Two mathematicians used large language models to discover counterexamples to long-standing conjectures, automating hypothesis generation and testing with minimal guidance. The article calls this “brute intelligence,” where AI runs iterative search loops to tackle any problem framed like a math exercise. It argues we’ll need to reshape tasks into testable, calculable formats for AI to industrialize discovery across fields.

Karpathy outlines three software eras: human-written code, trained neural weights, and now natural language prompts. He claims LLMs treat English as code, making traditional coding steps optional. The linked talk will explain why this shift matters in 40 minutes.

Deep Think with Confidence (DeepConf) is introduced as a method to improve reasoning efficiency and performance in large language models by using internal confidence signals to filter out low-quality reasoning traces. It requires no additional training or tuning and can be easily integrated into existing systems. Evaluations show significant accuracy improvements and a reduction in generated tokens on various reasoning tasks.