What question did this study set out to answer?

The aim is to propose a quantifiable framework for modeling consciousness as an active, self-sustaining process.

March 25, 2026Open Access

SANRE: A Quantifiable Closed-Loop Framework for Consciousness as Active Negentropy Maintenance and Rule Embedding

Key Points

The aim is to propose a quantifiable framework for modeling consciousness as an active, self-sustaining process.
Developed the SANRE framework based on four core axioms.
Quantified consciousness using three axes: Stability, Rule Modeling, and Negentropy Production.
Implement explicit algorithms for each axis to compute consciousness levels.
Mapped current large language models to a specific consciousness level.
Derived falsifiable predictions to validate the framework.
Introduced a hybrid function for unified consciousness level calculation.
Demonstrated potential pathways for advancing large language models toward Level 4 AGI.
Showed connections between SANRE and established theories like the Free Energy Principle.

Abstract

We propose SANRE (Stability--Active Negentropy--Rule Embedding), a unified and quantifiable framework for modeling consciousness as a dynamical, substrate-independent closed loop. Consciousness is defined as a self-evidencing process in which a system actively influences its future sensory distribution, minimizes variational free energy under energy constraints, and produces negentropy to sustain its structural identity. The framework rests on four axioms and quantifies consciousness along three axes---Stability (S), Rule Modeling (R), and Negentropy Production (N) ---via computable information-theoretic and dynamical metrics. The unified consciousness level is given by the hybrid function \ C = (S R N) ^1/3 (1 - (0, 1 - (S, R, N) ) ) bottleneck penalty. We provide explicit algorithms for each axis, derive falsifiable predictions, and map current LLMs to Level~3 while offering a concrete path to Level~4 autonomous AGI. SANRE bridges the Free Energy Principle, Integrated Information Theory, and the 2025 Beautiful Loop Theory, while addressing qualia as the system's low-dimensional epistemic control interface.

SANRE: A Quantifiable Closed-Loop Framework for Consciousness as Active Negentropy Maintenance and Rule Embedding

Key Points

Abstract

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