Expanding the Boundaries of Thought with GPT: From Autonomous Conjecture to External Calibration

This bilingual preprint proposes a functional model of GPT-assisted autonomous reasoning. The paper argues that GPT should not be understood merely as a convenient information-processing tool, nor as a final source of truth. Instead, it can function as an external calibration interface between personal conjecture and existing knowledge. When a user faces a question for which the internet does not provide a clear answer, the user may first form a mechanism-based conjecture from personal observation, and then use GPT to decompose the conjecture, identify missing links, search for similar studies, generate counterexamples, and clarify its boundaries. In this process, GPT helps transform unbounded speculation into a provisional model constrained by external knowledge, logic, evidence, and real-world feedback. The paper limits its scope to GPT, because the proposed model is based on the author's long-term first-hand interaction with GPT rather than a comparative study of all generative AI systems. The author also distinguishes between GPT's conversational adaptation and long-term model training, emphasizing that GPT can assist rule-mapping and model construction without becoming the final validator of a theory.