Reality-Constrained Systems: A Framework for Reducing Drift in AI and Decision Systems

A systems-level framework for maintaining alignment between outputs and the realities they are meant to represent.

As AI systems and decision processes scale, they become increasingly effective at producing coherent and plausible outputs. However, coherence does not guarantee alignment. This creates a class of systems that function well on the surface while gradually drifting away from real-world conditions.

This paper defines Reality Drift as a structural failure mode and introduces Reality-Constrained Systems as a response.

The framework is built on three components:
– Reality Anchors (external grounding)
– Cognitive Constraints (structured reasoning)
– Drift Diagnostics (misalignment detection)

Together, these mechanisms limit how far a system can drift under optimization pressure.

This is not about making systems more intelligent. It is about keeping them tied to reality as they improve.

Download the full PDF: Reality-Constrained Systems: A Framework for Reducing Drift in AI and Decision Systems

Extended Material:

• Reality Drift Canonical Definition
• Visual Reference Material
• Extended Working Papers
• Glossary
• Mechanics & Taxonomy
• Drift/Fidelity Index
• Connection Papers
• Recognition Guides
• Lineage Papers

Note: This site hosts a reader edition for archival and reference purposes.