Stateful Intelligence: Engineering Trust, Continuity, and Dignity in Human-AI Experiences

This preprint introduces the Presence Engine™, a foundational framework for privacy-first, personality-adaptive AI systems designed to maintain behavioral continuity and support human cognitive development. Current AI architectures operate stateless, each interaction begins from zero context, undermining trust formation and preventing meaningful long-term collaboration.

The Presence Engine™ proposes an alternative: stateful AI infrastructure grounded in social learning theory and critical thinking dispositions research. The system maintains emotional and behavioral continuity through local-first processing, models productive thinking patterns (reflection, perseverance, truth-seeking) through a curated Character Brain of 47,000–50,000 staged reflections per personality vertical, and guarantees user privacy through encrypted, user-controlled data architecture that eliminates data extraction incentives.

Key contributions:

• Theoretical framework integrating Bandura's social learning theory with dispositional psychology and OCEAN/HEXACO personality adaptation for human-AI interaction

• Four core principles: thinking patterns over skills, privacy-first as requirement, continuity enabling specialization, dignity by default

• Technical architecture specifications for local-first processing, personality-adaptive interfaces, and ethical safeguards (manipulation detection, honesty requirements, anti-coercion mechanisms)

• 12-month validation methodology (Phase 1: 50–100 users; Phase 2: 500+ users; Phase 3: longitudinal + control group)

• Failure mode analysis and mitigation strategies for personality misclassification, disposition modeling backfire, and system abuse

• Competitive positioning analysis differentiating stateful continuity architecture from existing memory features and personality platforms

Keywords: Human-Centric AIX, Presence Engine, Stateful AI, Privacy-First Architecture, Personality Adaptation, Social Learning Theory, Critical Thinking Dispositions, AI Alignment, Human Development, Affective Computing

Living Thesis | DOI: https://zenodo.org/records/17280692