For the last four months I have been experimenting a lot with LLM models in an attempt to simplify the creation of reasoning chains without using an auxiliary programming language. The idea comes from the control unit in classical processors. I see LLM as a more complex ALU, but the models lack a unit that can perform more complex sequences of prompts, including loops. In psychoanalysis there is a concept of the central executive system, which is somewhat similar to the control unit in CPUs.
> The central executive is responsible for controlled processing in working memory, including but not limited to, directing attention, maintaining task goals, decision making, and memory retrieval.
My first thought was that it would be possible to implement a prompt for the LLM that would perform the functions of the central executive. As a result, I came up with this prompt structure:
https://ibb.co/nCTD9X5.
The data in the green blocks changes depending on the active prompt. Prompts can refer to each other using a reference table. This makes it possible to build graphs of prompts where prompt is a node. The idea can be extended to dynamically created prompts that are added to the initial graph and form new graph segments (in the process of experimentation).
Github: https://github.com/turing-machines/mentals-ai
