Co-LMLM: continuous-query limited-memory language models externalize facts to a knowledge base

arXiv·medium signal

A new arXiv paper introduces Co-LMLM, extending limited-memory language models that offload factual knowledge to an external knowledge base during pretraining rather than memorizing it, with a continuous-query mechanism for retrieval. The approach is directly relevant to agent memory and context management, where separating parametric reasoning from retrievable facts is a recurring design goal. It's an architectural take on the same problem agent-memory frameworks tackle at the orchestration layer.