PSV is a training loop where an autonomous proposer generates formal problem specifications, a solver attempts programs/proofs, and a formal verifier accepts only fully proven solutions; verified wins become high‑quality training data for the solver. By replacing unit‑test rewards with formal verification as the selection mechanism, PSV makes self‑generated, provably correct mathematics and software a scalable outcome.
— If PSV generalizes, it changes the landscape of scientific discovery, software assurance, and industrial R&D—creating systems that can autonomously create and verify high‑confidence results and thus shifting regulatory, safety and workforce policy.
Alexander Kruel
2026.01.09
100% relevant
The article documents an AI‑generated/formalized Erdős problem (Terence Tao's page) and explicitly describes the PSV loop as replacing unit tests with formal verification and using difficulty‑targeted proposers to keep problems on the frontier (GPT‑5.2 Pro and Aristotle cited).
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