This article details a self-corrected modular, brain-inspired LLM architecture, moving beyond unverified hypotheses to extract engineering-able principles from neuroscience. It offers rigorous solutions to key problems, including entity alignment, and presents a prototype-ready design.
The article critiques current LLM architectures for their bloat, black-box nature, and context failures, attributing these issues to an entangled parameter space. It proposes a modular, brain-inspired architecture, drawing parallels to the human brain's specialized processing areas integrated by the prefrontal cortex.
This article, part of 'The Learn Arc' series on Active Inference, details how abstract mathematical concepts translate into actual neural circuits. It posits the cortex functions as a physical factor graph, where cortical columns are factor nodes and white-matter fibres act as messages in a message passing computation.