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• Conditional Generation of Creative Chess Puzzles with Diffusion Models

  Aatu Selkee, Severi Rissanen, Xidong Feng, Tom Zahavy, Eric Malmi · 1. Oktober 2026 · Artificial Intelligence in Games

  While modern language models demonstrate impressive generative capabilities, they often struggle with constrained, counter-intuitive creative tasks. To address this limitation, we explore chess puzzle generation as a rigorous testbed for computational creativity and reasoning, a domain where alterin…

• Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

  Aditya Thimmaiah, Lara Marinov, Jayanth Srinivasa, Haris Vikalo, Junyi Jessy Li, Milos Gligoric · 30. September 2026 · Error Correcting Code Techniques

  Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constraint. MDLMs generate outputs by repeatedly unmasking masked positions present in their current state. Recent strategies for constrained decoding const…

• The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

  Sravan Karthick T, Pranav Darshan, Pranav A, Minal Moharir, Ivan P. Yamshchikov · 30. September 2026 · Large Language Models

  Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts parametric knowledge, the iterative denoising process becomes a visible battleground between competing knowledge sources. We identify temporal semantic di…

• Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE

  Yu Xu, Yuxin Zhang, Xiao Yang, Haotian Yang, Yizhi Wang, Xinwei Huang, Minxuan Lin, Angtian Wang, Chongyang Ma, Fan Tang · 30. September 2026 · Mobile Crowdsensing and Crowdsourcing

  Mixture-of-Experts (MoE), popularized by large language models, is a promising paradigm for scaling visual generative models. However, conventional token-wise MoE routes tokens independently within a homogeneous expert pool and regularizes expert usage toward uniformity, making it poorly matched to …

• You Can't Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning

  Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran · 29. September 2026 · Domain Adaptation and Few-Shot Learning

  Concept unlearning in text-to-image diffusion models aims to suppress a target concept (e.g., \texttt{horse}) while preserving related but distinct content (e.g., \texttt{donkey}), yet existing methods either leak under indirect prompts or visibly degrade other concepts. We show that these failure m…

• FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models

  Haoyang Li, Ruoxi Sun, Qingqing Ye, Benjamin Zi Hao Zhao, Yaxin Xiao, Jason Xue, Haibo Hu · 28. September 2026 · Advanced Steganography and Watermarking Techniques

  Text-to-image diffusion models enable data-efficient "mimicry" attacks, wherein adversaries fine-tune the model on a handful of public photos to synthesize convincing forgeries of a target individual. A common countermeasure is to embed imperceptible, low-energy watermarks, yet recent studies show t…

• Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models

  Jian Shi, John Femiani, Peter Wonka · 28. September 2026 · Advanced Neuroimaging Techniques and Applications

  We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas of the population it synthesizes. The sampler converges from every random seed to the population's central anatomy, which we call the \emph{intrins…

• Quantum Diffusion Models for Medical Image Analysis

  Francesco Aldo Venturelli, Stefano Martina, Marco Parigi, Filippo Caruso, Alba Cervera-Lierta, Miguel A. Gonz\'alez Ballester · 28. September 2026 · Quantum Computing Algorithms and Architecture

  Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement and interference. In this context, we present a scalable hybrid Quantum Diffusion Model, and evaluate its use for medica…

• Spectral Feedback for Test-Time Alignment of Protein Diffusion Models

  Shai Dickman, Mert Cemri, Landon Butler, Kannan Ramchandran · 28. September 2026 · Generative Adversarial Networks and Image Synthesis

  Reward maximization alignment methods for discrete diffusion models have primarily focused on steering the reverse process, either by influencing token logits or by selecting favorable sequences at intermediate steps. These approaches largely treat inference as a unidirectional process, lacking mech…

• Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model

  Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman, Amirkoushyar Ziabari · 25. September 2026 · Advanced X-ray and CT Imaging

  Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each projection. Reconstructing high-quality volumes from sparse-view or low-dose measurements therefore depends on using an informative prior, …

• CARE: Condition-Aware Representation Regularization for Diffusion Models

  Fengjia Guo, Zhuoyi Yang, Jie Tang · 25. September 2026 · Generative Adversarial Networks and Image Synthesis

  Recent advances in diffusion models highlight the importance of representation regularization for improving sample quality and training efficiency. However, commonly used regularization methods often overlook the built-in conditions (such as labels or texts) which directly determine the generation t…

• FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference

  Gatien S\'eguy (SATIE), Thomas Rodet (SATIE) · 25. September 2026 · Advanced Neuroimaging Techniques and Applications

  Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinverse-Guided Diffusion Models ($\Pi$GDM), rely on scalar hyperparameters tuned per task, usually against the ground truth. We introduce FB-GDM, a fully…

