让AI记住用户逻辑决策，提升个性化对话准确率。
Towards Root Memories: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs
- 提出根记忆结构，从长期对话中提取可复用的个性化逻辑
- 在长对话场景下，相比基线方法准确率显著提升
- 适合需要深度用户理解的个性化大模型应用
记忆系统对个性化大语言模型至关重要。然而，现有检索方法主要依赖语义相似性，可能遗漏语义重叠度低但逻辑关键的记忆。当前基准无法有效评估此问题。为此，我们构建了IMLogic，首个针对长对话场景中隐式逻辑记忆检索的高质量基准。受此启发，我们提出根记忆（root memory），一种结构化、决策保留的表示，能从长期用户历史中提炼可复用的个性化逻辑。进而提出RootMem，一个即插即用框架：先将原始历史转化为结构化的根记忆，再通过大模型路由器激活逻辑相关记忆，补充语义检索，实现个性化决策逻辑增强。大量实验表明，RootMem显著优于最强基线，并持续提升现有记忆代理的准确率。基准与代码将公开于https://anonymous.4open.science/r/IMLogic-DBB3。
原文摘要 · Abstract (English)
Memory systems are essential for personalized Large Language Models (LLMs). However, existing retrieval methods in these systems primarily rely on semantic similarity, potentially missing logically critical memories with limited semantic overlap. Current benchmarks remain inadequate for evaluating this problem. To address this gap, we construct IMLogic, the first high-quality benchmark targeting implicit logical memory retrieval in long-dialogue scenarios. Motivated by this challenge, we introduce root memory, a structured, decision-preserving representation that distills reusable personalized logic from long-term user histories. We then propose RootMem, a plug-and-play framework that first distills raw histories into structured root memories and then uses an LLM-based router to activate logically relevant ones, complementing semantic retrieval with personalized decision logic. Extensive experiments demonstrate that RootMem significantly outperforms the strongest retrieval baselines and consistently boosts the accuracy of existing memory agents. Our benchmark and codes will be available at https://anonymous.4open.science/r/IMLogic-DBB3.
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