Separating context encoding from reading allows LLM systems to compress context once and reuse it across multiple questions with different models, dramatically reducing computation and latency in multi-turn scenarios.
LatComm enables efficient communication between multiple LLMs by compressing context once into a compact, reusable representation that different models can selectively read.