Meta and NYU’s h1 system beats elaborate theorem-proving workflows at lower cost

A minimal agentic loop built on Lean feedback posts top benchmark scores and solves eight open problems Researchers at Meta and New York University have built a stripped-down AI system for proving math theorems. It matched or beat carefully engineered Lean proof workflows, and it did so for less money. The system is called h1. Its core argument is that simplicity can beat complexity in automated theorem proving, provided the AI gets good feedback. What h1 actually does The work comes from Meta’s FAIR research team and NYU. Their paper is titled “h1: A Taste of the Bitter Lesson in Automated Theorem Proving.” It was released on October 25, 2026. The researchers describe h1 as a minimal agentic loop. It connects frontier large language models to Lean 4 and feeds them verifier results in real time. Most automated provers wrap the language model in an elaborate scaffold. They use predefined workflows that split problems into pieces, route subtasks, and orchestrate many moving parts. The h1 framework skips that machinery. Instead, it leans on a few simple mechanisms: Persistent file editing, so the model works on a proof file that keeps its state between steps Immediate compiler feedbac...