Researchers have demonstrated a novel method for precisely manipulating quantum information in an Andreev molecule, offering a path toward scalable and sensor-free quantum computation.
A new agentic AI framework is proving remarkably effective at identifying security flaws in code changes before they’re committed, offering a significant leap forward in proactive vulnerability detection.
A new analysis reveals widespread public key reuse across diverse cryptocurrencies, exposing vulnerabilities in privacy and enabling improved entity clustering.
Researchers are exploring quantum optimization techniques to build more resilient and adaptable networks for unmanned aerial vehicles.
Researchers have developed a novel quantization technique that significantly improves the compression of large language models without sacrificing accuracy.
New research introduces a method for reliably training machine learning models with extremely limited precision, unlocking efficiency gains for large language models and beyond.
A new analysis reveals that despite the inherent security advantages of the CHERI architecture, vulnerabilities in core system components can undermine its compartmentalization features.
A new analysis reveals vulnerabilities in the communication protocols used by AI agents, potentially allowing attackers to hijack their functionality and bypass safety measures.
A novel federated learning framework allows financial institutions to share insights and improve anti-money laundering detection without compromising data privacy.
Researchers have developed an agentic framework that significantly improves the accuracy and cost-effectiveness of identifying vulnerabilities in Ethereum smart contracts.