Recursive Self-Improvement in Cognitive Systems: A Study on the Evolutionary Pathways of Autonomous Intelligence and Meta-Learning Architectures

Abstract

Recursive self-improvement (RSI) represents one of the most transformative concepts in artificial intelligence (AI) and cognitive science. It refers to the capacity of an intelligent system to iteratively refine its own algorithms, architecture, and learning processes without direct human intervention. This concept lies at the intersection of cognitive architectures, machine learning, meta-learning, and artificial general intelligence (AGI). The emergence of self-modifying systems presents both unprecedented opportunities and existential challenges. This paper explores the theoretical foundations, computational mechanisms, and architectural models underlying recursive self-improvement in cognitive systems. Furthermore, it investigates the ethical, technical, and epistemological implications of creating systems that can autonomously evolve their intelligence.

Keywords: Recursive self-improvement, cognitive systems, meta-learning, artificial general intelligence, self-modifying algorithms, machine consciousness, cognitive evolution.