Generative models are powerful tools for sampling from a learned distribution $\mathcal{P}(Y \mid X)$, and inverse-design methods invert this map to find an input $x$ that produces a desired point output $y^*$. However, many design goals are naturally distributional rather than pointwise, incorporating the inherent uncertainty of $Y$ and targeting a specific form for it, a task not addressed by standard inverse design. To address this issue we introduce Conditional Distribution Matching (CDM), a new inverse-design problem class in generative modeling: given a joint distribution $\mathcal{P}(X, Y)$ and a target distribution $\mathcal{G}(Y)$, find an input $x^*$ whose induced conditional distribution $\mathcal{P}(Y \mid X = x^*)$ matches $\mathcal{G}$. We formally define two variants: Conditional Distribution Matching Sampling (CDMS) and Conditional Distribution Matching Optimization (CDMO). To solve these problems, we propose MLGD-F (Matching-Loss Guided Diffusion with a Fast inner sampler), a plug-and-play inference-time algorithm that combines a pretrained score-based diffusion model with a pretrained fast conditional sampler, requiring no additional training or fine-tuning. By leveraging single-step conditional sampling, MLGD-F enables tractable gradient computation, making the estimation of $\mathcal{P}(Y \mid X)$ both memory-efficient and computationally lightweight. We validate MLGD-F on synthetic benchmarks, structured image transformations, and generative editing optimization, demonstrating reliable recovery of inputs whose conditional distributions match diverse user-specified targets, including discrete mixtures and continuous low-rank supports.

Nine people have died in an explosion at a coal mine in Colombia in the latest fatal accident to hit the country's mining sector. Emergency workers said they had rescued six miners from the shafts in Sutatausa, north of the capital, Bogotá. Colombia's national mining agency said a build-up of gases was thought to have caused the explosion at 16:00 (21:00 GMT) on Monday. It also published a list of recommendations it said it had made to the mine's operators after an inspection less than a month ago, in which it had warned of a potentially dangerous gas build-up. Many mines in Colombia are operated informally and without proper safety standards.

Detail from Holbein's sketch of an unidentified woman, which it is claimed may depict Anne Boleyn. Detail from Holbein's sketch of an unidentified woman, which it is claimed may depict Anne Boleyn. They are two small sketches by the Renaissance master Hans Holbein: one has long been considered to be a portrait of Henry VIII's doomed second wife, Anne Boleyn, and the other is of an unknown woman whose name was lost to time. Now researchers using AI have discovered that the unnamed woman might be the tragic queen after all, while the other figure could in fact be Boleyn's mother. The works, which belong to the royal collection and are known as the Windsor sketch and the Unidentified Woman respectively, were analysed by a team at the University of Bradford, who found that they might have been incorrectly inscribed in the 1700s, leading to a misunderstanding that has lasted centuries.

Our goal is to extract meaningful transformations from raw images, such as varying the thickness of lines in handwriting or the lighting in a portrait. We propose an unsupervised approach to learn such transformations by attempting to reconstruct an image from a linear combination of transformations of its nearest neighbors. On handwritten digits and celebrity portraits, we show that even with linear transformations, our method generates visually high-quality modified images. Moreover, since our method is semiparametric and does not model the data distribution, the learned transformations extrapolate off the training data and can be applied to new types of images.

This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models.

Foundation models are trained on increasingly immense and opaque datasets.

In the age of A.I., we endlessly debate what consciousness looks like. Can a camera see things more clearly? Earlier that day, she'd been onstage at the conference I was attending and had been teased for a gesture that looked as though she were flipping off the audience. Now she was in the hotel lobby, in a black gown, holding court. She stepped in front of a bright-orange wall. I had brought an 85-mm. "What are your hopes for the future of humanity?" She wasn't keen to answer, but she responded to the camera.

Since 2006, the project has photographed 17,000 species in the world's zoos, aquariums, and wildlife sanctuaries. Photographs from the Photo Ark will be featured in the inaugural exhibition at the National Geographic Museum of Exploration in Washington D.C. Breakthroughs, discoveries, and DIY tips sent every weekday. A picture is said to be worth a thousand words, but some photographs are worth 17,000. Well, 17,000 species, that is. For's Photo Ark project, photographer Joel Sartore is documenting all species living in the world's zoos, aquariums, and wildlife sanctuaries.

In multi-agent systems, explicit cognition of teammates' decision logic serves as a critical factor in facilitating coordination. Communication (i.e., ``\textit{Tell}'') can assist in the cognitive development process by information dissemination, yet it is inevitably subject to real-world constraints such as noise, latency, and attacks. Therefore, building the understanding of teammates' decisions without communication remains challenging. To address this, we propose a novel non-communication MARL framework that realizes the construction of cognition through local observation-based modeling (i.e., \textit{``Think''}). Our framework enables agents to model teammates' \textbf{active inference} process. At first, the proposed method produces three teammate portraits: perception-belief-action. Specifically, we model the teammate's decision process as follows: 1) Perception: observing environments; 2) Belief: forming beliefs; 3) Action: making decisions. Then, we selectively integrate the belief portrait into the decision process based on the accuracy and relevance of the perception portrait. This enables the selection of cooperative teammates and facilitates effective collaboration. Extensive experiments on the SMAC, SMACv2, MPE, and GRF benchmarks demonstrate the superior performance of our method.