Two open problems, exact-arithmetic checking and a proof assistant, over a single weekend. The post Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming appeared first on Towards Data Science .
arXiv:2606. 18119v1 Announce Type: new Abstract: To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors.
We share our AI model’s proof attempts for the First Proof math challenge, testing research-grade reasoning on expert-level problems.
An online simulation and a novel method for increasing power The post How to Get More Statistical Power from Fewer Research Participants appeared first on Towards Data Science .
arXiv:2605. 22763v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research.
arXiv:2604. 24021v4 Announce Type: replace Abstract: We present QED, an open-source multi-agent system that turns human-provided research questions into complete mathematical proofs without further human guidance.
arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science .
arXiv:2608. 11195v1 Announce Type: new Abstract: AI agents are increasingly used in mathematics research, but it is often unclear how to use them effectively.
arXiv:2603. 02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub.
AI agents are increasingly used in mathematics research, but it is often unclear how to use them effectively. Towards this, we present an extensive case study of how AI was used to improve bounds on the Grothendieck constant $K_G$, which captures the hardness between combinatorial problems and their continuous relaxations.
arXiv:2608. 15979v1 Announce Type: new Abstract: Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules.
arXiv:2606. 06526v1 Announce Type: new Abstract: Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs.