5 accepted papers
ML attacks on Learning with Errors (LWE) with binary or small secrets only succeed on LWE settings with very simple secrets. For example, they can recover secrets with up to three non-zero bits when models are trained on not-reduced LWE data, and three non-zero bits in the ''cruel region'' [9] when …
Scaling laws have played a major role in modern AI, providing predictive power over how model performance will improve with increasing resources. This has spurred intense interest in their origin, with a common suggestion being that they arise from power laws already present in the data. Here we stu…
Recent work showed that ML-based attacks on Learning with Errors (LWE), a hard problem used in post-quantum cryptography, outperform classical algebraic attacks in certain settings. Although promising, ML attacks struggle to scale to more complex LWE settings. Prior work connected this issue to the…
AI-powered attacks on Learning with Errors (LWE)—an important hard math problem in post-quantum cryptography—rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study an…
Despite their spectacular progress, language models still struggle on complex reasoning tasks, such as advanced mathematics. We consider a long-standing open problem in mathematics: discovering a Lyapunov function that ensures the global stability of a dynamical system. This problem has no known gen…