Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.18177.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

ArXiv paper proposes 'reversible forgetting,' a framework for enterprise AI agents in non-stationary environments, defining three memory states (active, dormant, retired) with reactivation transitions. It is instantiated as a Hysteretic Reversible Memory Controller using asymmetric thresholds, shadow-mode reactivation testing, and policy-gated retirement, illustrated with a finance use case.

Reframes forgetting as a design feature, not a failure, and offers a concrete memory-lifecycle model enterprise agent builders can adapt rather than just theorize about.

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