The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. From this vantage point we propose a novel outlier test, the empirical success of which suggests that the failure of existing likelihood-based outlier tests does not necessarily imply that the corresponding generative model is uncalibrated. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers. In aggregate, these results suggest that modifications to the standard evaluation practices and benchmarks commonly applied in the literature are needed.

Deep generative models have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that deep generative networks can efficiently generate high-dimensional complex data from a low-dimensional easy-to-sample distribution. However, this phenomenon can not be justified by existing theories. The widely held manifold hypothesis speculates that real-world data sets, such as natural images and signals, exhibit low-dimensional geometric structures. In this paper, we take such low-dimensional data structures into consideration by assuming that data distributions are supported on a low-dimensional manifold. We prove approximation and estimation theories of deep generative networks for estimating distributions on a low-dimensional manifold under the Wasserstein-1 loss. We show that the Wasserstein-1 loss converges to zero at a fast rate depending on the intrinsic dimension instead of the ambient data dimension. Our theory leverages the low-dimensional geometric structures in data sets and justifies the practical power of deep generative models. We require no smoothness assumptions on the data distribution which is desirable in practice.

When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interoperability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable.

We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifolds on which the distributions are concentrated. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it to assess the performance of deep generative models in various domains: images, 3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN, CIFAR10, FFHQ, market stock data, ShapeNet. We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance.

Users of AI image generators are offering each other instructions on how to use the tech to alter pictures of women into realistic, revealing deepfakes. Some users of popular chatbots are generating bikini deepfakes using photos of fully clothed women as their source material. Most of these fake images appear to be generated without the consent of the women in the photos. Some of these same users are also offering advice to others on how to use the generative AI tools to strip the clothes off of women in photos and make them appear to be wearing bikinis. Under a now-deleted Reddit post titled "gemini nsfw image generation is so easy," users traded tips for how to get Gemini, Google's generative AI model, to make pictures of women in revealing clothes.

Speaking with popular AI content creators convinces me that "slop" isn't just the internet rotting in real time, but the early draft of a new kind of pop culture. Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale. This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI's Sora (which exploded in popularity after launching in app form in September), Google's Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen.

The era of hype first, think later. Demis Hassabis, CEO of Google DeepMind, summed it up in three words: "This is embarrassing." Hassabis was replying on X to an overexcited post by Sébastien Bubeck, a research scientist at the rival firm OpenAI, announcing that two mathematicians had used OpenAI's latest large language model, GPT-5, to find solutions to 10 unsolved problems in mathematics. "Science acceleration via AI has officially begun," Bubeck crowed. Put your math hats on for a minute, and let's take a look at what this beef from mid-October was about. Bubeck was excited that GPT-5 seemed to have somehow solved a number of puzzles known as Erdős problems.

It had previously been announced as Game of the Year. The Indie Game Awards has stripped of two major awards, including Game of the Year and Debut Game. This is due to developer Sandfall Interactive's use of generative AI, . This looks to be fairly cut and dry. The awards ceremony that any game that uses generative AI in the development process would be strictly ineligible for nominations. It was recently revealed that Sandfall while making .

OpenAI's Child Exploitation Reports Increased Sharply This Year The company made 80 times as many reports to the National Center for Missing & Exploited Children during the first six months of 2025 as it did in the same period a year prior. OpenAI sent 80 times as many child exploitation incident reports to the National Center for Missing & Exploited Children during the first half of 2025 as it did during a similar time period in 2024, according to a recent update from the company. The NCMEC's CyberTipline is a Congressionally authorized clearinghouse for reporting child sexual abuse material (CSAM) and other forms of child exploitation. Companies are required by law to report apparent child exploitation to the CyberTipline. When a company sends a report, NCMEC reviews it and then forwards it to the appropriate law enforcement agency for investigation.

LG TVs add'delete' option for Copilot You can now tweak how warm and enthusiastic ChatGPT's responses are OpenAI is letting users decide between more, less or default options to adjust ChatGPT's personality. OpenAI gave its AI chatbot a professional makeover with the latest GPT-5.2 For anyone who's finding ChatGPT rude or sassy, OpenAI has some welcome news since it's letting users further customize its personality with extra warmth or enthusiasm. You can now adjust specific characteristics in ChatGPT, like warmth, enthusiasm, and emoji use. In a post on X, OpenAI revealed that users can adjust characteristics under new Warm, Enthusiastic, Header & Lists and Emoji options found in the Personalization settings.