What Should an Agent Forget? Separating What Is Stored from What Is Used· 代理应忘记什么？存储与使用分离
Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question. A superseded fact can mislead a current-state answer and still be essential for a historical query. We present RD-Forget, a training-free framework that separates what an agent stores from what it uses. A retained source archive preserves observations, and a query-conditioned memory view controls their influence on the current answer. A frozen language-model curator extracts relevant evidence, groups facts into semantic slots, and preserves the relations needed for multi-hop reasoning. Same-slot replacement links suppress superseded values in current-state contexts, while intent-aware retrieval makes earlier evidence eligible again. A rate-
提出了一种无训练框架RD-Forget，实现代理存储与使用的分离。
- 核心方法
- 通过保留源档案、查询条件记忆视图和冰冻语言模型策展人等机制，实现信息的存储与使用的分离，确保多跳推理的关系被保留，同时通过同槽替换链接和意图感知检索控制信息的影响。
- 适合谁读
- 研究者
- 要解决的问题
- 解决代理在存储信息时不恰当影响当前回答的问题，特别是当过时的信息对当前状态的回答可能造成误导时。
- 关键实验
- 未提供
- 主要贡献
- RD-Forget框架能够在无需额外训练的情况下，有效地管理代理的存储信息，确保信息的时效性和相关性。
- 意义与局限
- 该框架有助于提高代理在处理长期任务时的准确性和可靠性，但可能需要进一步实验验证其有效性和适用范围。