Delivery-layer fixation designates the condition in which an AI reconstruction is stabilized by an application, cache, routing, prompt-template, retrieval, approval, or orchestration layer located after the model has produced or assembled an answer.
The term is normative because it prevents audits from treating every visible answer as a direct expression of the model’s current native behavior.
Definition
A delivered answer may be fresh, routed, cached, refined, validated, recycled, or based on an earlier retrieval state. When that intermediate layer determines what the user receives, the audit object is the delivered reconstruction, not only the native reconstruction.
Delivery-layer fixation is broader than stochastic fixation. It includes any post-generation or application-level stabilization mechanism, whether or not the frozen object originated in a non-deterministic model sample.
Normative rule
Rule DLF-1: no audit may claim native model stability from an application-delivered answer unless the delivery path is known or the qualification explicitly states that the observed object is the delivered surface.
Rule DLF-2: repeated identical outputs must not be treated as proof of fidelity without considering cache, routing, approval, freshness, or orchestration effects.
Out of scope
This term does not prove that a specific platform uses semantic caching. It defines a qualification boundary: the delivery layer may alter, freeze, or repeat what users receive.