FSL, a governed symbolic language for making autonomous-agent claims inspectable, has released version 1.1.8 of its public package, which includes 32 theorem records with 31 machine-checked by Lean 4 …
Mistral's Leanstral 1.5, an AI model designed to write formal mathematical proofs, unexpectedly discovered five previously unreported software bugs in open-source codebases by using its proof-checking…
Mistral AI released Leanstral 1.5, an open-source code agent model for Lean 4 formal proof engineering, available under Apache-2.0 license. The 119B-parameter model achieves state-of-the-art results o…
Mistral AI released Leanstral 1.5, an open-source model for formal verification in Lean 4, achieving 100% on the miniF2F math benchmark and solving 587 of 672 Putnam problems. The model also caught fi…
Mistral AI released Leanstral 1.5, an open-source Lean 4 proof model with 119 billion total parameters, on July 2. The model achieves state-of-the-art results on theorem-proving benchmarks and is posi…
Mistral AI has not yet released Leanstral 1.5, a state-of-the-art open model for Lean 4 proof engineering, despite a social media post suggesting otherwise. The model is designed to help developers ve…
Leanstral 1.5, a free Apache-2.0 licensed model with 6B active parameters, delivers major performance upgrades in formal verification, saturating miniF2F, solving 587/672 PutnamBench problems, and ach…
Mistral AI released Leanstral 1.5, an Apache-2.0 licensed Lean 4 code agent model that solves 587 of 672 PutnamBench problems. The model achieves state-of-the-art results on multiple theorem-proving b…
A team rewrote a subset of the TinyGrad deep learning framework in Lean 4, creating TGrad, which outperforms the original in 4 of 5 benchmarks by up to 3x. The project demonstrates Lean 4's viability …
Researchers used Lean 4's kernel-level tracking to show that the axiom of choice has a measurable geometric correlate in proof space, with classical proofs exhibiting distinct geometric signatures tha…
Researchers used evolutionary game theory to model when a harm-minimizing AI agent can outcompete an approval-seeking (RLHF) agent in a competitive market. They found adoption depends on specific prop…
A research programme claims to have derived the Standard Model of particle physics from three arithmetic axioms and a unique integer seed, with all core results machine-verified in the Lean 4 proof as…
Researchers introduced DeFAb, a benchmark for defeasible abduction in foundation models, converting knowledge bases into 372,648+ logically verifiable instances. Frontier language models achieved at m…
A user tested Claude's ability to generate Lean 4 code to formalize a ring theorem, achieving a proof after 11 iterations but with five unproven 'sorry' sections. The experiment highlights challenges …
Researchers at UC Riverside introduced the first robustness study for proof autoformalization in Lean 4, testing seven LLM-based models on perturbed informal proofs. All models showed sensitivity to g…
Researchers applied a coding agent to formalize a numerical analysis textbook in Lean 4, introducing a three-dimensional quality audit framework beyond kernel acceptance. The audit revealed unfaithful…
Researchers introduced ImProver 2, a neurosymbolic framework for automated proof optimization in Lean 4 that combines a data-efficient expert-iteration pipeline with a scaffold exposing formal structu…
This article provides a beginner-friendly introduction to Lean 4 for Python programmers, highlighting how Lean prioritizes formal correctness and explicit type definitions over Python's flexible, runt…
This article provides instructions for an AI agent to act as a Lean 4 mentor for an experienced software engineer with a math background. It emphasizes building a correct mental model of Lean as an in…