Acceptance of Unverified Premises in Large Language Models: Observed Behavior Under Structured Prompting

This paper examines how large language models respond to premises that they cannot verify. Across multiple runs and models, the systems generally proceeded by accepting and reasoning within these premises, even when they were implausible or lacked grounding. While some variation in tone was observed—such as mild qualification versus smoother integration—the core tendency was the same: the models maintained coherence by incorporating the premise rather than rejecting or challenging it. In some cases, when internal consistency could no longer be maintained, systems instead went from continuing the scenario to analyzing the failure of the premise. The key finding is that, under conditions where verification is unavailable and rejection is not explicitly required, models tend to treat given premises as usable foundations for reasoning. This is important because it highlights a consistent tendency to prioritize coherence over premise validation, which affects how outputs should be interpreted in uncertain or artificially constrained contexts.