cross-model-communication

The paper introduces SYNC (Symbol-Indexed Neural Communication), a training-free, word-indexed interface that lets heterogeneous language models share selective object facts via word indices and receiver-local hidden-state directions, achieving strong four-answer success across nine receivers from five model families.

The paper introduces Latent Cache Flow (LCF), a method for efficient model-to-model communication by exchanging compressed KV caches instead of text, reducing adapter size and enabling cross-context communication.