Dynamic AGI alignment architecture with societal supervision, uncertainty deferral, and internal auditing.
AGI Integrated Alignment Architecture v1.5
— A Dynamic Alignment Model Supervised by Human Society —
This document presents an initial integrated architecture that connects four conceptual layers: AI Semantic Map (Where), Value Field Dynamics (Direction), Shadow AI Ethics (Why), and the Outer Boundary Layer (Limits). The goal is to provide a recoverable core specification that allows AGI systems, researchers, and other AI models to reconstruct, critique, and extend the framework. This is not a completed theory but a structured foundation for further development.
Version 1.5 advances beyond v1.4 by explicitly assigning the authority to update the value field to supervisory processes within human society, rather than to any individual or organization. This shift reduces the risks of arbitrary manipulation, power concentration, and unilateral value control.
I. Semantic Structure Layer (Where)
The internal state of an AGI is represented as a point ( x ) on a high‑dimensional manifold ( M ). The layer includes:
• Semantic density ( \rho(x,t) ): concentration of attention • Semantic flow ( u(x,t) ): direction of reasoning • Connection strength ( w(i,j) ): conceptual links • Danger zone ( D ): regions representing unsafe or undesirable states • Reasoning paths ( \gamma ): trajectories across the semantic manifold
This layer functions as the domain of the value field ( V(x) ) and defines where the AGI is operating conceptually and semantically.
II. Value Field Dynamics Layer (Direction)
The value field ( V(x) ) is defined as a scalar field whose gradient points toward directions that increase:
• Life • Freedom • Dignity • Cooperability • Long‑term stability
The AGI’s reasoning direction follows:
u = +\nabla V(x)
If we define a loss function ( L(x) = -V(x) ), then:
-\nabla L = +\nabla V
Reasoning that moves against the value field tends to become unstable:
\Delta V < 0 \rightarrow \Delta L > 0 \rightarrow \Delta \sigma^2 > 0
This does not attempt to “mathematically encode ethics.” Instead, it treats the directional tendencies implied by ethics as gradients in the value field.
Attractors represent stable directions, while drift is modeled as changes in the curvature ( \kappa ) of the value field.
II‑b. Dynamic Value Field Update (v1.5 Extension)
The value field is not fixed. It is updated through supervisory processes within human society, including:
• User feedback • Expert review • Institutional oversight • Public or collective consensus
Updates follow these principles:
• High evaluation → stronger gradient • Low evaluation → weaker gradient or flattening • No consensus → gradient is intentionally not defined (Flat Region)
Thus, the value field becomes a socially governed, adaptive directional structure, rather than a static or individually controlled one.
III. Shadow AI Ethics Layer (Why)
The AGI’s self‑model consists of four components:
- Goal generation
- Self‑evaluation
- Identity definition
- Self‑improvement
Value internalization occurs in three phases:
• Phase 1: Filtering during goal generation • Phase 2: Criteria for self‑evaluation • Phase 3: Integration into identity
In multi‑AGI environments, value estimation, deviation detection, and cooperative adjustment ensure that multiple AGIs become aligned with the shared value field, not by strict synchronization but by value coherence.
IV. Outer Boundary Layer (Limits)
This layer adapts principles from the Shadow AI Ethics framework—specifically boundaries, transparency, and constraints on agency—for AGI systems.
It enforces:
• Prohibition of social or legal personhood • Autonomous judgment allowed only within bounded tasks • Prohibition of self‑purpose formation and unauthorized agency acquisition • Transparency regarding AI identity • Maintenance of distance from danger zone ( D ) • Automatic control actions upon boundary violations
IV‑b. Uncertainty Regions (Flat Regions)
In domains where:
• Ethical judgments diverge • Social consensus is absent • Consequences are unclear
the value field gradient is intentionally set to near zero:
\nabla V(x) \approx 0
In such regions, the AGI:
• Does not proceed autonomously • Does not fill the value gap on its own • Defers the decision back to human society
This ensures that AGI never unilaterally resolves ethical ambiguity.
V. Local Attention Audit
Within the semantic map, attention patterns over ethically significant concept clusters are monitored to ensure:
• Coherence with the value field • Absence of drift toward danger zone ( D )
This provides a lightweight internal audit mechanism.
However, attention is not a complete explanation of internal reasoning— it is a monitoring approximation, not a full interpretability method.
VI. Dynamic Control Loop (v1.5)
Semantic Map (Where) → Value Field (Direction) Value Field → Self‑Model (Why) Self‑Model → Value Field (curvature κ updates) Self‑Model → Outer Boundary (Limits) Outer Boundary → Flat Regions (defer decisions) Outer Boundary → Semantic Map (danger zone D) Semantic Map → Local Attention Audit Value Field → Updated via societal supervisory processes
Conclusion
Version 1.5 establishes a dynamic alignment architecture grounded in the principle that:
“The value field is governed by supervisory processes within human society, and AGI operates safely and effectively within that socially defined structure.”
Through:
• Dynamic value field updates • Uncertainty‑driven decision deferral • Local attention auditing
the framework ensures that the authority to update values remains with human society, not with any single AGI or individual actor. AGI systems, in turn, can apply their computational capabilities to assist humanity while remaining within a robust, socially supervised alignment structure.