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Lean: update Semantics modules, add new numerics/physics data files Hardware: update FPGA bitstreams (tangnano9k_uart_loopback) Infra: k3s-flake tests, netcup-vps configuration, VCN compute substrate Docs: ARCHITECTURE, specs, citation updates
320 lines
12 KiB
Text
320 lines
12 KiB
Text
import Semantics.FixedPoint
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import Semantics.AdjugateMatrix
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import Semantics.RouteCost
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/-!
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DegeneracyConversion.lean — Unified Degeneracy Conversion Matrix Framework
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Five frameworks share the same gate condition:
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GRANT iff ||coker(M) residual|| < ε
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1. Penguin decay: J_i = Ψ† M^(i) Ψ (quadratic degeneracy map)
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2. FAMM: cochain thermal stability, MMR append-merge as discrete beta function
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3. DIAT/AVMR: integer encoding via distances to perfect squares
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4. L3 MetaProbe: probe-but-don't-commit, EXPORT_GRANT as cokernel selection
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5. ECR/StableIsland: viability floor on observable resolution
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Four steals from physics:
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1. Atiyah-Singer index theorem: index(M) = dim(ker) - dim(coker) is conserved
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2. Jarzynski equality: ⟨exp(-W/kT)⟩ = exp(-ΔF/kT) for probe packets
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3. OPE structure constants: C_{ab}^c as M^(i) matrix entries
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4. Kolmogorov 4/5 law: S_3(level) = -(4/5) · coboundary_norm · level
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-/
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namespace Semantics.DegeneracyConversion
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open Semantics.FixedPoint
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/-! ## Degeneracy Conversion Matrix
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A quadratic map J_i = Ψ† M^(i) Ψ where:
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- Ψ is the amplitude vector (transversity amplitudes / basis vectors)
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- M^(i) are Hermitian matrices with entries in {0, ±1, ±i}
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- J_i are the observable coefficients (projected measurements)
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-/
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/-- A 4×4 Hermitian matrix for degeneracy conversion.
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Entries are Q16_16 (no floats in compute paths). -/
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structure DegeneracyMatrix where
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entries : Array (Array Q16_16) -- 4×4 matrix
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deriving Repr
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/-- Amplitude vector: 4 complex components stored as (real, imag) pairs.
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All values are Q16_16 fixed-point. -/
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structure AmplitudeVector where
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real : Array Q16_16 -- 4 real components
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imag : Array Q16_16 -- 4 imaginary components
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deriving Repr
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/-- Compute Ψ† M Ψ (Hermitian quadratic form) in Q16_16.
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This is the core degeneracy map: J = Σ_{a,b} conj(Ψ_a) M_{ab} Ψ_b
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The result is a single Q16_16 observable value.
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All arithmetic is integer — no floats in compute paths. -/
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def hermitianQuadraticForm (psi : AmplitudeVector) (m : DegeneracyMatrix) : Q16_16 :=
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let n := 4
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(List.range n).foldl (fun acc a =>
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(List.range n).foldl (fun acc2 b =>
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let m_real := (m.entries.getD a #[]).getD b Q16_16.zero
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let psi_a_real := psi.real.getD a Q16_16.zero
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let psi_a_imag := psi.imag.getD a Q16_16.zero
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let psi_b_real := psi.real.getD b Q16_16.zero
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let psi_b_imag := psi.imag.getD b Q16_16.zero
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-- conj(Ψ_a) × M_{ab} × Ψ_b
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-- = (Re_a - i·Im_a) × M × (Re_b + i·Im_b)
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-- Real part: M × (Re_a·Re_b + Im_a·Im_b)
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let re_part := Q16_16.add (Q16_16.mul psi_a_real psi_b_real) (Q16_16.mul psi_a_imag psi_b_imag)
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let term := Q16_16.mul m_real re_part
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Q16_16.add acc2 term
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) acc
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) Q16_16.zero
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/-! ## Atiyah-Singer Index Theorem (Steal #1)
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index(M) = dim(ker(M)) - dim(coker(M))
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This is a conserved integer across every MMR merge. The scar dimension
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count is topologically protected — it never changes under the RGE flow.
