Automated views improve memory retrieval in long conversations
AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
Artificial Intelligence
Summary
Remembering details accurately is hard for AI chatbots during long talks. The authors show a way to organize the chatbot’s memories into separate groups based on topics, so it doesn’t mix up different types of information. This helps the AI find the right facts more easily when answering questions or personalizing chats. The system they created works better than older methods and keeps things simple when the AI responds.
What this means in practice
- •For conversational ai developers: Improve memory management in AI chat systems to maintain better context and personalization over long conversations.
- •For virtual assistant teams: Enhance long-term user profiling in virtual assistants by structuring stored memories for more relevant recall.$Commercial implications: Enables more accurate and personalized virtual assistant products by improving their memory retrieval capabilities.
Authors
Zijie Cao, Xijun Qu, Zhicheng Gu, Xiaoshu Chen, Duanyang Yuan, Yanning Hou, Sihang Zhou, Jianxing Gong, Jian Huang, Yang Mei
Abstract
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.