Self-Evolving Cognitive Architectures: Bridging Neural Optimization and Consciousness Simulation for AGI

Abstract: This paper presents the design and theoretical foundations of a Self-Evolving Cognitive Architecture (SECA), a unified framework that combines autonomous neural architecture evolution with consciousness-inspired cognitive mod- eling. The proposed system leverages evolutionary algorithms, particularly customized genetic operators, to iteratively redesign and optimize neural network architectures without human intervention. In parallel, it incorporates Theory of Mind (ToM) principles, recursive self-modeling, and agent-based logic to simulate subjective awareness, emergent be- havior, and adaptive reasoning. Conventional approaches to artificial intelligence typically emphasize either structural optimization, as seen in automated architecture search, or cognitive emulation, as pursued in consciousness simulators. SECA addresses this divide by providing a dual layered paradigm in which neural substrates evolve continuously while cognitive processes develop recursively. This synthesis offers both computational adaptability and higher order reasoning capabilities, enabling AI systems to dynamically restructure their architectures while simultaneously reflecting on their internal states, predicting the intentions of other agents, and adapting behavior to complex, dynamic environments. By integrating architectural self-optimization with consciousness simulation, SECA represents a significant advancement to- ward Artificial General Intelligence (AGI). The framework facilitates the creation of AI systems that are not only efficient and scalable but also capable of introspection, adaptive learning, and human-like social cognition.

Keywords: Self-Evolving Neural Architectures, Consciousness Simulation, Theory of Mind, Evolutionary Algorithms, AGI, Adaptive AI