Towards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents
arXiv:2608.18177v1 Announce Type: new Abstract: Continual learning has traditionally treated forgetting as a failure, emphasizing preservation of previously acquired knowledge as environments evolve. We argue that this objective is incomplete for enterprise AI agents operating in non-stationary environments, where customers, policies, tools, workflows, regulations, and market conditions change over time. Indiscriminate retention can allow obsolete knowledge to influence decisions, creating negative transfer and operational risk. We therefore propose reversible forgetting: a conceptual framework with three operational memory states: active, dormant, and retired, and a reactivation transition that can restore dormant knowledge when its relevance returns. We instantiate the framework as a Hysteretic Reversible Memory Controller that accumulates relevance evidence, uses asymmetric thresholds to prevent state oscillation, tests reactivation in shadow mode, and gates retirement through policy. The framework reduces the influence of obsolete information without conflating temporary suppression with permanent erasure. Finance illustrates the idea: knowledge useful under one market regime may become harmful under another yet regain relevance when similar conditions recur.
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[Submitted on 18 Aug 2026]
Title:Towards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents
View a PDF of the paper titled Towards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents, by Nilutpaul Sarker Yash and 2 other authors
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Abstract:Continual learning has traditionally treated forgetting as a failure, emphasizing preservation of previously acquired knowledge as environments evolve. We argue that this objective is incomplete for enterprise AI agents operating in non-stationary environments, where customers, policies, tools, workflows, regulations, and market conditions change over time. Indiscriminate retention can allow obsolete knowledge to influence decisions, creating negative transfer and operational risk. We therefore propose reversible forgetting: a conceptual framework with three operational memory states: active, dormant, and retired, and a reactivation transition that can restore dormant knowledge when its relevance returns. We instantiate the framework as a Hysteretic Reversible Memory Controller that accumulates relevance evidence, uses asymmetric thresholds to prevent state oscillation, tests reactivation in shadow mode, and gates retirement through policy. The framework reduces the influence of obsolete information without conflating temporary suppression with permanent erasure. Finance illustrates the idea: knowledge useful under one market regime may become harmful under another yet regain relevance when similar conditions recur.
Subjects:
Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2608.18177 [cs.LG]
(or arXiv:2608.18177v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.18177
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tirtho Roy [view email] [v1] Tue, 18 Aug 2026 00:38:46 UTC (2,189 KB)
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