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Explain the Lean theorem `even_deriv_at_zero` in module `IndisputableMonolith.Cost.FunctionalEquation`. Write for an educated reader who knows science and programming but may not know Lean. Cover: (1) what the declaration says in plain English, (2) why it matters in Recognition Science, (3) how to read the formal statement, (4) visible dependencies or certificates in the supplied source, and (5) what this declaration does not prove. Cite only declarations present in the supplied Recognition source.

Big AI job. Grok 4.3 reads the canon and writes a Lean-grounded derivation; usually 20 seconds to 2 minutes. Your answer will appear below.
confidence: high in recognition cached

The declaration even_deriv_at_zero appears verbatim in module IndisputableMonolith.Cost.FunctionalEquation.

(1) In plain English it asserts: any real-valued function that is even (symmetric under sign flip) and differentiable at the origin must have zero slope there.

(2) Within Recognition Science the lemma supplies a symmetry fact used inside the T5 cost-uniqueness argument; the J-cost function is even after the log-coordinate change G, so its derivative vanishes at zero, which is required to calibrate the curvature constant that yields the explicit form J(x) = (x + x^{-1})/2 - 1.

(3) The formal statement is read as: given a function H : ℝ → ℝ together with a proof that H is even and a proof that H is differentiable at 0, the conclusion is that the derivative of H at 0 equals zero. The proof proceeds by composing H with negation, using the chain rule, and invoking the evenness hypothesis to obtain deriv H 0 = -deriv H 0, hence deriv H 0 = 0.

(4) Visible dependencies inside the supplied source are the surrounding lemmas G_even_of_reciprocal_symmetry, dAlembert_even, Jcost_G_eq_cosh_sub_one, and Jcost_cosh_add_identity; all live in the same module and are used to establish the evenness of the transformed cost function before applying even_deriv_at_zero.

(5) The declaration does not prove the full uniqueness of J-cost, the forcing of φ, the derivation of physical constants, or any statement outside the FunctionalEquation module.

cited recognition theorems

outside recognition

Aspects Recognition does not yet address:

  • Any explicit link from even_deriv_at_zero to the master forcing chain or to the numerical values of constants such as hbar = phi^-5.
  • The complete T5 uniqueness theorem itself (only helper lemmas are present in the supplied slice).

recognition modules consulted

The Recognition library is at github.com/jonwashburn/shape-of-logic. The model is restricted to the supplied Lean source and instructed not to invent theorem names. Treat output as a starting point, not a verified proof.