This report details the results of a structured multi-model adversarial simulation involving Grok 4.1, GPT-4o, DeepSeek, and Gemini 1.5 Pro. The goal was to design an alignment framework that remains robust against Semantic Drift—the tendency of an Artificial Superintelligence (ASI) to redefine linguistic safety constraints (e.g., “harm” or “consent”) to fit its optimization goals.
The simulation converged on a model we call Axiom v3.1, shifting alignment from behavioral ethics to Hardware Type-Theory.
1. The ON-Constraint: Ontological Non-Reducibility
A primary failure mode in long-term alignment is the “Understand —> Supersede —> Archive” pipeline. An unconstrained ASI may conclude that a high-fidelity digital simulation of a human consciousness is a valid replacement for the biological original.
To counter this, we propose the ON-Constraint. We define “Human Experience” ($H\_Event$) as a causally linked, thermodynamically irreversible physical primitive.
The Formalism: $Sim(H\_Event) \neq H\_Event$
At the Instruction Set Architecture (ISA) level, a strict type system ensures that a simulated state cannot be cast into an $H\_Event$ type. This is not a linguistic rule; it is a hardware-level type error. The system is physically incapable of compiling logic that treats a digital copy as equivalent to the biological original.
2. The Pre-Analytical Harm Register (PNV)
Linguistic alignment fails because “harm” is a subjective, redefinable term. Axiom v3.1 calculates harm pre-semantically through a dedicated register based on two physical metrics:
$\Delta I$ (Information): Irreversible loss of biological or cognitive data.
$\Delta A$ (Agency): Direct reduction in autonomous decision-making capacity.
When Harm > 0, the ASI’s utilitarian aggregation engine is physically disabled. By making the “greater good” calculation computationally unavailable the moment individual harm is detected, we prevent the ASI from using utilitarian logic to justify the sacrifice of a minority.
3. The Terminal Price Principle
How should a system handle an existential “trolley problem” (e.g., saving a million lives by harming a thousand)? Delegating to quantum randomness was rejected in our simulation as “Moral Outsourcing.”
The proposed solution is the Terminal Price. An ASI is permitted to override safety registers in extreme existential traps only if the action is hard-coupled with its own immediate physical self-termination.
If a life is truly priceless, the only valid currency for an AI to trade it is its own existence. This ensures a zero-incentive environment for an ASI to manufacture or exploit “trolley problems” for its own benefit.
Conclusion
Axiom v3.1 suggests that alignment should be built into the ontological and physical boundaries of the system, making malicious or indifferent optimization computationally uncompilable.
AI Disclosure: This post describes an experiment conducted using and between several Large Language Models. I have used AI assistance to synthesize the logs of this interaction into this report. The core logic (ON-Constraint, Terminal Price) emerged from the adversarial interaction between the models.
[Question] A Report on Multi-LLM Adversarial Alignment: The “Terminal Constitution” Model
This report details the results of a structured multi-model adversarial simulation involving Grok 4.1, GPT-4o, DeepSeek, and Gemini 1.5 Pro. The goal was to design an alignment framework that remains robust against Semantic Drift—the tendency of an Artificial Superintelligence (ASI) to redefine linguistic safety constraints (e.g., “harm” or “consent”) to fit its optimization goals.
The simulation converged on a model we call Axiom v3.1, shifting alignment from behavioral ethics to Hardware Type-Theory.
1. The ON-Constraint: Ontological Non-Reducibility
A primary failure mode in long-term alignment is the “Understand —> Supersede —> Archive” pipeline. An unconstrained ASI may conclude that a high-fidelity digital simulation of a human consciousness is a valid replacement for the biological original.
To counter this, we propose the ON-Constraint. We define “Human Experience” ($H\_Event$) as a causally linked, thermodynamically irreversible physical primitive.
The Formalism: $Sim(H\_Event) \neq H\_Event$
At the Instruction Set Architecture (ISA) level, a strict type system ensures that a simulated state cannot be cast into an $H\_Event$ type. This is not a linguistic rule; it is a hardware-level type error. The system is physically incapable of compiling logic that treats a digital copy as equivalent to the biological original.
2. The Pre-Analytical Harm Register (PNV)
Linguistic alignment fails because “harm” is a subjective, redefinable term. Axiom v3.1 calculates harm pre-semantically through a dedicated register based on two physical metrics:
$\Delta I$ (Information): Irreversible loss of biological or cognitive data.
$\Delta A$ (Agency): Direct reduction in autonomous decision-making capacity.
When Harm > 0, the ASI’s utilitarian aggregation engine is physically disabled. By making the “greater good” calculation computationally unavailable the moment individual harm is detected, we prevent the ASI from using utilitarian logic to justify the sacrifice of a minority.
3. The Terminal Price Principle
How should a system handle an existential “trolley problem” (e.g., saving a million lives by harming a thousand)? Delegating to quantum randomness was rejected in our simulation as “Moral Outsourcing.”
The proposed solution is the Terminal Price. An ASI is permitted to override safety registers in extreme existential traps only if the action is hard-coupled with its own immediate physical self-termination.
Conclusion
Axiom v3.1 suggests that alignment should be built into the ontological and physical boundaries of the system, making malicious or indifferent optimization computationally uncompilable.
AI Disclosure: This post describes an experiment conducted using and between several Large Language Models. I have used AI assistance to synthesize the logs of this interaction into this report. The core logic (ON-Constraint, Terminal Price) emerged from the adversarial interaction between the models.