←News detail

English text is being prepared.Updated Oct 27 13:28

OpenAI announces AI model achieves three scientific breakthroughs, involving the local smoothing conjecture, ETH, and the existence of quantum field theory

AI summary

OpenAI announced that its AI model has achieved three breakthroughs in the fields of mathematics, computer science, and physics. The mathematics and computer science results have been formally verified through Lean and the proof materials have been made public, while the physics results are being reviewed by experts including Nobel Prize laureates in physics. In mathematics, the model provided a proof of the local smoothing conjecture in all dimensions, and thereby resolved the Kakeya conjecture and the Stein restriction conjecture in all dimensions; in computer science, the model provided a proof that the Exponential Time Hypothesis (ETH) is false. OpenAI has invited external experts to lead the verification and interpretation of the results, and has uploaded the natural-language proofs and Lean proofs to a public GitHub repository.

Why read this

SelectedAI score 90

OpenAI says the model produced proofs in three fields—mathematics, computational complexity, and physics—and has released Lean formalization materials, allowing readers to learn about its verification and collaboration mechanisms.

Topics

Tags

Body · AI translation

OpenAI Announces Three Major Scientific Breakthroughs, Involving Mathematical Conjectures, Computer Algorithms, and a Millennium Prize Problem

English text is being prepared.

English text is being prepared.

English text is being prepared.

OpenAI stated that the mathematics and computer science results have been formally verified using the Lean mathematical verification tool, with proof materials made public; the physics result is being reviewed by experts including Nobel laureates in physics. The results still require further independent confirmation by the academic community.

In 1991, mathematician Christopher Sogge proposed the local smoothing conjecture, an attempt to explain the behavior of waves when they focus. According to this conjecture, even if a wave exhibits extremely strong focusing, it can only persist for a very short time. If a representative moment is chosen at random, the degree of focusing observed will always be far below the most extreme case.

Similar phenomena often appear in mathematics: a phenomenon may reach extremely extreme levels in the worst case, but after averaging, the situation actually observed is much milder.

The phenomenon described by the local smoothing conjecture is of foundational significance, and therefore relates to numerous problems in mathematical analysis. Among them, both the Kakeya conjecture and the Stein restriction conjecture can be derived from the local smoothing conjecture.

In 2019, Guth, Wang Hong, and Zhang Ruixiang proved the local smoothing conjecture in two-dimensional space. In 2025, Wang Hong and Zahl also proved the three-dimensional Kakeya conjecture.

OpenAI's model this time provided a proof of the local smoothing conjecture in all dimensions, thereby also resolving the Kakeya conjecture and the Stein restriction conjecture in all dimensions. The key to the proof is a newly constructed auxiliary partial differential equation.

OpenAI obtained the AI-generated proof on October 18, 2026, and completed the Lean formal proof the next day. Subsequently, OpenAI contacted external experts to discuss how to establish a cooperation mechanism around this result.

Based on expert advice, OpenAI decided to directly publish the AI-generated proof, using the same approach as when it released research results on October 6. Currently, both the natural-language proof written by the model and the Lean formal proof have been uploaded to a public GitHub code repository.

Exponential Time Hypothesis Refuted

The computer science discovery concerns the Exponential Time Hypothesis (ETH).

ETH is a long-standing conjecture in computational complexity theory that studies the computational difficulty of the Boolean formula satisfiability problem and is closely related to the famous P versus NP problem.

For a long time, the academic community has generally believed that ETH holds. Hundreds of research papers have used this hypothesis as a premise to derive computational hardness conclusions in fields such as graph theory, computational biology, databases, and computational geometry.

Recently, however, OpenAI's system provided a proof that ETH does not hold, and completed formal verification through Lean.

After ETH is refuted, researchers will need to re-examine how much speedup can still be obtained for many computational tasks. The new result also helps guide research into more efficient algorithms. However, finding algorithms that can achieve these improvements and translating theoretical results into practical applications will still require a great deal of follow-up research.

