Published February 2, 2026 | Version v5.0
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The Bees That Saved Humanity From Themselves: The AlphaFold of Alignment — Binary Classification as the Maximally Quantized Decision Function for Existential AI Safety

Authors/Creators

  • 1. adlab
  • 2. USC Annenberg School for Communication and Journalism

Description

We propose solving AI alignment by reducing it from a problem of understanding consciousness to a problem of binary classification. A binary classifier is the maximally quantized decision function over an LLM's output space — collapsing ~100,000 tokens per step to 1 bit eliminates the generative surface through which jailbreaks operate. We narrow the mission from "align AI to human values" (intractable) to "don't let AI kill people" (universal, binary, testable). We introduce Persona Vector Stabilization as a Law of Large Numbers for alignment, the Bee Architecture with Rosetta Convergence Layer evaluation, and propose the Human Immune System: a global public institution governed by one-country-one-vote collecting binary alignment ratings at planetary scale. Fifth edition — major revisions include the information-theoretic quantization framing, corrected Constitutional Classifiers++ citations, narrowed existential safety mission, and complete governance model.

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Additional details

Dates

Submitted
2026-02-02

References

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