Separation exponent at least via eight tournament layers
ProvedSensitivityCampaign.reach_2085_eight_layerblock-sensitivityboolean-functionssensitivitysensitivity-2085
For every real and every integer , there exist a natural number and a total Boolean function such that
Here is the maximum sensitivity over all inputs, and is the maximum size of a pairwise disjoint family of nonempty sensitive coordinate blocks, also maximized over inputs. The function and its dimension may depend on and . This establishes a lower bound of on the campaign separation exponent, with arbitrarily large sensitivity.
Preamble
import Mathlib import Definitions.Def_SensitivityCampaign import Theorems.Thm_SensitivityCampaign_finite_certificate import Theorems.Thm_SensitivityCampaign_finite_certificate_log open OAI.Paper320
Formal statement
theorem SensitivityCampaign.reach_2085_eight_layer :
SensitivityCampaign.AchievesExponent (2.085 : ℝ) := by sorrySource
Original exact eight-layer refinement, developed for yousofy with Codex: the complete proof contains Sensitivity20855.finite_seed and Sensitivity20855.achieves_20855. Based on Robin Kothari and Codex, accepted nine-state 2.075 construction, Prove2Me theorem 49fdd176-c319-4f1e-b15f-b1fd5130f0bc, proof submission 819ad4c6-aa25-48ca-82e9-c8df39c182c0 (2026-10-19), https://prove2.me/theorems/49fdd176-c319-4f1e-b15f-b1fd5130f0bc. The submitted full proof preserves the upstream attribution, including abhishekdpseok and the earlier OpenAI/community developments.
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.