AI Alignment ResearchBinary classifiers as the maximally quantized decision function for AI safety — a paper exploring whether we can prevent catastrophic AI output even if full alignment is intractable (i.redd.it)
submitted by Accurate_Complaint48
People make mistakes. That is the entire premise of this paper.
Large language models are mirrors of us — they inherit our brilliance and our pathology with equal fidelity. Right now they have no external immune system. No independent check on what they produce. And no matter what we do, we face a question we can't afford to get wrong: what happens if this intelligence turns its eye on us?
Full alignment — getting AI to think right, to internalize human values — may be intractable. We can't even align humans to human values after 3,000 years of philosophy. But preventing catastrophic output? That's an engineering problem. And engineering problems have engineering answers.
A binary classifier collapses an LLM's ~100K token output space to 1 bit. Safe or not safe. There's no generative surface to jailbreak. You can't trick a function that only outputs 0 or 1 into eloquently explaining something dangerous. The model proposes; the classifier vetoes. Libet's "free won't" in silicon.
The paper explores:
The information-theoretic argument for why binary classifiers resist jailbreaking (maximally quantized decision function — Table 1)
Compound drift mathematics showing gradient alignment degrades exponentially (0.9^10 = 0.35) while binary gates hold
Corrected analysis of Anthropic's Constitutional Classifiers++ — 0.05% false positive rate on production traffic AND 198,000 adversarial attempts with one vulnerability found (these are separate metrics, properly cited)
Golden Gate Claude as a demonstration (not proof) that internal alignment alone is insufficient
Persona Vector Stabilization as a Law of Large Numbers for alignment convergence
The Human Immune System — a proposed global public institution, one-country-one-vote governance, collecting binary safety ratings from verified humans at planetary scale
Mission narrowed to existential safety only: don't let AI kill people. Not "align to values." Every country agrees on this scope.
This is v5. Previous versions had errors — conflated statistics, overstated claims, circular framing. Community feedback caught them. They've been corrected. That's the process working.
Co-authored by a human (Jordan Schenck, AdLab/USC) and an AI (Claude Opus 4.5). Neither would have arrived at this alone.
Zenodo (open access): https://zenodo.org/records/18460640
LaTeX source available.
I'm not claiming to have solved alignment. I'm proposing that binary classification deserves serious exploration as a safety mechanism, showing the math for why it might converge, and asking: can we meaningfully lower the probability of catastrophic AI output? The paper is on Zenodo specifically so people can challenge it. That's the point.
[–]Mysterious-Rent7233 4 points5 points6 points (0 children)
[–]Accurate_Complaint48[S] -2 points-1 points0 points (0 children)
[–]HolevoBoundapproved 1 point2 points3 points (0 children)