Bungie's Marathon arrives on March 5 How to claim Verizon's $20 outage credit With big power and big range, Volvo's next-gen EV efforts are off to a good start. Volvo hasn't exactly had a great run of EVs lately. The rollout of its flagship EX90 was stymied out of the gate by a bevy of software glitches. The EX30, meanwhile, was too expensive when it launched -- the promised $35,000 model was incompatible with the currently chaotic global tariff situation. Now, it's time for a new generation of EV from Volvo, one that's radically different at its core with a gigacast frame, a much higher-density battery and enough digital and literal horsepower to impress the most jaded of automotive enthusiasts.

How to watch today's Bosch CES 2026 press conference live The German engineering and tech giant will share new car cabin tech and AI enhancements. How to watch today's Bosch CES 2026 press conference live You might think of Bosch as the modern European equivalent to what the GE brand once was in America. It's a home appliance name (thanks to its partnership with Siemens), but the German multinational brand's core business is really about providing the underlying technology and engineering that powers cars, homes and factories around the world. That focus is reflected at CES 2026, where much of what Bosch is unveiling is intended to be licensed to other companies rather than sold as Bosch-branded products on store shelves. Case in point is Bosch's automotive plans at CES.

The German engineering and tech giant will discuss how it's bridging the physical and digital gap with people-centric tech. You might recognize Bosch as a home appliance name (thanks to its partnership with Siemens), but the German multinational brand's core business is really about providing the underlying technology and engineering that powers cars, homes and factories around the world. That focus is reflected at CES 2026, where much of what Bosch is unveiling is intended to be licensed to other companies rather than sold as Bosch-branded products on store shelves. Case in point is Bosch's automotive plans at CES. The company is showcasing what it calls AI in the car, or more specifically, in the cockpit of the car.

It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents. This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performance of RL compared to other learned representations or even low-dimensional, hand-engineered state information. Specifically, we propose to train an encoder that maps multiple image observations to a latent space describing the objects in the scene. The decoder built from a latent-conditioned NeRF serves as the supervision signal to learn the latent space. An RL algorithm then operates on the learned latent space as its state representation.

Your Out-of-Distribution Detection Method is Not Robust!

In a small room in San Diego last week, a man in a black leather jacket explained to me how to save the world from destruction by AI. Max Tegmark, a notable figure in the AI-safety movement, believes that "artificial general intelligence," or AGI, could precipitate the end of human life. I was in town for NeurIPS, one of the largest AI-research conferences, and Tegmark had invited me, along with five other journalists, to a briefing on an AI-safety index that he would release the next day. No company scored better than a C+. The threat of technological superintelligence is the stuff of science fiction, yet it has become a topic of serious discussion in the past few years.

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) networks, offering massive spatial degrees of freedom. Despite these advantages, the coexistence of near-field and far-field effects in hybrid-field channels presents significant challenges for accurate estimation, where traditional methods often struggle to generalize effectively. In recent years, large language models (LLMs) have achieved impressive performance on downstream tasks via fine-tuning, aligning with the semantic communication shift toward task-oriented understanding over bit-level accuracy. Motivated by this, we propose Large Language Models for XL-MIMO Channel Estimation (LLM4XCE), a novel channel estimation framework that leverages the semantic modeling capabilities of large language models to recover essential spatial-channel representations for downstream tasks. The model integrates a carefully designed embedding module with Parallel Feature-Spatial Attention, enabling deep fusion of pilot features and spatial structures to construct a semantically rich representation for LLM input. By fine-tuning only the top two Transformer layers, our method effectively captures latent dependencies in the pilot data while ensuring high training efficiency. Extensive simulations demonstrate that LLM4XCE significantly outperforms existing state-of-the-art methods under hybrid-field conditions, achieving superior estimation accuracy and generalization performance.

Accurate water quality assessment requires spatially resolved sampling, yet most unmanned surface vehicles (USVs) can collect only a limited number of samples or rely on single-point sensors with poor representativeness. This work presents a solar-powered, fully autonomous USV featuring a novel syringe-based sampling architecture capable of acquiring 72 discrete, contamination-minimized water samples per mission. The vehicle incorporates a ROS 2 autonomy stack with GPS-RTK navigation, LiDAR and stereo-vision obstacle detection, Nav2-based mission planning, and long-range LoRa supervision, enabling dependable execution of sampling routes in unstructured environments. The platform integrates a behavior-tree autonomy architecture adapted from Nav2, enabling mission-level reasoning and perception-aware navigation. A modular 6x12 sampling system, controlled by distributed micro-ROS nodes, provides deterministic actuation, fault isolation, and rapid module replacement, achieving spatial coverage beyond previously reported USV-based samplers. Field trials in Achocalla Lagoon (La Paz, Bolivia) demonstrated 87% waypoint accuracy, stable autonomous navigation, and accurate physicochemical measurements (temperature, pH, conductivity, total dissolved solids) comparable to manually collected references. These results demonstrate that the platform enables reliable high-resolution sampling and autonomous mission execution, providing a scalable solution for aquatic monitoring in remote environments.

Two pressing topics in the theory of deep learning are the interpretation of feature learning mechanisms and the determination of implicit bias of networks in the rich regime. Current theories of rich feature learning, often appear in the form of high-dimensional non-linear equations, which require computationally intensive numerical solutions. Given the many details that go into defining a deep learning problem, this complexity is a significant and often unavoidable challenge. Here, we propose a powerful heuristic route for predicting the data and width scales at which various patterns of feature learning emerge. This form of scale analysis is considerably simpler than exact theories and reproduces the scaling exponents of various known results. In addition, we make novel predictions on complex toy architectures, such as three-layer non-linear networks and attention heads, thus extending the scope of first-principle theories of deep learning.

Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conversion technologies. At these scales, phonon transport follows the Boltzmann Transport Equation (BTE), which captures non-diffusive (ballistic) effects but is too costly to solve repeatedly in inverse-design loops. Existing surrogate approaches trade speed for accuracy: fast macroscopic solvers can overestimate conductivities by hundreds of percent, while recent data-driven operator learners often require thousands of high-fidelity simulations. This creates a need for a fast, data-efficient surrogate that remains reliable across ballistic and diffusive regimes. We introduce a Physics-Enhanced Deep Surrogate (PEDS) that combines a differentiable Fourier solver with a neural generator and couples it with uncertainty-driven active learning. The Fourier solver acts as a physical inductive bias, while the network learns geometry-dependent corrections and a mixing coefficient that interpolates between macroscopic and nano-scale behavior. PEDS reduces training-data requirements by up to 70% compared with purely data-driven baselines, achieves roughly 5% fractional error with only 300 high-fidelity BTE simulations, and enables efficient design of porous geometries spanning 12-85 W m$^{-1}$ K$^{-1}$ with average design errors of 4%. The learned mixing parameter recovers the ballistic-diffusive transition and improves out of distribution robustness. These results show that embedding simple, differentiable low-fidelity physics can dramatically increase surrogate data-efficiency and interpretability, making repeated PDE-constrained optimization practical for nano-scale thermal-materials design.