Developing safe, high‑energy‑density energy storage systems is a central goal in electrochemistry. Silicon (Si) delivers a high theoretical specific capacity of 4200 mAh g-1, yet it suffers from severe volume expansion and interfacial degradation. Solid-state electrolytes (SSEs) can exert mechanical confinement and enable the formation of self-limited interfaces, rendering silicon-carbon (Si-C)/SSEs composite a highly promising anode system. This paper first analyzes the failure mechanisms in liquid-electrolyte systems, followed by an elaboration on the distinctive merits of Si-C-based solid-state anodes. Meanwhile, it identifies the core challenges confronting this system, including rigid interfacial contact, dynamic stress, and process compatibility issues. Recent research advances are reviewed from three critical perspectives: intrinsic material modification, interface engineering, and fabrication process optimization, covering diverse modification strategies at both the material and electrode levels. Finally, future research directions are prospected, with emphases on integrated material-device design, advanced in-situ characterization techniques, and artificial intelligence-empowered research and development, aiming to accelerate the practical deployment of low-voltage, high-energy-density solid-state batteries.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.