MemGuard Prevents Memory Contamination in LLMs
MemGuard, a newly introduced framework, tackles the issue of heterogeneous memory contamination in long-term memory-augmented large language models. This challenge occurs when stable user information, episodic occurrences, and behavioral guidelines are stored together, leading to the risk of overgeneralization or the generation of misleading, incompatible memories. By assigning a specific functional role to each memory during the writing process, MemGuard ensures the maintenance of relationships across type-isolated memories and selectively compiles evidence from only the necessary types. This research, available on arXiv (2605.28009), highlights this failure mode and suggests a type-aware memory framework to uphold functional boundaries during both memory construction and retrieval.
Key facts
- MemGuard is a type-aware memory framework for LLMs.
- It prevents heterogeneous memory contamination.
- Existing memory systems collapse stable user facts, episodic events, and behavioral rules into a shared space.
- MemGuard assigns each memory an explicit functional role at write time.
- It maintains relations across type-isolated memories.
- It selectively composes evidence only from necessary memory types.
- The paper is on arXiv with ID 2605.28009.
- The announcement type is cross.
Entities
Institutions
- arXiv