We introduce BayeSQP, a novel algorithm for general black-box optimization that merges the structure of sequential quadratic programming with concepts from Bayesian optimization. BayeSQP employs second-order Gaussian process surrogates for both the objective and constraints to jointly model the function values, gradients, and Hessian from only zero-order information. At each iteration, a local subproblem is constructed using the GP posterior estimates and solved to obtain a search direction. Crucially, the formulation of the subproblem explicitly incorporates uncertainty in both the function and derivative estimates, resulting in a tractable second-order cone program for high probability improvements under model uncertainty. A subsequent one-dimensional line search via constrained Thompson sampling selects the next evaluation point. Empirical results show that BayeSQPoutperforms state-of-the-art methods in specific high-dimensional settings. Our algorithm offers a principled and flexible framework that bridges classical optimization techniques with modern approaches to black-box optimization.

'The CEOs are telling us, "We're on track to create superhuman intelligence, which has a good chance of causing human extinction."' 'The CEOs are telling us, "We're on track to create superhuman intelligence, which has a good chance of causing human extinction."' Will it take a'Chernobyl-scale disaster' for us to regulate cyber weapons of mass destruction? T he AI company Anthropic has been making major headlines recently. Its trillion-dollar IPO plan and its blood feud with secretary of defense Pete Hegseth have attracted much attention, but two other events may be even more consequential.

WIRED goes on location--and on camera--with the cult hit. On March 16, 2026, at 5:45 pm in a leafy suburb of Atlanta called Sandy Springs, police pound on the door of a neglected French Country-style mansion, rifles at the ready, bodycams rolling. Minutes earlier, a distress call came from someone claiming to be hiding from a gunman in the mansion's downstairs bathroom. The dispatcher heard a gunshot ring out in the distance, then the line disconnected. "Open the door!" an officer yells. A calm young man with a mullet and woolly eyebrows steps out, hands raised. The police ask him who else is in the house. "Just my friends," he replies, as seven other young people, men and women, silently file out behind him, less evidently relaxed. They remain outside while two officers search the house. Inside the mansion there are no immediate signs of a massacre, but the decor alone arouses suspicion. All of the windows are frosted over, so only a chilly light leaks in. The place is a mess, and the walls are adorned with lurid, seemingly AI-generated art: a frowning baby holding an assault rifle, a rubber ducky bobbing in a mug of what looks like black coffee, a lidless and levitating eyeball crying into a martini glass. The rooms are painted primary colors, grass green and cherry red, like a kindergarten class. A vape dangles from a doorframe by a chain, suspended at mouth level. The pantry is practically empty. The bedroom is a dormitory featuring seven identical twin beds. No one is hiding in the bathroom. The call, it seems, was a prank. The police return to the driveway and ask, "What is it that you guys are doing here?" "We're just livestreaming," says a man in a camo hat named Matt. "You guys don't have any firearms or anything inside the house?" There are guns in the house, Matt says, for self-defense. Fans of their livestream can be obsessive, he explains, and tend to have perverse ideas about jokes. The officer asks to see their weapons, and they go downstairs. The room is cluttered with ergonomic swivel chairs, desks strewn with takeout containers and energy drinks, two flatscreen TVs, and a dozen computer monitors.

Generating realistic brain connectivity matrices is key to analyzing population heterogeneity in brain organization, understanding disease, and augmenting data in challenging classification problems. Functional connectivity matrices lie in constrained spaces--such as the set of symmetric positive definite or correlation matrices--that can be modeled as Riemannian manifolds. However, using Riemannian tools typically requires redefining core operations (geodesics, norms, integration), making generative modeling computationally inefficient. In this work, we propose DIFFEOCFM, an approach that enables conditional flow matching (CFM) on matrix manifolds by exploiting pullback metrics induced by global diffeomorphisms on Euclidean spaces. We show that Riemannian CFM with such metrics is equivalent to applying standard CFM after data transformation. This equivalence allows efficient vector field learning, and fast sampling with standard ODE solvers.

