The second day's play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia. In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use.

The second day's play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia. In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use.

Zhipu's AI service on the web, dubbed Z.ai. BEIJING/BENGALURU - Since DeepSeek shocked markets early last year with its cheap but powerful artificial intelligence model, global consumers have been faced with a choice: Chinese offerings with lower prices and less capability or OpenAI or Anthropic, which have poured billions into development. A model called GLM-5.2, launched last month by Beijing-based startup Z.ai, may finally be closing that gap in terms of Western interest. GLM-5.2 has Silicon Valley buzzing with its coding and agent capabilities, or the ability to execute complex tasks with minimal prompting, that almost rival leading U.S. offerings at a fraction of the cost, in what some experts are calling a "mini DeepSeek moment." In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ across tasks, these losses are generally not directly comparable, which makes it difficult to formulate a unified objective and may limit information sharing across tasks. We propose a multitask transformation framework in which task-specific responses may differ through unknown monotone transformations. Motivated by high-dimensional biological applications in which the predictor dimension may diverge with the sample size while only a common subset of predictors is informative, we consider shared sparsity across tasks. Under this framework, we estimate the target functions and identify important predictors by optimizing a smoothed rank-based criterion with a group-Lasso penalty, implemented through a multitask deep neural network with a shared first layer. We establish the nonasymptotic excess-risk bounds, and variable-selection consistency for the proposed estimator. Simulation studies show that the proposed method achieves competitive prediction and variable-selection performance compared with competing approaches. Analyses of gene-expression studies with continuous, binary, and mixed outcomes further illustrate that the proposed method improves prediction and identifies biologically meaningful shared predictors.

Traditional variational Kalman filtering with unknown noise statistics suffers from inconsistent process covariance estimation and slow convergence speed, limiting its practical utility. To address these issues, we introduce a surrogate variable representing the process-noise-free state, which enables explicit modeling and inference of process noise statistics. In addition, we reformulate the conventional coordinate ascent variation inference (CAVI) as a marginalized maximum a posteriori problem, followed by a single-step hyperparameter fitting. This reformulation obviates the need for multiple inner iterations inherent to CAVI and decouples the design of the covariance tracking filters. Consequently, this architecture permits the deployment of higher-order filters for covariance tracking and enables sliding-window hyperparameter estimation. Notably, when this window encompasses all historical data, the covariance tracking estimator intrinsically operates as a zero-phase filter. Numerical simulations validate the theoretical framework, demonstrating the enhanced convergence speed and superior estimation accuracy compared with existing methods.

Kawasaki Heavy Industries is collaborating with companies including Nvidia to integrate AI and robotics, and last month announced a development hub in Silicon Valley. Kawasaki Heavy Industries is finalizing plans to raise about ¥200 billion ($1.23 billion) by issuing new shares and convertible bonds to fund capital expenditure, according to two sources familiar with the matter. The company will decide on the details of the issuance as soon as this week, the sources said. The shares and convertible bonds will be sold mainly to overseas institutional investors, one of the sources said. The plan to raise funds has not been reported earlier. Kawasaki Heavy said in a statement that it is considering various capital strategies including issuing new shares and bonds but that nothing has been decided.

Eli Lilly faces a congressional investigation into its China clinical trials at People's Liberation Army hospitals and Xinjiang facilities amid biotechnology competition concerns.

Billions of dollars are flooding into efforts to reverse aging as scientists explore ways to return cells to a younger state. But how far off are these experimental treatments? Why "reprogramming" is the buzziest approach to reversing aging right now A startup claims it broke through a bottleneck that's holding back LLMs Will Douglas Heaven Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI's models Michelle Kim China has approved the world's first invasive brain-computer chip--here's what's next The country wants to become a global leader in brain implants. Strong government support is expected to help accelerate that process. Researchers are decoding how signals move between body and brain, with implications for how we understand and treat conditions from obesity to anxiety. In an early step towards artificial wombs, a biotech company claims it's developed a "fully artificial" chicken egg.

The industry ministry announced ¥387.3 billion in funding to develop a domestic foundation model for physical AI that controls robots, aiming to strengthen the country's competitiveness against the United States and China. The industry ministry said Tuesday that it would provide ¥387.3 billion in aid for a project to develop a domestic model that serves as the foundation of a physical artificial intelligence system that controls robots. The ministry aims to make the multimodal foundation model widely available to Japanese companies to help the country catch up with the United States and China in the technology. The project is led by Noetra, a Japanese company founded by firms including telecommunications operator SoftBank, to develop AI models domestically. Engineers from SoftBank and Japanese AI startup Preferred Networks will join the project.

We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to the small-stepsize central limit theorem and under an unbiased stochastic gradient oracle, we prove that the empirical average over a horizon of order the inverse squared stepsize satisfies a central limit theorem in the vanishing-stepsize regime. The limiting variance is characterized through the Poisson equation of the limiting full-gradient diffusion. We then rewrite this constant in an operator form that links it to the continuous-time asymptotic variance and, under standard operator-theoretic assumptions, derive a sufficient condition under which an anti-symmetric perturbation strictly reduces the leading-order fluctuation constant relative to the reversible baseline. We also identify bounded smooth predictive observables that re directly covered by the main theorem. As a separate Gaussian calculation beyond the bounded-test-function regime, we obtain closed-form formulas for quadratic Hamiltonians and linear observables. The framework covers non-reversible Langevin dynamics and augmented-state examples including Hessian-free high-resolution dynamics and a positive-definite subclass of gradient-adjusted underdamped Langevin dynamics that allow stochastic gradients. Numerical experiments on basic examples and Bayesian linear regression using synthetic data, and Bayesian logistic regression using real data support the predicted Gaussian fluctuations and show that the non-reversible schemes consistently reduce the root mean squared error (RMSE) relative to their reversible baselines.