The popularity of commercial nuclear reactors has surged in recent years as worries about climate change and energy independence drowned out concerns about meltdowns and radioactive waste. The problem is, building nuclear power plants is expensive and slow. A new generation of nuclear power technology could reinvent what a reactor looks like--and how it works. Advocates hope that new tech can refresh the industry and help replace fossil fuels without emitting greenhouse gases. Here's what that might look like . Next-gen nuclear is one of our 10 Breakthrough Technologies this year.

In response to a growing backlash, Microsoft said it would take steps to ensure that data centers don't raise utility bills in surrounding areas and address other public concerns. A Microsoft data center in Aldie, Virginia.Photograph: Bloomberg/Getty Images Microsoft said on Tuesday that it would be taking a series of steps toward becoming a "good neighbor" in communities where it is building data centers--including promising to ask public utilities to set higher electricity rates for data centers. Speaking onstage at an event in Great Falls, Virginia, Microsoft vice chair and president Brad Smith directly referenced a growing national pushback to data centers, describing it as creating "a moment in time when we need to listen, and we need to address these concerns head-on." "When I visit communities around the country, people have questions--pointed questions. They even have concerns," Smith said, as a slide showed headlines from various news outlets about opposition to data centers.

Plus: This company is developing gene therapies for muscle growth, erectile dysfunction, and "radical longevity" For decades, lithium-ion batteries have powered our phones, laptops, and electric vehicles. But lithium's limited supply and volatile price have led the industry to seek more resilient alternatives. They work much like lithium-ion ones: they store and release energy by shuttling ions between two electrodes. But unlike lithium, a somewhat rare element that is currently mined in only a handful of countries, sodium is cheap and found everywhere. Read why it's poised to become more important to our energy future. Sodium-ion batteries are one of 10 Breakthrough Technologies this year.

Traditional reinforcement learning usually assumes either episodic interactions with resets or continuous operation to minimize average or cumulative loss. While episodic settings have many theoretical results, resets are often unrealistic in practice. The infinite-horizon setting avoids this issue but lacks non-asymptotic guarantees in online scenarios with unknown dynamics. In this work, we move towards closing this gap by introducing a reset-free framework called the periodic framework, where the goal is to find periodic policies: policies that not only minimize cumulative loss but also return the agents to their initial state distribution after a fixed number of steps. We formalize the problem of finding optimal periodic policies and identify sufficient conditions under which it is well-defined for tabular Markov decision processes. To evaluate algorithms in this framework, we introduce the periodic regret, a measure that balances cumulative loss with the terminal law constraint. We then propose the first algorithms for computing periodic policies in two multi-agent settings and show they achieve sublinear periodic regret of order $\tilde O(T^{3/4})$. This provides the first non-asymptotic guarantees for reset-free learning in the setting of $M$ homogeneous agents, for $M > 1$.

Bayesian Optimization with multi-objective acquisition functions such as q-Expected Hypervolume Improvement (qEHVI) requires efficient candidate optimization to maximize acquisition function values. Traditional approaches rely on continuous optimization methods like Sequential Least Squares Programming (SLSQP) for candidate selection. However, these gradient-based methods can become trapped in local optima, particularly in complex or high-dimensional objective landscapes. This paper presents a simulated annealing-based approach for candidate optimization in batch acquisition functions as an alternative to conventional continuous optimization methods. We evaluate our simulated annealing approach against SLSQP across four benchmark multi-objective optimization problems: ZDT1 (30D, 2 objectives), DTLZ2 (7D, 3 objectives), Kursawe (3D, 2 objectives), and Latent-Aware (4D, 2 objectives). Our results demonstrate that simulated annealing consistently achieves superior hypervolume performance compared to SLSQP in most test functions. The improvement is particularly pronounced for DTLZ2 and Latent-Aware problems, where simulated annealing reaches significantly higher hypervolume values and maintains better convergence characteristics. The histogram analysis of objective space coverage further reveals that simulated annealing explores more diverse and optimal regions of the Pareto front. These findings suggest that metaheuristic optimization approaches like simulated annealing can provide more robust and effective candidate optimization for multi-objective Bayesian optimization, offering a promising alternative to traditional gradient-based methods for batch acquisition function optimization.

