Traditional recommendation systems tend to trap users in strong feedback loops by excessively pushing content aligned with their historical preferences, thereby limiting exploration opportunities and causing content fatigue. Although large language models (LLMs) demonstrate potential with their diverse content generation capabilities, existing LLM-enhanced dual-model frameworks face two major limitations: first, they overlook long-term preferences driven by group identity, leading to biased interest modeling; second, they suffer from static optimization flaws, as a one-time alignment process fails to leverage incremental user data for closed-loop optimization. To address these challenges, we propose the Co-Evolutionary Alignment (CoEA) method. For interest modeling bias, we introduce Dual-Stable Interest Exploration (DSIE) module, jointly modeling long-term group identity and short-term individual interests through parallel processing of behavioral sequences. For static optimization limitations, we design a Periodic Collaborative Optimization (PCO) mechanism. This mechanism regularly conducts preference verification on incremental data using the Relevance LLM, then guides the Novelty LLM to perform fine-tuning based on the verification results, and subsequently feeds back the output of the continually fine-tuned Novelty LLM to the Relevance LLM for re-evaluation, thereby achieving a dynamic closed-loop optimization. Extensive online and offline experiments verify the effectiveness of the CoEA model in serendipitous recommendation.

Artificial intelligence (AI)-driven models have the potential to revolutionize weather forecasting, but still rely on initial conditions generated by costly Numerical Weather Prediction (NWP) systems. Although recent end-to-end forecasting models attempt to bypass NWP systems, these methods lack scalable assimilation of new types of observational data. Here, we introduce XiChen, an observation-scalable fully AI-driven global weather forecasting system, wherein the entire pipeline, from Data Assimilation (DA) to medium-range forecasting, can be accomplished within only 15 seconds. XiChen is built upon a foundation model that is pre-trained for weather forecasting and subsequently fine-tuned to serve as both observation operators and DA models, thereby enabling the scalable assimilation of conventional and raw satellite observations. Furthermore, the integration of Four-Dimensional Variational (4DVar) knowledge ensures XiChen to achieve DA and medium-range forecasting accuracy comparable to operational NWP systems, with skillful forecasting lead time beyond 8.75 days. A key feature of XiChen is its ability to maintain physical balance constraints during DA, enabling observed variables to correct unobserved ones effectively. In single-point perturbation DA experiments, XiChen exhibits flow-dependent characteristics similar to those of traditional 4DVar systems. These results demonstrate that XiChen holds strong potential for fully AI-driven weather forecasting independent of NWP systems.

--Processing-in-Memory (PIM) architectures offer promising solutions for efficiently handling AI applications in energy-constrained edge environments. While traditional PIM designs enhance performance and energy efficiency by reducing data movement between memory and processing units, they are limited in edge devices due to continuous power demands and the storage requirements of large neural network weights in SRAM and DRAM. Hybrid PIM architectures, incorporating nonvolatile memories like MRAM and ReRAM, mitigate these limitations but struggle with a mismatch between fixed computing resources and dynamically changing inference workloads. T o address these challenges, this study introduces a Heterogeneous-Hybrid PIM ( HH-PIM) architecture, comprising high-performance MRAM-SRAM PIM modules and low-power MRAM-SRAM PIM modules. We further propose a data placement optimization algorithm that dynamically allocates data based on computational demand, maximizing energy efficiency. FPGA prototyping and power simulations with processors featuring HH-PIM and other PIM types demonstrate that the proposed HH-PIM achieves up to 60.43% average energy savings over conventional PIMs while meeting application latency requirements. These results confirm HH-PIM's suitability for adaptive, energy-efficient AI processing in edge devices. With the advent of artificial intelligence (AI), real-world applications are rapidly expanding, fueling a trend to embed AI capabilities into IoT devices across diverse fields. However, traditional server-centric data processing, such as cloud computing, faces significant energy and latency challenges due to processing and communication overloads.

Electronic devices are essential for robots but limit their usable environments. To overcome this, methods excluding electronics from the operating environment while retaining advanced electronic control and actuation have been explored. These include the remote hydraulic drive of electronics-free mobile robots, which offer high reachability, and long wire-driven robot arms with motors consolidated at the base, which offer high environmental resistance. To combine the advantages of both, this study proposes a new system, "Remote Wire Drive." As a proof-of-concept, we designed and developed the Remote Wire-Driven robot "REWW-ARM", which consists of the following components: 1) a novel power transmission mechanism, the "Remote Wire Transmission Mechanism" (RWTM), the key technology of the Remote Wire Drive; 2) an electronics-free distal mobile robot driven by it; and 3) a motor-unit that generates power and provides electronic closed-loop control based on state estimation via the RWTM. In this study, we evaluated the mechanical and control performance of REWW-ARM through several experiments, demonstrating its capability for locomotion, posture control, and object manipulation both on land and underwater. This suggests the potential for applying the Remote Wire-Driven system to various types of robots, thereby expanding their operational range.

Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists. This is because the vast majority of existing methodologies exploit various levels of approximations that lower accuracy and limit the flexibility of kernel and noise-model designs -- an unacceptable drawback at a time when expressive non-stationary kernels are on the rise in many fields. Here, we propose a methodology we term \emph{gp2Scale} that scales exact Gaussian processes to more than 10 million data points without relying on inducing points, kernel interpolation, or neighborhood-based approximations, and instead leveraging the existing capabilities of a GP: its kernel design. Highly flexible, compactly supported, and non-stationary kernels lead to the identification of naturally occurring sparse structure in the covariance matrix, which is then exploited for the calculations of the linear system solution and the log-determinant for training. We demonstrate our method's functionality on several real-world datasets and compare it with state-of-the-art approximation algorithms. Although we show superior approximation performance in many cases, the method's real power lies in its agnosticism toward arbitrary GP customizations -- core kernel design, noise, and mean functions -- and the type of input space, making it optimally suited for modern Gaussian process applications.

Patch robustness certification is an emerging kind of provable defense technique against adversarial patch attacks for deep learning systems. Certified detection ensures the detection of all patched harmful versions of certified samples, which mitigates the failures of empirical defense techniques that could (easily) be compromised. However, existing certified detection methods are ineffective in certifying samples that are misclassified or whose mutants are inconsistently pre icted to different labels. This paper proposes HiCert, a novel masking-based certified detection technique. By focusing on the problem of mutants predicted with a label different from the true label with our formal analysis, HiCert formulates a novel formal relation between harmful samples generated by identified loopholes and their benign counterparts. By checking the bound of the maximum confidence among these potentially harmful (i.e., inconsistent) mutants of each benign sample, HiCert ensures that each harmful sample either has the minimum confidence among mutants that are predicted the same as the harmful sample itself below this bound, or has at least one mutant predicted with a label different from the harmful sample itself, formulated after two novel insights. As such, HiCert systematically certifies those inconsistent samples and consistent samples to a large extent. To our knowledge, HiCert is the first work capable of providing such a comprehensive patch robustness certification for certified detection. Our experiments show the high effectiveness of HiCert with a new state-of the-art performance: It certifies significantly more benign samples, including those inconsistent and consistent, and achieves significantly higher accuracy on those samples without warnings and a significantly lower false silent ratio.

Self-driving laboratories (SDLs) have accelerated the throughput and automation capabilities for discovering and improving chemistries and materials. Although these SDLs have automated many of the steps required to conduct chemical and materials experiments, a commonly overlooked step in the automation pipeline is the handling and reloading of substrates used to transfer or deposit materials onto for downstream characterization. Here, we develop a closed-loop method of Automated Substrate Handling and Exchange (ASHE) using robotics, dual-actuated dispensers, and deep learning-driven computer vision to detect and correct errors in the manipulation of fragile and transparent substrates for SDLs. Using ASHE, we demonstrate a 98.5% first-time placement accuracy across 130 independent trials of reloading transparent glass substrates into an SDL, where only two substrate misplacements occurred and were successfully detected as errors and automatically corrected. Through the development of more accurate and reliable methods for handling various types of substrates, we move toward an improvement in the automation capabilities of self-driving laboratories, furthering the acceleration of novel chemical and materials discoveries.

Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization.

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.

Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupled, large-scale modeling of on-chip magnon-photon circuits. T o accelerate design workflows, we develop a physics-informed machine learning surrogate trained on the simulation data, reducing computational cost while maintaining accuracy. This combined approach reveals real-time energy exchange dynamics and reproduces key phenomena such as anti-crossing behavior and the suppression of ferromagnetic resonance under strong electromagnetic fields. By addressing the multiscale and multiphysics challenges in magnon-photon modeling, our framework enables scalable simulation and rapid prototyping of next-generation quantum and spintronic devices. 1 Introduction Hybrid quantum systems, which combine distinct physical platforms, are a promising route toward advanced quantum technologies, as they harness strong interactions that may not be readily achievable in a single platform [1, 2]. These systems take many forms, coupling any two (or more) quantum platforms -- for example, superconducting qubits [3, 4], microwave resonators [5], single spins [6], spin ensembles [4, 7-9], or mechanical resonators [10-12] -- to harness strong interactions. These heterogeneous systems leverage complementary advantages of each component, but their rich multi-physics interactions pose formidable modeling challenges. A prominent example is cavity magnonics, where collective spin excitations (magnons) couple with microwave photons in a resonant cavity to form hybrid magnon-polariton modes when tuned into resonance [13-15]. These states are essential for quantum operations such as mode swapping [16, 17], quantum state storage [4, 18, 19], and dynamic control of energy exchange [19, 20]. The hallmark experimental signature of strong magnon-photon coupling is a pronounced avoided crossing (mode splitting) in the frequency spectrum, in agreement with theoretical predictions [21] and observed in many 3D [13, 22] and on-chip 2D [7, 8, 23] cavity based systems.