给大模型记忆分类型，防止不同记忆互相干扰。
MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
- 写入时标记记忆功能类型，隔离不同类型记忆
- 减少5.8倍记忆调用，幻觉率降低28.27%
- 适合需要长期可靠记忆的对话系统
记忆增强型大语言模型通过跨交互保持长期记忆来扩展推理能力。但现有记忆系统常将用户事实、事件和行为规则混入共享空间，导致功能不同的记忆被互换使用，引发记忆污染。我们识别出这一问题为异构记忆污染：上下文相关事件被过度泛化，或语义相关但功能不兼容的记忆误导生成。为此提出MemGuard，一种类型感知的记忆框架，在记忆构建与检索中保持功能边界。它在写入时为每条记忆指定显式功能角色，维护类型隔离下的记忆关系，并仅从必要类型中选择证据进行组合，从而减少无关或功能不兼容证据的干扰。在幻觉检测和长程对话基准上，MemGuard将记忆可靠性提升最高达28.27%，同时内存调用量最多减少5.8倍。结果表明，可靠长程推理依赖于对异构记忆的有原则组织与选择性使用。
原文摘要 · Abstract (English)
Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. However, existing memory systems often collapse stable user facts, episodic events, and behavioral rules into a shared space, allowing functionally distinct memories to be retrieved and used as interchangeable evidence. We identify this failure mode as heterogeneous memory contamination, where context-specific events become overgeneralized claims, or semantically relevant but functionally incompatible memories mislead generation. To this end, we introduce MemGuard, a type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval. It assigns each memory an explicit functional role at write time, maintains relations across type-isolated memories, and selectively composes evidence only from necessary memory types, reducing contamination from irrelevant or functionally incompatible evidence. Across hallucination and long-horizon conversation benchmarks, MemGuard improves memory reliability by up to 28.27% while retrieving up to 5.8x fewer memory tokens than prior methods. These results suggest that reliable long-term reasoning depends on principled organization and selective use of heterogeneous memory.
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