Continual Learning under Open-World Distribution Shift

Authors

  • Adam Campbell Author

Keywords:

continual learning, open-world learning, distribution shift, forgetting, adaptation

Abstract

This review examines continual learning under open-world distribution shift. The organizing question is how adaptive models can acquire new knowledge while preserving prior capability and detecting unsupported novelty. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to measuring average retention without testing safety-critical forgetting. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in long-lived intelligent systems.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

License

Copyright (c) 2026 Advances in Adaptive Intelligence

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.