Diffusion models face confidence limits with dependent tokens, study finds

A new paper published on arXiv explores the limitations of confidence in discrete diffusion models, particularly when generating sequences with inherent dependencies between tokens. The research demonstrates that current methods, which generate multiple token positions per step from per-position distributions, only match the training distribution when these positions are conditionally independent. The study verifies this on a synthetic task, showing that generated distributions can be significantly higher than the sampling-noise floor, even when per-sample metrics appear normal. AI

影响 Highlights a theoretical limitation in diffusion models that could impact their reliability for sequential data generation.

排序理由 The cluster contains a research paper detailing theoretical limitations of diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]

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