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20222026
most citedeDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

223 citations · 727 across the 52 of their papers we have counts for

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10 papers · 1 filter

cs.CL2026

Large Language Continuous Diffusion Models

Zhihan Yang, Wei Guo, Jean-Marie Lemercier +14

Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and…

cs.CV2026

Scaling Reinforcement Learning for Diffusion Models via Velocity Matching

Jaemoo Choi, Wei Guo, Yuchen Zhu +4

Reward-based fine-tuning of diffusion models has largely inherited likelihood-based policy optimization developed for autoregressive large language models (LLMs). Diffusion models,…

cs.CV2026

Parallel Decoding Distillation for Fast Image and Video Generation

Neta Shaul, Chao Liu, Arash Vahdat +1

Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavi…

cs.CV2026

Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model

Xinyin Ma, Julius Berner, Chao Liu +3

Recent progress in large-scale generative models has substantially advanced video generation, yet existing methods remain constrained by a rigid inference paradigm. Bidirectional d…

cs.LG2026

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…

cs.CL2026

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…

cs.LG2026★ 1 cited

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Kieran Didi, Zuobai Zhang, Guoqing Zhou +11

Protein interaction modeling is central to protein design, which has been transformed by machine learning with applications in drug discovery and beyond. In this landscape, structu…

cs.CV2026

Mode Seeking meets Mean Seeking for Fast Long Video Generation

Shengqu Cai, Weili Nie, Chao Liu +8

Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to…

cs.LG2026

Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting

Jean Kossaifi, Nikola Kovachki, Morteza Mardani +15

The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fun…

cs.CV2026

Transition Matching Distillation for Fast Video Generation

Weili Nie, Julius Berner, Nanye Ma +3

Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to…