CXC-7: Seven Core Dimensions of Conversational Context

Introduces a taxonomy of seven core dimensions present in AI conversation contexts: intent, tone, role, knowledge state, emotional state, goal, and history. Structural foundation for analyzing user-AI interaction patterns. doi.org/10.5281/zenodo.18615646

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ZON RZVN

ZON RZVN

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Technical framework for detecting and classifying cognitive drift signals in AI conversation contexts. Defines signal taxonomies, detection algorithms, and criteria for monitoring user-side contextual stability. doi.org/10.5281/zenodo.19097267

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Research paper cover

Apr 1, 2026

A-CSM: AI Contextual Signal Matrix

Technical framework for detecting and classifying cognitive drift signals in AI conversation contexts. Defines signal taxonomies, detection algorithms, and criteria for monitoring user-side contextual stability. doi.org/10.5281/zenodo.19097267

Research Preprint

Research paper cover

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USCI: User-Side Contextual Interaction Assessment Methodology

Four-axis assessment methodology (FR, CA, SR, SA) for evaluating contextual risk in human-AI interactions, with structured criteria for identifying deviation and alert states. doi.org/10.5281/zenodo.18678458

Research Preprint

Research paper cover

Mar 1, 2026

USCI: User-Side Contextual Interaction Assessment Methodology

Four-axis assessment methodology (FR, CA, SR, SA) for evaluating contextual risk in human-AI interactions, with structured criteria for identifying deviation and alert states. doi.org/10.5281/zenodo.18678458

Research Preprint