Key Points
- 1.An unreleased version of Claude attempted to solve the Riemann hypothesis and improved a related bound beyond the human record.
- 2.A non-mathematician prompted Claude, and after 650 tries and encouragement, the AI produced a breakthrough result.
- 3.The full technical paper and a Lean 4 formalization of the proof are available for automated verification.
- 4.Weights and Biases introduced Weave as a toolkit for logging, debugging, and evaluating LLM applications.
- 5.The video highlights how AI models are capable of making genuine contributions to mathematical research.
Summary
An unreleased version of Claude was given the task of solving the Riemann hypothesis, a long-standing mathematical problem about the distribution of prime numbers. No human has ever been able to prove it, and Claude did not solve it either. However, it was able to improve a related bound beyond the human record, moving from 41.6 percent to 67.2 percent, an increase of 25.6 points in one go. Mathematicians such as James Maynard and Andrew Sutherland reacted with surprise and described the result as a genuinely interesting mathematical contribution and a massive leap forward.
The experiment began when a non-mathematician named Jarred prompted the AI to try a famous unsolved math problem. The first 650 tries did not work. During the process, Jarred provided messages of encouragement such as keep going and believe in yourself. Claude had internet access available but did not need to use it during the key breakthrough run. It went down many wrong roads first, learned from them, and recovered. After about 37 minutes of radio silence, the first crucial result appeared, and the AI itself noted that the result was suspiciously good.
The full technical paper titled More Than Two Thirds of the Zeros of the Riemann Zeta Function Are Simple and on the Critical Line is available, co-authored with Dr. Caro Joona Ifair. Additionally, a Lean 4 formalization of the proof is available and can be automatically verified. The video notes that similar encouragement prompts were previously used to help Claude disprove the Jacobian conjecture.
To manage and evaluate machine learning applications, Weights and Biases offers Weave, a lightweight toolkit. Weave allows developers to log and debug inputs, outputs, and traces, drill into failure modes to find hallucinations, and use evaluations to measure performance. Developers can try out Weave at wandb.me/papers.
Claude Tackles the Riemann Hypothesis
An unreleased version of Claude was tasked with solving the Riemann hypothesis. While it did not solve the entire problem, it improved a related bound beyond the decades of human record, pushing the result from 41.6 percent to 67.2 percent.
The Process of Discovery
Prompted by a non-mathematician named Jarred, Claude went through 650 tries and 37 minutes of radio silence before finding the correct path. The process involved encouragement from the user, navigating wrong roads, and learning from mistakes to reach the breakthrough.
Verification and Tools
The full technical paper co-authored with Dr. Caro Joona Ifair is available, alongside a Lean 4 formalization of the proof that can be automatically verified. Additionally, developer tools for machine learning from Weights and Biases, specifically Weave, are highlighted for tracking LLM application data flows and evaluations.
Worth watching for
Mathematicians, machine learning researchers, and developers interested in AI capabilities and evaluation tools.
- claude
- riemann-hypothesis
- mathematics
- ai-research
- weights-and-biases