Phase-Precession Computing Ultra-Low Power Analog AI and Temporal Logic via Dynamic Orientation

This paper introduces Phase-Precession Computing (PPC), a computing paradigm in which computation is encoded in the continuous-time evolution of orientation and phase, rather than discrete state transitions. Inspired by the physical behavior of gyroscopic systems, PPC treats precession trajectories as informational carriers, enabling ultra-low power inference, intrinsic temporal logic, and delay-based safety governance. Unlike conventional digital architectures that rely on instantaneous threshold decisions, PPC enforces time-structured resolution: inputs apply continuous “torque” to a dynamical state, whose trajectory must stabilize over time before irreversible actions are permitted. This naturally implements reflection-before-commit behavior, making PPC particularly suitable for safety-critical and autonomous systems. We present a minimal reference implementation demonstrating: (1) analog-style inference via coupled precession registers, (2) temporal logic evaluation using phase-based accumulation rather than clocks, and (3) a hardware-enforceable commit governor that halts on coherence collapse. The architecture is hardware-agnostic, audit-friendly, and compatible with ultra-low power analog substrates.