Science

As large language models increasingly take on evaluative roles, understanding their inherent biases becomes crucial for reliable decision-making.

Researchers have unveiled CVA6-CFI, a RISC-V core featuring hardware-accelerated control-flow integrity extensions designed to bolster security in embedded systems and beyond.

Researchers have developed an enhanced Monte Carlo simulation technique to more accurately model and understand the behavior of superfluid systems.

Researchers have developed a novel technique to predict relationships within complex knowledge graphs, offering improved accuracy and adaptability to unseen data.

New research demonstrates how hash functions can dynamically adjust to data distributions, minimizing collisions and boosting efficiency.

A new framework treats service reliability as code, enabling microservices to dynamically adapt and maintain performance based on defined objectives.

New research reveals that current methods for evaluating the security of code created by artificial intelligence are often misleading, and proposes a more robust, human-guided approach.

Researchers have developed a new training technique that dramatically reduces the size of large language models without sacrificing accuracy.

New research reveals that strategically selecting adversarial prompts can dramatically compromise the safety of AI control protocols, even when paired with trusted monitoring.

New research quantifies the minimal performance trade-off inherent in universal vector quantization techniques.