Search: diffusion models

More than 1,000 papers match: here are the 1,000 most recent, ranked by relevance.

• STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models

  Xin Yan, Aqiang Wang, Zhenglin Wan, Xingrui Yuand Ivor Tsang · 4 June 2026 · Advanced Neural Network Applications

  Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context. However, their large model sizes and iterative denoising process introduce substantial memory and computat…

• Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models

  Boyan Han, Yiwei Wang, Yi Song, Yujun Cai, Chi Zhang · 4 June 2026 · Large Language Models

  Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates. While straightforward fixed anchors can enforce such constraints, they oft…

• MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

  Yingzi Ma, Zhengyue Zhao, Xiaogeng Liu, Minhui Xue, Yue Zhao, Chaowei Xiao · 4 June 2026 · Computational and Text Analysis Methods

  Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs. Because mask tokens are native inputs and tokens are committed by confidence rather than position, harmf…

• dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching

  Zhiyuan Liu, Yicun Yang, Yaojie Zhang, Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, Yichen Zhu, Linfeng Zhang · 3 June 2026 · Generative Adversarial Networks and Image Synthesis

  Autoregressive Models (ARMs) have long dominated the landscape of Large Language Models. Recently, a new paradigm has emerged in the form of diffusion-based Large Language Models (dLLMs), which generate text by iteratively denoising masked segments. This approach has shown significant advantages and…

• A physics-informed foundation model for quantitative diffusion MRI

  Zihan Li, Jialan Zheng, Ziyu Li, Xun Yuan, Kasidit Anmahapong, Ziang Wang, Mingxuan Liu, Hongjia Yang, Yifei Chen, Zhuhao Wang, Yuhang He, Fang Chen, Rui Li, Huaiqiang Sun, Yi Liao, Congyu Liao, Yang Yang, Haibo Qu, Xue Zhang, Hongen Liao, Qiyuan Tian · 2 June 2026 · Advanced Neuroimaging Techniques and Applications

  Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring…

• Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models

  Nianyi Lin, Jiajie Zhang, Lei Hou, Juanzi Li · 1 June 2026 · Reinforcement Learning in Robotics

  A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) is the intractability of their likelihood functions, which are essential for the RL objective, necessitating corresponding approximation during training. While existing methods approximate the log-like…

• Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion

  Yizhuo Lu, Changde Du, Qingyu Shi, Hang Chen, Jie Peng, Liuyun Jiang, Shuangchen Zhao, Huiguang He · 29 May 2026 · EEG and Brain-Computer Interfaces

  Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior work is the prevailing paradigm of specialized, single-task models, which curtails versatility and neglects inter-task syne…

• Diffusion Large Language Models for Visual Speech Recognition

  Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha, Yong Man Ro · 28 May 2026 · Speech and Audio Processing

  Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on visually ambiguous tokens before sufficient context is available. We propose DLLM-VSR, to the best of our knowledge, the first Diffusion Large Language Mode…

• CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit

  Kangyu Wang, Zhiyun Jiang, Haibo Feng, Weijia Zhao, Lin Liu, Jianguo Li, Zhenzhong Lan, Weiyao Lin · 27 May 2026 · Natural Language Processing Techniques

  Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions while remasking the others. By analyzing dLLM denoising traces, we uncover a key inefficiency: models often predict the …

• How Accurate are Video Quality Models for Diffusion-Based Video Super-Resolution?

  Benjamin Herb, Steve Göring, Alexander Raake, Rakesh Rao Ramachandra Rao · 26 May 2026 · Image and Video Quality Assessment

  Recent video super-resolution (VSR) approaches use deep neural networks to enhance low-quality input videos and recover visual detail, with diffusion-based methods in particular showing promising results. In this paper, we investigate whether existing video quality models can be used to assess the p…

• DRM: Diffusion-based Reward Model With Step-wise Guidance

  Jaxon Zhang, Binxin Yang, Hubery Yin, Chen Li, Jing Lyu · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models. However, these reward models, pre-trained for semantic alignment, struggle to capture the essential perceptual qualities-such as aesthetics, composition, and visual harmony. In th…

• Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models

  Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani · 26 May 2026 · Generative Adversarial Networks and Image Synthesis

  We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distr…

• VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling

  Fatemeh Ghasemi Balouei, Nathan Willemsen, Mahesh Banavar, Bahman Moraffah · 25 May 2026 · Non-Invasive Vital Sign Monitoring

  Photoplethysmography (PPG) has become a ubiquitous physiological signal; however, current generative models still struggle to preserve realistic waveform morphology and learn a latent structure that captures cardiac and respiratory physiology. PPG generators trained with adversarial losses can produ…

• DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling

  Jean-Marie Lemercier, Tomas Geffner, Karsten Kreis, Morteza Mardani, Arash Vahdat, Ante Juki\'c · 25 May 2026 · Large Language Models

  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 issue, we propose DiLaDiff, a variant of masked diffusion language models with three components: (1) a continuous laten…

• FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation

  Runzhe Zhang, Letian Chen, Wenpeng Zhang, Zhouhan Lin, Peilin Zhao · 22 May 2026 · Large Language Models

  We present FlowLM, a flow matching language model transformed from pre-trained diffusion language models via efficient fine-tuning. By re-aligning the curved sampling trajectories of diffusion models into straight-line flows, FlowLM enables high quality few-step generation that rivals or even outper…

• Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images

  Liangrui Pan, Jiadi Luo, Yuxuan Xiao, Chenchen Nie, Xiaoshuai Wu, Songqing Fan, Ling Chu, Manqiu Li, Rongfang He, Zhenyu Zhao, Ruixing Wang, Shulin Liu, Yiyi Liang, Xiang Wang, Qingchun Liang, Shaoliang Peng · 18 May 2026 · AI in cancer detection

  Accurate intraoperative and postoperative diagnosis of spread through air spaces (STAS) is essential for guiding surgical decisions and postoperative management in lung cancer. However, histopathological assessment is labor-intensive and is prone to missed or incorrect diagnoses. We propose a Diffus…

• PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution

  Zihang Xu, Xiaoyang Liu, Zheng Chen, Yulun Zhang, Xiaokang Yang · 14 May 2026 · Advanced Image Processing Techniques

  Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character identity and break readability. Existing methods improve text fidelity with stronger recognition-based or generative priors, yet they still face two unre…

• DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions

  Zongyue Li, Xiao Han, Yusong Li, Niklas Strauss, Matthias Schubert · 14 May 2026 · Reinforcement Learning in Robotics

  Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforcement learning (RL). However, many existing methods do not directly generate actions alongside states and rewards, limiting their compatibility with standard val…

• Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models

  Yan Jiang, Ruihong Qiu, Zi Huang · 13 May 2026 · Domain Adaptation and Few-Shot Learning

  Recently, reinforcement learning (RL) has been widely applied during post-training for diffusion large language models (dLLMs) to enhance reasoning with block-wise semi-autoregressive generation. Block size has therefore become a vital factor in dLLMs, since it determines the parallel decoding granu…

• GPO-V: Jailbreak Diffusion Vision Language Model by Global Probability Optimization

  Yu Pan, Andi Zhang, Yi Wang, Sibei Yang, Wenjie Wang · 11 May 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion Vision-Language Models (dVLMs), built upon the non-causal foundations of Diffusion Large Language Models (dLLMs), have demonstrated remarkable efficacy in multimodal tasks by departing from the traditional autoregressive generation paradigm. While dVLMs appear inherently robust against con…

• CoBit: Language Modeling with Bitstream Diffusion

  Georgios Batzolis, Mark Girolami, Luca Ambrogioni · 11 May 2026 · Large Language Models

  Diffusion language models (DLMs) promise parallel, order-agnostic generation, but on standard benchmarks they have historically lagged behind autoregressive models in sample quality and diversity. Recent continuous flow and diffusion approaches have narrowed this gap. In this work, we further close …

• Token Time Continuous Diffusion for Language Modeling

  Parikshit Bansal, Sujay Sanghavi · 8 May 2026 · Large Language Models

  In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with so…

• Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast

  Jinyuan Feng, Xin Yu, Yiqun Chen, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Zhiqiang Pu · 6 May 2026 · Large Language Models

  The iterative denoising paradigm of Diffusion Large Language Models (DLMs) endows them with a distinct advantage in global context modeling. However, current decoding strategies fail to leverage this capability, typically exhibiting a local preference that overlooks the heterogeneous information den…

• Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning

  Yan Jiang, Ruihong Qiu, Zi Huang · 5 May 2026 · Large Language Models

  Recent diffusion large language models (dLLMs) have demonstrated both effectiveness and efficiency in reasoning via a block-based semi-autoregressive generation paradigm. Despite their progress, the fixed-size block generations remain a critical bottleneck for effective and coherent reasoning. 1. Fr…

• A unified perspective on fine-tuning and sampling with diffusion and flow models

  Carles Domingo-Enrich, Yuanqi Du, Michael S. Albergo · 4 May 2026 · Stochastic Gradient Optimization Techniques

  We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base density; a formulation that subsumes both sampling from unnormalized densities and reward fine-tuning of pre-trained models. This problem can be appro…