Either this already exists, or someone is going to implement that (should I implement that?): - assumption LLM can input/output in any useful language, - human languages are not exactly optimal away to talk with LLM, - internally LLMs keep knowledge as whole bunch of connections with some weights and multiple layers, - they need to decode human-language input into tokens, then into something that is easy to digest by further layers, then get some output, translate back into tokens and human language (or programming language, same thing), - this whole human language <-> tokens <-> input <-> LLM <-> output <-> tokens <-> language is quite expensive.

What if we started to talk to LLMs in non-human readable languages (programming languages are also just human readable)? Have a tiny model run locally that translates human input, code, files etc into some-LLM-understandable-language, LLM gets this as an input, skips bunch of layers in input/output, returns back this non-human readable language, local LLM translates back into human language/code changes.

Yesterday or two days ago there was a post about using Apple Fundamental Models, they have really tiny context window. But I think it could be used as this translation layer human->LLM, LLM->human to talk with big models. Though initially those LLMs need to discover which is "language" they want to talk with, feels like doable with reinforcement learning. So cheap local LLM to talk to big remote LLM.

Either this is done already, or it's a super fun project to do.