Efficient vision transformer remains a bottleneck for high-resolution images and long-video related real-world applications.

Pretrained vision-language models such as CLIP achieve strong zero-shot generalization but remain vulnerable to distribution shifts caused by input corruptions. In this work, we investigate how corruptions affect CLIP's image embeddings and uncover a consistent phenomenon we term as embedding variance collapse, where both intra-class and inter-class variances shrink as corruption severity increases. We find that this collapse is closely tied to performance degradation, with inter-class variance strongly correlated with classification accuracy. To explain this phenomenon, we analyze how corruptions alter the structure of the embedding space. Our theoretical results suggest that the visual encoder tends to encode corruption-related signals, which dilute class-discriminative features and compress the representation geometry. We further show that maximizing inter-class variance, even when estimated from pseudo-labels, can provably enhance embedding quality. Based on this insight, we propose Mint, a simple test-time adaptation method that maximizes pseudo-label-based inter-class variance on the fly using cumulative prototypes and gradient estimates. Mintoperates effectively with small batch sizes and consistently improves performance across multiple corruption benchmarks and CLIP architectures. Our code is available at https://github.com/baowenxuan/Mint.

We study fundamental connections between coalition formation games and clustering, illustrating the cross-disciplinary relevance of these concepts. We focus on graphical hedonic games where agents' preferences are compactly represented by a friendship graph and an enmity graph. In the context of clustering, friendship relations naturally align with data point similarities, whereas enmity corresponds to dissimilarities. We consider two stability notions based on single-agent deviations: local popularity and local stability.

Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods, including pruning and quantization, calibration data plays a vital role by informing the weight importance and activation dynamic ranges. However, how calibration data impacts the LLM capability after compression is less explored. Few of the existing works, though recognizing the significance of this study, only investigate the language modeling or commonsense reasoning performance degradation from limited angles, like the data sources or sample amounts. More systematic research is still needed to examine the impacts on different LLM capabilities in terms of compositional properties and domain correspondence of calibration data.

Could Israel sabotage the deal? Leaders of the G7 have pledged at a summit in France to strengthen Ukraine's air defences and increase pressure on Moscow's war economy, including by tightening sanctions on the Russian oil and gas sectors. "We, the Leaders of the G7, stand united in our unwavering support for Ukraine in defending its freedom, sovereignty, and territorial integrity," a statement released on Wednesday said. They added that the bloc, which includes Canada, France, Germany, Italy, Japan, the United Kingdom, the United States and the European Union, was "ready to consider extending to Ukraine the benefit of licenses to allow for an increase in Ukraine's military production". President Volodymyr Zelenskyy, who joined the summit on Tuesday and also held bilateral talks with US President Donald Trump and Secretary of State Marco Rubio, has been pressing allies for more than a year to allow Ukraine to produce its own interceptors because of a shortage of US anti-ballistic systems and interceptors.

Biomedical research data portals are essential resources for scientific inquiry, and interactive exploratory visualizations are an integral component for querying such data repositories. Increasingly, machine learning is being integrated into visualization systems to create natural language interfaces where questions about data can be answered with visualizations, and follow-up questions can build on the previous state. This paper introduces a framework that takes abstract low-level questions about data and a visualization grammar specification that can answer such a question, reifies them with data entities and fields that meet certain constraints, and paraphrases the question language to produce the final collection of realized data-question-visualization triplets. Furthermore, we can link these foundational elements together to construct chains of queries, visualizations, and follow-up queries. We developed an open-source review interface for evaluating the results of these datasets. We applied this framework to five biomedical research data repositories, resulting in DQVis, a dataset of 1.08 million dataquestion-visualization triplets and 11.4 thousand two-step question samples. Five visualization experts provided feedback on the generated dataset through our review interface. We present a summary of their input and publish the full reviews as an additional resource alongside the dataset.