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This paper identifies a gap between any-order prediction interface and actual any-order inference in masked diffusion models, proposing insertion-based and latent-space masked diffusion to enable genuine any-order inference for tasks like code generation. The authors train a 7B FlexMDM for Python and a 125M LatentMDM, showing improved performance.