The Provably Aligned AI Core: A Deterministic Safety Wrapper for Self-Improving Agents

Abstract

Self-improving artificial intelligence—systems that modify their own source code—promises exponential capability gains but also poses an existential risk: a singleunchecked code modification could permanently remove all safety constraints.Existing alignment methods (RLHF, Constitutional AI) rely on probabilistictraining and provide no formal guarantees. We propose the Provably Aligned AICore (PAAC) , a deterministic architecture that wraps any black-box self-improvingagent and mathematically guarantees that all code modifications preserve apredefined set of safety properties. The core intercepts every proposed code change,translates it into a logical specification, and submits it to an SMT-based formalverifier. Only modifications proved safe are executed. Safety properties areencoded as auditable first-order logic axioms, making the system transparent andaccountable. We describe the complete architecture, the verification protocol basedon bounded model checking with soundness guarantees under hard resource limits,and a threat model demonstrating resilience to circumvention attacks. A proof-ofconcept simulation shows PAAC blocking a backdoor attack while permitting safeoptimizations, with verification overhead under 120 ms per patch. This workprovides the first practical framework for provably safe recursive selfimprovement, bridging the gap between alignment theory and real-worlddeployment.