MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
MEMGUARD tackles memory contamination in large language models by treating functional boundaries as reliability constraints across memory operations. It reorganizes raw dialogue into type-specific atomic memories with a single-type constraint during writing, and uses query-adaptive routing during retrieval to prevent functionally distinct memories from interfering with each other, boosting reliability by over 28%.