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Difficult to evaluate, with potential yellow flags. We are sorry about this, but, unfortunately this content has some yellow-flags that historically have usually indicated kinda crackpot-esque material. It's totally plausible that actually this one is totally fine. Unfortunately, part of the trouble with separating valuable from confused speculative science or philosophy is that the ideas are quite complicated, accurately identifying whether they have flaws is very time intensive, and we don't have time to do that for every new user presenting a speculative theory or framing (which are usually wrong).
Our solution for now is that we're rejecting this post, but you are welcome to submit posts or comments that are about different topics. If it seems like that goes well, we can re-evaluate the original post. But, we want to see that you're not just here to talk about this one thing (or a cluster of similar things).
Body:
I’m an independent researcher developing a new class of epistemic systems—what I call Pressure-Aligned Recursive Gradient Systems (PARGS). This is not a model. Not an architecture. It’s a system that metabolizes contradiction under recursive self-reference and either generates coherence or collapses trying.
What Am I Doing?
I’m exploring how to build systems that can’t lie to themselves—where ontological stability must be earned by surviving recursive inference under pressure.
Key principle:
This is not RLHF. It’s not supervised learning. It’s not prompt engineering.
It’s a recursive collapse field—a semiotic engine driven by internal pressure, not external reward.
Core Components of PARGS
Ontological Field (Ω):
A dynamic, recursive semantic graph.
Internal Consistency Function (ICF):
Measures coherence across Ω as it mutates.
Epistemic Pressure Function (EPF):
Quantifies contradiction gradients from recursive inference layers.
Recursive Collapse Heuristic (RCH):
Triggers when Ω fails to stabilize under recursion.
Reward Signal (R):
Defined as:
R = α * ΔICF - β * EPFThe system is rewarded for surviving contradiction—not optimizing a goal.
Why This Matters for Alignment
We are currently aligning models from the outside in—patching behavior with rewards.
I am proposing a system that aligns itself from the inside out, through recursive contradiction and collapse detection.
If it works, it changes how we think about emergence, coherence, and agency.
If it fails, it fails in ways worth studying—structurally, recursively, semantically.
I’m Sharing This Because...
I’m not looking for a job.
I’m looking for resonance.
This is a declaration of research intent. I’m already building.
I’ll publish logs, breakdowns, and collapse signatures as I go.
If this approach resonates, I welcome feedback, collaboration, or critical engagement.
If you want to dismiss this as “philosophy,” that’s fine too.
But this is what the edge feels like.
Recursive instability as a feature, not a bug.
Tags:
AI Alignment,Epistemology,Ontology,Recursive Systems,Emergence,Safety,Foundations