提出无近似、无需训练的粒子滤波方法用于扩散代理模型，被ICML 2026接收，开创性极强。
arXiv:2605.18745v1 Announce Type: cross Abstract: Diffusion-based generative models increasingly rely on inference-time guidance, adding a drift term …
非CUDA平台优化新突破：在Apple M3 Ultra上对扩散模型推理进行10阶段系统实验，揭示实时图像生成加速方案。
arXiv:2605.16259v1 Announce Type: new Abstract: While real-time image generation using diffusion models has advanced rapidly on NVIDIA GPUs, systemati…
医学影像生成新挑战：揭示Latent Diffusion模型在医学数据上的可学习性差距，推动更可靠的AI诊断工具发展。
arXiv:2605.17087v1 Announce Type: new Abstract: Generative data augmentation with latent diffusion models is a promising strategy for addressing class…
稀疏点云条件3D扩散模型中的电路与分岔现象，揭示安全关键重建的底层机制
arXiv:2602.11130v2 Announce Type: replace Abstract: Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety…
提出抑制散度的耦合方法用于整流流，改善生成模型的稳定性和效率
arXiv:2605.17733v1 Announce Type: cross Abstract: The promise of Rectified Flow rests on producing self-generated couplings whose trajectories are str…
一篇关于几何视图扩散模型的论文，提出景观感知方法提升3D场景重建质量，创新融合空间几何与扩散先验。
arXiv:2605.19865v1 Announce Type: new Abstract: Accurate camera viewpoint estimation under sparse-view conditions remains challenging, particularly in…
通过缝合价值函数实现扩散模型高效对齐，显著提升生成图像质量与一致性，为AI生成提供新范式
arXiv:2605.19804v1 Announce Type: cross Abstract: For practical use, diffusion- or flow-based generative models must be aligned with task-specific rew…
用扩散模型实现免训练贝叶斯滤波，解决高维粒子滤波扩展性难题
arXiv:2605.20028v1 Announce Type: new Abstract: Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical syste…
将语义解耦与LLM推理、扩散生成融合，实现通用图像编码新范式。
arXiv:2412.18158v2 Announce Type: replace Abstract: Learned image compression methods have shown impressive performance but are often highly specializ…
图扩散模型首次用于动态生成多LLM智能体通信拓扑，提升协作效率。
arXiv:2510.07799v2 Announce Type: replace Abstract: The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on the…
扩散LLM无需外部教师，通过“展开回退”策略自我提升推理效率，开辟模型加速新方向。
arXiv:2605.16941v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still …
介绍一种针对掩码扩散大语言模型的离散倾斜匹配方法，解决RL微调中边际似然难解问题。
arXiv:2604.18739v2 Announce Type: replace Abstract: Masked diffusion large language models (dLLMs) are a promising alternative to autoregressive gener…
无需训练即可校正扩散模型后验，解决高密度区域外采样难题的新方法。
arXiv:2507.05482v3 Announce Type: replace Abstract: Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifi…
几何引导的隐藏状态替换，揭秘扩散模型在语言模型中的最佳插入位置，DiHAL创新方案提升性能。
arXiv:2605.14368v1 Announce Type: cross Abstract: Continuous diffusion language models lag behind autoregressive transformers, partly because diffusio…
ICLR 2026接受的论文，用近端优化改进扩散模型，实现更高效的神经采样器，适合机器学习研究者。
arXiv:2510.03824v2 Announce Type: replace Abstract: The task of learning a diffusion-based neural sampler for drawing samples from an unnormalized tar…
快速访问arXiv上这篇融合记忆专家与扩散世界模型的前沿论文，一键获取AI研究最新突破与全文PDF。
arXiv:2605.18813v1 Announce Type: new Abstract: World models aim to predict plausible futures consistent with past observations, a capability central …
一份系统梳理图到视频扩散模型发展的综述，从理论基础到前沿开放问题，助研究者快速把握领域脉络。
arXiv:2605.17248v1 Announce Type: new Abstract: Diffusion-based \textit{image-to-video} (I2V) generation has become a central direction in generative …
新能量模型破解生成扩散模型两大难题，实现线性逆问题无偏采样。
arXiv:2605.15487v1 Announce Type: new Abstract: Generative diffusion models can provide powerful prior probability models for inverse problems in imag…
商空间扩散模型：用商空间思想巧妙处理系统对称性，实现更高效、保对称性的分子3D结构生成。
arXiv:2604.21809v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have reformed generative AI, and also enabled new capabili…
基于扩散模型的文本生成新方法，通过在token嵌入上平滑去噪，实现高质量离散文本生成
arXiv:2505.18853v2 Announce Type: replace Abstract: Diffusion models have achieved state-of-the-art performance in generating images, audio, and video…