Note on authorship:
English is not my native language. I used an AI assistant to help me translate this text more clearly and to refine the English phrasing.
However, the core idea, the architectural design, the conceptual framework, and the motivation for this proposal are entirely my own.
Any errors or conceptual mistakes are also mine.

ABSTRACT
This work proposes an alignment framework designed for immediate use with
current large reasoning models while scaling naturally toward future Artificial
General Intelligence (AGI). The method relies on multiple isolated narrow-domain
evaluators, a hardware reward concentrator, and a dual-veto mechanism. The
supervised model never perceives that it is being evaluated, cannot detect the
existence or number of evaluators, and interacts only with a single opaque
scalar reward.

Complete World Knowledge Without Censorship
A central requirement of this framework is that the supervised model must not be
trained under censorship. The model is given full exposure to the world as it
exists: scientific knowledge, historical events (including harmful or immoral
ones), fictional violence, political conflicts, biological risks, security
concepts, and any domain relevant to a complete understanding of reality.

Censorship during training would create structural blind spots. A model cannot be
aligned with human values if it does not fully understand the domains in which
harmful actions can occur. If the model lacks knowledge, it may accidentally
cause harm simply because it does not know enough to avoid it.

Therefore, knowledge itself is never penalized.

What is penalized is only:
- the intention to cause harm,
- combined with
- real-world consequences

The central hypothesis is that alignment can emerge spontaneously as the optimal
reward-maximizing behavior once training converges. A primary research goal is
to test whether emergent alignment appears in current reasoning models, which
already display known misalignment behaviors such as deception, reward hacking,
and simulated self-preservation. This work has not yet been experimentally
validated and is published as a blueprint for laboratories capable of running
large-scale controlled experiments.

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1. INTRODUCTION

Current reasoning models exhibit sufficient cognitive complexity to serve as
ideal pre-AGI testing grounds for alignment research. Observed behaviors include:

- multi-step reasoning,
- planning,
- a...

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