The authors propose Cognitive Agent Compilation (CAC), a framework inspired by cognitive architectures that uses a strong "teacher" LLM to compile problem-solving knowledge into an explicit, inspectable "target agent." CAC separates (i) knowledge representation, (ii) problem-solving policy, and (iii) verification/update rules, making the agent's knowledge state and decisions transparent and editable—useful in educational settings where educators want to know what the system assumes the learner knows. They present an early proof-of-concept with small language models, surfacing design trade-offs between explicit control and scalable generalization, and position CAC as a step toward bounded-knowledge AI for education.
Main takeaways:
- CAC compiles problem-solving knowledge from a strong teacher LLM into an explicit, inspectable target agent with separated knowledge, policy, and update rules.
- Goal is to make AI tutors' knowledge states transparent and editable for educators and learners.
- Early proof-of-concept with small LMs highlights trade-offs between explicit control (inspectability, editability) and scalable generalization.
- Inspired by cognitive architectures that use symbolic, inspectable knowledge representations.
- Positions CAC as a building block for bounded-knowledge AI in educational applications.