Modern AI systems are often locked into isolated ecosystems, unable to share or reuse their learned representations across models. Our Cross-Model Embedding Handshake proposes a lightweight protocol to address this challenge. By using a cryptographic handshake and an alignment step, the protocol allows embeddings from one model to be translated into another’s space while preserving meaning and integrity.

This work is part of MIRE’s broader commitment to fostering collaboration, transparency, and open standards in artificial intelligence. By enabling models to “speak” across boundaries, we aim to lower barriers to innovation and encourage pluralism in AI development.

Read the full paper below and explore a demo CLI project on GitHub - https://github.com/VickM12/llmhs_demo