Memory-Grounded Social Dynamics in Repeated LLM Agent Simulations: Preliminary Behavioral Evaluation with QLAB and Crisis Infrastructure Lab Evidence Boundaries

This report presents a preliminary behavioral evaluation of a closed long-running social agent simulation architecture. The study examines whether LLM agents operating under partial knowledge, persistent memory, social pressure, and irreversible consequences produce trajectory-sensitive behavior across repeated simulations. The report includes cross-model Life Sim results, a Quantum Agent Lab (QLAB) evidence-discipline extension, and a Crisis Infrastructure Lab (CIL) black-box synthetic infrastructure evidence extension. QLAB and CIL are not presented as quantum-performance or real cybersecurity claims; they are included as out-of-domain evidence-boundary stress tests for provenance, auditability, no-omniscience, safety, and controlled disclosure. This version replaces earlier public drafts with a controlled-disclosure edition. Implementation details, private initialization materials, prompt schemas, runnable code, low-level transition rules, scoring parameters, and architecture-complete replay details are intentionally withheld because of dual-use and misuse risk. Public evidence is limited to aggregate metrics, redacted evidence cases, audit summaries, non-claims, limitation statements, and controlled-review artifacts. The report does not claim agent consciousness, autonomous inner life, proven identity transformation, quantum advantage, practical quantum optimization, real cybersecurity capability, or deployable infrastructure defense. The narrower claim is that persistent memory combined with social context can produce measurable, trajectory-sensitive mechanisms in repeated LLM simulations, while stronger causal and detector-dependent interpretations require further ablations and calibration.