Replacing dense orthogonal matrices with sign-randomized DCTs accelerates Kashin-based LLM quantization to O(N log N) with guaranteed convergence, achieving 4-bit accuracy competitive with OPTQ and QuIP while maintaining numerical stability and native 2-bit hardware compatibility.
TUBE introduces a variational upper bound with unbiased Monte Carlo estimation to evaluate discrete diffusion model log-likelihoods, revealing that block diffusion and any-order autoregressive models remain below exact autoregressive baselines.