arXiv:2608. 08101v1 Announce Type: new Abstract: Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content.
arXiv:2605.29713v2 Announce Type: replace-cross Abstract: This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence....
The article discusses how vision generative AI models, while rapidly advancing, have largely been developed with a focus on output quality, leading to hardware that adapts reactively to increasing model demands. It evaluates the parameter cost and energy efficiency of these models across various accelerator platforms and aligns four generative model families with seven real-world application domains. The authors propose a software‑hardware co‑design strategy that considers deployment constraints from the outset, ensuring that the appropriate model runs on suitable hardware for specific applications, thereby making generative AI deployment more sustainable and widely accessible.
arXiv:2506. 23546v2 Announce Type: replace-cross Abstract: Fixed points of recurrent neural networks can be leveraged to store and generate information.
The paper "Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling" investigates the use of flow matching for image generation and identifies that this approach can produce hallucinations—unrealistic images. It proposes an iterative refinement process that can be incorporated into virtually any generative modeling technique to improve performance and robustness. The authors demonstrate how their method corrects the generation path and gradually refines outputs to mitigate hallucinations.
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they might be going.
arXiv:2407. 09013v2 Announce Type: replace Abstract: The attempt to utilize machine learning in PCG has been made in the past.
arXiv:2606. 15344v1 Announce Type: cross Abstract: Generative modelling requires efficient stochastic nonlinear transformations and physical platforms that can naturally realise them.
The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process.
arXiv:2607. 21522v1 Announce Type: cross Abstract: Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging.
The paper introduces a method that applies physically based rendering (PBR) within the latent space of variational autoencoders used in image diffusion models. By modifying the rendering equation and using a differentiable renderer, the authors can generate latent maps that guide content creation with physically accurate lighting. The approach is trained on a single rendered image and then shown to generalize to changes in scene geometry, lighting, and camera viewpoint.
The article surveys recent progress in tractable probabilistic generative modeling, with a focus on Probabilistic Circuits (PCs). It offers a unified view of the trade‑offs between expressivity and tractability, outlining design principles, algorithmic extensions, and a taxonomy of the field. The review also covers deep and hybrid PCs that integrate ideas from deep neural models, and highlights challenges and open questions for future research.