超越静态摘要：为LLM代理的主动记忆提取
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
AI总结：
本文提出ProMem，一种主动记忆提取方法，通过递归反馈机制提升LLM代理的记忆完整性和问答准确性，实现高质量与低成本的平衡。
摘要：
记忆管理对于LLM代理处理长期交互和个性化至关重要。大多数研究关注如何组织和使用记忆摘要，但往往忽视初始的记忆提取阶段。在本文中，我们基于递归处理理论认为，现有的基于摘要的方法有两个主要局限性。首先，摘要是“提前”的，作为一种盲目的“前馈”过程，因为它不知道未来任务，从而遗漏了重要的细节。其次，提取通常是“一次性”的，缺乏反馈循环来验证事实，导致信息丢失的积累。为了解决这些问题，我们提出了主动记忆提取（即ProMem）。不同于静态摘要，ProMem将提取视为一个迭代的认知过程。我们引入了一个递归反馈回路，其中代理通过自我提问主动探测对话历史。这种机制使代理能够恢复缺失的信息并纠正错误。我们的ProMem显著提高了提取记忆的完整性和问答准确性。它还实现了提取质量与token成本之间的优越权衡。
英文摘要：
Memory management is vital for LLM agents in long-term and personalized interactions. Most previous work studies how to retrieve and use memory, but pays less attention to how memory is extracted. We find two main limitations in existing methods. First, extraction is "ahead-of-time": the agent saves information before it knows future tasks. A single summary prompt often mixes details, events, and relations, so useful information is lost. Second, extraction is usually one-off. Without verification, errors and hallucinations may stay in memory for a long time. To address these limitations, we propose ProMem, a proactive memory extraction framework. It separates details, events, and relations, and uses different extraction strategies for each type. It also checks completeness to recover missed events and verifies facts at the atomic level to reduce hallucinations. Experiments show that ProMem improves memory completeness and QA accuracy, while keeping a good balance between quality and token cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
