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ContinuousCombinerAnalysisInputs

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ContinuousCombinerAnalysisInputs packages the finite-order mollifier smoothness, bootstrapped second-derivative identity, and psi-affine completion needed to derive bilinearity from a continuous Law of Logic. Researchers generalizing the Translation Theorem beyond polynomial route-independence cite this hypothesis package when applying Aczél-Kannappan classification inside Recognition Science. The declaration is a structure definition that simply records the three named inputs without any proof steps.

claimLet $C$ be a comparison operator and let $h$ witness that $C$ satisfies the continuous laws of logic (identity, non-contradiction, excluded middle, scale invariance, continuous route independence, and non-triviality). Then ContinuousCombinerAnalysisInputs$(C,h)$ holds precisely when the derived cost $G(t)=F(e^t)$ admits $C^n$ regularity for every finite $n$ via the mollifier route, the second-derivative identity $2G''(t)=ψ(G(t))G''(0)$ holds after the smoothness bootstrap, and the psi-affine completion produces a constant $c$ such that the log-bilinear identity is satisfied.

background

The GeneralizedDAlembert module carries out Move 3 of the Recognition Science program: replace the polynomial-degree-≤2 restriction on the route-independence combiner with mere continuity, using the classical Aczél–Kannappan trichotomy for continuous solutions of the d'Alembert equation. SatisfiesLawsOfLogicContinuous is the continuous analogue of the original Law of Logic structure; it keeps the four Aristotelian conditions plus scale invariance but substitutes ContinuousRouteIndependence for the polynomial version. ContinuousCombinerMollifierFiniteSmoothness asserts that the log-coordinate derived cost $G(t)=F(e^t)$ is $C^n$ for every natural number $n$. The upstream bootstrap theorem upgrades any such finite-order control to full $C^∞$ smoothness on the log-cost.

proof idea

The declaration is a structure definition with an empty proof body. It assembles the three inputs by direct field references: the mollifier finite-smoothness predicate, the continuous_combiner_log_smoothness_bootstrap theorem applied to that predicate, and the two named predicates ContinuousCombinerSecondDerivativeInput and ContinuousCombinerPsiAffineCompletion that encode the Stetkær/Aczél derivative extraction and log-bilinear conclusion.

why it matters in Recognition Science

The structure is the explicit hypothesis package required by the downstream theorems continuous_combiner_bilinear, continuous_combiner_bilinear_classification, laws_continuous_subsumes_polynomial, and RCL_is_unique_functional_form_of_logic_continuous. It marks the precise point at which the quartic-log obstruction prevents continuity alone from discharging the Translation Theorem, thereby keeping finite pairwise polynomial closure as the sharp hypothesis for the Law-of-Logic paper. The package therefore sits between the polynomial Translation Theorem in LogicAsFunctionalEquation and any future result that might close the gap.

scope and limits

formal statement (Lean)

 569structure ContinuousCombinerAnalysisInputs
 570    (C : ComparisonOperator)
 571    (h : SatisfiesLawsOfLogicContinuous C) : Prop where
 572  finite_smoothness : ContinuousCombinerMollifierFiniteSmoothness C h
 573  second_derivative :
 574    ContinuousCombinerSecondDerivativeInput C h
 575      (continuous_combiner_log_smoothness_bootstrap C h finite_smoothness)
 576  psi_affine :
 577    ContinuousCombinerPsiAffineCompletion C h
 578      (continuous_combiner_log_smoothness_bootstrap C h finite_smoothness)
 579      second_derivative
 580
 581/-- **Residual input 2 assembled:** smoothness plus the derivative identity
 582and ψ-affine completion give the required log-bilinear identity. -/

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