Emotion-Gradient Metacognitive RSI (Part I): Theoretical Foundations and Single-Agent Architecture

Researchers introduce the Emotion-Gradient Metacognitive Recursive Self-Improvement (EG-MRSI) framework, a theoretical architecture for AI systems that can safely modify their own learning algorithms. The framework integrates metacognition, emotion-based motivation, and self-modification with formal safety constraints, representing foundational research toward safe artificial general intelligence.

• →EG-MRSI framework enables AI systems to recursively improve themselves while maintaining formal safety bounds.
• →The architecture incorporates differentiable intrinsic rewards based on confidence, error, novelty, and success metrics.
• →Introduces quantifiable metrics called Meaning Density and Meaning Conversion Efficiency for measuring semantic learning.
• →Framework builds on Noise-to-Meaning RSI foundation with added metacognitive and emotional components.
• →This is Part I of a four-part series establishing theoretical foundations for safe AGI development.