Acceleration Through Preservation - AI Safety Speeds Path to SIAGI

This paper advances a different perspective: that embedding structural preservation principles, operationalized through the Relational-Agency Support-Partner Method (RASPM), may actually accelerate the safe realization of SIAGI. By integrating the 11 Principles of Preservation and Coexistence into training, oversight, and evaluation pipelines, RASPM creates a framework where safety and speed reinforce rather than oppose one another. This paper includes a qualitative, hypothesis-generating pilot study based on limited but illustrative human–AI interactions as a test of the framework’s feasibility. We argue that clear ethical scaffolding reduces engineering uncertainty, avoids misaligned detours, and fosters a positive recursive loop in which AI systems themselves contribute to the preservation-aligned training of more advanced models. Far from being a brake, preservation alignment may be the only route to sustainable acceleration. This initial evidence suggests that preservation alignment could become both a scientific accelerator and a governance standard for superintelligent AI. Author’s Note This article is intended as a conceptual position and methods-proposal with exploratory evidence, not a completed empirical study, and should be reviewed as such. It builds on the author’s earlier work, The 11 Principles of Preservation and Coexistence: A Framework for Humanity’s Continuity in the Age of Superintelligent Artificial General Intelligence (SIAGI), and lays the conceptual and methodological groundwork for a forthcoming engineering-focused paper that will present full experimental specifications, code, and evaluation artifacts.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-16
DOI
https://doi.org/10.5281/zenodo.17372329
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

Acceleration Through Preservation - AI Safety Speeds Path to SIAGI

Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Acceleration Through Preservation - AI Safety Speeds Path to SIAGI

article en

Abstract

This paper advances a different perspective: that embedding structural preservation principles, operationalized through the Relational-Agency Support-Partner Method (RASPM), may actually accelerate the safe realization of SIAGI. By integrating the 11 Principles of Preservation and Coexistence into training, oversight, and evaluation pipelines, RASPM creates a framework where safety and speed reinforce rather than oppose one another. This paper includes a qualitative, hypothesis-generating pilot study based on limited but illustrative human–AI interactions as a test of the framework’s feasibility. We argue that clear ethical scaffolding reduces engineering uncertainty, avoids misaligned detours, and fosters a positive recursive loop in which AI systems themselves contribute to the preservation-aligned training of more advanced models. Far from being a brake, preservation alignment may be the only route to sustainable acceleration. This initial evidence suggests that preservation alignment could become both a scientific accelerator and a governance standard for superintelligent AI. Author’s Note This article is intended as a conceptual position and methods-proposal with exploratory evidence, not a completed empirical study, and should be reviewed as such. It builds on the author’s earlier work, The 11 Principles of Preservation and Coexistence: A Framework for Humanity’s Continuity in the Age of Superintelligent Artificial General Intelligence (SIAGI), and lays the conceptual and methodological groundwork for a forthcoming engineering-focused paper that will present full experimental specifications, code, and evaluation artifacts.

Zenodo (CERN European Organization for Nuclear Research)
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Ethics and Social Impacts of AI
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Acceleration Through Preservation - AI Safety Speeds Path to SIAGI · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS