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arXiv Machine Learning

Towards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents

The paper proposes a new approach called reversible forgetting for continual learning in enterprise AI agents. It introduces a framework with three memory states—active, dormant, and retired—and a reactivation transition that can restore dormant knowledge when it becomes relevant again. The authors implement this as a Hysteretic Reversible Memory Controller that uses relevance evidence, asymmetric thresholds, shadow mode testing, and policy gating to reduce the influence of obsolete information without permanently erasing it.

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Harness Continual Learning: Continual Adaptation Beyond Model Parameters

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