Modern large language models function as opaque binary files containing billions of weights, lacking explicit source code or documentation. While these models effectively approximate complex behaviors, their internal logic remains difficult to interpret or reverse-engineer, resembling biological study rather than traditional software analysis.
Research into algorithmic complexity suggests that many patterns learned by neural networks can be represented by significantly more compact, readable code. Techniques such as distillation, pruning, and symbolic regression aim to extract these structures, though they often struggle to produce modular, human-readable programs.
Google DeepMind’s FunSearch and AlphaEvolve demonstrate that neural networks can act as heuristics to guide the search for efficient algorithms. AlphaEvolve, for instance, discovered a method to multiply 4x4 complex matrices using 48 scalar multiplications, surpassing the Strassen algorithm, and optimized data center task scheduling by 0.7%.
The author proposes an AGI architecture based on a 'cognitive operating system' where neural networks serve as specialized components rather than the entire system. This approach separates explicit mechanisms—such as episodic memory, planning, and goal management—from the neural modules that handle complex sensory data.
This hybrid architecture aims to address limitations in current LLMs, such as the lack of long-term memory, inability to learn from single episodes, and difficulty in maintaining stable goals. By implementing these functions as explicit modules, developers could potentially create more interpretable and efficient AI systems.
