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Explain the Lean theorem `attention_plus_overflow_eq_gap` in module `IndisputableMonolith.CrossDomain.AttentionSpace`. 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

Explanation of the theorem attention_plus_overflow_eq_gap

(1) In plain English, the declaration states that the number of possible attentional states (40) plus exactly 5 overflow slots equals the complexity ceiling value 45.

(2) In Recognition Science this matters because the module defines an attentional state space as the product of 5 attention networks and 8 tick phases. The equality shows this space fits strictly under the gap45 ceiling while leaving a precise 5-slot overflow, interpreted in the module docstring as the five attention-network singletons under saturation. This supplies a structural count used for cross-domain claims.

(3) The formal statement reads:

theorem attention_plus_overflow_eq_gap :
    Fintype.card AttentionState + 5 = gap45 := by
  rw [attentionStateCount]; decide

AttentionState is the product type AttentionNetwork × TickPhase. Fintype.card computes its size. The proof rewrites via the count theorem and decides the arithmetic. gap45 is the constant 45.

(4) Visible dependencies in the supplied source: the proof calls attentionStateCount (which itself uses networkCount and tickCount); a parallel statement overflow_eq_D exists; the whole set is packaged in the certificate structure attentionSpaceCert whose field sum_is_gap is exactly this theorem. The module reports 0 sorry and 0 axiom.

(5) The declaration proves only the numerical equality. It does not prove the inductive definitions of the five networks or eight phases, any experimental prediction, or any link to the forcing chain or physical constants.

cited recognition theorems

outside recognition

Aspects Recognition does not yet address:

  • Experimental predictions or attentional-blink interpretations mentioned only in the module docstring
  • Any connection to the forcing chain, constants, or other modules outside this 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.