The Rotation-Retention Law: Knowledge Loss Is Proportional to Representational Rotation in Fine-Tuned Language Models. With EgoRA: Entropy-Governed Orthogonality Regularization for Adaptation

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-03
DOI
https://doi.org/10.5281/zenodo.19398708
Primary Topic
Neurobiology of Language and Bilingualism
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article
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article

The Rotation-Retention Law: Knowledge Loss Is Proportional to Representational Rotation in Fine-Tuned Language Models. With EgoRA: Entropy-Governed Orthogonality Regularization for Adaptation

Mark Dillerop
Zenodo (CERN European Organization for Nuclear Research)
Neurobiology of Language and Bilingualism
article

The Rotation-Retention Law: Knowledge Loss Is Proportional to Representational Rotation in Fine-Tuned Language Models. With EgoRA: Entropy-Governed Orthogonality Regularization for Adaptation

Mark Dillerop
article en

Abstract

No abstract available for this paper.
Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 7%
Neurobiology of Language and Bilingualism
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The Rotation-Retention Law: Knowledge Loss Is Proportional to Representational Rotation in Fine-Tuned Language Models. With EgoRA: Entropy-Governed Orthogonality Regularization for Adaptation — Mark Dillerop · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS