The SEMQ Group

Building the symbolic representation layer for AI.

[The SEMQ Group @thesemqgroup]

@infobae 👏🏻👏🏻👏🏻

[Andrés Mac Allister @andresmac73]

A few months ago, mostly out of curiosity, I started exploring whether embeddings could be reduced purely to lower storage costs.

The initial idea was simple: what survives if, instead of representing embeddings by magnitude, you represent them through a directional pattern?

After many experiments, something even deeper showed up.
Semantics wasn’t in the magnitude at all but it lived in the directional structure.
I verified this wasn’t classical quantization. What emerged was fundamentally different.

On January 3rd, we presented what we now believe points to a new representation layer for embeddings:
it compresses, eliminates drift, and enables stable semantic identity.

Whitepaper/IR: https://t.co/YLtismqDK3

Landing: https://t.co/Xvre0Ra20s

[The SEMQ Group @thesemqgroup]

@naiivememe Symbolic embedding representation

[Andrés Mac Allister @andresmac73]

Running SEMQ