OpenAI · 277-01 · 2026-09-25 · Mathematical physics
Threshold parallel repetition for finite-dimensional entangled games
preprints/Threshold-parallel-repetition-for-finite-dimensional-entangled-games-September-25-2026
Family 277. Threshold repetition for entangled games.
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
Proves exponential threshold repetition for every finite two-player one-round game: if its entangled value is , the probability of winning at least a fraction of independent repetitions decays exponentially in , for . Arbitrary joint finite-dimensional entangled strategies and correlated question distributions are allowed.
For every finite two-player game with entangled value v < 1, we prove exponential decay for the probability of winning at least a fraction of k independent repetitions, uniformly over all finite-dimensional joint strategies. The result allows arbitrary correlated question distributions and holds for every and k ≥ 1. Its universal rate is proportional to , where are the answer alphabets. For each fixed question distribution, we also obtain an explicit cubic rate in δ.
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 277: Threshold repetition for entangled games. 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.