Representational Quarantine and Semantic Vaccination (RQSV)

Representational Quarantine and Semantic Vaccination (RQSV) is a controlled causal research framework designed to evaluate whether weight-level safety interventions applied to a parent large language model influence the recurrence of specified safety failures in descendant models produced through standardized adaptation procedures. The framework reframes safety alignment as a lineage-level causal problem, treating the parent model as the site of intervention and independently adapted descendants as the units of outcome assessment. A structured literature audit of eighteen related works positions RQSV within existing research on safety degradation during fine-tuning, safety restoration, model merging, and activation steering while arguing that its randomized parent-to-descendant experimental design occupies a distinct methodological niche. The document specifies a three-branch experimental architecture comprising treated, sham-treated, and reference conditions; defines behavioral safety markers; outlines evaluation protocols and pilot-study requirements; and establishes explicit go/no-go criteria for empirical deployment. The work is presented as a research proposal and methodological framework intended for feasibility testing and preregistration prior to confirmatory experimentation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-09
DOI
https://doi.org/10.5281/zenodo.24065630
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

Representational Quarantine and Semantic Vaccination (RQSV)

Craig Kyrle Strachan Davidson, Alfie Christian Strachan Davidson
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Representational Quarantine and Semantic Vaccination (RQSV)

Craig Kyrle Strachan Davidson, Alfie Christian Strachan Davidson
article en

Abstract

Representational Quarantine and Semantic Vaccination (RQSV) is a controlled causal research framework designed to evaluate whether weight-level safety interventions applied to a parent large language model influence the recurrence of specified safety failures in descendant models produced through standardized adaptation procedures. The framework reframes safety alignment as a lineage-level causal problem, treating the parent model as the site of intervention and independently adapted descendants as the units of outcome assessment. A structured literature audit of eighteen related works positions RQSV within existing research on safety degradation during fine-tuning, safety restoration, model merging, and activation steering while arguing that its randomized parent-to-descendant experimental design occupies a distinct methodological niche. The document specifies a three-branch experimental architecture comprising treated, sham-treated, and reference conditions; defines behavioral safety markers; outlines evaluation protocols and pilot-study requirements; and establishes explicit go/no-go criteria for empirical deployment. The work is presented as a research proposal and methodological framework intended for feasibility testing and preregistration prior to confirmatory experimentation.

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