A Handshake Protocol for Cross-Model Embedding Interoperability

The Problem with Embeddings
If you’ve ever studied a foreign language, you know the difficulty of learning not only a new alphabet but also new pronunciations. Each letter sounds different from what you’re used to.
Embeddings in AI work the same way. Each embedding model “speaks its own dialect.” Words and data are mapped into multi-dimensional coordinates, but every model does it differently. Without translation, they can’t easily talk to one another.

For most users, this is invisible. But for engineers working with Retrieval-Augmented Generation (RAG), the stakes are higher. You’re often tied to a single embedding model: the one used to store vectors in your database. If your agent queries with a different model, you may see mismatched dimensions—or worse, wildly inaccurate retrievals because “cat” sits at [0.21, –0.4,…] in one space but [–0.06, 0.34,…] in another. Switching models often means rebuilding indexes or retraining from scratch.

A Cryptographic Handshake
I propose a solution: a cryptographic handshake protocol for cross-model embedding interoperability.
When two models interact, they exchange hellos with nonces (single-use random values) and policies. They negotiate shared anchors—reference points both models recognize—and commit cryptographically to prevent tampering. Using Orthogonal Procrustes analysis and Singular Value Decomposition (SVD), the protocol computes a rotation mapping to bring the spaces into alignment. For security, HMACs and privacy-preserving techniques protect integrity.

Why This Is Different
Alignment techniques exist, but none combine linear algebra and cryptography in a stateful protocol. Anchors can’t be spoofed. Nonces make replay impossible. That combination builds trust into the translation itself.

Below is an example of a query and retrieval between two embedding models and the before and after when using this hand-shake protocol

Results
In tests, cosine similarities improved dramatically. Queries that initially scored –0.021 rose to 0.637 after alignment, with some reaching as high as 0.744 (74% similarity).

Real-World Impact
As corporations adopt Agentic AI, interoperability becomes critical. Different companies prefer different embedding models. Without translation, agents are limited. With it, they can cooperate. Data migration between vector stores also becomes less lossy. This could open the door to embedding standards bridging open-source and proprietary ecosystems alike.

Patent Pending & Next Steps
A provisional patent has been filed, securing this innovation while I prepare a formal publication for arXiv.
This is Patent Pending.

If your team is experimenting with agentic AI, RAG, or cross-platform embeddings, I’d love to hear your perspective. Collaboration is where standards begin.

Read the protocol in full below! Patent Pending.

And find a demo app on GitHub here: https://github.com/VickM12/llmhs_demo