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