Uniform diffusion models rely on a leave-one-out denoiser rather than the usual denoising posterior, with exact conversions derived; an absorbing-state reformulation is introduced that matches or exceeds masked diffusion on language modeling while preserving the original joint distribution.
Generalized interpolating discrete diffusion
8 Pith papers cite this work. Polarity classification is still indexing.
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Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
DMax uses On-Policy Uniform Training and Soft Parallel Decoding to enable aggressive parallelism in dLLMs, raising TPF on GSM8K from 2.04 to 5.47 and on MBPP from 2.71 to 5.86 while preserving accuracy.
CCDD defines a joint multimodal diffusion on continuous representation space and discrete token space to combine expressivity with explicit token supervision for diffusion language models.
Cola DLM proposes a hierarchical latent diffusion model that learns a text-to-latent mapping, fits a global semantic prior in continuous space with a block-causal DiT, and performs conditional decoding, establishing latent prior modeling as an alternative to token-level autoregressive language model
Progressive supervised block merging plus stage-wise intra-diffusion distillation converts Qwen3-VL into a large-block dVLM with claimed SOTA open-dVLM scores and up to 3× decoding speedup on ≤4.4M data.
Introduces a standardized evaluation setup and SQL-D1 agent for diffusion language models on NL2SQL, claiming structural robustness advantages over autoregressive models.
Re-evaluation finds post-hoc remasking (WINO) yields little-to-no gain over confidence unmasking in standard dLLM settings and can worsen diversity collapse under stochastic decoding.
citing papers explorer
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Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
Uniform diffusion models rely on a leave-one-out denoiser rather than the usual denoising posterior, with exact conversions derived; an absorbing-state reformulation is introduced that matches or exceeds masked diffusion on language modeling while preserving the original joint distribution.
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Support Before Frequency in Discrete Diffusion
Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
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DMax: Aggressive Parallel Decoding for dLLMs
DMax uses On-Policy Uniform Training and Soft Parallel Decoding to enable aggressive parallelism in dLLMs, raising TPF on GSM8K from 2.04 to 5.47 and on MBPP from 2.71 to 5.86 while preserving accuracy.
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Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
CCDD defines a joint multimodal diffusion on continuous representation space and discrete token space to combine expressivity with explicit token supervision for diffusion language models.
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Continuous Latent Diffusion Language Model
Cola DLM proposes a hierarchical latent diffusion model that learns a text-to-latent mapping, fits a global semantic prior in continuous space with a block-causal DiT, and performs conditional decoding, establishing latent prior modeling as an alternative to token-level autoregressive language model
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BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation
Progressive supervised block merging plus stage-wise intra-diffusion distillation converts Qwen3-VL into a large-block dVLM with claimed SOTA open-dVLM scores and up to 3× decoding speedup on ≤4.4M data.
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Are Diffusion Language Models Good Database Analysts?
Introduces a standardized evaluation setup and SQL-D1 agent for diffusion language models on NL2SQL, claiming structural robustness advantages over autoregressive models.
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Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Re-evaluation finds post-hoc remasking (WINO) yields little-to-no gain over confidence unmasking in standard dLLM settings and can worsen diversity collapse under stochastic decoding.