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cs.CL2026
Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models
Heecheol Yun, Joonhyung Park, Joowon Kim +1
Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important q…
cs.CL2026
CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge
Seyun Bae, Seokhan Lee, Eunho Yang
The inability to filter out in advance all potentially problematic data from the pre-training of large language models has given rise to the need for methods for unlearning specifi…