I saw a lot of ppl think about what jev could use under the hood and could someone explain why this can't just be an embedding model where we just embed all the input + decisions and give back the cosine (or whatever) similarities?
Embeddings just convert the tokens to a vector that represents the text in an abstract semantic space. JEV goes a step further and actually processes the instructions/meaning of those embeddings to produce output, just not the usual series-of-tokens output we expect from an LLM.
K0IN · · focus · HN ↗
svachalek · · focus · HN ↗