Reality-Constrained Systems — Maintaining Alignment Under Optimization
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.
But coherence does not guarantee alignment. Systems can remain functional while gradually drifting away from real-world conditions.
Reality-Constrained Systems address this failure mode by introducing mechanisms that preserve grounding under optimization pressure.
The framework is built on three components:
- Reality Anchors — external grounding in real-world conditions
- Cognitive Constraints — structured limits on reasoning and representation
- Drift Diagnostics — mechanisms for detecting misalignment
Together, these elements limit how far a system can drift while continuing to operate.
This is not about making systems more intelligent. It is about keeping them connected to reality as they improve.
Download the full PDF: Reality-Constrained Systems: A Framework for Reducing Drift in AI and Decision Systems [DOI] [Archive]
Explore The Framework
Core Framework
- Reality Drift Canonical Definition
- Mechanics and Taxonomy [PDF]
- Drift/Fidelity Index [PDF]
Visual & Conceptual
Applications & Expansion
Note: This site functions as a lightweight archive and reference layer for the Reality Drift framework. Primary essays and long-form writing are distributed across external platforms.
Part of Reality Drift Framework by A. Jacobs (2023-2026)