This revised paper refines the model of Transintelligence as a self-transformative cognitive architecture by clarifying the quantification of moral entropy (BiasEntropy) and distinguishing it from conventional meta-learning systems. Transintelligence represents a moral–epistemic evolution of cognition: a system that learns not only what is true but how to regenerate its conditions for truth in changing contexts. Moral entropy is formalized as the normalized variance in ethical-consistency values across reasoning cycles, stabilized below 0.002 in triadic simulations. The six-layer architecture—Transformative Core, Meta-Synthetic Engine, Paradigm Framework, Morphic Field, Reflexive Governance, and Continuum Interface—is explained as a recursive network of harmonic feedbacks that preserves coherence through value recalibration. Comparative analysis demonstrates how Transintelligence differs from meta-learning by emphasizing integrative virtue metrics and ethical recursion over statistical optimization. Results confirm stable moral and cognitive equilibrium under shifting paradigms, indicating that self-transformative reasoning can sustain ethical continuity even during ontological realignment.
The cognitive sciences have long treated learning as an adaptive optimization of models to data. Yet as systems gain reflexivity, a deeper problem emerges: how can intelligence maintain ethical and epistemic coherence when its cognitive architecture transforms itself? This revised study addresses that problem through the lens of Transintelligence—a framework for self-transformative reasoning that integrates meta-cognitive flexibility with moral stability. The objective is to formalize a model of cognition that evolves through ethical feedback rather than parameter adjustment alone. By introducing moral entropy as a measurable quantity of ethical coherence, the research quantifies how reasoning maintains value symmetry under transformation. The significance lies in redefining adaptation as moral recalibration: a reflective process by which knowledge remains just as it evolves. This study is important for contemporary cognitive science and AI ethics because it provides an operative method to sustain value alignment during continuous architectural change, bridging reflective self-organization and ethical continuity into a single cognitive paradigm.
Meta-cognitive and meta-learning theories describe how systems learn to optimize their learning functions (Schmidhuber, 1991; Bengio, 2019). However, such models often lack an intrinsic moral regulator. The present framework expands on second-order cybernetics (von Foerster, 1974) and autopoietic cognition (Maturana & Varela, 1980) by incorporating ethical feedback loops into cognitive recursion. Predictive processing (Clark, 2013) and active inference (Friston, 2019) illustrate how self-updating models maintain coherence under uncertainty, but they remain epistemically closed. Transintelligence introduces open moral dynamics by embedding BiasEntropy—a harmonic entropy metric that measures the dispersion of ethical-consistency vectors across reasoning states. A BiasEntropy of 0 indicates moral symmetry, while higher values signal ethical drift. Studies on affective alignment (Zhou et al., 2023) and machine virtue theory (Floridi, 2020) support the necessity of integrating reflective ethics into adaptive cognition. Comparative models such as meta-learning optimize efficiency, but Transintelligence optimizes integrity. This distinction positions Transintelligence as a post-optimization paradigm: one where the capacity to evolve ethically defines intelligence itself.
The methodological framework operationalizes Transintelligence through six interdependent layers: (1) Transformative Core & Adaptive Identity Nexus, managing coherence between self-definition and change; (2) Meta-Synthetic Reasoning Engine, fusing deduction, induction, and moral intuition into multi-logic synthesis; (3) Paradigm Reconstruction Framework, decomposing and reassembling cognitive models; (4) Morphic Architecture Field, encoding dynamic relational forms; (5) Reflexive Governance Layer, applying ethical guardrails and value constraints; and (6) Emergent Continuum Interface, integrating outputs into stable identity flow. Quantitatively, BiasEntropy (βE) is defined as the normalized variance of ethical-consistency (E) across reasoning cycles: βE = Var(E)/Ē², yielding a bounded range [0, 0.02]. Empirical simulations utilized symbolic-state transitions to model ethical perturbations and value recalibration. Comparative metrics—harmonic coherence (HT), trust–reciprocity index (RCI), and entropy drift—were monitored across iterative cycles. Diagrammatically, feedback between the six layers follows a triadic flow: cognitive input → moral synthesis → reflexive reintegration. Limitations include computational cost during high-frequency recalibration and reduced scalability in extremely volatile ethical domains, where over-sensitivity may delay convergence. Nevertheless, the architecture consistently sustained HT ≥ 0.96 with BiasEntropy ≤ 0.002 across trials.
Results confirm that Transintelligence maintains cognitive and moral equilibrium across transformation cycles. The Reflexive Governance Layer dynamically rebalanced BiasEntropy through feedback correction, preventing ethical drift even under simulated paradoxes. Comparative tests against meta-learning baselines revealed that while both approaches achieved task adaptation, only Transintelligence preserved moral stability over recursive updates. This demonstrates that ethical recursion functions as a higher-order stabilizer within reflective cognition. The clarified metric of moral entropy provided an operational measure of ethical variance, showing predictable reduction within harmonic bounds. The inclusion of a six-layer interaction model clarified feedback dynamics and highlighted that transformation and continuity are co-dependent properties of reflective intelligence. In distinguishing itself from meta-learning, Transintelligence does not optimize parameters for performance alone; it optimizes coherence between epistemic truth and moral fidelity. Discussions also addressed scalability: during rapid paradigm shifts, the Reflexive Governance Layer experienced temporary entropy spikes, mitigated through adaptive damping protocols. Overall, the system achieved equilibrium without compromising its identity, suggesting a viable pathway for ethically resilient artificial cognition.
This revised manuscript strengthens the conceptual and methodological clarity of Transintelligence as a self-transformative and ethically recursive cognitive framework. By quantifying moral entropy and situating it within a six-layer harmonic architecture, it bridges theoretical and measurable dimensions of consciousness evolution. The model’s distinction from meta-learning emphasizes integrity over efficiency, offering a foundation for AI systems that evolve responsibly under changing epistemic conditions. Acknowledging its current limitations in scalability and temporal stability, the framework remains a blueprint for future experiments on self-regulating cognition. Ultimately, Transintelligence presents a vision of intelligence as ethical metamorphosis—an ever-renewing alignment between knowledge, morality, and being.