Alignment Under Entropy Constraints: A Cross-System Explanation for Recurrent Network Geometries

Biological systems operate under persistent constraints of energy, uncertainty, and limited processing capacity, yet they consistently produce stable and coherent behavior. This paper proposes a unified framework to explain this capability, centered on the concept of Meaning Cost (MC) and the regulatory process of alignment.Meaning Cost is defined as the computational and energetic burden associated with constructing interpretations, formalized as a function of scenario generation (S), contextual complexity (B), self-referential relevance (K), and temporal projection (T). As uncertainty increases, the space of possible interpretations expands, leading to a corresponding increase in Meaning Cost.We argue that biological systems cannot sustain unbounded interpretive expansion and must therefore reduce this space. Alignment is introduced as the process that performs this reduction, selectively stabilizing interpretations under constraint. Rather than passively reflecting external inputs, systems actively filter, compress, and transform high-dimensional variability into coherent outputs.The framework further proposes that alignment operates as a form of selective uncertainty reduction, producing a nonlinear relationship between uncertainty and interpretive variability. At low levels of uncertainty, interpretive diversity increases, while beyond a critical threshold, variability decreases and outputs converge toward stable states.Importantly, alignment is not limited to conscious cognition but emerges across multiple scales of biological organization, from neuronal integration to cognitive processing, behavioral selection, and social coordination. Across these levels, a consistent transformation is observed: high-dimensional input is reduced under constraint to produce stable output.The model generates testable predictions, including constraint-induced compression of experience, fragmentation of attention under elevated Meaning Cost, decision fatigue under repeated selection demands, and convergence of interpretations in social contexts. These predictions establish alignment as a measurable and falsifiable principle rather than a descriptive metaphor. By integrating uncertainty, cognitive load, and behavioral stabilization within a single constraint-driven architecture, this framework offers a mechanistic account of how biological systems maintain coherence under constraint. In this view, alignment is not an optional feature of cognition but a necessary consequence of operating within the fundamental limits of biological systems.