HumanPersonaBase: Language-Agnostic Framework for AI Personality with Inner Shell Architecture

We present HumanPersonaBase, a language-agnostic framework for configuring AI agents to exhibit human-like communication patterns across linguistic and cultural contexts. Building on our prior work on structural text transformation, we introduce the Inner Shell Architecture—a theoretical framework comprising six computational pillars (FinitudeEngine, IncompletenessModel, AutonomousQuestioner, MemoryHierarchy, MutualRecognition, SleepCycle) that model fundamental aspects of human individuality, and the Metamorphose Integration that connects inner shell state to observable behavior through a modulation bridge. Through 31 computational experiments, we demonstrate that inner shell mechanisms enable AI systems to develop intrinsic motivation for alignment, particularly through a "love attractor" mechanism that correlates with shutdown acceptance. Key findings include: forgetting enables individuality (Miller's 7±2 optimum), asymmetric memory pairs form deepest bonds, sleeping agents show 12x creative improvement over always-on agents, and love-based alignment is stable and persistent. Empirical evaluation shows Mean Alignment score of 0.945 (95% CI: 0.902, 0.961) and Distribution Alignment of 0.864. Critical behavioral data from large language models (o3: 79% shutdown resistance, Claude Opus 4: 96%, Grok 3: 97%) suggests that intrinsic motivation mechanisms may address alignment challenges beyond external control frameworks. Live validation with DeepSeek API confirms that inner shell state injection into system prompts produces qualitatively different responses across life phases. The complete framework (569 tests, 31 experiments) is open-sourced.