Systems Evaluating Relationship-Aware AI: Metrics for Execution Control and Relational Alignment

This paper introduces a formal evaluation framework for Relationship-Aware AI systems. Conventional AI evaluation frameworks are fundamentally response-centric, measuring correctness, preference, and task performance under the assumption that inference should always execute once input is received. This paper demonstrates that such evaluation paradigms cannot capture the central problem of Relationship-Aware AI:whether execution should occur at all. To address this limitation, the paper establishes evaluation as an intrinsic component of execution-controlled AI systems, where execution and non-execution are governed and evaluated under relational conditions. The framework defines:- execution validity,- relational alignment,- intervention optimality,- relationship consistency,- inference efficiency,- and autonomy preservation. Rather than evaluating outputs independently of interaction dynamics, the proposed framework evaluates execution behavior through relational consequences across temporal interaction structures. Crucially, execution validity cannot be reduced to response quality, task performance, or user preference alone. Evaluation must instead determine whether execution decisions are appropriate under relational conditions. The framework further introduces standardized comparison protocols between response-centric systems and relationship-controlled systems, establishing relational evaluation as a foundational requirement for execution-centered AI architectures. This publication serves as a foundational systems evaluation paper for the Relationship-Aware AI Research initiative, establishing relational evaluation as the governing evaluation layer for execution-controlled AI systems.