Score-based diffusion models have become a powerful framework for generative modeling, with score estimation as a central statistical bottleneck. Existing guarantees for score estimation largely focus on light-tailed targets or rely on restrictive assumptions such as compact support, which are often violated by heavy-tailed data in practice. In this work, we study conventional (Gaussian) score-based diffusion models when the target distribution is heavy-tailed and belongs to a Sobolev class with smoothness parameter $β>0$. We consider both exponential and polynomial tail decay, indexed by a tail parameter $γ$. Using kernel density estimation, we derive sharp minimax rates for score estimation, revealing a qualitative dichotomy: under exponential tails, the rate matches the light-tailed case up to polylogarithmic factors, whereas under polynomial tails the rate depends explicitly on $γ$. We further provide sampling guarantees for the associated continuous reverse dynamics. In total variation, the generated distribution converges at the minimax optimal rate $n^{-β/(2β+d)}$ under exponential tails (up to logarithmic factors), and at a $γ$-dependent rate under polynomial tails. Whether the latter sampling rate is minimax optimal remains an open question. These results characterize the statistical limits of score estimation and the resulting sampling accuracy for heavy-tailed targets, extending diffusion theory beyond the light-tailed setting.

It's easy to be cynical about technology these days. Many of the "disruptions" of the last 15 years were more about coddling a certain set of young, moneyed San Franciscans than improving the world. Yet you can be sympathetic to the techlash and still fully buy into the idea that technology can be good. We really can build tools that make this planet healthier, more livable, more equitable, and just all-around better. And some people are doing just that, pushing progress forward across a number of fundamental, potentially world-changing technologies. These are exactly the technologies we aim to spotlight in our annual 10 Breakthrough Technologies list.

Our reporters and editors constantly debate which emerging technologies will define the future. Once a year, we take stock and share some educated guesses with our readers. Here are the advances that we think will drive progress or incite the most change--for better or worse--in the years ahead. Rubrik is the exclusive sponsor of the 10 Breakthrough Technologies 2026 and had no editorial influence on this list. Rubrik is a security and AI operations company that aims to secure and accelerate the world's AI transformation.

Modern engineering and scientific workflows frequently require simultaneous prediction across related tasks and fidelity levels [1-6]. In such contexts, some outputs are scarce and expensive to obtain, while others are cheaper and more abundant. Multi-task Gaussian processes (MTGPs), also known as multi-output Gaussian processes, offer a principled Bayesian framework to exploit inter-task correlations, enabling knowledge sharing that improves predictive accuracy and reduces the demand for large high-fidelity datasets [7-9]. Over decades of development, MTGPs have been applied across diverse domains, including time series forecasting, multitask optimization, and multifidelity classification, demonstrating their broad utility wherever data cost asymmetries and cross-task dependencies are present [10-16]. The central motivation for MTGPs is to leverage dependencies among related tasks to enhance predictive quality when high-fidelity information is limited [17]. For example, predicting an airfoil's lift coefficient from limited, expensive high-fidelity computational fluid dynamics (CFD) simulations can benefit from correlating with sufficient low-fidelity simulations [3]. Recent work in joint multi-objective and multifidelity optimization has also utilized MT - GPs to balance exploration and exploitation across tasks, improving predictive performance and decision-making by explicitly modeling relationships among outputs and fidelities [12].

Meta will finance Oklo's purchase of uranium for its reactors. It's a massive vote of confidence for both the startup and nuclear power, but challenges remain. There are two ways for tech companies to invest in nuclear power right now. One is to buy power from traditional reactors that are already built, either by purchasing electricity from the plants directly or financing the reconstruction of decommissioned units. The other is to invest in one of the dozens of reactor startups promising to commercialize designs and technologies never before used in the American market to generate electricity.