Semantic Fidelity Paper Series: Representational Systems in AI
This page collects the Representational Systems Briefs, a series of short papers on how AI systems preserve, transform, retrieve, and act upon representations across time. The papers examine memory, context engineering, agentic systems, model monitoring, and machine learning drift through the broader lenses of Semantic Fidelity and Reality Drift.
The series connects established technical concepts such as agent memory, context engineering, Model Context Protocol, concept drift, model monitoring, retrieval augmentation, and responsible AI to a common structural problem: a system can remain coherent and operational while the meanings carried by its representations gradually weaken.
The central question is not only whether an AI system can remember, retrieve, predict, plan, or complete a task. It is whether the information moving through those processes continues preserving the distinctions, constraints, and relationships required for correct understanding.
Part I: AI Agents, Memory, and Persistent Reasoning
AI Agents, Long-Term Memory, and Semantic Fidelity (PDF)
Examines how persistent memory, retrieval, summarization, and repeated reuse can preserve continuity while gradually changing the meaning of earlier interactions.
AI Agents, Memory, and Semantic Fidelity (PDF)
Explains why autonomous agents are larger representational systems rather than language models alone, and how meaning can drift across memory, planning, retrieval, and tool use.
Part II: Governance, Monitoring, and Operational Alignment
AI Governance, Model Monitoring, and Reality Drift (PDF)
Shows how governance processes, audits, dashboards, and compliance systems can remain active while losing contact with the changing behavior of deployed AI systems.
Concept Drift, Model Drift, and Reality Drift (PDF)
Connects concept drift, data drift, distribution shift, and model degradation to the broader problem of learned representations becoming less answerable to changing real-world conditions.
Part III: Context, Retrieval, and Meaning Preservation
Context Engineering, Prompt Engineering, and Semantic Fidelity (PDF)
Explains why larger context windows, better retrieval, and more available information do not automatically preserve understanding when meaning changes during selection, compression, and integration.
Related Framework Concepts
Semantic Fidelity Resources:
- Semantic Fidelity Definition
- Glossary
- Visual Frameworks
- Semantic Fidelity Paper Series
- Failure Modes in LLM Systems
- Drift Detection in AI Systems
Core Concepts: