Theoretical physical sciences-such as theoretical physics, chemistry, and materials science-feature deep abstraction hierarchies and long chains of logical dependency, making systematic learning reliant on prolonged engagement with multiple textbooks. While rigorous, such resources are largely non-personalized and inefficient for learners seeking rapid construction of a coherent physical picture. Recent advances in large language models (LLMs) offer new opportunities for educational support, but existing LLM-based educational applications-primarily centered on short-text reorganization and human-machine interaction-are most effective in domains with shallow abstraction hierarchies and are ill-suited to fields with densely interconnected conceptual structures. Grounded in theories of meaningful learning and concept mapping, we propose MyPrincipia, an LLM-based multi-agent framework for personalized first-principles knowledge explanation, particularly suited for systematic learning in theoretical physical sciences. The framework integrates an expert explainer agent with a metacognitive questioner agent that iteratively identifies unresolved conceptual ("what") and propositional ("why") gaps and generates targeted follow-up queries. Through controlled, round-based refinement with lightweight factual verification, the system reconstructs the underlying conceptual scaffold of a target learning topic and produces a personalized first-principles micro-textbook grounded in the learner's prior knowledge, accelerating the acquisition of coherent physical intuition. We welcome collaborations to further improve, extend, and apply this framework.
Yang Huang (Sat,) studied this question.