# Delta (Diffusion Expected Length Targeting Algorithm)

For summarization settings such as headlines and previews that must fit an exact character count, Delta estimates the expected character length of the current output inside a masked diffusion language model's denoising loop and dynamically inserts or removes masked positions to steer generation toward the target. It needs no fine-tuning and applies to off-the-shelf LLaDA and Dream. Across the XSum, CNN/DailyMail and arXiv summarization benchmarks, character-length error falls by an order of magnitude relative to the baselines while summary quality stays competitive under automatic and human evaluation.

For summarization settings such as headlines and previews that must fit an exact character count, Delta estimates the expected character length of the current output inside a masked diffusion language model's denoising loop and dynamically inserts or removes masked positions to steer generation toward the target. It needs no fine-tuning and applies to off-the-shelf LLaDA and Dream. Across the XSum, CNN/DailyMail and arXiv summarization benchmarks, character-length error falls by an order of magnitude relative to the baselines while summary quality stays competitive under automatic and human evaluation.