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From seemingly AI-generated videos to repurposed old footage, TikTok, Instagram, and X did little to stop the onslaught of misleading posts in the wake of the US invasion of Venezuela. A crowd outside of Miami reacts to the news of the capture of Venezuelan President Nicolás Maduro on January 3, 2026. Within minutes of Donald Trump announcing in the early hours of Saturday morning that US troops had captured Venezuelan president Nicolás Maduro and his wife, Cilia Flores, disinformation about the operation flooded social media. Some people shared old videos across social platforms, falsely claiming that they showed the attacks on the Venezuelan capital Caracas. On TikTok, Instagram, and X, people shared AI-generated images and videos that claimed to show US Drug Enforcement Administration agents and various law enforcement personnel arresting Maduro.

Some AI chatbots have a surprisingly good handle on breaking news. Supporters of Nicolás Maduro and the late Hugo Chávez hold posters with their images after explosions and low-flying aircraft were heard on January 3, 2026, in Caracas, Venezuela. At around 2 am local time in Caracas, Venezuela, US helicopters flew overhead while explosions resounded below. A few hours later, US president Donald Trump posted on his Truth Social platform that Venezuelan president Nicolás Maduro and his wife had been "captured and flown out of the Country." US attorney general Pam Bondi followed with a post on X that Maduro and his wife had been indicted in the Southern District of New York and would "soon face the full wrath of American justice on American soil in American courts."

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The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We first show that the solution of PDEs that satisfy reasonable and verifiable assumptions can be approximated by neural networks, thus the approximation error tends to zero as the number of neurons tends to infinity. Then, we derive the gradient flow that the training process follows in the ``wide network limit'' and analyze the limit of this flow as the training time tends to infinity. These results combined show that the generalization error of DGFMs tends to zero as the number of neurons and the training time tend to infinity.

Multilingual language models achieve strong aggregate performance yet often behave unpredictably across languages, scripts, and cultures. We argue that mechanistic explanations for such models should satisfy a \emph{causal} standard: claims must survive causal interventions and must \emph{cross-reference} across environments that perturb surface form while preserving meaning. We formalize \emph{reference families} as predicate-preserving variants and introduce \emph{triangulation}, an acceptance rule requiring necessity (ablating the circuit degrades the target behavior), sufficiency (patching activations transfers the behavior), and invariance (both effects remain directionally stable and of sufficient magnitude across the reference family). To supply candidate subgraphs, we adopt automatic circuit discovery and \emph{accept or reject} those candidates by triangulation. We ground triangulation in causal abstraction by casting it as an approximate transformation score over a distribution of interchange interventions, connect it to the pragmatic interpretability agenda, and present a comparative experimental protocol across multiple model families, language pairs, and tasks. Triangulation provides a falsifiable standard for mechanistic claims that filters spurious circuits passing single-environment tests but failing cross-lingual invariance.

As generative AI systems are increasingly deployed in real-world applications, regulating multiple dimensions of model behavior has become essential. We focus on test-time filtering: a lightweight mechanism for behavior control that compares performance scores to estimated thresholds, and modifies outputs when these bounds are violated. We formalize the problem of enforcing multiple risk constraints with user-defined priorities, and introduce two efficient dynamic programming algorithms that leverage this sequential structure. The first, MULTIRISK-BASE, provides a direct finite-sample procedure for selecting thresholds, while the second, MULTIRISK, leverages data exchangeability to guarantee simultaneous control of the risks. Under mild assumptions, we show that MULTIRISK achieves nearly tight control of all constraint risks. The analysis requires an intricate iterative argument, upper bounding the risks by introducing several forms of intermediate symmetrized risk functions, and carefully lower bounding the risks by recursively counting jumps in symmetrized risk functions between appropriate risk levels. We evaluate our framework on a three-constraint Large Language Model alignment task using the PKU-SafeRLHF dataset, where the goal is to maximize helpfulness subject to multiple safety constraints, and where scores are generated by a Large Language Model judge and a perplexity filter. Our experimental results show that our algorithm can control each individual risk at close to the target level.

In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformation for each spline parameter tuning, with survival analysis computations relying on the entire dataset, posing difficulties for large datasets. Meanwhile, existing neural network-based survival analysis methods focus on accuracy but often overlook the interpretability of cumulative exposure patterns. To bridge this gap, we introduce CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data. Evaluated on two diverse real-world datasets, CENNSurv revealed a multi-year lagged association between chronic environmental exposure and a critical survival outcome, as well as a critical short-term behavioral shift prior to subscription lapse. This demonstrates CENNSurv's ability to model complex temporal patterns with improved scalability. CENNSurv provides researchers studying cumulative effects a practical tool with interpretable insights.

AI-Powered Dating Is All Hype. Dating apps and AI companies have been touting bot wingmen for months. But the future might just be good old-fashioned meet-cutes. I am, admittedly, a big flirt. I love everything about the exchange of getting to know another person.

Having made colossal profits as well as losses on previous investments, founder Masayoshi Son has pivoted SoftBank toward artificial intelligence. Japanese tech investor SoftBank said Wednesday that its stake in OpenAI is now around 11% after completing the second stage of a $41-billion investment in the maker of ChatGPT. Having made colossal profits as well as losses on previous investments, flamboyant founder Masayoshi Son has pivoted SoftBank toward artificial intelligence. SoftBank had announced in April its planned investment of up to $40 billion in Open AI, and on Wednesday it said that the second tranche of $22.5 billion was completed. The final investment reached $41 billion and includes $30 billion from SoftBank's Vision Fund plus $11 billion from other third-party co-investors, it said.