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A team of researchers from leading institutions including Shanghai Jiao Tong University and Zhejiang University has developed what they’re calling the first “memory operating system” for artificial intelligence, addressing a fundamental limitation that has hindered AI systems from achieving human-like persistent memory and learning.
The system, called MemOS, treats memory as a core computational resource that can be scheduled, shared, and evolved over time — much like how traditional operating systems manage CPU and storage resources. The research, published July 4th on arXiv, demonstrates significant performance improvements over existing approaches, including a 159% boost in temporal reasoning tasks compared to OpenAI’s memory systems.
“Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the development of long-context reasoning, continual personalization, and knowledge consistency,” the researchers write in their paper.
AI systems struggle with persistent memory across conversations
Current AI systems face what researchers call the “memory silo” problem — a fundamental architectural limitation that prevents them from maintaining coherent, long-term relationships with users. Each conversation or session essentially starts from scratch, with models unable to retain preferences, accumulated knowledge, or behavioral patterns across interactions. This creates a frustrating user experience where an AI assistant might forget a user’s dietary restrictions mentioned in one conversation when asked about restaurant recommendations in the next.
While some solutions like Retrieval-Augmented Generation (RAG) attempt to address this by pulling in external information during conversations, the researchers argue these remain “stateless workarounds without lifecycle control.” The problem runs deeper than simple information retrieval — it’s about creating systems that can genuinely learn and evolve from experience, much like human memory does.
“Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods,” the team explains. This limitation becomes particularly apparent in enterprise settings, where AI systems are expected to maintain context across complex, multi-stage workflows that might span days or weeks.
New system delivers dramatic improvements in AI reasoning tasks
MemOS introduces a fundamentally different approach through what the researchers call “MemCubes” — standardized memory units that can encapsulate different types of information and be composed, migrated, and evolved over time. These range from explicit text-based knowledge to parameter-level adaptations and activation states within the model, creating a unified framework for memory management that previously didn’t exist.
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