Search: diffusion models

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

• HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models

  Guoshenghui Zhao, Tan Yu, Weijie Zhao · 30 April 2026 · Large Language Models

  Diffusion large language models generate text through multi-step denoising, where hallucination signals may emerge throughout the trajectory rather than only in the final output. Existing detectors mainly rely on output uncertainty or coarse trace statistics, which often fail to capture the richer h…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models

  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · 30 April 2026 · Large Language Models

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross-arch…

• Scaling Properties of Continuous Diffusion Spoken Language Models

  Jason Ramapuram, Eeshan Gunesh Dhekane, Amitis Shidani, Dan Busbridge, Bogdan Mazoure, Zijin Gu, Russ Webb, Tatiana Likhomanenko, Navdeep Jaitly · 28 April 2026 · Speech Recognition and Synthesis

  Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational and data demands to match text models. Since discretizing continuous speech for AR creates bottlenecks, we explore wheth…

• LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model

  Inclusion AI, Tiwei Bie, Haoxing Chen, Tieyuan Chen, Zhenglin Cheng, Long Cui, Kai Gan, Zhicheng Huang, Zhenzhong Lan, Haoquan Li, Jianguo Li, Tao Lin, Qi Qin, Hongjun Wang, Xiaomei Wang, Haoyuan Wu, Yi Xin, Junbo Zhao · 23 April 2026 · Multimodal Machine Learning Applications

  We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a natively integrated framework. Its architecture combines a fully semantic discrete tokenizer, a MoE-based dLLM backbone, and a diffusion decoder. By discre…

• Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models

  Lin Yao · 22 April 2026 · Natural Language Processing Techniques

  Diffusion large language models (dLLMs) gain speed by committing multiple tokens in parallel at each denoising step, but any erroneous commitment persists as conditioning context and biases every subsequent prediction. LLaDA2.1 repairs such errors with Token-to-Token (T2T) editing, which re-examines…

• $R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

  Zhenbang Du, Kejing Xia, Xinrui Zhong, Yonggan Fu, Nicolai Oswald, Binfei Ji, Brucek Khailany, Pavlo Molchanov, Yingyan Lin · 22 April 2026 · Large Language Models

  Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding still suffers from high inference latency, which limits deployment. In this work, we observe that a substantial part of…

• Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models

  Zhengnan Guo, Fei Tan · 14 April 2026 · Mental Health via Writing

  While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their faithfulness, specifically regarding hallucination, remains largely underexplored. To bridge this gap, we present the first controlled comparative stu…

• Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models

  Shun Zou, Yong Wang, Zehui Chen, Lin Chen, Chongyang Tao, Feng Zhao, Xiangxiang Chu · 13 April 2026 · Large Language Models

  Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive (Semi-AR) decoding is widely employed in base dLLMs and advanced decoding strategies due to its superior performance. However, our observations rev…

• Advantage-Guided Diffusion for Model-Based Reinforcement Learning

  Daniele Foffano, Arvid Eriksson, David Broman, Karl H. Johansson, Alexandre Proutiere · 13 April 2026 · Reinforcement Learning in Robotics

  Model-based reinforcement learning (MBRL) with autoregressive world models suffers from compounding errors, whereas diffusion world models mitigate this by generating trajectory segments jointly. However, existing diffusion guides are either policy-only, discarding value information, or reward-based…

• From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits

  Xiaoning Wang, Ted Underwood, Zhewei Sun · 23 September 2026 · Language and cultural evolution

  Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phrases such as Canon Event meaning an embarrassing but pivotal event, go viral online, it becomes increasingly important to…

• Reliability Challenges in Diffusion Vision-Language Models

  Md. Atabuzzaman, Chris Thomas · 2 September 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion-based Large Vision-Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advantages in parallel decoding, bidirectional context, and controllable generation. Despite rapid progress, their reliability properties remain largely unch…

• StereoDiffuer: Diffusion-based Progressive Geometry Modeling with Saliency Attention Perception for Stereo Matching

  Bohan Li · 25 August 2026 · Advanced Vision and Imaging

  With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stereo matching methods still struggle to preserve fine-grained geometric details, resulting in blurred edges and over-smoothed predictions in challenging…

• Understanding Why Foundation Models Work for Diffusion-Generated Image Detection

  Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva · 13 August 2026 · Digital Media Forensic Detection

  Vision foundation models have recently emerged as powerful feature extractors for detecting AI-generated images, achieving strong generalization across generators and robustness to common image degradations. However, the reason behind their effectiveness is poorly understood. In this work, we invest…

