Why is entropix important?
The buzz is there! The cooks are cooking! But what is entropix? I wrote a fluff piece here explaining it, but the most comprehensive and complete description of entropix is here.
You should think of entropix as a new framework for LLM execution that uses the model’s own signals to dynamically switch between a lot of existing LLM techniques. That dynamic part is where the magic is at.
In the ideal state, entropix makes prompt engineering obsolete. I doubt that’s going to be true out of the gate, and maybe never, but that’s effectively where this is going.
Hallucinations happen when the model gets to a point where it’s uncertain, but the rules demand that it choose a path and continue on with boldness, even if the model isn’t feeling bold. In other words, hallucination is a behavior issue, not a knowledge issue.
Entropix offers a new path. If the model feels uncertain or stuck, we can give it other options. We can drop into Chain of Thought or tweak the temperature or top-k parameters to make it more creative, whatever is needed in the moment.
