R2LM combines causal attention with a reverse Mamba SSM sidecar to supply right-side context in dLLMs, claiming 2.4x-12.9x throughput gains over bidirectional dLLMs and 1.9x-2.9x over AR baselines while matching or exceeding quality.
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6 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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DiBS ranks candidate values with a diffusion model inside a symbolic Sudoku solver to reduce nodes, backtracks, and long-tail search cost on the Royle 17-clue benchmark.
Learned Relay Representations add a differentiable per-token channel to masked diffusion models so they can propagate latent information across iterative denoising steps, yielding better coding performance and up to 32% lower latency on Fast-dLLM v2 than standard supervised finetuning.
GraphBSI uses Bayesian Sample Inference as noise-controlled SDEs to generate discrete graphs in one shot, achieving state-of-the-art results on molecular benchmarks Moses and GuacaMol.
The paper proposes CDCD, a continuous-time and continuous-space diffusion framework for categorical data, and reports results on language modeling tasks.
An educational exposition that layers core definitions, simplified estimates, and research-level theorems on diffusion sampling for probability-background graduate students.
citing papers explorer
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Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
R2LM combines causal attention with a reverse Mamba SSM sidecar to supply right-side context in dLLMs, claiming 2.4x-12.9x throughput gains over bidirectional dLLMs and 1.9x-2.9x over AR baselines while matching or exceeding quality.
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DiBS: Diffusion-Informed Branch Selection
DiBS ranks candidate values with a diffusion model inside a symbolic Sudoku solver to reduce nodes, backtracks, and long-tail search cost on the Royle 17-clue benchmark.
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Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
Learned Relay Representations add a differentiable per-token channel to masked diffusion models so they can propagate latent information across iterative denoising steps, yielding better coding performance and up to 32% lower latency on Fast-dLLM v2 than standard supervised finetuning.
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Discrete Bayesian Sample Inference for Graph Generation
GraphBSI uses Bayesian Sample Inference as noise-controlled SDEs to generate discrete graphs in one shot, achieving state-of-the-art results on molecular benchmarks Moses and GuacaMol.
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Continuous diffusion for categorical data
The paper proposes CDCD, a continuous-time and continuous-space diffusion framework for categorical data, and reports results on language modeling tasks.
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A Mathematical Introduction to Diffusion Models
An educational exposition that layers core definitions, simplified estimates, and research-level theorems on diffusion sampling for probability-background graduate students.