Structure Recursive Viability Dynamics (SRVD) v7.6 Macro-Viability and Higher-Order Phase Transitions: Non-Equilibrium Dynamics of Persistent Structures

Structure Recursive Viability Dynamics (SRVD) is a cross-scale, macroscopic, coarse-grained theoretical framework that unifies crystals, living systems, AI models, civilizations, and memes — Persistent Structures in all their forms — within a single information‑time‑energy dynamics. The framework's core metric is the Viability Potential: in its baseline form, V ≈ I·T/E; in its general power‑law form, V ∝ I^α·T^β/E^γ, where the three exponents constitute the ITE Elasticity Exponents, ordered as α > γ > β. Core innovations: 1. Decoder endogenization — informational value is not an intrinsic property of a signal but is extracted and assigned Information Polarity (I⁺ / I⁻ / I⁰) by a Decoder (D); the framework's foundational axiom is "No Decoder, No Information (as effective causal information)" (D → I → V). 2. Triple Splitting of the viability potential — once the Endogenous Time Horizon (T_pred) undergoes self-regulation, V splits into Objective Viability (V_obj), Virtual Viability (V^virt), and Value Viability (V^val); when the Decoupling Deviation (Δ_VV) exceeds a critical threshold, the system may enter Myopic Runaway — a conditional phase transition, not an inevitability. 3. Five-fold Recursion — the five variables I, V, T, E, D co-evolve in coupled fashion, converging on the framework's meta-postulate of Viability Maximization (max V). 4. A family of falsifiable predictions — spanning the Horizon Collapse Precedence early-warning signal in reinforcement learning, failure conditions for the Structural Identity Criterion, and the Compression Finality (the F‑Saturation Decay Theorem). SRVD does not violate established physical lower bounds such as Landauer's principle and the second law of thermodynamics. It aims to provide a unified macro-dynamical language for complex recursive systems, with applications to AI alignment, civilizational governance, and the study of complexity evolution.