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cs.LG2026
CORE: Context-Robust Remasking for Diffusion Language Models
Kevin Zhai, Sabbir Mollah, Zhenyi Wang +1
Standard decoding in Masked Diffusion Models (MDMs) is hindered by context rigidity: tokens are retained based on transient high confidence, often ignoring that early predictions l…
cs.LG2025
Dynamic Feedback Engines: Layer-Wise Control for Self-Regulating Continual Learning
Hengyi Wu, Zhenyi Wang, Heng Huang
Continual learning aims to acquire new tasks while preserving performance on previously learned ones, but most methods struggle with catastrophic forgetting. Existing approaches ty…
cs.LG2025
Understanding Catastrophic Interference: On the Identifibility of Latent Representations
Yuke Li, Yujia Zheng, Tianyi Xiong +2
Catastrophic interference, also known as catastrophic forgetting, is a fundamental challenge in machine learning, where a trained learning model progressively loses performance on…