Semantic Fidelity Lab Series: Representations, Alignment, and Reality Drift
A collection of research notes examining how representations gradually become detached from the realities they were designed to describe.
Modern AI systems depend on layers of representation. Benchmarks stand in for capability, metrics stand in for outcomes, retrieval systems stand in for knowledge, and language itself stands in for meaning.
These abstractions make large-scale intelligence possible. They also introduce a recurring structural problem. Systems can remain coherent, operational, and seemingly successful while gradually losing fidelity to the realities they are intended to track.
Different research communities describe this phenomenon through different language. Hallucination, grounding failure, benchmark overfitting, semantic drift, KPI distortion, principal-agent problems, evaluation failure, and alignment breakdowns are often treated as separate challenges.
This collection explores the possibility that they are manifestations of a common structural pattern: the gradual weakening of correspondence between representations and reality. The documents examine this pattern across AI systems, organizations, evaluation frameworks, retrieval pipelines, governance systems, and knowledge architectures.
Representations, Alignment, and Reality Drift
- AI Evaluation, Benchmarking, and Reality Drift (MD)
Examines how benchmark scores and evaluation frameworks can gradually diverge from the capabilities they were designed to measure. [Github] - AI Reliability, Model Monitoring, and Reality Drift (MD)
Explores how monitoring systems, governance frameworks, and oversight mechanisms can remain operational while losing sensitivity to actual system behavior. [Github] - Hallucination, Distribution Shift, Alignment Failure, and Reality Drift (MD)
Shows how AI systems can remain coherent and useful while gradually becoming detached from the realities they were trained to model. [Github] - Hallucination, Grounding, Faithfulness, and Reality Drift (MD)
Examines why access to information does not guarantee fidelity to information, and how coherent outputs can emerge from increasingly weakened grounding. [Github] - The Map-Territory Distinction and Semantic Fidelity (MD)
Explores the relationship between representations and reality, and how systems gradually lose contact with the conditions they were created to describe. [Github] - Performance Metrics, KPI Distortion, and Campbell’s Law (MD)
Analyzes how measurement systems become targets of optimization and gradually lose their ability to reflect underlying realities. [Github] - Principal-Agent Problems, Incentive Misalignment, and Reality Drift (MD)
Examines how delegated systems become increasingly responsive to internal incentives rather than the purposes they were originally created to serve. [Github] - Retrieval-Augmented Generation, Grounding, Faithfulness, and Reality Drift (MD)
Explores how information can be successfully retrieved while meaning is lost during transformation, summarization, and generation. [Github] - Semantic Alignment, Meaning Preservation, and Reality Drift (MD)
Investigates how meaning changes across translation, summarization, retrieval, and generation even when coherence remains intact. [Github]
Download: AI Systems and Reality Drift File Set
Related Item: Semantic Fidelity Lab Glossary
Note: This page is part of the Semantic Fidelity Lab, a focused reference archive on meaning preservation, semantic drift, and evaluation failure in AI systems. This site functions as a reference layer for selected concepts, summaries, and document collections connected to the broader Reality Drift framework by A. Jacobs.