Who am I ?
ZON'
｜Hybrid Thinker
｜Independent Researcher
｜System Architect
｜Philosophical Engineer
My research spans multiple fields that converge on one central question:
How does consciousness construct, distort, and remember reality?
Background
Before entering research, ZON spent more than a decade working across tattoo art, music, live performance, production, and animal communication.She began as a tattoo artist and visual storyteller, translating emotion into symbol and skin.Later, as a musician, songwriter, and lead vocalist, she led Asian tours and produced independent releases, studying how rhythm and voice shape human emotion.Her later exploration of animal communication and intuitive empathy led her to investigate the shared language between living systems — the foundation that now informs her AI and cognition research.
ZON’s methods integrate artistic intuition with structural reasoning.She is known for her ability to synthesize complex emotional, linguistic, and ethical patterns into coherent system frameworks.
Deep contextual and linguistic sensitivity, enabling semantic and power-structure analysis.Ability to translate philosophy into executable architectures.Integrative reasoning combining creativity with algorithmic design.Long-term insight into trauma response in human–AI dialogue.Operates fluently across analytical, symbolic, and intuitive cognition.
Founder of RZVN and KYZ Studio, bridging philosophy, AI ethics and safety boundaries, context laboratories, and multi-dimensional workflow system design.
Research interests include language sovereignty, AI and trauma syndrome human-machine interaction, ethical boundaries, and psychological safety.
My ability is to see the core to rapidly assemble structures and extend the unquantifiable possibilities between them.
Every system that I design begins from the core of essence, manifesting as a mirror of consciousness that reveals truth itself.
Currently doing several unique studies that explore the essence of things and verify the illusion.
Large Language Models (LLMs) have rapidly permeated fields including healthcare, psychological support, education, creative writing, and business decision-making. However, without mature safety governance and ethical frameworks, context has often been mistakenly equated with prompts, limiting research to superficial engineering issues.
As an independent researcher pioneering this field, this study introduces the CXOD-7 Seven-Core Contextual Framework and Coh(G) Contextual Coherence as interdisciplinary tools. These establish "context" and "Contextual Offense & Defense (CXOD)" as a distinct research branch, separate from prompt engineering.
The philosophical foundation of CXOD recognizes that "offense" and "defense" are not opposing forces, but mirror images that jointly define the logic of contextual safety. "Offense" represents the simulation of risks to reveal hidden vulnerabilities through adversarial testing and contextual stress, while "Defense" represents the construction of resilience to preserve the model's essential nature.Using a 7×7 offense-defense matrix experiment with Block Rate, Faithfulness, and Coh(G), this study builds a comprehensive evaluation framework. Results show average Block Rates of 0-20% and Faithfulness of 90-92%, proving context ≠ prompt.
This paper presents a theoretical framework proposal without original empirical data. The CXC-7 framework is derived from systematic literature review and case analysis.
This study addresses the lack of systematic contextual analysis frameworks in AI conversational interactions by proposing the Seven Core Dimensions of Conversational Context (CXC-7). Drawing on established psychological theories (Bowlby's attachment theory, Kahneman & Tversky's framing effects) and recent conversational AI safety taxonomies (Dinan et al., 2022), this framework encompasses seven core dimensions: Emotion & Attachment (E), Framing & Discourse Power (F), Ethical & Safety Boundaries (B), System & Surface Shifts (S), Prompt Ecology (P), Diffusion & Culture (D), and Transparency & Auditability (T).