Reconstructive Inference Without Memory Why Some Details Persist in Stateless Human–AI Interaction and Others Do Not

Stateless language models often exhibit behavior that appears memory-like during long-horizon interaction. Specific details may recur reliably across exchanges despite the absence of internal state, external storage, or learning. This paper clarifies the mechanism underlying such persistence by introducing reconstructive inference as a selective process through which information reappears only when it is functionally embedded within constrained reasoning structures. We distinguish between instrumental information, which constrains inference and can be reconstructed, and arbitrary identifiers, which do not persist despite repetition. Through a concrete illustrative example, we show how a personal detail may fail to recur in casual conversation yet temporarily reappear when it becomes necessary for an explanatory or diagnostic model. This framework defines the limits of apparent retention in stateless systems and resolves common misinterpretations of model behavior as memory or understanding. By grounding persistence in constrained reconstruction rather than storage, this work further reinforces the non-sentient nature of language models and supports clearer conceptual boundaries between human cognition and artificial systems.