arXiv Machine Learning
Trust but Verify: Designing Region-Inhomogeneous Forward Dynamics for Discrete Diffusion
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arXiv AI
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UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective
arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.
arXiv Machine Learning
1d ago
Corrective Diffusion Language Models
The paper introduces Corrective Diffusion Language Models (CDLMs), which enhance masked diffusion language models by explicitly supervising visible corrupted tokens to enable confidence-guided refinement of incorrect tokens. Experiments show that this correction-oriented post‑training improves iterative refinement and parallel decoding across models ranging from 0.5B to 8B, outperforming standard masked‑diffusion fine‑tuning on benchmarks such as Code Revision, HumanEval(+), and MATH. The authors also provide a new Code Revision Benchmark and release code and data for reproducibility.
arXiv AI
Jun 19
How Transparent is DiffusionGemma?
arXiv:2606. 20560v1 Announce Type: cross Abstract: LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors.
arXiv AI
Aug 13
CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference
arXiv:2608. 11235v1 Announce Type: new Abstract: Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon.
arXiv Machine Learning
Jun 3
Backdooring Masked Diffusion Language Models
arXiv:2605. 19262v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) are emerging as a compelling new paradigm for text generation, but their training-time security remains largely unexplored.
arXiv Computation and Language
Aug 21
Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Language Models
arXiv:2604. 18738v3 Announce Type: replace Abstract: Diffusion language models (dLLMs) generate text through iterative denoising, filling multiple masked positions at each step.