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This paper introduces LEAP, a training-free method to accelerate inference in Diffusion Language Models (dLLMs) by detecting early-converging tokens, reducing denoising steps by 30% without losing accuracy.
This paper introduces TABOM, a self-distilled trajectory-based post-training framework for Diffusion Language Models that aligns training with inference trajectories using Boltzmann modeling to mitigate the training-inference discrepancy and reduce catastrophic forgetting.
This paper introduces Repr-Align, a method to adapt autoregressive language models into diffusion language models via representation alignment, achieving up to 4x training acceleration without retraining representations from scratch.
This paper introduces DiffRetriever, a method that uses diffusion language models to generate multiple representative tokens in parallel for efficient information retrieval, outperforming autoregressive baselines in speed and accuracy.
DALM proposes a domain-algebraic language model that generates text under exact structural constraints derived from a domain lattice, addressing hallucination by organizing knowledge into separate domain fibers with algebraic guarantees. The model uses three-phase structured denoising (domain → relation → concept) with domain-annotated training data to prevent cross-domain contamination.