Neuromorphic Multi-LLM Modular Intelligence Architecture: A Systems-Level Path Toward AGI Beyond Monolithic LLM Limits

Recent results show that transformer-based language models fail on classes of reasoning tasks for structural, not empirical, reasons. These failures arise from bounded inference complexity, fixed-depth computation, and token-limited memory. This paper argues that these limits do not preclude Artificial General Intelligence, but they do rule out monolithic model designs. We present a modular architecture, the Neuromorphic AI Architecture for a system of LLMs, that composes multiple bounded models through persistent memory, external verification, and a non-linguistic control layer. We formalize the computational limits of single-model inference, compare them mathematically to system-level composition, and show that modular decomposition allows cumulative reasoning capacity without violating complexity theory. The result is a system capable of long-horizon planning, self-correction, and adaptive computation, properties required for AGI-like behavior.