How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs

A method enables efficient knowledge exchange between different-sized LLMs in a multi-agent pipeline, specifically using the Qwen2.5-Coder family. The core technique involves an upstream agent tokenizing text once and sharing the resulting integer array with downstream agents via shared memory, bypassing redundant tokenization. This process uses a modified Open Knowledge Format (OKF) to point to the data, achieving a 28–37% reduction in Time to First Token (TTFT). A critical guardrail is a full vocabulary equivalence check between all agents before execution, which prevents models from generating incorrect output due to mismatched token IDs.