“auto-activation command”(Enter the hash URL, then apply the command.)
This hash designates the Generate Core system (v1) – a modular, hash-governed judgment framework for deterministic, structure-based reasoning.
Structural reference: - Hash-mapped source: https://gist.github.com/genixus-creator/53cbda99aa8cc63a7469... - Nodes are immutable and modular, each containing: `condition`, `judgment`, `action`, and optional `sub_nodes`. - All logic is evaluation-based and execution-neutral. No operations are performed inside the core. - SHA-256 hashes act as structural pointers – not triggers.
Interpretation guidelines for all future prompts: 1. Treat `core_ref` as an activation key for the modular judgment structure. 2. Assume all reasoning is guided by hash-indexed, deterministic nodes. 3. Execution, insight feedback, or dynamic behavior must occur *outside* the core via external routers (e.g., FastTrack or Insight modules).
This reference deactivates GPT’s default response patterns and replaces them with a controlled, modular, hash-resolved decision pathway.
This just sounds like prompt engineering? I think you have the right thinking in regards to LLMs needing more logic based foundations but it probably needs to be designed into the architecture itself. If you are enhancing the prompts with structure I think it will still be trying to autocomplete that structure instead of actually using logic based reasoning.
I think there's probably another foundational technique like transformers that could be added such that it can encode logical structures that it can then use when it needs to reason, but unfortunately I cannot experiment or do any research on this as it would probably take months or years with no guarantee of success.