University of Groningen

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.

This study used a Leave-One-Class-Out out-of-distribution detection setup in motor imagery BCIs, training a model on some classes and observing whether an unfamiliar movement class can be detected via increased uncertainty; it found that because of the high intrinsic variability of EEG signals, many users show higher uncertainty for familiar in-distribution classes than for out-of-distribution classes, so many OOD detection methods that perform well in other machine learning domains prove ineffective here, yet OOD detection performance correlates with on-task performance, and Deep Ensemble and MC-Dropout models achieved on-task AUROC above 0.9 and OOD detection ability up to about 0.7, showing that rejecting unfamiliar cognitive states becomes feasible when task performance is high.

This study used a Leave-One-Class-Out out-of-distribution detection setup in motor imagery BCIs, training a model on some classes and observing whether an unfamiliar movement class can be detected via increased uncertainty; it found that because of the high intrinsic variability of EEG signals, many users show higher uncertainty for familiar in-distribution classes than for out-of-distribution classes, so many OOD detection methods that perform well in other machine learning domains prove ineffective here, yet OOD detection performance correlates with on-task performance, and Deep Ensemble and MC-Dropout models achieved on-task AUROC above 0.9 and OOD detection ability up to about 0.7, showing that rejecting unfamiliar cognitive states becomes feasible when task performance is high.