AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing memory systems typically rely on fixed granularities or static schemas, but these designs struggle when heterogeneous information, such as preferences, events, constraints, and temporal updates, is embedded in a single mixed representation. The resulting semantic interference makes top-K retrieval sensitive to noise and often leaves relevant evidence poorly ranked. We present AutoViewMem, a data-driven framework that organizes long-term conversational memory into self-configuring, low-overlap semantic views before indexing. AutoViewMem discovers candidate views from interaction traces, selects a compact complementary view set, and uses these v
自动配置语义视图以优化大型语言模型的长期记忆。
- 核心方法
- 提出AutoViewMem框架，通过从交互记录中发现候选视图，选择紧凑的互补视图集，减少语义重叠。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有的长期记忆系统在处理异构信息时存在语义干扰，导致检索不准确。
- 关键实验
- 通过实验验证了AutoViewMem在多种场景下的性能，包括不同类型的异构信息处理能力。
- 主要贡献
- 能够有效组织长期对话记忆，提高检索准确性，增强语言模型代理的一致性和个性化。
- 意义与局限
- 意义在于改善了大型语言模型在长期对话中的表现，局限性在于需要大量交互数据来训练自配置视图。