A new paradigm treats memory not as passive retrieval or LLM fine-tuning, but as a separate, fully trainable model that can be plugged into any existing large language model.

MeMo introduces a modular framework with a novel data synthesis pipeline, model merging for continual learning, and a black-box inference protocol. It delivers strong performance on complex benchmarks like BrowseComp-Plus, NarrativeQA, and MuSiQue while keeping inference costs constant and remaining robust to noisy data.