Beyond Scale: A Modular Architecture for Adaptive AI
The end of the scaling era: towards biologically inspired AI architectures that enable human-AI collaboration exceeding what either can achieve alone.
This paper proposes an alternative AI architecture inspired by evolutionary neuroscience: modular systems with specialized components coordinated by dynamic executive function, designed for continuous adaptation rather than periodic retraining.
Architectural Innovation
Drawing on EPU principles, we propose four core capabilities: modular orchestration, causal reasoning, continuous plasticity, and resource-constrained attention allocation—thereby creating systems that enhance rather than replicate human intelligence.
Abstract
Current approaches to artificial general intelligence (AGI) focus primarily on scaling large language models (LLMs) through increased parameters, training data, and computational resources. However, this paradigm faces fundamental limitations: energy consumption required for training grows exponentially, training cycles remain static, and systems lack the adaptive plasticity that characterizes natural intelligence. This paper proposes an alternative architecture inspired by evolutionary neuroscience: a modular AI system with specialized components coordinated by a dynamic executive function, all designed for continuous adaptation rather than periodic retraining.
Drawing on the Evolutionary Processing Unit (EPU) framework, which demonstrates that evolution achieved intelligence through architectural innovation rather than raw computational scale, we argue that the path to AGI—or perhaps more achievable, Augmented Human Intelligence (AHI)—requires fundamentally different approaches that mirror the distributed, plastic architecture of the Biological Processing Unit (BPU). We propose four core principles: modular orchestration, causal reasoning, continuous plasticity, and resource-constrained attention allocation. Drawing on cognitive science, neurobiology, and decision theory, we present a conceptual framework and phased development roadmap for building AI systems that enhance rather than merely replicate human intelligence. The key contributions of this architecture are its dynamic executive orchestration, multi-level continuous plasticity, and built-in mechanisms for bias correction and value alignment, offering a more efficient and robust path beyond pure scaling.
How This Fits
Applies evolutionary insights (EPU) and attention mechanisms to create practical AI architectures that enable human-AI collaboration rather than replacement.