Diffusion LLMs can act as their own efficiency teachers by using revokable parallel decoding to identify reliable token orders and then distilling those orders into the model parameters for faster inference.
High- resolution image synthesis with latent diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
InfoTok uses mutual information constraints to regularize shared visual tokenization in unified MLLMs, improving both understanding and generation performance without extra training data.
SafeRedir achieves robust unlearning of unsafe concepts in image generation models by adaptively redirecting prompt embeddings toward safe semantic regions at inference time via a multi-modal classifier and token delta generator.
citing papers explorer
-
Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers
Diffusion LLMs can act as their own efficiency teachers by using revokable parallel decoding to identify reliable token orders and then distilling those orders into the model parameters for faster inference.
-
InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
InfoTok uses mutual information constraints to regularize shared visual tokenization in unified MLLMs, improving both understanding and generation performance without extra training data.
-
SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models
SafeRedir achieves robust unlearning of unsafe concepts in image generation models by adaptively redirecting prompt embeddings toward safe semantic regions at inference time via a multi-modal classifier and token delta generator.