Tootfinder

End4: End-to-end Denoising Diffusion for Diffusion-Based Inpainting Detection
Fei Wang, Xuecheng Wu, Zheng Zhang, Danlei Huang, Yuheng Huang, BoWang
https://arxiv.org/abs/2509.13214

End4: End-to-end Denoising Diffusion for Diffusion-Based Inpainting Detection
The powerful generative capabilities of diffusion models have significantly advanced the field of image synthesis, enhancing both full image generation and inpainting-based image editing. Despite their remarkable advancements, diffusion models also raise concerns about potential misuse for malicious purposes. However, existing approaches struggle to identify images generated by diffusion-based inpainting models, even when similar inpainted images are included in their training data. To address …

Anomalous statistics in the Langevin equation with fluctuating diffusivity: from Brownian yet non-Gaussian diffusion to anomalous diffusion and ergodicity breaking
Takuma Akimoto, Jae-Hyung Jeon, Ralf Metzler, Tomoshige Miyaguchi, Takashi Uneyama, Eiji Yamamoto
https://arxiv.org/abs/2509.12571

Anomalous statistics in the Langevin equation with fluctuating diffusivity: from Brownian yet non-Gaussian diffusion to anomalous diffusion and ergodicity breaking
Diffusive motion is a fundamental transport mechanism in physical and biological systems, governing dynamics across a wide range of scales -- from molecular transport to animal foraging. In many complex systems, however, diffusion deviates from classical Brownian behaviour, exhibiting striking phenomena such as Brownian yet non-Gaussian diffusion (BYNGD) and anomalous diffusion. BYNGD describes a frequently observed statistical feature characterised by the coexistence of linear mean-square disp…

Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer
Travis Davies, Yiqi Huang, Yunxin Liu, Xiang Chen, Huxian Liu, Luhui Hu
https://arxiv.org/abs/2509.11865

Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer
Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains challenging. We study design choices that most affect stability and performance for diffusion-transformer policies trained on heterogeneous, multimodal robot data, and introduce Tenma, a lightweight diffusion-transformer for bi-manual arm control. Tenma integrates multiview RGB, proprioception, and language via a cross-emb…

The Stakeholder Engagement Division, the Infrastructure Security Division, and likely the Integrated Operations Division are believed to have been impacted.
Multiple CISA divisions targeted in shutdown layoffs, people familiar say
https://www.

Multiple CISA divisions targeted in shutdown layoffs, people familiar say
CISA units including its Stakeholder Engagement division are believed to have been hit. A DHS spokesperson said that the RIFs are meant to help get CISA “back on mission.”

Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling
Yi Zhang, Rui Guo, Yonina C. Eldar
https://arxiv.org/abs/2509.12857 https://ar…

Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling
We propose a posterior sampling algorithm for the problem of estimating multiple independent source signals from their noisy superposition. The proposed algorithm is a combination of Gibbs sampling method and plug-and-play (PnP) diffusion priors. Unlike most existing diffusion-model-based approaches for signal separation, our method allows source priors to be learned separately and flexibly combined without retraining. Moreover, under the assumption of perfect diffusion model training, the prop…

AFC anarchy: Will all 4 divisions have new champs? Appraising the Broncos, Colts, Patriots and Steelers https://www.nfl.com/news/afc-anarchy-will-all-4-divisions-have-new-champs-appraising-the-broncos-colts-patriots-and-s…

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Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness
Zixuan Fu, Yan Ren, Finn Carter, Chenyue Wen, Le Ku, Daheng Yu, Emily Davis, Bo Zhang
https://arxiv.org/abs/2509.12024

Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness
Diffusion models have achieved unprecedented success in image generation but pose increasing risks in terms of privacy, fairness, and security. A growing demand exists to \emph{erase} sensitive or harmful concepts (e.g., NSFW content, private individuals, artistic styles) from these models while preserving their overall generative capabilities. We introduce \textbf{SCORE} (Secure and Concept-Oriented Robust Erasure), a novel framework for robust concept removal in diffusion models. SCORE formul…

Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Xiangtong Yao, Yirui Zhou, Yuan Meng, Yanwen Liu, Liangyu Dong, Zitao Zhang, Zhenshan Bing, Kai Huang, Fuchun Sun, Alois Knoll
https://arxiv.org/abs/2509.11621

Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle to accommodate new task requirements at inference time. Addressing this typically requires costly data recollection and policy retraining for each new hardware or task configuration. To overcome this, we introduce an adaptation-projection strategy that enables a diffusion policy to perform zero-shot adaptation to novel…

LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence
Zixin Yin, Xili Dai, Duomin Wang, Xianfang Zeng, Lionel M. Ni, Gang Yu, Heung-Yeung Shum
https://arxiv.org/abs/2509.12203

LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence
The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in a fundamental compromise on weakened inversion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities of diffusion models, suppressing high-fidelity inpainting and text-guided creation. In this paper, we introduce LazyDrag, the first drag-based image editing method for Multi-Modal Diffusion Transformers, which directly elim…