Published July 26, 2026 | Version v1
Journal article Open

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

Authors/Creators

  • 1. Landry Industries

Description

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.

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References

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