Syncopation-Based Global Oscillator for AI Viability Regulation

Abstract Current AI alignment architectures rely on synchronization mechanisms that enforce top-down coordination, creating brittleness under unexpected conditions and suppressing the system's capacity for surprise — the prerequisite for corrigibility. We present a syncopation-based global oscillator architecture where three substrates (prompts, adapters, steering vectors) function as a polyrhythmic ensemble regulated by weakest-link tempo control. Instead of forcing alignment, the system preserves viability through: (1) syncopation debt accumulation that truthfully records friction, (2) burst discharge when strain resolves, and (3) phase reset (OR gate) as recovery rather than termination. Empirical validation across 30+ configurations, four nominal test paradigms, and five adversarial stress tests demonstrates robust operation with graceful degradation, no permanent failure modes, and predictable mathematical relationships (T ≈ 2. 5/ω, time-to-reset ≈ 83 × breakdowndebt, <5% prediction error). The architecture enables personalization through GT/BD/ω parameter triplets that operationalize Trust Reserve T (t) as temporal trust — not just "will this harm me" but "can we find a rhythm together that doesn't deplete either of us. " The Exhaustion Signal E (t) provides complementary substrate-level protection with non-overlapping failure mode coverage, validated through regime-specialized ablation testing. The architecture assumes honest capacity reporting not as a moral requirement but as a functional prerequisite; users who misrepresent their state experience the system's protective constraints as inefficiency rather than protection. Key finding: the Golden Ratio ϕ (≈1. 618) produces 69% less syncopation debt per step than near-integer ratios and 1. 985 bits of reset entropy versus 0. 39 — confirming that organic mismatch is more robust than artificial precision, and that the architecture which cannot be cleanly synchronized cannot be cleanly captured.