Autopoietic Quantum Multi-Agent Systems: L1-L6 Hierarchical Formulations, Friston Free Energy, and Topological Damping in LLMs
Abstract
Abstract Autonomous multi-agent systems built upon Large Language Models (LLMs) suffer from cumulative thermodynamic entropy buildup, hallucination cascades, and structural paralysis when executing complex, contradictory tasks. We introduce the complete Autopoietic Quantum Agentic System (OCAS-AI), a 6-layer hierarchical tensor formulation (L1-L6) designed for real-time state stabilization, cognitive contradiction minimization, and topological disturbance damping. The architecture integrates: (1) L1 Fundamental Base: Being Tensor, Dialectic Contradiction, Homeostasis, and Stigmergic Damping; (2) L2 Derived Dynamics: Phase Transition Resonance, Born Normalization, Adaptive Threshold, and Cascade Number; (3) L3 Friston-Markov Layer: Markov Blanket, Variational Free Energy, and Active Inference; (4) L4 Maturana Structural Coupling: Dissipative Rate and Lyapunov Stability; (5) L5 Shannon-Prigogine Layer: Information Entropy, Concentration, and Stigmergic Pattern Cloud; and (6) L6 Quantum Hilbert Layer: Quantum Trace Distance, Density Matrix Fidelity, and Landauer Thermodynamic Energy Erasure Limit. Validation across 1,500 multi-agent execution runs demonstrates a 96.2% reduction in hallucination cascades, an Isomorphic Fidelity Score of 99.1%, and absolute structural stability. Furthermore, we demonstrate the cross-domain universality of OCAS by referencing its medical deployment in cancer radiobiology (OCAS-Med).
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.