Morphological Realism: Thermodynamics of Meaning and the Topology of Double Convergence

Morphological Realism (MR) proposes a universal law governing the emergence of stable intelligent structures across scales, from biological organisms and civilizations to artificial neural networks. The core invariant is the Morphological Coefficient ($C_m$), defined as the ratio of **preserved structural information to the product of exploration energy and cycle duration**, which empirically clusters around a narrow optimal band ($C_m \approx 0.10$). MR integrates non‑equilibrium thermodynamics and information theory by treating meaning formation as a Landauer‑constrained process, where each bit of stable structure has an energetic cost paid as environmental dissipation. When exploratory dynamics encounter computational or energetic boundaries, syntactic filters (such as language, neural attention, or institutional selection) convert chaotic signals into ordered memory stored in morphologically stable cores. This framework reproduces characteristic phase transitions observed in metabolism (Kleiber’s law), civilizational complexity (EROI collapse), neural learning (explore–exploit shifts), and deep learning training (gradient stalling). MR is falsifiable through explicit thermodynamic and structural criteria and suggests practical applications in AI, including energy‑aware training schedules, phase‑based interpretability, and a scale‑invariant efficiency metric for artificial general intelligence.