ChatGPT has generated a complete proof for a 50‑year‑old Erdős conjecture, demonstrating that large language models can perform formal mathematical reasoning. The solution was verified with a proof‑assistant workflow, marking a pivotal step toward AI‑augmented research and suggesting a new paradigm for tackling long‑standing mathematical challenges.
Breakthrough Overview
Cambridge undergraduate Kevin Barreto and independent researcher Liam Price prompted ChatGPT with the Erdős statement. The model produced a concise argument that the pair judged sophisticated. To ensure rigor, they fed the natural‑language proof into the AI verification tool Aristotle, which translated it into Lean and confirmed its logical validity.
About the Solved Erdős Conjecture
The conjecture belongs to the extensive catalog of Erdős problems, a collection of over 1,100 questions posed by Paul Erdős. While many remain open, this particular case resisted resolution for five decades despite its relatively simple formulation.
Implications for Mathematical Research
The hybrid workflow—generating conjectural arguments with a language model and validating them with a formal proof assistant—could become a standard practice. It allows mathematicians to focus on intuition and high‑level strategy while delegating routine deduction to AI.
- Accelerated discovery: AI can quickly produce candidate proofs, shortening the exploratory phase.
- Broader accessibility: The approach lowers the barrier for non‑experts to contribute to advanced research.
- Enhanced verification: Formal tools like Lean provide a solid foundation for peer review.
Challenges and Next Steps
Community scrutiny remains essential. Although the Lean verification offers strong confidence, peer review will determine whether the argument meets established mathematical standards. Extending this method to more complex, higher‑dimensional problems—such as the remaining Millennium Prize conjectures—will require further experimentation.
Future Outlook
As AI tools integrate deeper into the mathematical workflow, the line between human insight and machine computation will continue to blur. This breakthrough suggests that longstanding mathematical mysteries may soon be within reach, heralding a new era of AI‑assisted discovery.
