Computer Science Β· Inha University
Researching how humans discover structure, assign meaning, and generate research questions.
This profile is the entry point to the Structure Recognition Research Program, a unified long-term research program investigating how humans discover structure, assign meaning to it, and generate research questions. The program comprises five papers organized under Architecture B (Empirical Foundation Model): three empirical case studies in Boolean function analysis (Karnaugh map visual pattern interpretation, symmetric Boolean function structure, variable rearrangement invariance), a meta-theoretical integration (Structure Recognition Theory, SRT), and a methodological self-reflection on long-term human-AI research collaboration. The program's central question is: How do humans come to see something new?
This profile is the entry point to a unified long-term AI-assisted research program.
How do humans come to see something new?
The program follows a connected sequence from empirical observation to theoretical synthesis. Papers 1β3 provide the observational foundation; Paper 4 synthesizes them into a general framework:
| Step | Repository | Topic | Status |
|---|---|---|---|
| 1 | KMap Structure Invariance | Karnaugh map visual patterns; XOR/XNOR checkerboard structures | Stable |
| 2 | Symmetric Boolean Functions | Symmetric Boolean functions; Hamming Weight layers; ring structures | Stable |
| 3 | Variable Rearrangement Invariance | Variable rearrangement; structural invariance under transformation | Active |
| 4 | Structure Recognition Theory | Meta-theory explaining why structures become research-worthy | Active |
Empirical Foundation Layer
Paper 1 β Karnaugh Map Structure Invariance
Paper 2 β Symmetric Boolean Function Visual Patterns
Paper 3 β Variable Rearrangement Invariance
β
Theoretical Integration Layer
Paper 4 β Structure Recognition Theory (SRT)
β
Application Domains
Human-AI Collaboration Β· AI Education Β· Structure-Based Mathematics
The central navigation point for the entire research program is the Research-Portfolio repository.
It provides:
- Full program overview and research timeline
- Concept genealogy mapping how ideas developed
- Cross-repository navigation and planning documents
An active thread of the program investigates human-AI research collaboration as both a method and a research subject. Humans detect structure and generate research questions; AI connects concepts and expands the explanation space. This division of cognitive labor is itself under study.
This work also explores knowledge transfer, context management, and AI-assisted research workflows across multi-session research programs β areas increasingly relevant as AI tools become integral to academic inquiry.
| Repository | Role | Status |
|---|---|---|
| 5HumanAIResearchCollaboration | Long-term Human-AI research collaboration; externalized memory; AI-to-AI handover | Active |
| ANTIGRAVITY | AI collaboration workspace; session continuity; handover documents | Monitoring |
Boolean Structure Studies (Papers 1β3)
Observing and documenting specific structural phenomena in Boolean functions visualized through Karnaugh maps. Each paper is an independent case study with its own publication path.
Structure Recognition Theory β SRT (Paper 4)
A meta-theoretical framework investigating how and why humans detect explanatory significance in observed patterns. Current hypotheses (H1βH7) span structure discovery, attention filtering, and the role of representation in research generation.
Human-AI Collaboration (Branch 5)
An ongoing observation: humans detect structure and generate research questions; AI connects concepts and expands explanation space. This division of cognitive labor is itself a research subject.
AI Collaboration Education (Branch 6)
Exploratory. How should collaboration with AI be taught? What skills distinguish productive human-AI inquiry?
Structure-Based Elementary Mathematics (Branch 7)
Exploratory. Reinterpreting elementary mathematics through pattern, structure, and meaning.
The following sequence represents the actual historical development of the research program β not a logical reconstruction, but a record of discovery:
Pattern
β (1st observation: Karnaugh map checkerboards)
Layer Structure
β (Hamming Weight layers explain pattern positions)
Equivalence
β (functions with same pattern under different arrangements)
Structural Invariance
β (what is preserved across variable rearrangements?)
Structure Recognition Theory
β (why do certain structures become research-worthy?)
Human-AI Collaboration Model
β (what roles do humans and AI play in discovery?)
[Future: AI Education Β· Structure-Based Mathematics]
Difference β Attention β Structure β Meaning β Research Worthiness β Question β Research
Karnaugh map Β· Boolean function Β· structural invariance Β· structure recognition
symmetric Boolean function Β· variable rearrangement Β· Hamming weight
human-AI collaboration Β· AI-assisted research Β· knowledge transfer Β· context management
visual pattern analysis Β· research question generation Β· research program
Active work: Repository standardization Β· GitHub Pages deployment Β· Structure Recognition Theory development
Program hub: Research-Portfolio
"Understanding a structure is different from memorizing its result."