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Q2Presentation.Lifting.FoxLedger

Elements of an exponent-2 kernel are their own inverses.

The additive Fox shadow of a word along a base marking q, evaluated at a kernel-valued perturbation n of the generators. The powOmega2 case uses the uniform exponent (omega2Mod |Y|).val.

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    The perturbed marking a ↦ q a · n a.

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      theorem Q2Presentation.Lifting.mul_mem_pow {Yt : BoundaryFramedTarget} (E : ElementaryKernel Yt) [Finite Yt.Y] (y : Yt.Y) (m : ↥E.N) (k : ℕ) :
      (y * ↑m) ^ k = y ^ k * ↑(Additive.toMul ((∑ i ∈ Finset.range k, conjEndN E y ^ i) (Additive.ofMul m)))

      Multiplicative-form power telescope: (y·m)^k = y^k · (Σ conj-telescope). The kernel term is the geometric sum of conjugations applied to m.

      theorem Q2Presentation.Lifting.eval_perturb {Yt : BoundaryFramedTarget} (E : ElementaryKernel Yt) [Finite Yt.Y] (q : Marking Yt.Y) (n : Gen → ↥E.N) (w : GExpr) :
      w.eval (perturb E q n) = w.eval q * ↑(Additive.toMul (foxShadow E q (fun (a : Gen) => Additive.ofMul (n a)) w))

      The Fox ledger (exact first-order law): evaluating any word at a kernel-perturbed marking multiplies the value by the word's Fox shadow.

      theorem Q2Presentation.Lifting.foxShadow_eq_zero_of_eval_eq {Yt : BoundaryFramedTarget} (E : ElementaryKernel Yt) [Finite Yt.Y] (q : Marking Yt.Y) (n : Gen → ↥E.N) (w : GExpr) (h1 : w.eval q = 1) (h2 : w.eval (perturb E q n) = 1) :
      foxShadow E q (fun (a : Gen) => Additive.ofMul (n a)) w = 0

      Row vanishing: if a word dies at both a marking and its perturbation, the perturbation's Fox shadow along that word is zero — the linear condition the relator rows impose on differences of lifts.

      Linearity of the shadow in the perturbation #

      theorem Q2Presentation.Lifting.foxShadow_add {Yt : BoundaryFramedTarget} (E : ElementaryKernel Yt) [Finite Yt.Y] (q : Marking Yt.Y) (n₁ n₂ : Gen → NAdd E) (w : GExpr) :
      foxShadow E q (n₁ + n₂) w = foxShadow E q n₁ w + foxShadow E q n₂ w

      The shadow of a fixed word, as an additive hom in the perturbation.

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        The shadow of a fixed word, as a linear map in the perturbation.

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          The kernel count under row-surjectivity #

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          The kernel count: a surjective row map has kernel of size |N|² (rank–nullity over the four generator slots and two relator rows).