Frames Axiomatics: An Axiomatic Framework for Structure-Ordered Information

Frames Axiomatics is a structural framework for the ordered representation, referencing, and long-term stabilization of information. The framework is based on the idea that information should be treated primarily as observable, referencable structure, rather than as mutable content or implicit meaning. Frames Axiomatics introduces a disciplined separation between existence, order, reference, and interpretation, allowing information to be recorded, related, and revisited without requiring semantic agreement or centralized authority. At its core, the framework defines Frames as atomic, non-overwriting units of record. Frames are designed to be: append-only, structurally identifiable, referencable over time, and independent of execution or interpretation contexts. Frames may be grouped, indexed, and related through explicit structural mechanisms, forming higher-level constructs such as frame blocks, indices, and archival bundles. These constructs do not assign meaning; they describe relationships, order, and presence only. A key design principle of Frames Axiomatics is the explicit separation of layers: structural representation, verification and referencing, interpretation and semantics (external). This separation enables stability across time, environments, and tooling, and supports reproducibility and auditability without enforcing a specific worldview or execution model. In addition to its archival and data-structuring aspects, Frames Axiomatics is also being explored as a structural foundation for stabilizing complex information systems, including experiments in the structured handling of outputs from Large Language Models (LLMs). In this context, frames are used to reduce drift, enforce explicit boundaries between observations and interpretations, and enable traceable evolution of model-generated artifacts. This work is exploratory and structural in nature and does not propose changes to model internals. Frames Axiomatics is presented as an open, evolving framework. The focus of this work is on clarity of structure, explicit boundaries, and long-term stability, rather than optimization or performance. No claims are made regarding semantic correctness, truth, or authority of recorded content