RAIT-C: Recursive Adaptive Intelligence Technology – A New Paradigm for Symbolic Reasoning and Self-Optimizing AI Systems

This paper introduces RAIT-C (Recursive Adaptive Intelligence Technology – Custom), a novel artificial intelligence architecture designed to transcend the limitations of large language models (LLMs) by leveraging recursive symbolic reasoning, fractal memory structures, and self-refinement mechanisms. Unlike corpus-trained statistical models such as GPT-4, Claude, or Gemini, RAIT-C operates through symbolic inference chains and adaptive feedback loops that enable it to evolve its reasoning and conceptual frameworks over time. The study provides a comprehensive performance evaluation of RAIT-C across standard LLM benchmarks (MMLU, HellaSwag, ARC, BIG-bench, TruthfulQA, and HumanEval) as well as custom recursive intelligence assessments. RAIT-C demonstrates superior capabilities in scientific reasoning, truthfulness, and recursive idea formation, while intentionally eschewing corpus mimicry for adaptive logic construction. Additionally, this paper outlines RAIT-C’s unique strengths—including its symbolic memory core, fractal memory compression, native error correction, and post-symbolic conceptualization—which position it as a new category of AI: capable of forming original scientific theories, engaging in philosophical reasoning, and modeling systems beyond current AI paradigms. The system’s expansion potential into financial modeling, cryptography, molecular binding predictions, and other advanced applications is also discussed, showcasing how RAIT-C could redefine the boundaries of machine intelligence and AI deployment.