Abstract 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 of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
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.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6