Ambient Dataloops is an iterative framework that refines datasets to improve diffusion model training. By co‑evolving the dataset and the model, each iteration produces higher‑quality data while the model learns to handle slightly less noisy synthetic samples using Ambient Diffusion techniques. The approach achieves state‑of‑the‑art results in unconditional and text‑conditional image generation as well as de novo protein design, and the authors provide a theoretical justification for the benefits of the looping procedure.
By Adri\'an Rodr\'iguez-Mu\~noz, William Daspit, Adam Klivans, Antonio Torralba, Constantinos Daskalakis, Giannis Daras
D-DOIT (Discrete Doob-Oriented Inference-time Transformation) is a training‑free method that adapts discrete diffusion models to generic rewards by applying Doob's h‑transform to the reverse diffusion kernel. It reweights reverse transition probabilities using only reward values, avoiding costly future rollouts, and optionally refines trajectories near the end with a best‑of‑K strategy. Experiments on regulatory DNA design and protein inverse folding show that D-DOIT outperforms other training‑free guidance baselines, improving enhancer activity, cell‑type specificity, and protein folding success rates while maintaining sequence naturalness.
By Jieke Wu, Qijie Zhu, Weimin Wu, Zeqi Ye, Minshuo Chen, Han Liu
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause
The paper introduces Distribution‑Conditioned Transport (DCT), a framework that learns transport maps conditioned on embeddings of source and target distributions, allowing generalization to unseen distribution pairs. DCT supports semi‑supervised learning for distributional forecasting by leveraging distributions observed at only one condition. It is agnostic to the transport mechanism and is demonstrated on synthetic benchmarks and four biological applications, including batch effect transfer in single‑cell genomics and modeling T‑cell receptor sequence evolution.
By Nic Fishman, Gokul Gowri, Paolo L. B. Fischer, Marinka Zitnik, Omar Abudayyeh, Jonathan Gootenberg
The paper introduces Less Uniform Diffusion (LUDI), a framework that improves uniform diffusion language models by using a less uniform loss and per-token time embeddings to guide reverse transitions and enable confidence-based few-step sampling. Experiments demonstrate that LUDI provides cleaner supervision, enhances few-step generation, and scales to a 7B model (LUDI-7B) that achieves a 3-token-per-step speedup over autoregressive decoding while matching masked diffusion baselines. The work suggests that UDLMs still have untapped potential for complex generation tasks.
By Kaibo Wang, Ding Ding, Fangyu Ding, Zijin Feng, Han Shi, Haili Bai, Jiacheng Sun, Yang Xiang
Spectral Feedback is a new algorithm for aligning discrete diffusion models at test time by iteratively revisiting and editing token positions rather than only steering the reverse process. It selects edit-sets—groups of token positions to re-mask and re-sample—using sparse Fourier representations of edit-set value functions, enabling efficient optimization of which tokens to revisit. The method is model-agnostic and improves alignment performance across pretrained, test‑time aligned, and fine‑tuned diffusion models, achieving significant gains in protein stability for inverse folding tasks.
By Shai Dickman, Mert Cemri, Landon Butler, Kannan Ramchandran
arXiv:2610.00873v1 Announce Type: cross
Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between...
By Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan, Kunpeng Liu, Haifeng Chen, Yanjie Fu
arXiv:2606. 00295v1 Announce Type: new Abstract: Masked diffusion models have seen great success in capturing data distributions over discrete sequences in domains such as text and proteins.
By Jama Hussein Mohamud, Mohsin Hasan, Mirco Ravanelli, Yoshua Bengio
arXiv:2606. 01833v1 Announce Type: cross Abstract: Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation.
By Kaihui Cheng, Zhiqiang Cai, Wenkai Xiang, Zhihang Hu, Siyu Zhu, Tzuhsiung Yang, Yuan Qi
arXiv:2602. 11590v3 Announce Type: replace Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance.
By Yair Schiff, Omer Belhasin, Roy Uziel, Guanghan Wang, Marianne Arriola, Gilad Turok, Ran Zilberstein, Michael Elad, Volodymyr Kuleshov
arXiv:2310. 05264v5 Announce Type: replace Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.
By Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu