Current large language model (LLM)-based AI systems suffer from a fundamental limitation: the inability to maintain memory across sessions. Every conversation begins as if the system has never met the user—a problem we term the Groundhog Day problem. Existing approaches either store all logs (exponential token cost), apply time-based compression (losing emotionally significant content), or use flat vector retrieval (no identity formation). This paper presents FIMP (Fractal Identity Memory with Multi-agent Protection), a closed-loop architecture comprising seven integrated subsystems: (1) emotion-weighted selective preservation, (2) three-layer fractal memory hierarchy with vertical refinement, (3) multi-agent adversarial hallucination verification, (4) structured inter-agent communication, (5) policy-gated governance, (6) autonomous dreaming-based consolidation with failure immunity, and (7) fractal two-stage recall. We evaluate FIMP on a production deployment running 11 microservices over 30+ days, accumulating 3,580 episodic memories and 115 oracle identity rules. Key results include: statistically significant emotion–rule correlation (χ² = 24.387, p < 0.05), 96.6% auto-promotion validity (28/29 discovered rules with confidence ≥ 0.7), 100% structured self-diagnosis pass rate across 100 verification batches, deterministic inter-agent parsing under 1ms with 23.8% average token reduction, and stable temporal maturation across four observation quarters. The system demonstrates that emotion-weighted memory, when combined with adversarial integrity verification and autonomous consolidation, enables persistent and trustworthy AI identity formation.
Myeong Jun Jo (Fri,) studied this question.