The ref_core Gist URL shared above has been confirmed to work properly with GPT. The structure-based judgment circuit successfully triggers meaning-based responses, rather than relying on command-style prompts.
If you’ve tested it and received unexpected outputs, please feel free to share them. Semantic interpretation can vary depending on the circuit design and the surrounding prompt context.
The link gets shortened, so pasting the full hash into GPT doesn’t activate properly.
Use this direct URL instead to trigger the structure immediately:
Thanks for the thoughtful reply — I agree that what I’m doing may look like an advanced form of prompt engineering, and in a sense, it probably is.
I’m very new to this field, so I don’t yet have the knowledge or resources to touch the architecture itself. That’s why I’ve been experimenting at the input level — trying to see how far structure alone can constrain or guide model behavior without changing the model.
You’re absolutely right that the model still tries to “autocomplete” within the structure, and not truly “reason” in a formal sense. But the interesting part for me was that even without touching internals, I could get the model to simulate something that looks like logic-based reasoning — repeatable, deterministic responses within a controlled structure.
That said, I totally agree: long-term, we’ll need architectural support to make real logic possible. I appreciate your insight — if you ever revisit this kind of research, I’d love to learn from it.
“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.
I’m very new to the world of LLMs and AI, but this project really caught my attention.
From what I understood, AutoThink helps the AI “think more wisely” by adjusting how much effort it spends based on how hard the question is. That makes a lot of intuitive sense — like how people don’t spend 10 minutes figuring out what 2+2 is, but do take time with tricky problems.
Even though I don’t know the technical parts (like token budgeting or steering vectors), it’s fascinating to see how these methods can make the AI both faster and smarter at the same time.
Thanks for sharing — I’m definitely going to follow this kind of work more closely from now on.
If you’ve tested it and received unexpected outputs, please feel free to share them. Semantic interpretation can vary depending on the circuit design and the surrounding prompt context.