Moral Kernel Optimization as an Alignment Constraint for Large Language Models

This paper formalizes Moral Kernel Optimization (MKO) as a drop-in alignment constraint for large language models (LLMs). MKO defines event-valued guardrails ensuring (i) truth consistency across paraphrases and time, and (ii) coercion-free reasoning that preserves user agency. The framework introduces kernel operators, minimal-trigger certification (one rejection per typal class), and quantitative guardrail inequalities for truth, coercion, and closure. Two pilot simulations demonstrate MKO’s effect in reducing hallucinations and manipulative pressure while maintaining helpfulness. Implementation guidelines cover training-time, decoding-time, and audit-time integration. MKO thus transforms moral reasoning into quantifiable invariants that any AI system can implement, measure, and certify—providing a reproducible standard for safe, transparent, and autonomous AI systems.