Zero-shot prompts evaluate native memory by querying the LLM with isolated input frames while web-search grounding is disabled, revealing its baseline training recall. By testing these prompts across ungrounded model instances without live retrieval intervention, systems can map the model's direct parametric associations and isolate genuine training-data recall using token-level log probability analysis.