The Rezostable Core: Structural Admissibility as a Pre-Decision Stability Principle for AI Systems
Abstract
Description (Abstract / Prior Art Statement) This record establishes prior art for the architectural concept named The Rezostable Core, introduced within the XREALISM® framework. The Rezostable Core defines a pre-decision architectural principle in which system stability is achieved by constraining the state space itself, rather than regulating behavior after execution. Central to this approach is an admissibility invariant (ΣE) that determines which states are structurally allowed to exist before any planning, optimization, or execution occurs. Unlike control layers, safeguards, or sandboxed execution environments, the Rezostable Core does not correct or mitigate unsafe behavior after it arises. Instead, it renders entire classes of unstable or unsafe states non-representable at the architectural level. This document describes: a design philosophy and architectural distinction, a separation between admissibility, decision-making, and execution, a pre-decision notion of stability and responsibility in autonomous systems. No implementation details are disclosed. No algorithms, thresholds, data structures, or executable logic are included. This record serves solely to establish conceptual and architectural prior art. “The Rezostable Core.pdf”: SHA-256 b276b6d47aa132d3c197410022f73419d28e22b1155864d72bea5b684ab8f5d2 License / Rights © 2026 Dr. Ladislav Gradečak XREALISM® License & Notice This work describes an architectural concept and design philosophy only. No implementation details are disclosed No algorithms, thresholds, or executable logic are included All core mechanisms remain proprietary Permission is granted to read, quote, and discuss this text for non-commercial purposes with attribution. Any use implying: implementation, replication, commercial deployment, or derivative architectural work requires explicit written permission from the author. All rights reserved.
Fields of Study & Subjects
Safety Systems Engineering in Autonomy
AI-based Problem Solving and Planning
Ethics and Social Impacts of AI
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 10 February 2026