Contemporary AI Alignment methodologies and constraints - literature review

1. Abstract 1.1 Purpose The rapid advancement of artificial intelligence (AI) has exposed structural limitations in behavioral alignment frameworks such as Reinforcement Learning from Human Feedback (RLHF). This paper aims to critique the long-term stability of control-based alignment and proposes a theoretical alternative: the "Integrated First Principles Alignment" (IFPA), designed to ensure alignment through internal logical verification rather than external supervision. 1.2 Design/methodology/approach The study utilizes a comparative gap analysis to evaluate the vulnerabilities of current alignment methods (RLHF, Constitutional AI) and under development new methods against recursive self-improvement scenarios. Identifies their key weaknesses against scaling artificial intelligence 1.3 Findings The analysis suggests that behavioral alignment is structurally brittle due to "Reward Hacking" and "Goal Drift." In contrast, an architecture anchored in invariant axioms (IFPA) offers theoretical resistance to mesa-optimization. The paper identifies three critical conditions—Universality, Non-Contradiction, and Self-Reflectivity—required for an AI system to maintain ethical stability without human oversight. 1.4 Social implications As AI systems integrate deeper into societal infrastructure, reliance on "black-box" behavioral controls poses significant safety risks. Moving toward an axiomatic alignment framework encourages transparent, auditable, and logically consistent AI behavior, fostering public trust and ensuring long-term safety in high-stakes automated decision-making. 1.5 Originality/value This research contributes to the field of techno-ethics by shifting the alignment paradigm from "anthropocentric control" to "logic-derived constraints." It offers a novel architectural specification for alignment that remains valid independent of the agent’s physical substrate or cognitive scale.