12 papers
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
Generative Model Proposal based Particle Filtering for Data Assimilation
Chandni Nagda, Mayank Shrivastava, Gudrun Thorkelsdottir +3
Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the pos…
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithf…
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
Jean-Marie Lemercier, Tomas Geffner, Karsten Kreis +3
Diffusion language models intrinsically fail to capture correlations between decoded tokens, which leads to a harsh trade-off between sampling quality and throughput. To solve this…
Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
Jaihoon Kim, Taehoon Yoon, Prin Phunyaphibarn +3
Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a f…
Continuous Diffusion Scales Competitively with Discrete Diffusion for Language
Zhihan Yang, Wei Guo, Shuibai Zhang +5
While diffusion has drawn considerable recent attention from the language modeling community, continuous diffusion has appeared less scalable than discrete approaches. To challenge…