GPT-6 Astra gives mathematicians a breather, and OpenAI says that's by design
OpenAI's GPT-6 Astra tops ulam.ai's ErdosBench math benchmark with 106 of 226 problems solved, while the company prioritizes recursive self-improvement over math optimization.
OpenAI's GPT-6 Astra leads ulam.ai's ErdosBench with a score of 3.23, solving 106 of 226 open math problems and fully solving 43, ahead of GPT-5.6 Sol's 78 solved problems. Chief scientist Jakub Pachocki said OpenAI deliberately avoided targeted math optimization to prioritize recursive self-improvement and automated alignment research. Benchmark developer Przemek Chojecki estimated the gain at 5-10% across tested math-research skills. Mathematician Terence Tao warned at the 2026 International Congress of Mathematicians that AI-generated proofs could shift mathematics from proof scarcity to proof overload.
- Astra scored 3.23 on ErdosBench, solving 106 problems, 43 fully, and disproving 27
- GPT-5.6 Sol trails with 3.12 and 78 problems solved at maximum reasoning
- Pachocki says OpenAI skipped targeted math optimization to focus on RSI and alignment
- Terence Tao warns of proof overload as AI generates proofs faster than verification
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- OpenAI
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- GPT-6 AstraGPT-5.6 Sol
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- GPT-6 AstraGPT-5.6 Sol
From the original article
3 min read at the-decoder.com
OpenAI's GPT-6 Astra tops the ErdosBench for open math problems, even though chief scientist Jakub Pachocki says math was deliberately not a priority. Instead, OpenAI is pouring resources into recursive self-improvement and alignment research.
By Matthias Bastian · the-decoder.com
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