Intelligent devices have become deeply integrated into everyday life, generating vast amounts of user interactions that form valuable personal knowledge. Efficient organization of this knowledge in user memory is essential for enabling personalized applications. However, current research on memory writing, management, and reading using large language models (LLMs) faces challenges in filtering irrelevant information and in dealing with rising computational costs. Inspired by the concept of selective attention in the human brain, we introduce a memory discrimination task. To address large-scale interactions and diverse memory standards in this task, we propose a Scene-Aware Memory Discrimination method (SAMD), which comprises two key components: the Gating Unit Module (GUM) and the Cluster Prompting Module (CPM). GUM enhances processing efficiency by filtering out non-memorable interactions and focusing on the salient content most relevant to application demands. CPM establishes adaptive memory standards, guiding LLMs to discern what information should be remembered or discarded. It also analyzes the relationship between user intents and memory contexts to build effective clustering prompts. Comprehensive direct and indirect evaluations demonstrate the effectiveness and generalization of our approach. We independently assess the performance of memory discrimination, showing that SAMD successfully recalls the majority of memorable data and remains robust in dynamic scenarios. Furthermore, when integrated into personalized applications, SAMD significantly enhances both the efficiency and quality of memory construction, leading to better organization of personal knowledge.

翻译：智能设备已深度融入日常生活，产生大量用户交互，形成宝贵的个人知识。在用户记忆中高效组织这些知识对于实现个性化应用至关重要。然而，当前基于大语言模型（LLMs）的记忆写入、管理和读取研究面临两大挑战：无关信息过滤困难与计算成本不断攀升。受人类大脑选择性注意机制的启发，我们提出记忆判别任务。针对该任务中大规模交互和多样化记忆标准的问题，我们提出场景感知记忆判别方法（SAMD），该方法包含两个核心组件：门控单元模块（GUM）和聚类提示模块（CPM）。GUM通过过滤非记忆性交互并聚焦于与应用需求最相关的显著内容，显著提升处理效率。CPM建立自适应记忆标准，引导LLMs判别信息应被记忆或丢弃，同时分析用户意图与记忆上下文的关系以构建有效的聚类提示。全面的直接与间接评估证明了我们方法的有效性和泛化能力。我们独立评估了记忆判别性能，表明SAMD能成功召回大部分可记忆数据，并在动态场景中保持鲁棒性。此外，当集成到个性化应用中时，SAMD显著提升了记忆构建的效率和质量，从而实现更优的个人知识组织。