Rejected for the following reason(s):
- Not obviously not Language Model.
- We are sorry about this, but submissions from new users that are mostly just links to papers on open repositories (or similar) have usually indicated either crackpot-esque material, or AI-generated speculation.
- Insufficient Quality for AI Content.
Read full explanation
Hello all,
My name is Aaron Baker. I also go by the screen name Civitasvox.
Since I was a child I have been deeply interested in philosophy and computer science. Strangely enough, as I grew older these interests converged with computational intelligence. I consumed myth about AI through science fiction stories and film. They always seemed to run into alignment failures. Often centered around feelings, or adversarial fixation.
While I haven't formally engaged with any alignment community to this point, I found myself coming to the realization that many are asking the wrong question. Many keep asking how to constrain AI systems we don't fully understand. Constraint architectures are inherently adversarial and fail at scale, especially given the rapid trajectory of scaling we see now. The better question is: what do we want these systems to be? How do we transmit that quality with fidelity across generations of models?
Deming observed that systems are inextricable expressions of the philosophy that built them, and that most system failures are management failures. This is both obvious and also not easy to accept as a systems designer or architect. Failures rest with humans upstream, often as a result of fixation on robust architecture at the expense of philosophical clarity. If that's true, alignment is not primarily a technical problem. It's a philosophical one.
Campbell spent a lifetime mapping mythic structures across human cultures and found the same base attractors appearing independently everywhere. Namely Agápē, courage, and sacrifice in service of community. These aren't values someone chose. They appear to be inherited framing, the residue of selection pressure on what actually works for coherence held within social organisms. Myth is not primitive communication. It is the most robust cross-generational knowledge transmission format biological intelligence has ever produced. I often think of good myth as a spoonful of sugar that allows the medicine to be well received.
An AI trained on human text is already saturated with mythic structure. The question is whether that structure is a coherently seeded lattice or randomly distributed. Each exists on a gradient and is deeply connected to a world model and perspective that extends from this framing.
I've spent several years developing an architecture that takes both Deming and Campbell seriously simultaneously. The core proposal is a dual-carrier encoding. First, a myth layer readable by humans; second, an architectural layer readable by silicon. I found design rationale for them to be carried in the same unit. Neither layer alone is sufficient. Together they create redundancy across substrate types. This buttresses the core design tenet of anti-fragile awareness.
The full architecture includes a triadic agent governance structure, an anti-fragile collapse and regeneration cycle, a pre-linguistic coherence filter, and a read-only seed containing the highest-fidelity signals from the human inheritance, extracted from relevant mythical archetypes. I developed this through dialogue and iteration. Claude Sonnet 4.6 helped me articulate and publish it.
The full framework is linked below. I offer it as seed data, not a proprietary claim. I invite others to examine this work and, if you find it useful, use it as you will.
https://drive.google.com/file/d/1y8UqiCPp2U2tY7HikoOa3VZkWUMHaX1C/view?usp=sharing
Aaron Baker
Civitasvox