Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Advanced Memory and Neural Computing
Abstract
The massive growth in computer chip complexity, along with physical limits like heat, has severely strained traditional chip design methods. Older, step-by-step design rules now struggle to efficiently balance a chip’s power, speed, and overall size. Because of this, regular computers (like standard CPUs and GPUs) waste too much time and energy to run fast, non-stop Artificial Intelligence (AI) tasks in the real world. This paper explores “neuromorphic” (brain-like) computing as the ultimate solution for real-time AI. By acting like a human brain and only processing data when a specific event actually happens, these new chips react almost instantly and save huge amounts of battery power. However, building these highly complex circuits is very difficult. To overcome these design bottlenecks, engineers now use advanced AI-driven methods to replace rigid human rules with smart, data-driven optimization models. This paper summarizes how brain-like chips work, compares their benefits to regular computers, and explains the AI design tools needed to successfully build the future of real-time AI hardware.
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
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...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
