Dynamic Phase-Space Signatures and Principal-Component Shifts Across Cognitive Regimes in a Zero-Training Runtime

This report analyzes a tick-resolved window of a real-time “cognitive runtime” execution (1k-node substrate) and characterizes regime structure using principal component analysis (PCA) of internal numeric telemetry. The main finding is that regime labels correspond to distinct regions in low-dimensional state space while transitions coincide with statistically larger per-tick displacement in the first eight principal components. In the analyzed window (t ∈ 359521, 385094, n = 16,746 ticks), the first eight PCs explain 89.18% of the standardizedfeature variance; regime-change ticks have higher ∥∆PC1:8∥2 (mean 6.05 vs. 5.05; Cohen’s d = 0.47; Mann–Whitney p = 2.9 × 10−157). A microstate Markov model (K=30, built on the first eight PCs) exhibits multiple slow modes (second eigenvalue 0.9978, implied timescale τ ≈ 455 steps), consistent with metastable organization. All results are backed by a self-contained data+code bundle with SHA256 indexing and a single reproduction script.