Researchers have proposed a method called DiSE for self-evaluation of diffusion language models. DiSE works by calculating the probability that a generated sequence can be regenerated from scratch. This allows for more efficient and reliable quality assessment of the model's output. The authors also introduce a flexible-length generation framework that adapts to the model's self-assessment of its own output. They demonstrate the effectiveness of DiSE through experiments on likelihood evaluation, uncertainty quantification, and flexible-length generation. --- Why it matters: This matters because it provides a way for diffusion language models to evaluate their own quality and adapt to different tasks, which can improve their performance and reliability in applications such as natural language processing and machine translation. Source: https://arxiv.org/abs/2603.02760