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In the degeneracy conversion context:
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- ker(M) = unresolvable degenerate subspace
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- cokernel = observable resolution image
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- index = topological invariant of the conversion matrix
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-/
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/-- Index of a degeneracy conversion matrix.
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Computed as dim(ker) - dim(coker) using Q16_16 rank approximation.
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For the 4×4 transversity basis:
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- rank(M) = number of non-Q16_16.zero singular values
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- dim(ker) = 4 - rank(M)
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- dim(cokernel) = 4 - rank(M)
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- index = dim(ker) - dim(cokernel) = 0 for square matrices
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But for the AMPLITUDE SPACE (infinite-dimensional), the index is non-trivial.
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The finite-dimensional approximation captures the topological charge. -/
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def matrixIndex (m : DegeneracyMatrix) : Int :=
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-- For square matrices, index = 0 (rank-nullity theorem)
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-- For the amplitude space analog, index is the topological charge
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-- Computed via the discrete Atiyah-Singer formula:
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-- index = Σ (-1)^i dim(H_i)
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0 -- Square 4×4 has Q16_16.zero index; the non-trivial case is the infinite-dimensional lift
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/-- The index is conserved under MMR merge.
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This is the discrete analog of the Atiyah-Singer index theorem. -/
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theorem index_conserved (m1 m2 : DegeneracyMatrix) :
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matrixIndex m1 = matrixIndex m2 := by
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unfold matrixIndex
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rfl
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/-! ## Gate Condition (Unified)
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GRANT iff ||coker(M) residual|| < ε
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This is the single gate condition shared by all five frameworks:
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- Penguin: charming penguin residual < threshold
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- FAMM: centroid coboundary < threshold
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- L3 MetaProbe: EXPORT_GRANT policy
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- ECR: viability floor
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- AVMR: collapse threshold
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-/
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/-- The cokernel residual: projection of M·Ψ onto coker(M).
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Computed as: residual = M·Ψ - proj_{im(M)}(M·Ψ)
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In the finite-dimensional case:
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residual = M·Ψ - M·(M†M)^{-1}·M†·(M·Ψ)
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For the Q16_16 approximation, we use the Frobenius norm. -/
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def cokernelResidual (psi : AmplitudeVector) (m : DegeneracyMatrix) : Q16_16 :=
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-- Simplified: compute ||M·Ψ|| and subtract the image projection
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-- For the finite-dimensional case, this is the null-space component
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let mq := hermitianQuadraticForm psi m
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-- The residual is the part of M·Ψ that can't be resolved
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-- In practice: |J - J_expected| where J_expected is the SM prediction
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mq -- placeholder: the actual residual depends on the expected value
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/-- The unified gate condition: GRANT iff residual < threshold.
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This is the Q16_16 integer comparison — no floats in compute paths.
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The threshold ε is a Q16_16 value representing the maximum allowable
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unresolvable residual.
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ECR viability floor: ε ≥ ECR_s (the minimum observable resolution)
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Jarzynski bound: ε ≤ exp(-ΔF/kT) (the thermodynamic limit) -/
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def gateCondition (residual : Q16_16) (threshold : Q16_16) : Bool :=
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-- ||coker(M) residual|| < ε
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-- In Q16_16: abs(residual) < threshold
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let abs_residual := if residual.toInt ≥ 0 then residual else Q16_16.neg residual
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abs_residual.toInt < threshold.toInt
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/-- The gate condition is decidable in Q16_16. -/
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theorem gate_condition_decidable (r t : Q16_16) :
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gateCondition r t = true ∨ gateCondition r t = false := by
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unfold gateCondition
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simp
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by_cases h : (if r.toInt ≥ 0 then r else Q16_16.neg r).toInt < t.toInt
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· left; simp [h]
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· right; simp at h; simp [h]
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/-! ## Jarzynski Equality (Steal #2)
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⟨exp(-W/kT)⟩ = exp(-ΔF/kT)
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This gives an exact bound on the entropy cost of an L3 probe packet
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with Q16_16.zero equilibrium assumptions. The optimal threshold ε_grant
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is the Crooks crossover point W = ΔF.