• CSGen: A Multi-Domain Curvilinear Structure Generation Model via Hierarchical Multimodal Diffusion

  Zhe Shan, Ziming Yang, Lei Zhou, Wenwen Zhang, Cong Lin, Xia Xie · 6 August 2026 · Generative Adversarial Networks and Image Synthesis

  Curvilinear structure analysis is an important and fundamental task in multimedia. However, the controllable generation of images with precise curvilinear structure objects remains an open challenge. To address this, we propose CSGen, a hierarchical multimodal diffusion model that synthesizes high-f…

• DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models

  Yongkang Zhou, Xiang Xia, Cheng Yan, Fan Xu, Wuyang Zhang · 4 August 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial recurring inference cost. Unlike autoregressive decoding, diffusion generation repeatedly revisits the entire response as …

• One-step lowest-variance selection in a Gaussian random-field model motivated by masked diffusion: Total correlation and a square root collision threshold

  Linjun Li · 21 July 2026 · Diffusion and Search Dynamics

  Motivated by confidence-guided parallel unmasking in masked discrete diffusion, we study a single selection step in a stylized Gaussian random-field model. A locally dependent nonnegative score field represents position wise uncertainty, and the scheduler selects the K positions with the smallest sc…

• Diffusion-Driven State Space Models

  Jack Ruder, Michael Wojnowicz · 23 June 2026 · Gaussian Processes and Bayesian Inference

  In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typically sacrifice one of these goals: deep state space models often assume Gaussian latent transitions, limiting fit and forecasting, while diffusion mo…

• MODIP: Efficient Model-Based Optimization for Diffusion Policies

  Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome, Olivier Sigaud · 10 June 2026 · Reinforcement Learning in Robotics

  Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However, while their success has largely been confined to BC, direct reinforcement learning (RL) fine-tuning remains challenging …

• TBD-VLA: Temporal Block Diffusion Vision Language Action Model

  Sung-Wook Lee, Xuhui Kang, Yen-Ling Kuo · 9 June 2026 · Multimodal Machine Learning Applications

  Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores the temporal structu…

• FLARE: Diffusion for Hybrid Language Model

  Yuchen Zhu, Jing Shi, Chongjian Ge, Hao Tan, Yiran Xu, Wanrong Zhu, Jason Kuen, Koustava Goswami, Rajiv Jain, Yongxin Chen, Molei Tao, Jiuxiang Gu · 2 June 2026 · Large Language Models

  Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-latency deployment. Recent efficient-inference work has progressed along two axes: reducing the cost of each model invocation through efficient architectur…

• Masked Diffusion Vision-Language Models for Temporal Action Localization

  Fengshun Wang, Zhengbo Zhang, Zhigang Tu · 29 May 2026 · Human Pose and Action Recognition

  Temporal action localization (TAL) requires recognizing the target event and localizing its start and end times precisely in untrimmed videos. Recent vision-language formulations improve semantic reasoning and support language-conditioned outputs, but their autoregressive decoders still generate tok…

• Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization

  Xiaoyuan Cheng, Wenxuan Yuan, Zhancun Mu, Yuanzhao Zhang, Yiming Yang, Hai Wang, Zhuo Sun, Che Liu · 27 May 2026 · Reinforcement Learning in Robotics

  Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approaches remains fundamentally limited. A commonly recognized challenge is model bias and error compounding, which degrade long-horizon predictions. Bey…

• Diffusion and Flow Matching Models for Tabular Data: A Survey

  Zhong Li, Qi Huang, Lincen Yang, Jiayang Shi, Zhao Yang, Niki van Stein, Thomas B\"ack, Matthijs van Leeuwen · 25 May 2026 · Data Management and Algorithms

  Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, generative modeling remains difficult: a dataset may contain numerical and categorical attributes, missing values, sensiti…

• FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction

  Qi Si, Penglei Wang, Yushuai Wu, Yifeng Jiao, Xuyang Liu, Xin Guo, Yuan Qi, Yuan Cheng · 19 May 2026 · Single-cell and spatial transcriptomics

  Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce …

• SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer

  Haoyi Zhu, Haozhe Liu, Yuyang Zhao, Tian Ye, Junsong Chen, Jincheng Yu, Tong He, Song Han, Enze Xie · 15 May 2026 · Human Pose and Action Recognition

  We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p, minute-scale videos with precise camera control. SANA-WM achieves visual quality comparable to large-scale industrial baselines such as LingBot-Worl…