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A classic essay by physicist Eugene Wigner discussing the remarkable and unexpected effectiveness of mathematics in describing natural phenomena.
OpenAI released AI-generated math breakthroughs that experts are calling research misconduct due to lack of academic rigor.
Tencent reports that its Hy3-powered Hyra research agent assisted in settling a 50-year-old sum-difference problem in mathematics, with the paper published on arXiv.
This paper investigates how visual evidence should be represented for predicting item difficulty in mathematics assessments, comparing text-only, visual textualization, and image-native modeling with LLMs and VLMs. It finds image-native modeling is a competitive alternative to textualization, with performance depending on VLM adaptation.
OpenAI's unreleased model Astra reportedly solved ten major open mathematics problems, with results formalized in Lean certificates, signaling a major leap in AI mathematical reasoning.
The author expresses surprise that Google DeepMind is not leading mathematical AI benchmarks despite its past foundational work in the area, while noting OpenAI's recent progress.
OpenAI releases manuscripts, formal Lean certificates, and reasoning walkthroughs for ten AI-achieved advances in mathematics and theoretical computer science, including results on sphere packing, non-sofic groups, and quantum parallel repetition.
OpenAI's internal next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.
This paper introduces a three-stage LLM pipeline for systematically generating and validating major mathematical conjectures, using Lean 4 formal verification and reflective validation to discover problems with high 'problem taste'.
Fields Medal winner Jacob Tsimerman is joining OpenAI to work on AI safety, using his mathematical expertise to address existential risks from AI.
A philosopher's essay on how AI reasoning models are disrupting mathematics, solving long-standing conjectures and forcing academia to rethink the future of research.
A tweet recommends an arXiv paper that explains the mathematical foundations of Transformers, covering tokenization, embeddings, multi-headed attention, and KV caching for applied mathematicians.
An essay argues that humans should receive primary credit for AI-assisted discoveries, countering OpenAI's claim that AI systems generating mathematical arguments should be attributed as discoverers.
A tweet highlights an arxiv paper by Michel Fabrice Serret that introduces Transformers and attention mechanisms from an applied mathematics perspective, covering vectorization, multi-head attention, and methods to reduce attention costs like KV caching and latent attention.
A reflective essay on OpenAI's AI solving ten open math problems, arguing that AI will eventually surpass human mathematicians and reshape mathematics and science.
A mathematician reflects on how recent advances in AI are affecting the field of mathematics and broader intellectual life.
Martin Casado asks for book recommendations about how the math and physics communities reacted to early computers solving integrals previously thought impossible to solve analytically, noting this reception is a missing piece of tech history knowledge.
OpenAI used an internal model, Astra, to solve ten mathematical problems that had stalled for over a decade, spending under $2,000 per problem and releasing Lean 4 formalizations and a paper. The results prompt reflections on AI's role in mathematics.
Noam Brown claims OpenAI's internal Astra model solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science, potentially marking a major step for scientific reasoning.
OpenAI's internal version of its next major model family Astra reportedly solved ten major open problems in mathematics and theoretical computer science, marking a major step for scientific reasoning.