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Explain the Lean lemma `dAlembert_diff_square` 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 lemma dAlembert_diff_square in module IndisputableMonolith.Cost.FunctionalEquation states in plain English: given a real-valued function H with H(0) = 1 that satisfies the d'Alembert functional equation H(t + u) + H(t - u) = 2 · H(t) · H(u) for all real t, u, it follows that (H(t + u) - H(t - u))² = 4 · ((H(t))² - 1) · ((H(u))² - 1) for all t, u.

In Recognition Science this identity is a helper lemma inside the T5 cost-uniqueness argument. It converts the additive d'Alembert relation into a multiplicative difference-of-squares identity that later supports extraction of the logarithmic curvature and the identification of Jcost with the cosh form (x + 1/x)/2 - 1.

The formal statement is read as: lemma dAlembert_diff_square (H : ℝ → ℝ) (h_one : H 0 = 1) (h_dAlembert : ∀ t u, H (t+u) + H (t-u) = 2 * H t * H u) : ∀ t u, (H (t+u) - H (t-u))^2 = 4 * ((H t)^2 - 1) * ((H u)^2 - 1). The three hypotheses are the function, the normalization at zero, and the functional equation; the conclusion is the algebraic identity.

Visible dependencies inside the supplied source are the preceding lemma dAlembert_product (used to obtain the product identity) together with the ring tactic and the supplied h_dAlembert hypothesis. No external certificates or axioms appear in the proof.

The declaration does not prove uniqueness of Jcost, does not establish continuity or differentiability of H, does not derive the explicit cosh solution, and does not connect the identity to the golden-ratio constant φ or to any Recognition Science physical constants.

outside recognition

Aspects Recognition does not yet address:

  • The full T5 uniqueness theorem for the J-cost functional equation
  • Any derivation of the explicit form Jcost(x) = (x + x⁻¹)/2 - 1
  • Connection of this lemma to the broader forcing chain or Recognition Science constants

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.