arXiv AI September 15, 2026 By Yue Jin

Homeostatic Continual Learning

The paper introduces a new approach called Homeostatic Continual Learning, designed to allow an AI agent to learn continuously in a changing environment without catastrophic forgetting. The method identifies outliers in environmental data when the agent’s output deviates, enabling the agent to incrementally refine its model and policy across increasingly diverse contexts. The authors also propose extending the method to build a world model that factorizes objects into features, abstracts them into comparable concept instances, and maps concepts to intents via features, while outlining necessary future work and broader AI connections.

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By Jeong Min Kong, Richard S. Sutton

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