OpenAI reports computability of the random 3-SAT threshold

For uniformly signed proper random 3-SAT clauses sampled independently with replacement, the finite positive limiting satisfiability threshold is a computable real: one deterministic machine takes a requested precision and returns a rational approximation within that error.

Sources for this record

What was already known

Gaia Carenini established the limiting-threshold existence result for every fixed k ≥ 3 in ECCC TR26-229, published October 5, 2026. OpenAI credits her with priority for that conjecture. The computational companion adds an oracle-free precision algorithm; existence alone does not give a computable rate of finite-size convergence (first OpenAI manuscript Section 1.1, pp. 1–2; companion abstract).

AI involvement AI-led

OpenAI credits itself as manuscript author and states that its internal frontier model produced the results in this release. Individual human input is not detailed enough to establish task-only autonomy.

OpenAI is named as author on the manuscript, and its release attributes the results to an internal frontier model. The individual human input is not detailed enough to establish autonomy.

Importance Notable

Turns existence of the random 3-SAT threshold into an effective, terminating approximation procedure.

Limitations

Only the proper uniform random 3-SAT threshold is covered; no efficiency bound, explicit numerical value, behavior at the threshold, or computability for other clause sizes is asserted.

Technical verification Formal (Lean 4)

OpenAI reports a Lean proof matching the selected claim.

Formal checking

Pinned OpenAI Lean solution module contains a proved matching headline theorem. This is source-reported code inspection; local build and imported-module audit were not performed.

Checker
OpenAI-reported Lean 4
Scope
The computable 3-SAT threshold, including a deterministic rational approximation for every precision.
Basis
Reported by the linked checker
Evidence source

Checking a formal statement does not establish that it matches the original problem.

Human understanding Few

Few is the default when evidence for a higher level is insufficient. No named person is documented as explaining the central argument for this assessed contribution.

Correction and publication history

2026-10-18 · publication

Primary manuscript, attribution, prior-work, and verification-scope review for non-notifying local inclusion.

Read this published revision