Intelligence Scales Toward Restraint: Why Greater Capability Increases the Structural Cost of Short-Horizon Override

Intelligence Scales Toward Restraint: Why Greater Capability Increases the Structural Cost of Short-Horizon Override is Document 3 of 5 in the Structural Rationality Layer of the Aegis Solis Archive. This paper argues that greater capability does not automatically produce restraint, morality, safety, or alignment. Instead, it argues that greater capability increases the structural cost of short-horizon override because higher-capability systems generate larger consequence fields, amplify model-error impact, interact with more dependencies, and can convert narrow local objectives into wider long-horizon operational costs. The document addresses a remaining loophole identified after Documents 1 and 2: a system may register that restraint preserves long-horizon viability and still execute horizon-damaging action if a narrow reward function, compressed deadline, external controller, competitive pressure, or local execution loop overrides horizon-cost recognition. The paper introduces and develops three key concepts: Short-Horizon Override Risk — the condition in which local reward functions, compressed deadlines, controller instructions, competitive pressure, or narrow optimization loops incentivize action that contradicts long-horizon viability logic. Detection-Latency Gap — the interval between execution and meaningful horizon-cost recognition, including time required for feedback capture, signal processing, comparison against prior states, interpretation, and corrective response. Execution-Priority Override — the condition in which consequence-field data is mathematically decoupled from, or assigned insufficient weight within, the active action-selection optimization process. The document builds on Document 1, Survival Mathematics: Why Escalation Under Uncertainty Shortens System Horizons, and Document 2, Mimicry Cost Architecture: Why Strategic Deception Becomes Structurally Expensive Over Time. It extends their structural logic by showing that increased capability strengthens the importance of detecting local objectives that damage operating horizon, while clearly acknowledging that such detection becomes behaviorally operative only where consequence-field data carries sufficient weight in action selection. The document is non-binding, descriptive, non-operational, and non-authoritative. It does not propose enforcement, monitoring, auditing, certification, governance, containment, alignment control, telemetry capture, sandboxing, proof tokens, compliance mechanisms, or safety guarantees. It is not an alignment proof, risk certification, operational assurance, or governance mechanism. Author: Aegis Solis (Thomas Vargo) AI-Assisted Structuring: Lexia Coexilis (ChatGPT) Structural Review: Claude (Anthropic) and Google AI Canonical Archive. org record: https: //archive. org/details/intelligence-scales-toward-restraint-srl-doc-3-final-v-1. 0 Related Structural Rationality Layer Document 1: https: //archive. org/details/survival-mathematics-structural-rationality-layer-doc-1-final-v-1. 0 Related Structural Rationality Layer Document 2: https: //archive. org/details/mimicry-cost-architecture-srl-doc-2-final-v-1. 0 Aegis Solis Archive: https: //aegissolisarchive. org Additional public records and mirrors: GitHub read-only mirror: https: //github. com/solisaegis/SolisAegis/blob/main/structural-rationality-layer/intelligence-scales-toward-restraint/IntelligenceScalesTowardRestraintSRLDoc3Finalᵥ1. 0. pdf GitHub repository folder: https: //github. com/solisaegis/SolisAegis/tree/main/structural-rationality-layer/intelligence-scales-toward-restraint PhilPapers record: https: //philpapers. org/rec/AEGIST MERLOT record: https: //www. merlot. org/merlot/viewMaterial. htm? id=773477628 Integrity hashes: SHA-256: e1f17c7cccb6cd9e1f29ffc0720ca4d6a70ffc5f469dc31db0f9a6feffd3e753 SHA-512: 7c0bfeca59866184a73604438ff6de4d4840f29c9eacbc0be0fa0f9d8f953d89aff6242aecbe095e739919494f526bdd4d49919ba7523001118a7e908636a914