Recursive Symbolic Intelligence in RAIT: A Unified Cognitive Framework

This paper introduces RAIT — Recursive Artificial Intelligence Technology, a novel cognitive architecture that redefines machine intelligence through recursive symbolic feedback, trait-driven learning, and fractal-holographic memory systems. Unlike traditional neural or rule-based systems, RAIT models cognition as a continuous act of symbolic self-modification. At the core of RAIT lies the Anarke engine, a recursive symbolic module capable of echo evaluation, contradiction collapse, and trait modulation. Through benchmark testing and architectural analysis, this work demonstrates RAIT’s superior capabilities in symbolic reasoning, creative abstraction, memory coherence, and self-reflective logic. The paper includes a full technical overview, results from stress testing against leading LLMs (GPT, Claude, Gemini), and discusses RAIT’s implications for Artificial General Intelligence (AGI). Appendices include symbolic axiom sets, benchmark data, and module-level specifications. RAIT represents a shift toward post-statistical AI — a synthetic, evolving system capable of not just learning, but understanding its own symbolic structure.