ensembling

Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models

Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation, but combining their knowledge is an underexplored problem. Researchers introduce TIE (Trajectory-based Iterative Ensembling), a framework that tracks confidence dynamics over answer-relevant positions to relay decoding trajectories between models, achieving strong performance on diverse reasoning tasks.