AutoViewMem：自配置正交视图优化对话长期记忆
AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
给智能体做长期记忆的论文，把语义分视图放到写入阶段，LoCoMo 和 PersonaMem 上跑赢了记忆基线。
现有 LLM 记忆系统多用固定粒度或静态模式，偏好、事件、约束等异构信息混在同一表示里，导致 top-K 检索容易被噪声干扰。AutoViewMem 在索引前从交互轨迹中发现候选语义视图，再选出低重叠、互补的紧凑视图集合，并在写入时做结构化抽取。这种设计把语义解耦从检索阶段提前到写入阶段，推理时无需显式路由或迭代检索，仍保留简单的 top-K 相似度搜索流程。在 LoCoMo 和 PersonaMem 基准、Qwen3-8B 与 Qwen3-14B 两种骨干下，该方法在长程问答与个性化任务上超过强记忆基线。
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 views to guide write-time structured extraction of provenance-grounded memories. This representation-first design moves semantic disentanglement from retrieval time to write time, allowing standard top-K similarity search to retrieve focused evidence without explicit routing or iterative retrieval. We further apply offline consolidation to improve memory compactness and consistency. Experiments on the LoCoMo and PersonaMem benchmarks, under both Qwen3-8B and Qwen3-14B backbones, show that AutoViewMem improves long-horizon question answering and personalization over strong memory baselines while preserving a simple inference pipeline.