Generative language modeling for automated theorem proving
Summary
OpenAI presents GPT-f, a transformer-based automated theorem prover for the Metamath formalization language, which discovered new short proofs accepted into the main Metamath library — marking the first time a deep-learning system contributed proofs adopted by a formal mathematics community.
View Cached Full Text
Cached at: 04/20/26, 02:55 PM
Similar Articles
Better language models and their implications
OpenAI introduces GPT-2, a 1.5 billion parameter transformer-based language model trained on 40GB of internet text that achieves state-of-the-art performance on language modeling benchmarks and demonstrates zero-shot capabilities in reading comprehension, translation, question answering, and summarization. Due to safety concerns, only a smaller model and technical paper are released publicly rather than the full trained model.
openai-community/gpt2
This page details GPT-2, a transformer-based language model pretrained on English text for text generation, available on Hugging Face with usage instructions and examples.
Advancing science and math with GPT-5.2
OpenAI releases GPT-5.2, featuring GPT-5.2 Pro and GPT-5.2 Thinking variants optimized for scientific and mathematical work. The models achieve state-of-the-art performance on benchmarks like GPQA Diamond (93.2%) and FrontierMath (40.3%), demonstrating improved reasoning capabilities designed to accelerate scientific research across physics, chemistry, biology, and mathematics.
Researchers are already significantly improving on OpenAI’s recent math results, verified in Lean
Researchers are quickly surpassing OpenAI's recent AI-generated math results, with improved proofs formally verified in Lean, highlighting rapid progress in AI-assisted theorem proving.
OpenAI just dropped 700 preprints of mathematical proofs and counterexamples
OpenAI released a repository containing 722 mathematical manuscripts (700 preprints) grouped into 372 families of proofs and counterexamples generated by an unreleased internal model, along with some Lean formalizations and abridged reasoning traces. The results span areas like the Riemann zeta function, Mahler conjectures, and free group factors, and were produced using roughly three hours of ChatGPT Pro thinking compute per result on about 4,000 posed problems.