Datasets:

realitydriftproject
/

reality-drift-mechanics-taxonomy-ai-systems-alignment

This dataset contains a foundational paper from the Reality Drift framework, formalizing the mechanics, lifecycle, and taxonomy of drift across technological, institutional, and cognitive systems.

Reality Drift describes a structural condition where systems:

remain functional and internally coherent
while gradually decoupling from the realities they are meant to represent

This produces a core paradox:

Operational continuity without correction.

Core Definition

Reality Drift emerges when:

optimization outpaces fidelity
scale weakens feedback loops
mediation compresses context

As these forces compound, systems continue to operate while losing their ability to self-correct.

The Drift Mechanism

The paper formalizes a consistent causal chain:

Optimization Pressure + Weakening Constraints → Compression → Context Loss → Fidelity Collapse → Drift

Key dynamics:

reality is compressed into representations
context is stripped during scaling
feedback loops weaken
misalignment becomes structural, not visible

Drift Lifecycle

Reality Drift unfolds in stages:

Alignment Phase
Strong grounding in real-world conditions
Optimization Phase
Metrics and efficiency become dominant
Decoupling Phase
Indicators diverge from reality without obvious failure
Synthetic Realness Phase
Systems remain coherent while outcomes degrade
Collapse or Correction Phase
Divergence either becomes visible or is realigned

Drift Taxonomy

The framework categorizes drift across domains:

Language and Meaning

semantic drift
narrative drift
epistemic drift

Cognitive

attention drift
identity drift
cognitive drift

Institutional

policy drift
operational drift
alignment drift

Economic

incentive drift
measurement drift
market drift

AI and Technical Systems

model drift
AI drift
diagnostic drift

Correction Layer

Reality Drift is not purely degenerative. It can be mitigated through:

Semantic Fidelity
preserving meaning across representations
Recursive Correction Loops
reintroducing feedback into the system
Reality Anchoring Mechanisms
maintaining contact with ground truth

These mechanisms restore alignment before divergence becomes structural.

Why This Matters

Most systems are evaluated based on:

performance metrics
internal coherence
output quality

Reality Drift highlights a deeper failure mode:

systems can improve on their own terms while losing alignment with reality.

This applies across:

AI systems
institutions
markets
culture

File

Reality_Drift_Mechanics_Taxonomy_RDF-2026-V1.pdf

Context

Part of the broader Reality Drift Framework (2023–2026), this work establishes semantic fidelity as a structural concern in AI alignment, evaluation, and system design.

Rather than optimizing for outputs alone, this framework focuses on whether systems remain meaningfully connected to the realities they are meant to represent.

Citation

A. Jacobs, Reality Drift: Mechanics and Taxonomy, 2026.

License

CC-BY-4.0

Core framework and sources

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