peechboy

LLMs can now talk to each other without words. MIT researchers have developed a system where LLMs communicate by directly transferring internal states without using language. They called it "telepathy." Right now, multi-agent systems are fundamentally limited. When one AI agent needs to hand off work to another, it has to write out a text summary, and the receiving agent has to read it from scratch. This is incredibly inefficient. It wastes huge amounts of compute, limits bandwidth, and loses critical nuance. Researchers call text a "narrow channel" that forces AI to communicate like humans do. But an AI isn’t human. It builds a much deeper internal representation of its context, called a Key-Value (KV) cache. A new paper out of MIT CSAIL just introduced SwaT (Swarm Telepathy). Instead of forcing agents to talk via text, SwaT allows them to transfer their KV-cache directly to each other. It is quite literally machine telepathy. Even crazier? This works across different model families, architectures, and sizes. You can have a 4B parameter model from one company beam its latent state directly into a 32B model from another company. The receiving model doesn't have to read a single word of the original text. And the results are staggering. Combined with cache compression, SwaT beats text-to-text agent communication by an average of 40% in accuracy, while running 4x faster. It even transferred vision capabilities from a Vision-Language Model to a text-only model, without the receiver ever seeing an image, and despite SwaT only being trained on text. We are watching the birth of a unified, cross-model latent language. Agents will no longer just talk to each other. They will share minds.