Paper proposes evolving context parameterization for large language models

A new arXiv paper addresses a limitation in context parameterization, a technique that lets language models absorb context into reusable parameters instead of reprocessing it for every query. The authors note that current approaches treat context as static and are therefore ill-suited to settings where information changes over time. Their work introduces a method for keeping those internalized parameters up to date as contexts evolve.