CAVE-Mem：面向记忆搜索的边界感知经验验证

中文摘要 AI 辅助

长期记忆智能体日益依赖迭代搜索和可复用经验来回答涉及大规模个人、事实或叙事历史的问题。然而，当前的经验记忆系统主要优化相关性：它们检索与当前状态相似的历史搜索教训并将其注入提示中。当记忆基底、问题意图、答案粒度或证据边界发生变化时，相关经验仍可能有害。我们提出CAVE-Mem，一个无需训练的框架，将经验表示为带适用性、边界和效用条件的类型化干预算子。CAVE-Mem首先获取基础记忆搜索答案，然后仅当算子匹配当前基底、答案契约、证据边界和交叉拟合效用时才允许其改变答案；否则系统弃权（不执行）。在长期对话记忆、多跳问答和长文档叙事推理上的实验表明，相较于仅相关性的经验复用，该方法持续获得性能提升。

英文摘要

Long-term memory agents increasingly rely on it- erative search and reusable experience to answer questions over large personal, factual, or narrative histories. However, current experience-memory systems largely optimize relevance: they re- trieve past search lessons that appear similar to the current state and inject them into the prompt. A relevant experience can still be harmful when the memory substrate, question intent, answer granularity, or evidence boundary changes. We propose CAVE- Mem, a training-free framework that represents experience as a typed intervention operator with applicability, boundary, and utility conditions. CAVE-Mem first obtains a base memory-search answer, then allows an operator to change it only if the oper- ator matches the current substrate, answer contract, evidence boundary, and cross-fitted utility; otherwise the system abstains. Experiments across long-term conversational memory, multi-hop question answering, and long-document narrative reasoning show consistent gains over relevance-only experience reuse.

发表机构

• Kent State University（肯特州立大学）

机构由 AI 辅助整理，请以论文原文为准。