MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
arXiv:2605.28009v1 Announce Type: cross Abstract: 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 memori
The rapid advancement of large language models necessitates robust memory management solutions as their applications become more complex and require long-term interaction.
Sophisticated memory management is critical for the stability, reliability, and trustworthiness of advanced AI agents and their ability to operate autonomously over extended periods.
This research introduces a framework to prevent 'memory contamination,' potentially enabling more stable and reliable long-term memory in AI, crucial for real-world applications.
- · AI developers
- · AI platforms
- · Enterprise AI users
- · AI-driven automation
- · Unsophisticated memory-augmented LLM architectures
- · Applications requiring high factual consistency without robust memory
- · Current methods prone to memory collision
Improved reliability and reduced hallucinations in long-term conversational AI systems and autonomous agents.
Accelerated deployment of AI agents in sensitive domains requiring factual recall and consistent behavior.
Enhanced trust in AI systems could lead to broader societal integration of autonomous AI agents across various sectors.
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Read at arXiv cs.LG