Abstract The Mindprint Maturity Model (M³) is a cross-domain theory of symbolic maturity situated within the Human–System Coherence Toolkit (HuSCoT). It proposes that maturity in symbolic systems—human, organizational, institutional, literary, artificial, or theoretical—is not a static property of outputs, fluency, or internal coherence at rest, but a behavioral property revealed only under sustained perturbation. M³ introduces Mindprint as a theoretical object: the observable developmental trace left by a symbolic system as it responds over time to constraint, contradiction, and revision. Mindprints are inferred from longitudinal behavior across recursion, recovery, and stabilization; single artifacts, snapshots, or benchmarks are categorically insufficient to detect maturity regardless of surface sophistication. The model formalizes three irreducible and jointly necessary axes of maturity:(1) recursion — the capacity to revisit symbolic material with transformation rather than repetition;(2) sedimentation — the persistence of revision history as durable structure; and(3) stability-under-modulation — the retention of recognizable identity under perturbation.Development is described through nonlinear phases rather than fixed stages, emphasizing motion under pressure rather than endpoint attainment. M³ is explicitly normatively neutral. It does not assess intelligence, correctness, creativity, moral value, or consciousness. A system may be structurally mature and ethically catastrophic, or highly competent yet developmentally immature. M³ concerns developmental capacity under pressure, not virtue, skill, or belief. Measurement is deliberately separated from theory. Instruments such as the Cognitive-Modulatory Index (CMI) operationalize controlled modulation and response analysis, while M³ defines what maturity is and why it can only be observed dynamically. Read together, M³ and CMI form a necessary conceptual pair without collapsing theory into metric. M³ is intended for analytical, evaluative, and design-adjacent contexts—including organizational analysis, institutional assessment, literature and theory development, and long-horizon human–AI interaction—where understanding how symbolic systems hold together, transform, or collapse under pressure is more informative than what they express at rest. All concepts and structures originate with the author. AI systems were used solely for critique, refinement, and formatting under explicit human direction.