Catégorie : Contextual Hyper-Embedding

Comparison between Google S2R, Pribor’s Combinatorial Magic and Pribor’s CHE (Contextual Hyper-Embedding)

This document presents the characteristics, divergences and synergies between three approaches: Google S2R, Pribor’s Combinatorial Magic and Pribor’s CHE (Contextual Hyper-Embedding).

1. Google’s S2R *

“S2R” means Speech-to-Retrieval. It is a recent voice search architecture that Google is deploying, which bypasses the explicit speech → text transcription step to try to directly establish a match between the spoken audio and the information sought. The model relies on a dual encoder: one processes the audio, the other the candidate texts, in order to bring their vector representations closer together in the same semantic space.

2. Pribor’s Combinatorial Magic

Combinatorial Magic is a bijective, lossless and fixed-dimensional encoding of simple sentences into 4D or 5D vectors: three symbolic components (Subject, Verb, Object) plus a “meta” register of 8 or 16 bits. It is distinguished by O(1) complexity, total absence of information loss, and perfect interpretability.

3. CHE (Contextual Hyper-Embedding uint8)

CHE is an extremely economical contextual encoding approach, which represents each token by a uint8 integer. Unlike the floating-point attention of Transformers, it avoids matrices and softmax, reducing energy consumption by a factor of up to 5000.

4. Comparative Table

5. Synergies and Integration

The three approaches can be integrated into a hybrid architecture: S2R provides the global semantic geometry, CHE ensures contextual efficiency through uint8 quantisation, and Combinatorial Magic formalises symbolic propositions without loss. This combination gives rise to a family of S2R–CHE–CM models combining semantic generalisation, energy frugality and complete interpretability.

* Ehsan Variani and Michael Riley, Research Scientists, Google Research, « Speech-to-Retrieval (S2R): A new approach to voice search », October 7, 2025