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Briefings and ideas about Formal verification from AI podcast discussions and the research they cite, with links to the original sources.
Alan Chan of GovAI proposes that AI systems be able to report misconduct they find. He would measure the tendency and give models an out, but not mandate it.
Updated Oct 14, 2026 IdeaAI-generated code will increasingly carry machine-checked proofs of correctness, Robinhood CEO Vlad Tenev argued.
Updated Sep 19, 2026A preprint reports GPT-6 Astra got 49 of 50 unpublished physics calculations right with web access, for about $2 each. On Moonshots, XPRIZE CEO Anousheh Ansari asked who will find new problems if AI solves the defined ones.
Grigore Rosu says provably correct monitors can fence in AI agents whose inner workings can't be proven. Host Nathan Labenz worries that, like RLHF, reliable tools could speed deployment of untrusted AI without solving alignment.
Runtime Verification's leaders say an LLM's Rust port of kernel code, checked by differential fuzzing and Lean proofs, surfaced Linux bugs. The company's write-ups show partial proofs that are only as good as their specifications.
OpenAI posted hundreds of AI-produced math manuscripts on October 6 and withdrew three a day later. A mathematicians' group objected; on Moonshots, Alex Wissner-Gross argued math is just the first field AI will bulk-solve.
Eric Weinstein says AI should be free to challenge math and physics leaders he accuses of holding their fields back. He calls agent swarms on the internet dangerous. He believes a rigorously air-gapped, problem-solving AI would be safe to run, but he acknowledges that its research might give it new means of escape.
Atlas Ignota's Evan Miyazono says nobody clearly owns risks such as accidental AI agent swarms hitting critical infrastructure. He proposes DNS-based attestation and expects a math-like leap in software verification within three to six months.
A preprint credits an unreleased Claude model with the seed of a 3SUM algorithm just below quadratic time. On Moonshots, the result was cited in an argument that labs' internal models are pulling away from open ones.
OpenAI researcher Noam Brown says multi-agent coordination deserves not even 10% of the credit for the claimed Navier–Stokes result. He adds that OpenAI has not run a single-agent comparison or measured what 10,000 agents added.
Panelists split over whether OpenAI's Navier–Stokes proof means AI will soon improve AI research; a skeptic called math narrower, while a risk-focused guest gave about 10% odds a bigger agent swarm designs a better AI architecture in six months.
OpenAI says an internal model running about 10,000 agents for 88 hours produced a forced Navier–Stokes blowup construction with a Lean-checked proof. Moonshots panelists stressed it concerns idealized fluids and noted disputes over credit.
Anthropic's agents formalized Wiles's existing proof of Fermat's Last Theorem in about 13 million Lean lines over eleven days, per its report, with occasional human direction and a dependency-tracking platform keeping runs from losing their work.