already identify individual images in the da

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