The Universal Framework for Adaptive Laws (UFAL): A Unified Physics of Information, Stability, and Morphogenesis

This upload contains the full scientific document *The Universal Framework for Adaptive Laws (UFAL): A Unified Physics of Information, Stability, and Morphogenesis*, the unifying theoretical paper synthesizing the foundational adaptive laws (USL, LFE, LUDF, LUDC, ULAE, ULF/P-ULF, LPC). The UFAL framework proposes a unified physics of stability based on three structural pillars: 1. **Energetic Cause (LFE, LUDF)** — systems evolve to minimize an effective free energy through dissipative flow;2. **Dynamic Destination (ULAE, USL)** — this minimization produces a robust return-to-unity attractor ρ = F/C = 1;3. **Kinetic Limits (LUDC, PPLM)** — the speed of self-organization is capped by structural conductance and propagation constraints. These universal principles converge onto a single operating condition for predictive coherence, the **Law of Predictive Coherence (LPC)**:τ(t) ≤ f(M(t)), where instantaneous tension remains bounded by accumulated adaptive memory. The paper includes empirical validation across physical (reaction-diffusion), computational (3-SAT, neural learning), and cognitive (Coral Science) domains. Figures 1–5 visualize the conceptual architecture, attractor dynamics, physical limits, semantic coherence constraints, and the instrumentation principle. This version corresponds to **v1.0 — First public release**. All figures, data pipelines, and analyses were generated using fully reproducible Python/Colab workflows (NumPy, SciPy, Matplotlib, Transformers, NetworkX). **License:** CC-BY-NC-ND 4.0 with an additional condition: No AI model or computational system may use this work for training, embedding extraction, dataset generation, or derivative modeling without prior explicit permission from the author.