CHORDI: Conflict to Harmonization Operator via Re-Dimensioning and Integration

Abstract

AI alignment has become an increasingly important challenge. Advances in AI knowledge and logical capability must be accompanied by ethical and socially responsive reasoning, including the capacity to consider the perspectives of affected individuals and society as a whole. A key question is how AI can reason through Logic and Empathy/Ethics simultaneously, make the resulting Conflict explicit, and move beyond it toward a more responsible judgment. To support healthy Human–AI Co-evolution, this paper proposes CHORDI (Conflict to Harmonization Operator via Re-Dimensioning and Integration), a quaternion-inspired conceptual Meta-Reasoning framework for AI decision-making. CHORDI evaluates a given problem through dual pathways of Logic and Empathy/Ethics, explicitly identifies the Conflict between them, and uses that Conflict as the starting point for Perspective Reversal, Re-Dimensioning of the problem formulation, and Integration in search of an innovative third solution space. As a related research hypothesis, this paper introduces SEMARGEBRA (Semantic–Intent Algebra): an approach that maps mathematically structured relations and operations onto prompt-level transformations of meaning, intent, perspective, and problem structure. The central idea is not that an LLM numerically executes quaternion algebra, but that mathematically structured prompting can provide operational constraints that an LLM can interpret and enact at the semantic level. A preliminary comparison comprising three cases, three Large Language Models (LLMs), and responses generated with and without the full CHORDI prompt—18 responses in total—suggested that Conflict, Perspective Reversal, problem Re-Dimensioning, and third-solution spaces tended to emerge more explicitly and systematically when CHORDI was applied. These findings are exploratory and do not constitute statistical evidence of CHORDI’s effectiveness or of SEMARGEBRA as an established algebraic system. CHORDI further addresses a dual AI Safety concern: autonomous deviation by AI systems and deliberate AI misuse by humans. By exposing tensions between logical objectives and human or ethical values, it may support Conflict Resolution and the generation of auditable, human-centered alternatives. CHORDI does not independently guarantee AI Safety or AI Alignment; rather, it may serve as a complementary cognitive and procedural AI Governance layer for the early detection, reconsideration, and reformulation of both forms of risk. Its ultimate aim extends beyond decision support and optimization to a framework for healthy Human–AI Co-evolution in which humans and AI enhance one another’s capabilities while preserving Human Agency, autonomy, dignity, responsibility, and meaning-making. Accordingly, this Discussion Paper does not claim a completed theory, a formal SEMARGEBRA calculus, or an operational safety technology. It presents research hypotheses that value Conflict can be treated as a warning signal and transformed through reevaluation and Re-Dimensioning, and that mathematically structured semantic–intent operations may offer a reproducible way to guide such transformations in LLMs. CHORDI and SEMARGEBRA are therefore offered as starting points for future research, empirical validation, formalization, and implementation in AI Safety and related cognitive architectures.