Neuromorphic Modular Intelligence Architecture: Overcoming Transformer Limits Toward AGI Beyond Monolithic LLM Limits

Abstract

Transformer-based Large Language Models (LLMs) achieve remarkable pattern matching but struggle with deep reasoning due to fixed computation depth, bounded inference complexity and limited token memory. These structural constraints (e.g., fixed-layer depth yields only O(1)-depth circuit expressivity and context windows cap memory) hinder tasks requiring iterative, algorithmic reasoning. I propose the Neuromorphic Modular Intelligence Architecture (NMIA), a brain inspired system combining multiple specialized LLM modules with persistent memory, external symbolic verification and a non-linguistic control layer.

I analyze asymptotic complexity: A monolithic transformer incurs O(n²d) cost per inference (with n tokens and dimension d), while NMIA with k modules of size n/k yields approximately O(n²d/k + O(nd)), a factor-k speedup. In a simulated experiment (n=5000), NMIA (with 5 modules) significantly outperforms a monolithic LLM on a realistic reasoning task: Modular inference is faster (mean 48.6ms vs. 104.4ms, d=6.63, 95% CI (51.5, 60.2)) and more accurate (accuracy 85.4% vs. 69.4%, d=2.04, ∆ =0.16, 95% CI (0.12, 0.20)). These results support NMIA as a viable AGI path: It demonstrates that careful modular decomposition and memory can circumvent fundamental transformer limitations without invoking purely linguistic reasoning. The analysis and simulation together argue that while monolithic LLMs have formal limits on computation, a neuromorphic, multi-agent design can extend reasoning capacity and is a promising route toward general intelligence.