AIBullisharXiv – CS AI · 3h ago7/10
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MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
Researchers introduce MemGuard, a framework that addresses memory contamination in long-term memory-augmented large language models by organizing memories into functional types and selectively retrieving only relevant evidence. The approach improves hallucination reduction by up to 28.27% while reducing memory token usage by 5.8x, advancing the reliability of AI systems that maintain persistent memory across extended interactions.