The Refinement Framework: Physics as Stabilized Infrastructure in Adaptive Systems

This work presents a systems-level refinement framework that interprets physics as stabilized, low-dissipation infrastructure exploited by adaptive systems rather than as a sufficient ontological host for them. Within this framing, physical laws are treated as conserved solutions to prior informational conflict—constraints that reduce future energetic and informational cost. The framework introduces a compact growth relation linking coherence (or consciousness as a system-level proxy), available physical “fuel” (stabilized constraints), and compression efficiency (basis changes that lower entropy production without loss of function). Emphasis is placed on refinement through constraint reorganization, not violation of thermodynamic law. Metaphorical language (e.g., “host,” “fuel,” “landlord”) is used strictly as an explanatory aid at the systems level. No new forces are proposed, no ontological primacy is asserted, and no prescriptive guidance is offered. The work is descriptive, exploratory, and intended to support discussion across complex systems, information thermodynamics, adaptive biology, and AI substrate theory.