Cognitive Science and Domain Stewards
What if the path to artificial general intelligence isn't through bigger language models, but through smarter structures around existing ones?
Recent discussions around Domain Stewards have highlighted their potential to achieve domain-specific artificial general intelligence through specialized focus and structured knowledge implementation. Building on previous analyses of AI architecture evolution and the parallels with database wrappers, we can examine how cognitive science principles illuminate why Domain Stewards are particularly effective and how they can be optimized further.
Domain Stewards represent a novel approach to bootstrapping artificial intelligence by deliberately constraining its operational context while simultaneously enriching its environment with well-structured knowledge and clear action pathways. Rather than waiting for more powerful language models, this approach leverages existing LLM capabilities by reducing cognitive complexity through careful system design. Like training wheels on a bicycle, these constraints paradoxically enable greater capability by providing stability and structure.
Cognitive Load Theory, originally developed to understand human learning and problem-solving, provides valuable insights into why Domain Stewards are so effective. By pre-organizing domain knowledge into well-structured formats, we dramatically reduce the extraneous cognitive load on the LLM. This structured approach allows the LLM to focus its computational resources on germane cognitive load – the actual problem-solving and decision-making required for the task at hand. The parallel with human cognition is striking: just as students learn better when provided with worked examples and clear frameworks, LLMs perform better when operating within well-defined knowledge structures and action boundaries.
