arXiv:2605.15156v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications require timely, domain-specific information, motivating the need for efficient mechanisms to incorporate new knowledge. In this paper, we introduce MeMo (Memory as a Model), a modular framework that encodes new knowledge into a dedicated memory model while keeping the LLM parameters unchanged. Compared to existing methods, MeMo offers several advantages: (a) it c
The rapid expansion of LLM applications into real-world scenarios necessitates immediate solutions for knowledge integration and continuous learning to overcome their static nature after pretraining.
This development offers a potential path to make large language models more dynamic and efficient, addressing a critical bottleneck in their real-world utility and reducing the computational cost of continuous retraining.
LLMs can now theoretically incorporate new, domain-specific information without expensive full retraining, opening avenues for real-time adaptation and personalization.
- · AI developers
- · Enterprises deploying LLMs
- · LLM application users
- · Companies reliant on frequent, expensive LLM re-pretraining
- · Generic LLM providers without adaptation mechanisms
MeMo enables LLMs to efficiently integrate new information, addressing the 'frozen after pretraining' limitation.
This could lead to more specialized and adaptable LLM applications across various industries, accelerating their practical deployment.
The modular approach might foster a new ecosystem of 'memory models' creating more robust and continuously evolving AI systems that redefine what an LLM 'is'.
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Read at arXiv cs.LG