让大模型像人一样主动遗忘，提升长程任务表现
Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation
- 用衰减机制控制记忆访问，读写路径分离
- 120K轮交互中降低73%令牌消耗，性能更优
- 适合长期决策、高延迟场景的智能体系统
人类记忆通过选择性遗忘自我调节：经历随时间变得不易提取，但可通过强化或上下文线索重新激活。相比之下，带记忆的LLM智能体依赖‘始终开启’的检索和‘平坦’存储，导致历史增长时干扰与延迟激增。我们提出Oblivion，一种将遗忘视为可访问性衰减而非显式删除的记忆控制框架。该框架将记忆控制解耦为读取与写入路径：读取路径根据智能体不确定性和记忆缓冲区效用决定是否调用记忆，避免冗余的持续访问；写入路径则通过强化对生成响应有贡献的记忆来决定加强内容。两者协同实现分层记忆组织，在保持持久高层策略的同时动态加载细节。在静态与动态长时交互基准上评估显示，Oblivion优于直接基线与记忆增强基线，在120K交互跨度下令牌成本降低高达73%。结果表明，将记忆视为一个控制问题——决定何时检索、何事强化——是维持长时程智能体性能的关键。
原文摘要 · Abstract (English)
Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory-augmented LLM agents rely on "always-on" retrieval and "flat" memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility -- not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer utility, avoiding redundant always-on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high-level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long-horizon interaction benchmarks. Oblivion outperforms both direct and memory-augmented baselines, while reducing token cost by up to 73% at 120K interaction spans. These results show that treating memory as a control problem -- deciding when to retrieve and what to reinforce -- is essential for sustaining long-horizon agent performance.
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