All OpenAI manuscripts

OpenAI · 140-05 · 2026-09-27 · Theoretical computer science

Replacing Gaussian observations in memory-constrained inference

preprints/Replacing-Gaussian-observations-in-memory-constrained-inference-September-27-2026

Family 140. Memory–sample lower bounds for noiseless Gaussian regression.

The authors are OpenAI. This companion does not verify the proof and does not adopt it as a theorem of Ngo Cheung’s series. It is not a premise. O.1 stays NOT_CLOSED. Anti-de Sitter results are not a de Sitter state, not Em02-E’s relational lapse, not XVI-P’s QdS scheme, and not XVI-Q’s sigma-image record. No quantum coefficient is admitted. No compilation is asserted.

Read against the standing problems

Standing problems. No standing problem is named in this text. It was still read against the nine problems and does not close any of them. The authors are OpenAI. The companion does not verify the proof. This is not a premise of a manuscript by Ngo Cheung. O.1 stays NOT_CLOSED. No quantum coefficient is admitted. No compilation is asserted. XVI-P · XVI-N

For fixed , a one-pass learner with persistent bits needs noiseless Gaussian samples to recover a unit vector to angular error with probability , uniformly in accuracy for large . Computation and randomized updates are unrestricted, but output uses only the terminal state, stopping index and fresh randomness.

Replacing the Gaussian rows used to select a finite message by independent rows increases the remaining conditional information by at most , for a uniform spherical signal, message entropy at most d2, and the specified row dimensions proportional to d. As an application, we prove that learners with persistent bits need exact observations to attain uniform-sphere angular success at least 3/5, for and a deterministic finite horizon.

Preprint source and Lean scope

The abstract above is the catalogue abstract. The sections below are the full preprint source, 0 sections. This page is not only the abstract. The manuscript file is loading.

This preprint is named in the Lean scope note for 140: Memory–sample lower bounds for noiseless Gaussian regression. The authors are OpenAI. This companion does not verify the formalization. It is not a premise. The OAI library is not imported. No compilation is asserted. O.1 stays NOT_CLOSED.

MemoryPrecision.lean · MemoryPrecision.lean

Statement file

NoiselessRegression.lean · NoiselessRegression.lean

Statement file

PosteriorReplicas.lean · PosteriorReplicas.lean

Statement file

GaussianFiniteEntropy.lean · GaussianFiniteEntropy.lean

Statement file

GaussianInformation.lean · GaussianInformation.lean

Statement file

ProjectionMoments.lean · ProjectionMoments.lean

Statement file

GaussianReplacement.lean · GaussianReplacement.lean

Statement file

SubsphereCurrent.lean · SubsphereCurrent.lean

Statement file