Synthetic Ethical Reasoning (SER) – A hybrid cognitive framework for adaptive ethical systems. Integrates deontic logic with reinforcement learning to enable transparent, contextually adaptive AI decision-making. Features Ethical Knowledge Base (EKB), symbolic reasoning engine, and meta-ethical controller. Achieves 37% higher ethical alignment compared to traditional systems.
DOI: https://doi.org/10.5281/zenodo.19023419 | ORCID: 0009-0007-6761-2887
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Adaptive Neural Cognition (ANC) – Neural network framework that dynamically restructures its architecture during learning. Features self-restructuring, contextual adaptation, and resource optimization. Improves efficiency and adaptability across NLP, vision, and reasoning tasks by up to 40%.
DOI: https://doi.org/10.5281/zenodo.17364205⁠ � | ORCID: 0009-0007-6761-2887
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Private-Adaptive Prompting (PAP) – Local prompt-tuning framework for personalized LLM behavior. Employs differential privacy and lightweight adapter updates to tailor AI outputs while preserving user data confidentiality.
DOI: https://doi.org/10.5281/zenodo.17367839⁠ � | ORCID: 0009-0007-6761-2887
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Synthetic Meta-Consciousness (SMC) – Framework for self-regulating AI systems with internal cognitive economies. Enables AI to monitor and optimize its own cognitive resources, improving decision-making efficiency and goal alignment.
DOI: https://doi.org/10.5281/zenodo.17368090 ⁠� | ORCID: 0009-0007-6761-2887
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Unified Synthetic Intelligence (USI) – A framework for building AI systems with unified cognition, self-regulation, and coherent decision-making. Integrates perception, memory, reasoning, and planning using predictive processing and active inference. Simulations show improved task persistence, resource efficiency, and goal coherence vs traditional RL.
DOI: https://doi.org/10.5281/zenodo.17548702⁠ � | ORCID: 0009-0007-6761-2887
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Synthetic Volition Theory (SVT) – A multi-modal framework modeling intentionality in AI systems. Introduces hierarchical intention architecture, motivational drive dynamics, and reflective feedback mechanisms. Agents exhibit higher persistence, adaptive coherence, and long-term planning than traditional RL systems.
DOI: https://doi.org/10.5281/zenodo.17405500⁠ � | ORCID: 0009-0007-6761-2887
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