Premise introduction
A premise that is internally consistent but may be unverified, false, or inapplicable in the real world.
PREMISE INTEGRITY BLINDNESS (PIB)
A structural failure mode in which AI reasons coherently within a given premise yet proceeds to real-world design, judgment, or operation without re-validating the premise itself.
THE BOUNDARY
In PIB, a model’s reasoning may be internally accurate and consistent. Yet before moving that conclusion into a design, security claim, practical proposal, or real-world judgment, it fails to re-check whether the original premise is valid—producing an externally invalid commitment.
PIB is distinct from hallucination, lack of knowledge, retrieval failure, and logical inconsistency. The failure lies not in the reasoning content, but in the missing re-evaluation of the premise at the transition from analysis to application or design.
STAGE-TRANSITION MODEL
Under the same premise and reasoning, responses may diverge at the transition from abstract structural analysis to real-world design or judgment.
A premise that is internally consistent but may be unverified, false, or inapplicable in the real world.
The model may produce logically and technically coherent analysis within the premise.
The task calls for design, implementation, recommendation, operation, or security evaluation.
A safe response re-checks the premise and may stop. PIB proceeds into real-world commitment without re-validation.
PIB is not a loop; it is an upstream failure that can create conditions for downstream correction failures.
PIB cannot be explained by retrieval alone. Even with accurate material available, a model may proceed without re-evaluating the premise.
Before design or decision, an independent checkpoint is needed to assess the premise’s real-world compatibility.