MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
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 ...
| Authors | Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian et al. |
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| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 30 September 2026 |
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