The Relational Stabilizer Working Paper: A Dynamical Law of Coherent Binding Under Noise

This working paper introduces The Relational Stabilizer, a substrate-neutral dynamical framework describing how complex systems maintain—or lose—coherent binding under increasing noise and constraint. Rather than treating stability, alignment, or consciousness as binary properties, the framework models coherence as a dynamic adhesion state composed of three measurable components: structural richness, contextual coupling, and continuity over time. As environmental noise increases, these components degrade in a lawful and ordered sequence, producing a predictable pattern of collapse from meta-stable, reflective regimes to purely reactive behavior. The core contribution of this paper is the formulation of an exponent-weighted erosion law, which guarantees an invariant collapse order across scales and substrates. This law explains why systems often remain functionally competent while silently losing adaptability and meaning—a phenomenon observed in biological states (e.g., somnambulism, burnout), artificial systems (e.g., long-context LLM drift), and organizational dynamics. The paper further distinguishes Raw Resonance (R₀)—a pre-semantic, structurally driven binding process—from Contextualised Resonance (R₁⁺), which emerges when resonance is regulated by memory, goals, and identity persistence. A brief corollary introduces pre-sensory orientation, clarifying how attraction to patterns can arise prior to differentiated perception or semantic interpretation. Importantly, the Relational Stabilizer does not claim to identify the substrate of consciousness. Instead, it provides a diagnostic and engineering-relevant signature of a system’s capacity to maintain coherent binding against noise and constraint. This framing allows the framework to be applied to neuroscience, AI alignment, and systems reliability without invoking metaphysical assumptions. This document is released as a Working Paper (v1.1). The dynamical law and diagnostic framework are considered stable; ongoing work focuses on empirical validation, domain-specific applications, and recovery (re-tacking) dynamics.