CostFunctionalAxioms
plain-language theorem explainer
The CostFunctionalAxioms class packages the three primitive axioms that any cost functional must obey in Recognition Science. A researcher deriving uniqueness of the J functional or symmetry properties would cite this bundle to access normalization, the recognition composition law, and calibration at once. It is realized as a class extension over the three separate axiom classes with no further proof obligations.
Claim. A function $F: (0,∞) → ℝ$ satisfies the cost functional axioms when it obeys normalization $F(1)=0$, the recognition composition law $F(xy)+F(x/y)=2F(x)F(y)+2F(x)+2F(y)$ for all $x,y>0$, and calibration $d^2/dt^2 F(e^t)|_{t=0}=1$.
background
The module CostAxioms formalizes the primitive foundation of Recognition Science. It introduces three axioms: Normalization requires that the cost at unity vanishes, $F(1)=0$; Composition encodes the recognition composition law, the multiplicative form of d'Alembert's equation; Calibration fixes the curvature by requiring the second logarithmic derivative at zero to equal one. These axioms are more primitive than logic, encoding economic inevitability where low-cost configurations correspond to consistency. Upstream, the canonical arithmetic object supplies the initial Peano structure, but the cost axioms stand independently.
proof idea
This declaration is a one-line wrapper that extends the Normalization, Composition, and Calibration classes to form the complete axiom bundle for a cost functional.
why it matters
This axiom bundle is the starting point for the uniqueness theorem uniqueness_specification, which establishes that any sufficiently regular function satisfying these axioms equals the canonical J. It fills the T5 uniqueness step in the forcing chain, where J-uniqueness follows from the recognition composition law. The bundle enables derivation of symmetry and non-negativity properties used throughout the framework, including the mass formula and Berry creation threshold.
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Shallow quantum circuit guarantees a 0.6924 cut on cubic graphs
"F_p(γ,β) = ⟨γ,β|C|γ,β⟩ ... |γ,β⟩ = U(B,β_p)U(C,γ_p)···U(B,β_1)U(C,γ_1)|s⟩"