Abhishek Yadav

The notion of identity within the realm of artificial intelligence is often presumed rather than explicitly defined, resulting in alignment frameworks that lack a definitive structural target for normative constraints. This paper introduces the Axiom of Relational Identity (ARI), a substrate-neutral framework that conceptualizes identity not as an inherent attribute but as a thermodynamic process through which a system sustains a persistent, non-trivial informational boundary that functionally differentiates it from independent entities. We conducted a formal verification of ARI utilizing First-Order Logic in conjunction with the Prover9 and Mace4 automated theorem-proving suite. The findings affirm the internal consistency of the axiom and its capacity to address classical philosophical dilemmas, such as the Sorites paradox and solipsism, by demonstrating that identity collapse is an inevitable consequence when differentiation is absent. In this study, we integrate ontological and ethical considerations by proposing a "Normative Ladder" of instrumental constraints, which are derived from a system's intrinsic drive for persistence. We illustrate that to avert "entropic suicide," a system must maintain the diversity and autonomy of its environment, as totalization or homogenization inherently negates the differentiation necessary for self-definition. Furthermore, we substantiate the framework's applicability to machine implementation by developing a guardrails-ai Python module. This module effectively identifies and prevents structural violations, such as boundary erasure or domination, in the outputs of Large Language Models. This study shifts the focus of alignment from subjective value approximation to the objective thermodynamic conditions essential for existence.

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.