迈向可逆遗忘：在持续式企业智能体中管理过时知识
Towards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents
AI总结：
针对企业AI智能体在非平稳环境中面临的过时知识问题，提出含三种记忆状态的可逆遗忘框架，实例化为滞后可逆记忆控制器，可减少过时信息影响且不混淆临时抑制与永久擦除，金融场景可验证其思路。
AI中文摘要：
传统持续学习将遗忘视为失败，强调在环境演变时保留先前习得的知识。我们认为，这一目标对于在非平稳环境中运行的企业AI智能体而言并不完整，这类环境中的客户、政策、工具、工作流程、法规及市场状况会随时间变化。不加区分地保留知识会让过时知识影响决策，造成负迁移和运营风险。因此，我们提出可逆遗忘：一个包含三种操作记忆状态（活跃、休眠、退役）以及重新激活过渡机制的概念框架，当休眠知识的相关性回归时可将其恢复。我们将该框架实例化为滞后可逆记忆控制器，其会积累相关性证据、使用非对称阈值防止状态振荡、在影子模式下测试重新激活、通过政策管控退役。该框架可减少过时信息的影响，且不会将临时抑制与永久擦除混为一谈。金融领域可说明这一思路：在某一市场 regime（制度）下有用的知识，可能在另一制度下有害，但当相似条件再次出现时又会恢复相关性。
英文摘要：
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.
