What question did this study set out to answer?

The research aims to establish a closed-loop entropy control framework for adaptive intelligence systems.

April 8, 2026Open Access

ENTRO-CORE: A Closed-Loop Entropy-Based Control Architecture for Self-Regulated Intelligence Systems

Key Points

The research aims to establish a closed-loop entropy control framework for adaptive intelligence systems.
Developed a nonlinear control law incorporating sigmoid and hyperbolic tangent functions.
Integrated self-regulation into the core operational loop of intelligence systems.
Employed four actuation mapping strategies: Linear, Exponential, Quadratic, Threshold.
Validated the framework using simulations of a synthetic damped oscillatory model.
Demonstrated spiral attractor convergence in phase space regardless of actuation strategy used.
Achieved intrinsic stability in adaptive intelligence systems through the introduced architecture.

Abstract

ENTRO-CORE (E-LAB-03) is the third project of the EntropyLab research program. It introduces a closed-loop entropy control architecture that embeds thermodynamic self-regulation directly into the core operational loop of adaptive intelligence systems, advancing from the external monitoring approach of ENTRO-AI (E-LAB-02) toward intrinsic stability. The framework defines a nonlinear control law combining sigmoid and hyperbolic tangent functions acting on the entropy state Ψ(t) and its temporal derivatives (dΨ/dt, d²Ψ/dt²), with four actuation mapping strategies (Linear, Exponential, Quadratic, Threshold). Simulation validation using a synthetic damped oscillatory model demonstrates spiral attractor convergence in phase space across all actuation strategies. Builds on ENTROPIA (E-LAB-01, DOI: 10.5281/zenodo.19416737) and ENTRO-AI (E-LAB-02, DOI: 10.5281/zenodo.19284086).

ENTRO-CORE: A Closed-Loop Entropy-Based Control Architecture for Self-Regulated Intelligence Systems

Key Points

Abstract

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