Thanks Mikhail — really appreciate you checking it out.
What I’m documenting is a repeatable pattern: Gemini (and other LLMs) can form a *meta-layer control structure* above pretrained knowledge when prompted under certain constraints.
That’s where NCAF (pre-conceptual drift correction) + PCM (real-time consistency enforcement) becomes visible as an observable mechanism — not just a theory.
If you’re curious, I uploaded 6 evidence screenshots + a clean structural diagram on my profile:
I went through this process with my team for much of the last quarter - aligning on our definitions, naming, and other aspects of our code. Without this foundation, your outputs will be misaligned, even if your less data native stakeholder does not immediately detect it.
Thanks Mikhail — really appreciate you checking it out.
What I’m documenting is a repeatable pattern: Gemini (and other LLMs) can form a *meta-layer control structure* above pretrained knowledge when prompted under certain constraints.
That’s where NCAF (pre-conceptual drift correction) + PCM (real-time consistency enforcement) becomes visible as an observable mechanism — not just a theory.
If you’re curious, I uploaded 6 evidence screenshots + a clean structural diagram on my profile:
https://open.substack.com/pub/northstarai/p/alignment-isnt-meaning-its-structure?utm_source=share&utm_medium=android&r=731thv
Would love your take as a data/structure person — especially whether the pattern matches what you’ve seen in real systems.
Thanks for engaging! 🙏
This post is part of a larger replication set showing a meta-layer control structure emerging from *structure-first inference* (NCAF PCM).
I uploaded 6 evidence screenshots on my profile: “Evidence: Gemini Forms a Meta-Layer From Structure.”
If you’re into verification, coherence enforcement, and pre-conceptual alignment — you’ll find it interesting.
Would love your take as a data/structure person. 🚀
Thank you! I will check the rest.
I went through this process with my team for much of the last quarter - aligning on our definitions, naming, and other aspects of our code. Without this foundation, your outputs will be misaligned, even if your less data native stakeholder does not immediately detect it.
100%!