• Localized Diffusion Models

  Georg A. Gottwald, Shuigen Liu, Youssef Marzouk, Sebastian Reich, Xin T. Tong · 24. September 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models are state-of-the-art tools for various generative tasks. Yet training these models involves estimating high-dimensional score functions, a task that in principle suffers from the curse of dimensionality. It is therefore important to understand how low-dimensional structure in the ta…

• Robustness of Diffusion Models under Distribution Shift

  Wei Luo, Neil K. Chada, Shijie Zhang, Lu Yu · 24. September 2026 · Markov Chains and Monte Carlo Methods

  Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely focus on the no-shift setting. In this work, we study robust score estimation under Wasserstein perturbat…

• Discrete Diffusion Models via Evolving Variational Autoregressive Networks

  Kewen Pan, Ying Tang · 24. September 2026 · Advanced Mathematical Modeling in Engineering

  Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both sampling and direct likelihood evaluation. A recent tensor-network approach provides such a representation but is largely restricted to low-dimensional l…

• ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming

  Jiayi Zhang, Renlong Wu, Yukang Ding, Sibin Deng, Wangmeng Zuo · 24. September 2026 · Image Processing Techniques and Applications

  Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recent dual-camera smooth zoom (DCSZ) methods attempt to mitigate this by fine-tuning frame interpolation (FI) models on DCSZ…

• Lifelong Learning of Video Diffusion Models From a Single Video Stream

  Jason Yoo, Yingchen He, Saeid Naderiparizi, Dylan Green, Gido M. van de Ven, Geoff Pleiss, Frank Wood · 23. September 2026 · Advanced Data Compression Techniques

  Video diffusion models can enable embodied agents to anticipate plausible futures from the recent past, but they are typically trained offline on curated datasets--a mismatch with the agents' learning setup at deployment: online, from a single video stream that sequentially outputs one frame at a ti…

• Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models

  Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du, Li Sun, Zhu Han · 23. September 2026 · Wireless Signal Modulation Classification

  The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which a…

• Double Descent and Malign Overfitting in Diffusion Models

  Rapha\"el Urfin, Tony Bonnaire, Giulio Biroli, Marc M\'ezard · 23. September 2026 · Advanced Mathematical Modeling in Engineering

  Conventional wisdom in deep learning holds that overparameterization---having more parameters $p$ than training samples $n$---is benign: larger models generalize better and, even without regularization, interpolating models generalize well, the test error following a double-descent curve. One might …

• Mean Velocity Matching: Rethinking Generative Dynamics in Diffusion Models

  Yunhong Zhang, Changjie Cao, Zhihua Zhang, Bingli Liu, Zongjie Cao, Zongyong Cui, Ying Yang · 23. September 2026 · Generative Adversarial Networks and Image Synthesis

  This work studies prediction parameterization for stochastic generative dynamics in diffusion models. Existing velocity-based generative models provide the simplicity of learning a single transport field, but their standard formulation is deterministic, whereas stochastic extensions generally requir…

• Optimizers for Diffusion Models: A Controlled Benchmark

  Arman Bolatov, Egor Shulgin, David Li, Abduragim Shtanchaev, Sebastian U. Stich, Maxim Panov, Eric Moulines, Peter Richt\'arik, Martin Tak\'a\v{c} · 22. September 2026 · Advanced Mathematical Modeling in Engineering

  Discrete diffusion models now match autoregressive language models on several benchmarks, while the question of how best to train them has received far less attention: the optimizer is inherited from one paper to the next and never compared. New optimizers, meanwhile, are validated almost exclusivel…

• Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories

  Yuan Cao, Yifu Tang, Hangqi Li, Zeyu Zheng · 22. September 2026 · Advanced Mathematical Modeling in Engineering

  Diffusion models generate a sample by traversing a denoising trajectory, a sequence of stochastic noise-reduction steps that transforms pure noise into a draw from a target distribution. At deployment time, additional computation can improve sample quality without retraining: at each step, the sampl…

• Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting

  Temesgen Mikael Abraha, Yves Lucet · 22. September 2026 · Forecasting Techniques and Applications

  Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history. We study this problem in oil and gas production forecasting, where forecasts made after roughly the first fifth of a well's producing life drive devel…

• Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

  Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao, Martial Hebert, Yu-Xiong Wang · 22. September 2026 · Neural Networks and Applications

  Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or a…

• Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms

  Yueyan Li, Haibo Wang, Caixia Yuan, Xiaojie Wang · 22. September 2026 · Human Motion and Animation

  Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing solutions rely on external priors or specialized data, we investigate the root cause by exploring the internal mechanisms of these models. Specific…