The χ-twisted crossed derivations, d̄-additivity, and tail separation #
Piece 4/6 of GQ2.Roe.Labute.StageLemma (see that module for the mathematical overview
and the statement freeze). SL1's separating functionals: the finite lift group
WL N = ℤ/2^N ⋊ (ℤ/2^N)ˣ, the two coordinate derivations out of the towers, the
d̄-image subgroup structure, and the endgame showing the derivations separate the
two tails.
The χ-twisted crossed derivations (SL1's separating functionals) #
SL1 needs functionals on Zₖ that kill Im d̄ and the defect but pair non-degenerately with
the two tails. The numerics report (docs/orchestration/sl1-numerics.md §6) identifies them
as digit-(k−1) shadows of θ-crossed derivations; the repo already carries the exact
(un-truncated) version of that calculus — Labute's descent condition
IsLabuteOrientationDatum (GQ2/Roe/CrossedDerivation.lean), which says that for the
canonical orientation χ every crossed derivation D(gh) = Dg + χ(g)·Dh kills the
presenting relator. So the functionals descend to the towers on the nose, with no relation
module theory: a derivation is just the .u-component of a hom into A ⋊ ℤ₂ˣ.
Since ℤ₂ ⋊ ℤ₂ˣ is not known here to be pro-2 (the universal properties drLiftHom /
d0LiftHom demand that), we run the whole calculus at the finite shadow
WL N = ℤ/2^N ⋊ (ℤ/2^N)ˣ, which is a finite 2-group by cardinality. All the sharp 2-adic
input (the orientation values, η⁻¹ = −3, v₂(X−1) = v₂(S−1) = 2, v₂(Y+1) = 3) stays in
ℤ₂ and is pushed down by the reduction ring hom.
The finite lift group ℤ/2^N ⋊ (ℤ/2^N)ˣ (WordLift, product law
(u,g)(v,h) = (u + g·v, gh)): the mod-2^N shadow of Labute's ℤ₂(χ) ⋊ ℤ₂ˣ.
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- GQ2.Roe.Labute.WL N = GQ2.FoxH.WordLift (ZMod (2 ^ N)) (ZMod (2 ^ N))ˣ
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The reduction ℤ₂ ⋊ ℤ₂ˣ → ℤ/2^N ⋊ (ℤ/2^N)ˣ (a group hom: reduction is a ring hom, so it
intertwines the two product laws).
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2-power divisibility in ℤ/2^N, read through the reductions #
Divisibility by 2^j in ℤ/2^N is read off any 2-adic lift (j ≤ N).
The derivations out of the two towers #
Direction 1: the derivation D_R → WL N with generator data (v i, χ_R). It exists
because χ_R is Labute's orientation — every crossed derivation kills r₂
(isLabuteOrientation_chiR), which is exactly the relator hypothesis drLiftHom wants.
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The base component is χ_R (mod 2^N): both sides are continuous homs D_R → (ℤ/2^N)ˣ
agreeing on s, x, y, so dr_hom_ext applies. The right-hand side is continuous because it
factors through the discrete level quotient Q_N.
The r₀-side Labute datum: with the orientation values (−1, 1, η) every crossed
derivation kills r₀ = A²S⁴[S,Y]. The computation is the one 2-adic miracle behind SL1:
the S⁴-block contributes 4·Ds and the commutator (η⁻¹ − 1)·Ds, and η⁻¹ = −3 exactly,
so the total (3 + η⁻¹)·Ds vanishes on the nose.
Direction 2: the derivation D₀ → WL N with generator data (v i, (−1, 1, η)).
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The derivation kernel in Zₖ, and the tail pairing #
Transport of the filtration bound λ_j(WL N) ⊆ K_j to the tower along a derivation.
The vanishing subgroup 𝒜_Φ ⊆ Q_{k+1}: classes admitting a λₖ-representative whose
derivation offset is divisible by 2^k. This is the Lean form of "the coker functional
digit_{k-1} ∘ D vanishes"; derivKer_dvd shows the condition does not depend on the
representative.
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Additivity of the shift word in the modification (Im d̄ is a subgroup) #
SL1 must produce a single modification, so the d̄-image has to be closed under products.
It is: every factor of d̄ is 𝔽₂-linear in w up to central corrections, and for k ≥ 3
all corrections lie in Zₖ, which is central.
Interleaving three central pairs.
Additivity of d̄ in the modification, r₀-side.
Additivity of d̄ in the modification, r₂-side.
The shift word lands in the central layer Zₖ.
The endgame: the coordinate derivations separate the two tails #
The tail parity relation: if a tail combination lies in every coordinate derivation's
kernel, the two coordinate vectors satisfy the corresponding 𝔽₂-linear relation.
Independence of a generating triple's coordinate vectors. The coordinate map
𝔽₂³ → 𝔽₂³, c ↦ ∑ cᵢ·θ(aᵢ), hits the standard basis (the classes of the aᵢ generate
Q_{k+1}, and θ factors through it), hence is onto, hence — same finite type — injective.