DSFB Structural Semiotics Engine for General Systems: A Deterministic Endoduction Framework for Residual-Based Meaning Extraction

We propose a deterministic structural semiotics framework that treats residual evolution as an interpretable signal rather than noise, enabling inference to proceed directly from structured deviation without requiring exhaustive prior specification of system behaviors. This paper introduces a deterministic structural semiotics framework in which residual dynamics are treated as primary inferential objects, yielding finite-time detectability guarantees governed by their evolution relative to admissible envelopes rather than by residual magnitude alone.This paper develops a deterministic structural semiotics engine for dynamic engineered systems, reframing residuals not as noise to be minimized but as structured signals carrying system-internal meaning. Building on the Deterministic Structural Feedback (DSFB) framework, we formalize residuals as signs, drift and slew as temporal syntax, and residual envelopes as admissibility grammar. The resulting engine extracts a compositional semiotic layer from system dynamics, enabling interpretation, diagnosis, and prediction without reliance on probabilistic modeling. We introduce a unified methodology in which system behavior is mapped into a structured sign space, where deviations encode latent structural information. A growing heuristics bank serves as a reusable semantic layer, accumulating invariant signatures of known failure modes, regime transitions, and structural transformations. The framework is presented independently of any specific application domain, establishing a general-purpose deterministic alternative to stochastic estimation paradigms. This work positions DSFB not only as an estimation framework, but as a structural language for interpreting dynamic systems, with implications for diagnostics, certification, and autonomous system introspection.