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OpenAI's model independently solved the plane unit distance problem posed by Erdős in 1946, marking the first time AI has autonomously solved a core open problem in mathematics—a historic achievement.
An internal OpenAI model has disproved Erdős's unit distance conjecture, solving a famous open problem in mathematics and demonstrating AI's potential to contribute to high-level research.
An OpenAI researcher claims that their model's solution to an Erdős problem in discrete geometry is the biggest AI achievement to date, but predicts it will be overshadowed by end of year.
Sam Altman announces that a general-purpose AI model has solved a major open problem in mathematics, calling it a big milestone and expressing mixed feelings about AI's expanding capabilities.
OpenAI claims its general-purpose reasoning model discovered a counterexample to the conjectured upper bound in Erdős's planar unit-distance problem, producing a proof reviewed by mathematicians.
An internal OpenAI model achieved a breakthrough in the unit distance problem, a famous open conjecture in discrete geometry that had seen no progress in 80 years, by finding a new construction beating the grid's bound.
OpenAI's general-purpose reasoning model autonomously solved the planar unit distance problem, a famous open problem in mathematics posed by Paul Erdős in 1946, marking the first time AI has independently solved a prominent open problem in a field of mathematics.
An OpenAI model has autonomously solved the planar unit distance problem, a famous open mathematics question from 1946, disproving an 80-year-old assumption and achieving a first for AI.
OpenAI has achieved a breakthrough in mathematics: an AI model autonomously solved the planar unit distance problem, a famous open question from 1946, by discovering a new family of constructions that outperform square grids. This marks the first time AI has independently solved a prominent open problem in mathematics.
An article explaining the concepts of strong convexity and L-smoothness in optimization, known as the quadratic sandwich, and their role in gradient descent performance.
A comprehensive resource detailing multiple fast algorithms for computing the factorial function, including prime swing, split recursive, and Moessner's algorithm, with implementations in various programming languages.
An exploration of the meaning and implications of Gödel's incompleteness theorems, featuring insights from logicians, mathematicians, philosophers, and a physicist on how these theorems challenge the axiomatic method and the nature of mathematical truth.
Terence Tao pointed out that the math behind current LLMs is actually very simple, but the real puzzle lies in the intermediate zone of natural language data, which leads to unpredictable model behavior.
Stanford University offers a free online course on mathematical methods for computer vision, robotics, and graphics, including a full PDF textbook and video lectures, making high-quality education accessible to everyone.
Terence Tao states that the mathematics underlying modern LLMs is simple, using basic linear algebra and calculus, but the unpredictability of model performance across tasks remains a mystery due to the complex nature of natural language data.
Introduces a GitHub repository called Awesome Math that organizes free high-quality resources (videos, textbooks, problem sets) across 30+ math fields including algebra, geometry, analysis, etc. Updated continuously, suitable for math learners.
Researchers release SU-01, a 30B-A3B reasoning model achieving gold-medal-level performance on physics and math Olympiad problems using a unified scaling recipe for proof search.
A researcher trained small language models on their own self-generated coding mistakes and corrections, achieving 80% on HumanEval and surpassing GPT-3.5 on math, demonstrating effective self-improvement with minimal resources.
Two MIT students, Sunshine Jiang and Rupert Li, have been named 2026 Knight-Hennessy Scholars, receiving funding for graduate studies at Stanford University. Jiang researches embodied AI and robotics, while Li works in probability and discrete geometry.
GoodfireAI found that neural networks perform math by rotating shapes, uncovering a shape-rotating calculator inside an LLM that is used for more than just math.