This repository contains the Python implementation and generated figures for a minimal consensus-network experiment demonstrating that the dynamic cross-phase between state and flow approaches π/2 (90°) near a critical coupling value λ ≈ 1.7. The effect appears robust across both ring (degree-2) and star (max-degree) topologies, indicating that the π/2 phase-lock arises from a structural transition in the consensus dynamics rather than from symmetry or connectivity alone. The update rule is linear: x(t+1) = (I - \beta L)x(t), \quad \beta = \alpha \lambda, where L is the graph Laplacian. We sweep λ ∈ [0.1, 2.0] and compute: Time-to-consensus (variance threshold), Log-variance decay trajectories, and Dynamic cross-phase φ(λ) via Hilbert and FFT estimators. A stable π/2 band emerges near λ ≈ 1.7, consistent with relational threshold predictions in coherence-based physical models. The included script regenerates all figures in the associated research note and is fully deterministic (fixed random seed). Contents: consensus_phase_lag.py – Reproducible simulation script fig1_time_to_consensus.png – Convergence rate vs. coupling fig2_log_variance.png – Variance decay comparison fig3_cross_phase.png – Phase-lock plateau near λ ≈ 1.7 README.md – Instructions and requirements python3 consensus_phase_lag.py