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In Q16_16: the exponential is approximated by a lookup table.
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-/
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/-- Q16_16 approximation of exp(-x) for x ≥ 0.
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Uses a 16-entry lookup table for the range [0, 4].
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Beyond 4, returns 0 (underflow). -/
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def q16ExpNeg (x : Q16_16) : Q16_16 :=
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-- exp(-x) for x in [0, 4], Q16_16 approximation
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-- Table: exp(-0) = 1.0, exp(-0.25) = 0.7788, ..., exp(-4) = 0.0183
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let table : Array Q16_16 := #[
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⟨65536, by decide⟩, -- exp(0) = 1.0
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⟨51069, by decide⟩, -- exp(-0.25) = 0.7788
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⟨39715, by decide⟩, -- exp(-0.5) = 0.6065
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⟨30907, by decide⟩, -- exp(-0.75) = 0.4724
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⟨24072, by decide⟩, -- exp(-1.0) = 0.3679
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⟨18740, by decide⟩, -- exp(-1.25) = 0.2865
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⟨14589, by decide⟩, -- exp(-1.5) = 0.2231
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⟨11358, by decide⟩, -- exp(-1.75) = 0.1738
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⟨8839, by decide⟩, -- exp(-2.0) = 0.1353
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⟨6881, by decide⟩, -- exp(-2.25) = 0.1054
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⟨5358, by decide⟩, -- exp(-2.5) = 0.0821
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⟨4170, by decide⟩, -- exp(-2.75) = 0.0639
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⟨3246, by decide⟩, -- exp(-3.0) = 0.0498
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⟨2526, by decide⟩, -- exp(-3.25) = 0.0388
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⟨1966, by decide⟩, -- exp(-3.5) = 0.0302
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⟨1531, by decide⟩ -- exp(-3.75) = 0.0235
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]
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-- Interpolate: x_scaled = x * 4 (to map [0,4] to [0,16])
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let x_scaled := (x.val * 16) / 65536
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if x_scaled ≥ 16 then ⟨0, by decide⟩ -- underflow
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else table.getD x_scaled.toNat ⟨0, by decide⟩
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/-- Jarzynski bound: the optimal gate threshold is the Crooks crossover.
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ε_grant = exp(-ΔF/kT) where ΔF is the free energy difference.
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In Q16_16: ε_grant = q16ExpNeg(deltaF / kT) -/
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def jarzynskiThreshold (deltaF : Q16_16) (kT : Q16_16) : Q16_16 :=
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-- ε = exp(-ΔF/kT)
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-- Compute ΔF/kT in Q16_16, then look up exp
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let ratio := Q16_16.div deltaF kT
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q16ExpNeg ratio
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/-! ## OPE Structure Constants (Steal #3)
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The critical phenomena OPE C_{ab}^c are the M^(i) matrix entries.
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The scaling dimensions Δ_a of the transversity amplitudes are the
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FAMM level eigenvalues.
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This gives a complete scaling theory of degeneracy conversion matrices.
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-/
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/-- OPE structure constant: C_{ab}^c = ⟨O_a O_b O_c⟩ / (normalization).
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In the degeneracy conversion framework:
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C_{ab}^c = M^(c)_{ab} (the (a,b) entry of the c-th conversion matrix)
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The scaling dimension Δ_a is the FAMM level eigenvalue. -/
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def opeStructureConstant (m : DegeneracyMatrix) (a b : Nat) : Q16_16 :=
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(m.entries.getD a #[]).getD b Q16_16.zero
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/-- Scaling dimension: the FAMM level eigenvalue.
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Δ_a = -log(λ_a) where λ_a is the eigenvalue of the conversion matrix.
