Masked diffusion language models (DLMs) generate text through iterative parallel decoding, but at low step budgets, committing many tokens independently can produce premature termination or repetitive outputs. Continuous embedding-space denoising can mitigate this failure mode by maintaining soft states for all positions, yet training such models from scratch at scale remains challenging. We show that a pretrained masked DLM can instead be efficiently adapted to support continuous denoising. Starting from LLaDA-8B-Instruct, we perform only 1,000 steps of continued pretraining with Discrete Stochastic Localization (DSL), replacing binary masking with continuous per-token Gaussian noise mapped to soft embedding mixtures. The adapted model supports SDE-based inference that evolves all positions jointly and defers hard token commitment to the final step. At ≤16 denoising evaluations, DSL-LLaDA substantially mitigates the premature-termination and repetition tradeoff in open-ended generation and achieves the best ROUGE-1 among the evaluated diffusion baselines on all four zero-shot summarization benchmarks. The same adaptation also enables selective correction of corrupted tokens while preserving more than 98% of clean tokens. Same-compute controls indicate that these behaviors arise from continuous-noise exposure rather than additional training alone.

Large Language Models (LLMs) have shown promising performance in summary evaluation tasks, yet they face challenges such as high computational costs and the Lost-in-the-Middle problem where important information in the middle of long documents is often overlooked. To address these issues, this paper introduces a novel approach, Extract-then-Evaluate, which involves extracting key sentences from a long source document and then evaluating the summary by prompting LLMs. The results reveal that the proposed method not only significantly reduces evaluation costs but also exhibits a higher correlation with human evaluations. Furthermore, we provide practical recommendations for optimal document length and sentence extraction methods, contributing to the development of cost-effective yet more accurate methods for LLM-based text generation evaluation.