EU Regulatory Changes

This paper, published on arXiv, reveals a significant privacy vulnerability in federated learning for large language models. It demonstrates that while federated learning is designed to protect dat...

This paper, published on arXiv, introduces a new technical framework called Sovereign Execution Brokers, which proposes a method for enforcing certificate-bound authority in AI agentic control plan...

This publication introduces a novel probabilistic verification framework for AI agents, designed to formally assess the safety and reliability of autonomous decision-making systems. The authors pro...

A new research paper published on arXiv, titled "Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software," raises significant co...

This publication introduces A-COMPASS, a formal mathematical framework for analyzing anonymity in microdata, which is detailed, individual-level data often used in research and analytics. The paper...

This paper, published on arXiv, presents a new analysis of defensive techniques against automated attacks on agentic AI systems—AI that can autonomously take actions. It specifically examines how "...

This publication from arXiv presents a novel image encryption algorithm that integrates convolutional neural networks with dynamic S-box generation. While not a regulatory change itself, it signals...

This paper, published on arXiv, presents a novel technical approach for classifying malware using large language models (LLMs) through a process called multi-view decompilation. Rather than a regul...

This paper, published on arXiv, presents a new framework for evaluating the safety of large language model (LLM) agents, specifically focusing on "multi-turn red-teaming" and adversarial robustness...