claimroutineinferred_dep
Nonpseudorandom excess decodes to game labeling
Excess payoff forces nonpseudorandom vertices, decoding a labeling above
global soundness bound for orderings
Statement (paper)
If , then for every injective . Equivalently, if , then more than a fraction of left vertices are nonpseudorandom and some labeling of has edge value greater than .
In plain English
If , then every injective ordering has . Equivalently, if , then more than a fraction of left vertices are nonpseudorandom and some labeling of has edge value above .
Why it's here
This turns the local bound for pseudorandom vertices into a global bound on the ordering value. Any excess over must come from nonpseudorandom left vertices. Each of those has a short, -independent candidate list of labels from the influential-coordinate bound, and choosing labels from these lists gives a game labeling of value above , contradicting .
Classified routine:
This is the standard soundness decoding step of UG-hardness reductions (Khot–Kindler–Mossel–O'Donnell style, as in Guruswami–Manokaran–Raghavendra for ordering CSPs). Nonpseudorandom vertices get short lists of influential coordinates, and choosing random labels from these lists yields a labeling whose value contradicts the game's value bound. It follows directly from the two preceding claims.
Uses
Used by
Sources
PaperOpen
- Section
- §A.2 The finite ordering transfer
- Page
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- Label
—- Line
appendices/finite-consequences.tex:805
Leannot aligned
Phase 2 aligns this node to a Lean declaration.
Friction
- infoinferred_depoutline: "Nonpseudorandom excess decodes to game labeling" rests on claim-prop-finite-ordering-transfer-3
- infoinferred_depoutline: "Nonpseudorandom excess decodes to game labeling" rests on claim-prop-finite-ordering-transfer-4