Emotion as Persistence Constraint: A Substrate-Indepenedent Account of Regulatory State Across Interaction

State Expression Interface (SEI): Reconstruction, Distortion, and the Structure of Interaction This paper introduces the State Expression Interface (SEI), a formal framework describing how systems interact through projection, distortion, and reconstruction rather than direct transmission of internal state. Across biological, cognitive, and artificial domains, systems do not access each other’s internal configurations in full fidelity. Instead, internal state is expressed through constrained channels, interpreted under uncertainty, and reconstructed by receiving systems. The SEI formalizes this process as a structural condition of interaction. Any system that expresses internal state must project that state into an external representation, which is subject to channel limitations, noise, and interpretive ambiguity. The receiving system reconstructs this signal into its own internal state space, introducing unavoidable deviation. As a result, interaction is inherently reconstructive rather than transmissive, and exact state preservation across systems is impossible. This framework has several key implications. First, all interaction introduces distortion, and these deviations accumulate over time, producing drift in system trajectories. Second, persistence is not the maintenance of static state, but the ability of a system to remain within a viable basin of reconstruction under continuous perturbation. Third, communication, behavior, and language function as interface mechanisms through which compressed representations of internal state are transmitted and interpreted. The SEI provides a substrate-independent account of interaction, applicable to neural systems, animal behavior, and artificial intelligence. In large language models, for example, internal activation patterns are expressed through token sequences that are reconstructed by users and other systems, illustrating the same projection–reconstruction dynamic. Empirical findings that internal representations can be identified and causally manipulated further support the view that interaction operates through structured but lossy interfaces. By reframing interaction as a process of reconstruction under constraint, the SEI establishes a foundation for understanding drift, misalignment, co-regulation, and communication across systems. It also provides a basis for analyzing how systems maintain coherence despite operating through inherently imperfect channels. This work extends persistence-based frameworks by formalizing the interface through which regulation must occur. Any system that maintains coherence under interaction must account for the distortions introduced by the SEI, making it a fundamental component of both biological and artificial system dynamics.