Paradigm Swarm: Structural Prevention of Catastrophic Forgetting through Semantic Weight Isolation

We propose Paradigm Swarm — an architecture for continual learningwhere knowledge is split into isolated paradigm-experts with frozenweights. This yields six architectural properties: zero forgettingby construction, emergent gap detection, unlimited compute ceiling,coalition compilation, expert distillation, and dissipative structurediscovery. Validated across 28 experimental configurations (syntheticto CIFAR-100), Paradigm Swarm matches Oracle within 0.002 accuracywhile SGD forgets up to 57pp. 45 reproducible NumPy experiments.Code: github.com/sensus-stoa/paradigm-swarm

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-26
DOI
https://doi.org/10.5281/zenodo.20923795
Primary Topic
Domain Adaptation and Few-Shot Learning
Type
preprint
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preprint

Paradigm Swarm: Structural Prevention of Catastrophic Forgetting through Semantic Weight Isolation

Evgeniy V. Dolgov
Zenodo (CERN European Organization for Nuclear Research)
Domain Adaptation and Few-Shot Learning
preprint

Paradigm Swarm: Structural Prevention of Catastrophic Forgetting through Semantic Weight Isolation

Evgeniy V. Dolgov
preprint en

Abstract

We propose Paradigm Swarm — an architecture for continual learningwhere knowledge is split into isolated paradigm-experts with frozenweights. This yields six architectural properties: zero forgettingby construction, emergent gap detection, unlimited compute ceiling,coalition compilation, expert distillation, and dissipative structurediscovery. Validated across 28 experimental configurations (syntheticto CIFAR-100), Paradigm Swarm matches Oracle within 0.002 accuracywhile SGD forgets up to 57pp. 45 reproducible NumPy experiments.Code: github.com/sensus-stoa/paradigm-swarm

Zenodo (CERN European Organization for Nuclear Research)
Domain Adaptation and Few-Shot Learning
DOIOpen Access PDFOpenAlex
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