Context Representation Drift (CRD)

Document ID: SF0039
Version: 1.3
Status: Active / Public
Document Type: Methodological Research Report
Application: AI-Assisted Research and Evaluation

The Context Representation Drift (CRD) v1.3 defines a formal analytical framework for identifying, measuring, and reasoning about semantic drift in AI system context representations across extended interactions.

CRD addresses a core failure mode in multi-turn AI usage: the gradual divergence between an initially established context and the internal representations used by an AI system to generate subsequent responses. This drift can occur even when no explicit contradictions or errors are present, leading to subtle but compounding misalignment over time.

Core Objectives

CRD introduces structured mechanisms for:

• Detecting context degradation across interaction turns

• Identifying drift onset points

• Differentiating surface-level coherence from internal representational consistency

• Characterizing drift types and severity

These mechanisms enable researchers to diagnose context instability before it results in incorrect reasoning, hallucinated assumptions, or silent analytical failure.

Relationship to Other Protocols

CRD is designed to operate independently but is intended to be used in conjunction with the Ingestion Verification Protocol (IVP), which ensures that source materials are fully processed prior to downstream reasoning.

Together, the protocols establish complementary safeguards:

• IVP constrains input integrity

• CRD constrains context stability across reasoning steps

This pairing enables auditable, multi-stage verification in AI-assisted research workflows.

Design Characteristics

• Platform-agnostic by design

• Does not require access to internal model states

• Operates entirely through observable interaction behavior

• Suitable for longitudinal and high-risk analytical contexts

CRD does not assume privileged system access and can be applied consistently across different AI platforms and deployment environments.

Scope and Limitations

CRD does not guarantee correctness of conclusions. Instead, it functions as a diagnostic and audit framework, ensuring that downstream reasoning occurs within a stable and traceable contextual envelope.

The protocol is intended to support researchers, not replace scholarly judgment or responsibility.