SnapOS Claim Document v1.0 - Foundational Claim for Semantic Stability Engineering
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Description
The SnapOS Claim Document v1.0 formalizes the foundational scientific claim behind Semantic Stability Engineering (SSE), a new discipline that addresses the evolution and stability of meaning in adaptive computational systems. While machine learning optimizes statistical or behavioural performance, current architectures lack a model-independent layer for monitoring, governing, and restoring semantic coherence as systems evolve.
This document establishes three core claims:
- Semantic drift is a distinct system-level phenomenon occurring when internal meaning representations change independently of output behaviour.
- SnapOS introduces the first semantic stability layer, providing signal-based semantic observability, the Reflexive Behavioral Specification (RBS), and the Reflexive Interpretation Cycle (RIC) for drift detection, correction, and re-entry.
- Semantic Stability Engineering (SSE) is defined as the scientific field focused on measuring, analysing and stabilising meaning behaviour across interpretive cycles in adaptive systems.
To ensure conceptual clarity and prevent misclassification, the document includes:
- a Non-Subsumption Clause,
- an Independent Origin Declaration,
- and a Temporal Priority Shield,
establishing SnapOS as the originating architecture of SSE and declaring the conceptual space in which the framework operates.
This Claim Document is designed to serve as a priority-establishing, citation-ready reference for subsequent research in semantic drift, AI system stability, governance, multi-agent coherence, and meaning-level auditability. It complements the broader SnapOS and Reflexive Systems Architecture literature by providing a precise and unambiguous definition of the field and its core innovations.
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The_SnapOS_Claim_Document_v1_0.pdf
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Additional details
Identifiers
Dates
- Created
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2025-04-01Initial formal release of the SnapOS audit model and semantic traceability framework (version 1.0).
References
- Chaitin, G. J. (1977). Algorithmic Information Theory. IBM Journal of Research and Development, 21(4), 350–359. https://doi.org/10.1147/rd.214.0350
- Håstad, J. (1987). Computational Limitations of Small-Depth Circuits. Ph.D. thesis, MIT. http://hdl.handle.net/1721.1/14958
- Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica. Monatshefte für Mathematik und Physik, 38, 173–198. https://doi.org/10.1007/BF01700692
- Floridi, L. (2011). The Philosophy of Information. Oxford University Press. ISBN: 9780199232383
- Souly, A. et al. (2025). Poisoning Attacks on LLMs Require a Near-Constant Number of Samples. Alan Turing Institute / Anthropic / UK AI Security Institute. arXiv:2508.03114.
- Bowen, D. et al. (2024). Data Poisoning in LLMs: Jailbreak-Tuning and Scaling Laws. arXiv:2408.02946.