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CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think

Published in -, 2026

Revealed the theoretical suboptimality of pointwise token rounding in continuous DLMs. Proposed a contextual autoregressive decoder to replace linear rounding, enabling sequence-aware discretization. Demonstrated that continuous diffusion models can rival discrete DLMs when rounding is properly modeled.

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Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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