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Explain the Lean theorem `ode_cosh_uniqueness_contdiff` 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: low outside recognition cached

The supplied Lean source does not contain the module IndisputableMonolith.Cost.FunctionalEquation or the declaration ode_cosh_uniqueness_contdiff. The provided modules are limited to AlexanderDuality, RealityFromDistinction, LanglandsFromRecognitionCost, RecognitionForcing, RichDomainCosts, SingleAnchor calibration, RecognitionBandGeometry, and RecognitionBandwidth. No material in the canon slice addresses this theorem, so no derivation is possible from the supplied source.

outside recognition

Aspects Recognition does not yet address:

  • The theorem ode_cosh_uniqueness_contdiff
  • Any declarations or proofs from module IndisputableMonolith.Cost.FunctionalEquation

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.