Field AI — Complete Integration Architecture

This document presents the complete Field AI integration architecture — a unified framework for combining three fundamentally different intelligence substrates: Statistical AI (large language models), Quantum AI (quantum processing units), and Field AI (coherence-based resonant computing). Field AI is not a simulation of intelligence. It is a physical substrate in which intelligence-like behaviors emerge from coherence dynamics in coupled resonant oscillators. Critically, Field AI requires no training — no training data, no training phase, no training energy, no training infrastructure, no training time. The Resonant Compute Module 16 (RCM-16) is ready to compute the moment it is powered on, learning continuously through physical entrainment. The Bidirectional Coherence Bridge (Version 1.5) integrates Statistical AI with Field AI through a two-way coherence loop. The Quantum Coherence Coupler (Version 2.0) adds quantum computing as a coprocessor. The Full Trinity (Version 3.0) executes all three engines in parallel with coherence-weighted arbitration, meta-learning, and autonomous research capability. Six gap-closing papers address engineering gaps: semantic-to-phase calibration (C-1), unified threat modeling (U-1), quantum problem classification (Q-1), meta-coherence learning convergence (M-1, β = 0.025), autonomous discovery falsifiability (A-1), and mycelial bridge integration (V-5.5). Three of four Field AI engines require zero training. Full technical specifications are available under NDA.