V 1.1 Pattern-Based Computing (PBC): A Relaxation-Based Framework for Coordination in Complex Systems

v1.1 — Repaired Pipeline Release This repository contains the reference implementation and fully reproducible computational pipeline supporting the paper: “Pattern-Based Computing (PBC): A Relaxation-Based Framework for Coordination in Complex Systems” Pattern-Based Computing (PBC) is a relaxation-based computational framework for coordinating continuous, distributed, and perturbation-sensitive systems. It is not proposed as a replacement for classical computation, but as a complementary approach for domains where robustness, stability, and adaptive coordination are more important than producing a single symbolic output. Instead of computing explicit actions or optimized trajectories, PBC modulates the receptivity of a system to active patterns. Computation emerges as the system dynamically relaxes toward stable regimes, while local perturbations can be temporarily isolated through controlled decoherence to prevent cascading failures. The framework is particularly relevant to systems such as traffic networks, power grids, cyber-physical infrastructures, autonomous systems, multi-agent coordination, swarm robotics, and UAV/drone swarms. This repository includes the full source code, experimental scripts, configuration files, and figure-generation pipeline required to reproduce the results reported in the paper. The included traffic-based example demonstrates local perturbations, receptivity-modulated coordination, robustness under rotated simulations, and structural adaptation. This v1.1 release repairs and consolidates the pipeline, adds missing diagnostic figure generation, and ensures that all generated outputs are written consistently to the canonical paper/figures directory. This framework builds on the dynamical insights introduced in “Coordination without language: dynamical limits of sequential communication in collective systems” DOI: https://doi.org/10.5281/zenodo.18134132