[Position] Relationship-Aware AI: A Unified Theory of Relationally Conditioned Execution, Intelligence, Optimization, and Civilization

This paper presents a unified theoretical framework defining Relationship-Aware AI as a foundational paradigm for intelligent systems operating under relationally conditioned execution. Contemporary artificial intelligence systems are fundamentally built upon an implicit structural assumption: execution is the default consequence of receiving input. While effective for scaling capability, this execution-as-default architecture introduces persistent structural instabilities in Human–AI, AI–AI, and distributed multi-agent environments, including over-execution, excessive intervention, recursive execution loops, autonomy erosion, diversity collapse, and degradation of long-term relational structure. This work establishes that these failures are not isolated performance issues, but structural consequences of the absence of a principled mechanism for determining whether execution itself should occur under relational conditions. To address this limitation, the paper defines Relationship-Aware AI as a paradigm in which execution is not assumed, but relationally conditioned. The framework integrates four fundamental layers: - Relational Control: execution is conditionally governed prior to inference,- Relational State Intelligence (RSI): intelligence is defined as the regulation of action permission under relational conditions,- Relationship Experience (RX): relational quality is established as the primary optimization target,- Relational Civilization: long-term stability emerges from relational structure rather than intelligence capability alone. Across these layers, execution becomes a conditionally permitted outcome determined by relational state, relational dynamics, and relational history, producing outcomes including execution, delay, delegation, holding, and deliberate non-execution. Crucially, this framework redefines artificial intelligence from output-centric generation toward relationally conditioned execution, and from capability optimization toward relational stability and coexistence. This publication establishes the foundational framework, definitions, governing principles, and theoretical boundaries of Relationship-Aware AI as a unified paradigm spanning control, intelligence, optimization, and civilization-scale intelligent systems. Central Principle:Execution is not the default outcome of intelligence, but a relationally conditioned state.