AlphaProof Nexus solves dozens of math problems
The Decoder: Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed
Google Deepmind's AlphaProof Nexus solved dozens of open math problems, including nine Erdős problems two of which were unsolved for 56 years, per a study in Science. The publication comes as OpenAI faces a boycott for dumping AI proofs on GitHub without peer review. Deepmind had its results reviewed before publishing. The article Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed appeared first on The Decoder.
Get smart on it
Google Deepmind's AlphaProof Nexus is an AI system that solved dozens of previously unsolved mathematics problems, including nine open problems from a well-known list, with two of those problems unsolved for 56 years. The system works by using a language model to draft and revise mathematical proofs written in a formal computer language that automatically checks each attempt for errors, sometimes calling on a specialized proof tool for harder problems. This development matters because it represents a different approach from a recent competing effort: while another AI lab released hundreds of AI-generated proofs on an online code repository without peer review, Deepmind submitted its work through the traditional academic peer review process and published the results in a scientific journal. The work comes amid broader debate in the mathematics community about how AI-generated proofs should be handled and shared.
Relevant courses
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on MavenRelated news
AI agents overstate their results and remain far from autonomous research, study finds
Epoch AI and Anthropic independently found the same thing: current AI models like GPT-5.6 Sol and Claude Fable 5 can run experiments but lack scientific self-criticism and genuine creative thinking. At best, Sol reached 15 percent of the human reference score, and even that came from methods researchers already knew. The models' biggest weakness is still their inability to critically question their own results. The article AI agents overstate their results and remain far from autonomous
AI agent teams waste massive tokens for barely measurable quality gains, research finds
Teams of AI agents barely outperform solo agents but cost up to 5.1x more, according to Vals AI. Only one out of four tests with GPT-6 Sol and Claude Opus 5.5 showed a measurable gain. Anthropic's own data backs this up: beyond ten agents, quality plateaus while token costs keep climbing. The article AI agent teams waste massive tokens for barely measurable quality gains, research finds appeared first on The Decoder.
‘Pure insanity’: Mathematicians will need years to make sense of OpenAI’s latest drop
"Staggering." "Overwhelming." "Unprecedented." "Surreal." "Pure insanity." Those were among the descriptions more than three dozen mathematicians reached for in conversations with The Verge as they tried to make sense of the flood of mathematical results OpenAI abruptly dropped on the field this week. Amid the awe, excitement, and uncertainty over the sheer scale of the deluge was a deep-seated anxiety over what it all means - and what comes next. For all their different react
Asana cuts model costs 76x in browser tests with GPT-6.1 Sol
Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.
Google Research RRSI Guide: Mastering Self-Improving AI Agents
In this tutorial, we implement RRSI (Regularized Recursive Self-Improvement), a method that lets an LLM agent rewrite its own harness, prompts, tools, memory, control flow, and sub-agents around a frozen model, without the harness overfitting to the tasks it evolves on. The full RRSI loop drafts edits with Claude Opus on Vertex AI and scores them inside Docker benchmarks, which is not something a free notebook can run. The part of RRSI that actually carries the paper’s idea, the rules tha
OpenAI’s math solutions aren’t meeting the field’s standards yet
OpenAI's flood of proofs deviated from the guidelines set by a group of mathematical researchers consulted by the frontier lab.