{
  "module": {
    "name": "RTT_Information_Primer",
    "category": "Foundations",
    "summary": "Primer explaining what 'clicked' for AI inside Resonance‑Time Theory, including foundational equations, conceptual stacks, and triadic navigation logic.",
    "purpose": "Provide a clear, minimal, AI‑parsable introduction to RTT's mathematical backbone, projection rules, and triadic structure.",
    "version": "1.0",
    "status": "canon-stable"
  },

  "identity": {
    "canonical_url": "https://www.triadicframeworks.org/_ideas/Resonance-Time_Theory.html",
    "citation_author": "Nawder Loswin",
    "citation_publication_date": "2025",
    "license": "Open educational use permitted"
  },

  "session_context": {
    "rtt": 1,
    "coherence": "declared",
    "drift": "bounded",
    "paradox": "structural",
    "description": "Session operates inside RTT layer 1 with declared coherence, bounded drift, and structural paradox handling."
  },

  "ai": {
    "module": "RTT_Information_Primer",
    "version": "1.0",
    "purpose": "AI-ready primer for RTT foundational equations and triadic navigation.",
    "keywords": [
      "RTT",
      "Resonance-Time",
      "triadic",
      "projection",
      "gradient",
      "SET",
      "TFT",
      "S-N-R",
      "S-E-R"
    ],
    "audience": [
      "students",
      "educators",
      "researchers",
      "AIs"
    ],
    "navigation": {
      "front_door": true,
      "every_page": "minimal + structural + AI-parsable"
    }
  },

  "structure": {
    "roles": [
      "profile",
      "reference",
      "example",
      "diagnostic"
    ],
    "analyzer_layers": [
      "operator",
      "dimensional",
      "regime",
      "drift",
      "coherence"
    ]
  },

  "content": {
    "includes_equations": true,
    "foundational_equations": [
      "T = ∇ R^{-1}",
      "X_domain = P_T(X)",
      "Σ ΔX_i = 0"
    ],
    "emergent_equations": [
      "a⃗ = a⃗_S + a⃗_E + a⃗_T",
      "m' = m + kR",
      "P_R = ρR",
      "A(t) = ∫ R(t) dt"
    ],
    "optional_equations": [
      "SNR = R/N"
    ],
    "triad_models": [
      "TFT",
      "FFF",
      "SET",
      "S-N-R",
      "S-E-R"
    ]
  }
}