MIT CSAIL, in collaboration with the National University of Singapore and A*STAR, has introduced the MeMo framework, significantly boosting large language model (LLM) performance by up to 26.73%. Detailed in a paper released on May 20, 2026, MeMo, or Memory as a Model, allows LLMs to incorporate new knowledge without retraining. Instead, a smaller, dedicated Memory model is trained to store and recall information, enabling the main LLM to remain unchanged. The MeMo framework employs a five-step reflection QA synthesis pipeline, allowing the Executive LLM to query the Memory model through a structured protocol. This approach prevents catastrophic forgetting and eliminates the need for costly retraining of the main model. Benchmarks on datasets like BrowseComp-Plus and NarrativeQA demonstrated significant performance improvements, with the Memory model functioning as a universal adapter across various LLMs. This innovation holds potential for AI applications in crypto infrastructure, particularly for DeFi analysis agents that require up-to-date knowledge. By reducing the need for retraining, MeMo could lower operational costs for AI-driven crypto applications, although its real-world efficacy in dynamic environments remains to be fully tested.