Centaurian Architecture: A Quantum-Cognitive Architecture for Interpretable, Embodied AI with Neural Periphery
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2026
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| _version_ | 1866901859883548672 |
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| author | Drozd, Oleksii |
| author_facet | Drozd, Oleksii |
| contents | <p>Modern large language models achieve remarkable linguistic fluency but remain opaque, resource-intensive, and fundamentally uninterpretable — every output is a stochastic sample from a trillion-parameter black box. This paper presents the **Centaurian Architecture (CA)**, a multi-layered system for human-like AI that preserves the interpretability and traceability of symbolic cognitive modeling while selectively incorporating lightweight neural components exactly where they demonstrably outperform rule-based alternatives. A critical preliminary clarification: the architecture's Quantum Personality Model (QPM) is an instance of **Quantum-Like AI (QLAI)** — it employs the mathematical formalism of quantum mechanics (Hilbert spaces, density matrices, unitary evolution) as a modeling language for cognition, running entirely on classical hardware. No quantum computer is required.</p> <p>The architecture integrates four core subsystems: (1) a **QPM** encoding the Five-Factor Model of personality into a 12-qubit Hilbert space formalism with empirically calibrated entanglement parameters and operationalized Lindblad decoherence dynamics; (2) **small language models** serving as linguistic transducers (Qwen2.5-7B-Instruct standardized across both deployment tiers; 7B is the empirically validated minimum, established by Experiment 1, with 3.8B incoherent on JSON-based SCI prompts) that convert structured cognitive outputs into natural language without performing reasoning; (3) **custom JA/LI procedural facial animation** synchronized with lightweight neural text-to-speech; and (4) a **domain-specific knowledge architecture** combining RDF/OWL ontologies with embedded vector retrieval for grounded, hallucination-resistant content generation. We provide complete specifications for the quantum circuit design, the formal QPM-measurement-to-SLM translation protocol, the ontology schema and vector retrieval pipeline, LoRA fine-tuning methodology, edge deployment resource estimates, empirical validation framework (Experiments 1 and 2), the Self-Model Component architecture (Section 17), and an extension path from software-only virtual agents to physically embodied humanoid robots. The system's total memory footprint is ~5 GB (base CA, Tier 1) to ~6 GB (SMC-enabled, Tier 2 with the LoRA-10K SCI grounding adapter), enabling deployment on current edge hardware (NVIDIA Jetson Orin, Qualcomm Snapdragon 8 Elite, Apple Silicon) while maintaining end-to-end traceability — every behavioral decision is auditable from situative input through quantum state evolution to observable output, with neural components confined to bounded I/O transduction roles.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20256941 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Centaurian Architecture: A Quantum-Cognitive Architecture for Interpretable, Embodied AI with Neural Periphery Drozd, Oleksii Artificial intelligence Artificial Intelligence Cognitive Architecture Quantum Cognition Large Language Models Personality Psychology Human-Computer Interaction Natural language processing Natural Language Processing Explainable AI Embodied AI Robotics Robotics Knowledge Graphs Multimodal AI Edge Computing Large Language Models Five Factor Model Affective Computing Speech Synthesis Facial Animation AI Safety <p>Modern large language models achieve remarkable linguistic fluency but remain opaque, resource-intensive, and fundamentally uninterpretable — every output is a stochastic sample from a trillion-parameter black box. This paper presents the **Centaurian Architecture (CA)**, a multi-layered system for human-like AI that preserves the interpretability and traceability of symbolic cognitive modeling while selectively incorporating lightweight neural components exactly where they demonstrably outperform rule-based alternatives. A critical preliminary clarification: the architecture's Quantum Personality Model (QPM) is an instance of **Quantum-Like AI (QLAI)** — it employs the mathematical formalism of quantum mechanics (Hilbert spaces, density matrices, unitary evolution) as a modeling language for cognition, running entirely on classical hardware. No quantum computer is required.</p> <p>The architecture integrates four core subsystems: (1) a **QPM** encoding the Five-Factor Model of personality into a 12-qubit Hilbert space formalism with empirically calibrated entanglement parameters and operationalized Lindblad decoherence dynamics; (2) **small language models** serving as linguistic transducers (Qwen2.5-7B-Instruct standardized across both deployment tiers; 7B is the empirically validated minimum, established by Experiment 1, with 3.8B incoherent on JSON-based SCI prompts) that convert structured cognitive outputs into natural language without performing reasoning; (3) **custom JA/LI procedural facial animation** synchronized with lightweight neural text-to-speech; and (4) a **domain-specific knowledge architecture** combining RDF/OWL ontologies with embedded vector retrieval for grounded, hallucination-resistant content generation. We provide complete specifications for the quantum circuit design, the formal QPM-measurement-to-SLM translation protocol, the ontology schema and vector retrieval pipeline, LoRA fine-tuning methodology, edge deployment resource estimates, empirical validation framework (Experiments 1 and 2), the Self-Model Component architecture (Section 17), and an extension path from software-only virtual agents to physically embodied humanoid robots. The system's total memory footprint is ~5 GB (base CA, Tier 1) to ~6 GB (SMC-enabled, Tier 2 with the LoRA-10K SCI grounding adapter), enabling deployment on current edge hardware (NVIDIA Jetson Orin, Qualcomm Snapdragon 8 Elite, Apple Silicon) while maintaining end-to-end traceability — every behavioral decision is auditable from situative input through quantum state evolution to observable output, with neural components confined to bounded I/O transduction roles.</p> |
| title | Centaurian Architecture: A Quantum-Cognitive Architecture for Interpretable, Embodied AI with Neural Periphery |
| topic | Artificial intelligence Artificial Intelligence Cognitive Architecture Quantum Cognition Large Language Models Personality Psychology Human-Computer Interaction Natural language processing Natural Language Processing Explainable AI Embodied AI Robotics Robotics Knowledge Graphs Multimodal AI Edge Computing Large Language Models Five Factor Model Affective Computing Speech Synthesis Facial Animation AI Safety |
| url | https://doi.org/10.5281/zenodo.20256941 |