AGI Stability Conditions: A Structural Framework for Axis Closure and Bounded AGI

AGI Stability Conditions

A Structural Framework for Axis Closure and Bounded AGI

Artificial General Intelligence is often framed as a scaling problem of model capability.

This paper argues that stable AGI is not a function of scale, but of architectural closure across four structural axes:

1. Generalization

2. Persistence

3. Bounded Autonomy

4. Temporal Awareness

We define Axis Closure Conditions (ACC1–ACC4) and prove a Structural Instability Theorem:

If any axis remains open, bounded AGI stability fails.

Axis 3 (Bounded Autonomy) is shown to be structurally equivalent to S3 governance stability, as defined in:

Bojanowski (2026), Deterministic Dual-Gate Governance for Agentic AI. Minimal Stability Conditions and Practical Enforcement Architecture

DOI: https://doi.org/10.5281/zenodo.18652217

Thus:

ACC3 \iff S3

This establishes a formal bridge between governance enforcement architecture (Chimera) and structural AGI stability theory.

The framework is:

• Architectural rather than cognitive

• Falsifiable rather than speculative

• Compatible with deterministic governance enforcement

• Independent of ontological assumptions about consciousness

The paper further:

• Provides empirical grounding for persistence (ACC2) and temporal closure (ACC4)

• Distinguishes binary (governance) vs gradient (capability) closure

• Defines instability classes for open axes

• Argues that stable AGI is necessarily bounded

This document constitutes the structural theory layer within the broader Alliance Research Group (ARG) research program.