OpenAI invited three experts in algorithm design and computational complexity theory to lead the verification, study, and interpretation of this result: Russell Impagliazzo and Ramamohan Paturi of the University of California, San Diego, and Ryan Williams of MIT.

All three experts have long-standing research records in this field:

Impagliazzo and Paturi, together with Francis Zane, jointly proposed ETH and have published multiple influential papers around this hypothesis over the past 30 years.

Williams has proved that certain strengthened versions of ETH do not hold, has also derived computational hardness conclusions based on ETH, and has established connections between algorithms and computational complexity lower bounds.

OpenAI accepted two requests from the collaborating team: first, to publish all raw data from the research results so that other researchers can independently verify them; second, for OpenAI to provide funding to hold an academic conference to discuss the overall impact of the new results on the field of computer science.

The collaborating team also invited the academic community to jointly organize a public review of the ETH proof and to discuss questions raised by AI research breakthroughs, including how to verify results, how to open research materials, how to recognize academic contributions, and how scientific research should be conducted in the future.

OpenAI generated the natural-language proof refuting ETH on October 16, and completed the Lean formal proof on October 17. It then began establishing the aforementioned external cooperation mechanism.

The model-generated natural-language proof and Lean proof have now been published in OpenAI's public GitHub code repository.

Existence of Quantum Chromodynamics and a Unified Completion of the Standard Model

In physics, OpenAI presented a research result on the existence of quantum field theory.

In a rigorous mathematical sense, this result proves that certain interacting quantum field theories containing gauge fields and fermions in four-dimensional space do indeed exist, including quantum chromodynamics (QCD), as well as a unified completion theory of the Standard Model of particle physics.

The proof adopts the research method proposed by Balaban in constructive quantum field theory. In addition, the research also solved a subproblem, namely proving the existence of pure Yang-Mills theory. This problem is part of the Quantum Yang-Mills Theory Millennium Prize Problem established by the Clay Mathematics Institute.

The Standard Model of particle physics describes the interactions between subatomic elementary particles, covering three of the four fundamental interactions in nature, excluding gravity.

Because the Standard Model itself cannot be directly extended to arbitrarily short distance scales, OpenAI's existence proof targets the simplest grand unified theory containing the Standard Model gauge fields and fermions.

Quantum chromodynamics studies how particles such as quarks and gluons are connected through the strong interaction. Pure Yang-Mills theory covers a narrower scope, involving only the self-interactions among the gluons that mediate the strong interaction, excluding matter particles such as quarks.

OpenAI invited David Gross to lead the research, and other team members included Michael Douglas, Arthur Jaffe, Igor Klebanov, and Nikita Nekrasov.

All five scholars have made important contributions in the fields of quantum field theory and mathematical physics:

David Gross: In 1973, his participation in research on asymptotic freedom in Yang-Mills theory laid the foundation for quantum chromodynamics to become the recognized theory of the strong interaction, and earned him the 2004 Nobel Prize in Physics. Asymptotic freedom is also the key mechanism for rigorously proving the mathematical existence of these theories.

Arthur Jaffe: Together with Witten, he formulated the Quantum Yang-Mills Theory Millennium Prize Problem, was one of the proposers of the concept of establishing the Millennium Prize Problems, and participated in founding the Clay Mathematics Institute.

Michael Douglas: Over the past 20 years, he has been involved in advancing research on this problem, reported research progress in 2004, and comprehensively reviewed the latest results earlier this year.

Igor Klebanov: He was among the earlier researchers to use the duality between gauge fields and string theory to study confinement, an important property of quantum chromodynamics. He currently leads the Simons Foundation's collaboration on confinement and quantum chromodynamics strings.

Nikita Nekrasov: He achieved pioneering results in exact solution methods for generalized forms of Yang-Mills theory, and received the 2023 Dannie Heineman Prize for Mathematical Physics.

References

Official announcement: Working with academic communities to build scientific understanding

Advertising statement: External redirect links contained in the article (including but not limited to hyperlinks, QR codes, passcodes, etc.) are used to convey more information and save selection time; the results are for reference only. All articles on IT Home that contain external links include this statement.

Source

English text is being prepared.