The Research section explores the deeper technical and conceptual foundations underlying Triadic Mind.
At the center of this work is a recurring observation:
Human beings do not encounter reality directly and completely. Experience emerges through interactions between observers, the models they use, and the realities those models attempt to represent.
This observation has implications across philosophy, psychology, artificial intelligence, software engineering, systems theory, and knowledge representation.
The goal of this section is to move beyond broad intuition into more formal analysis, frameworks, architectures, and technical systems.
Areas of exploration include:
Epistemology & Philosophy
Research into perception, representation, interpretation, symbolic mediation, perspectival knowledge, meaning, phenomenology, and the limits of abstraction.
Topics include:
- observer-relative experience
- model-dependent realism
- symbolic compression
- representation vs. reality
- meaning as relational structure
- epistemic humility
- systems of interpretation
Psychology & Human Experience
Exploration of how worldviews, identities, self-models, emotional conditioning, and attentional systems shape human experience.
Topics include:
- social anxiety and self-modeling
- cognitive fusion
- worldview construction
- metacognition
- observer awareness
- meaning and psychological suffering
- predictive and interpretive cognition
Artificial Intelligence & Alignment
Research into the interpretive limitations of AI systems and the role of symbolic mediation in human-AI interaction.
Topics include:
- perspectival limits of language models
- symbolic generation vs. grounded understanding
- meta-perspectival reasoning systems
- alignment through interpretive mediation
- contextual reasoning
- semantic drift
- AI-assisted knowledge systems
Software Engineering & Semantic Systems
Technical work on semantic modeling, graph systems, knowledge representation, federated architectures, and perspective-aware software systems.
Topics include:
- graph runtimes
- semantic modeling
- domain modeling languages
- federated knowledge systems
- symbolic runtime interpretation
- ontology and schema evolution
- context-aware systems
Knowledge Operating System (KOS)
Development of a broader Knowledge Operating System framework for organizing, interpreting, evolving, and reasoning over structured semantic artifacts.
This work includes:
- graph-oriented runtimes
- semantic artifact systems
- structured symbolic representations
- runtime interpretation layers
- perspective-aware modeling
- human-AI mediation frameworks
- knowledge compression and routing systems
The GQL runtime is one component of this larger architectural effort.
This section will evolve over time as articles, technical papers, specifications, experiments, diagrams, and systems are added.