PReM: Learning What to Preserve and When to Refresh for Context Compression

Researchers have introduced PReM, a new technique designed to optimize how large language models handle long-context information during inference. By selectively determining which data to retain and when to update the compressed context, this method improves memory efficiency without sacrificing the accessibility of critical information.