This paper reconstructs Toegye Yi Hwang's philosophy into a five-stage EEFS architecture with design principles, scenario classifications, and an evaluation instrument for ethical emotion regulation in agentic AI.
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An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.
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Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework
This paper reconstructs Toegye Yi Hwang's philosophy into a five-stage EEFS architecture with design principles, scenario classifications, and an evaluation instrument for ethical emotion regulation in agentic AI.
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Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage
An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.