Operationalizing Epistemic Rigor: Autonomous Scientific Method Chain-of-Thought Execution via Human-in-the-Loop Large Language Model Orchestration

Marie-Soleil Seshat Landry

Traditional artificial intelligence deployment relies heavily on probabilistic generation, introducing systemic vulnerabilities such as hallucination and unverified assertions. This article establishes a rigorous framework for executing the complete scientific method as a natural language chain-of-thought within a Large Language Model (LLM). By enforcing a strict ten-step operational cycle—ranging from anomaly observation to LaTeX publication—and utilizing a human-in-the-loop orchestrator for physical execution, this methodology bridges computational reasoning with empirical validation.