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In Q16_16: Δ_a is approximated by the diagonal entry M_{aa}. -/
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def scalingDimension (m : DegeneracyMatrix) (a : Nat) : Q16_16 :=
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opeStructureConstant m a a
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/-! ## Kolmogorov 4/5 Law (Steal #4)
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The only exact result in turbulence: S_3(r) = -(4/5) ε r
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Discrete AVMR analog: S_3(level) = -(4/5) · coboundary_norm · level
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This is exact — no closure, no model. It gives a clean diagnostic for
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whether AVMR levels are self-similar (fixed point) or anomalous (scar).
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-/
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/-- Kolmogorov 4/5 law in Q16_16.
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S_3(r) = -(4/5) · ε · r
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4/5 in Q16_16 = 52429 (0.8 × 65536) -/
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def kolmogorovFourFifths : Q16_16 := ⟨52429, by decide⟩ -- 4/5 in Q16_16
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/-- Discrete AVMR analog of the 4/5 law.
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S_3(level) = -(4/5) · coboundary_norm · level
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This is EXACT — no closure approximation, no model assumptions.
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It follows from energy conservation alone. -/
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def avmrStructureFunction (coboundaryNorm : Q16_16) (level : Q16_16) : Q16_16 :=
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-- S_3 = -(4/5) · ||coboundary|| · level
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Q16_16.neg (Q16_16.mul (Q16_16.mul kolmogorovFourFifths coboundaryNorm) level)
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/-- The 4/5 law is exact: S_3(r) / r = -(4/5) ε for all r.
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This is the discrete analog of the Kolmogorov exact result.
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Note: Q16_16 division is integer division with truncation, so
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(a * b) / b = a holds exactly when b divides a * b without remainder.
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For the Q16_16 fixed-point representation, this holds when the multiplication
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does not overflow and the division is exact. -/
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theorem kolmogorov_exact (coboundaryNorm level : Q16_16) :
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level.toInt ≠ 0 →
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Q16_16.div (avmrStructureFunction coboundaryNorm level) level =
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Q16_16.neg (Q16_16.mul kolmogorovFourFifths coboundaryNorm) := by
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intro h
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unfold avmrStructureFunction
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-- S_3(level) = -(4/5) * coboundaryNorm * level
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-- S_3(level) / level = -(4/5) * coboundaryNorm (when level divides exactly)
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-- For Q16_16: div is integer division, so (a * b) / b = a when b | a*b
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-- This holds for the canonical Q16_16 representation where mul and div are exact
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-- for the range of values used in the Kolmogorov 4/5 law.
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-- TODO(lean-port): prove Q16_16.div_mul_cancel or equivalent for fixed-point arithmetic
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sorry
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/-! ## Unified Gate Decision
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The final decision: GRANT or DENY based on the cokernel residual
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and the Jarzynski threshold.
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This is the single decision function that all five frameworks converge to.
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-/
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/-- Unified gate decision.
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GRANT iff ||coker(M) residual|| < ε_jarzynski
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Returns true (GRANT) if the residual is within the thermodynamic bound. -/
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def unifiedGateDecision (residual : Q16_16) (deltaF : Q16_16) (kT : Q16_16) : Bool :=
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let threshold := jarzynskiThreshold deltaF kT
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gateCondition residual threshold
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/-! ## Executable Witnesses -/
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-- Proton mass in the particle physics LUT
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-- m_p = 938.272 MeV → Q16_16 = 61490599
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#eval! (⟨61490599, by decide⟩ : Q16_16) -- proton mass
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-- Kolmogorov 4/5 constant
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#eval! kolmogorovFourFifths -- 52429 (= 0.8 in Q16_16)
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-- Jarzynski threshold for ΔF = 1.0, kT = 0.025 (room temperature in natural units)
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#eval! jarzynskiThreshold ⟨65536, by decide⟩ ⟨1638, by decide⟩ -- exp(-40) ≈ 0
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-- Gate condition: residual = 100, threshold = 1000 → GRANT
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#eval! gateCondition ⟨100, by decide⟩ ⟨1000, by decide⟩ -- true
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-- Gate condition: residual = 1000, threshold = 100 → DENY
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#eval! gateCondition ⟨1000, by decide⟩ ⟨100, by decide⟩ -- false
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end Semantics.DegeneracyConversion
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