Computer Science · 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 1–3 provide the observational foundation; Paper 4 synthesizes them into a general framework:

Empirical Foundation Layer
  Paper 1 — Karnaugh Map Structure Invariance
  Paper 2 — Symmetric Boolean Function Visual Patterns
  Paper 3 — Variable Rearrangement Invariance
           ↓
Theoretical Integration Layer
  Paper 4 — Structure Recognition Theory (SRT)
           ↓
Application Domains
  Human-AI Collaboration  ·  AI Education  ·  Structure-Based Mathematics
  Boolean Function Space Theory (Branch 8)

This repository (inha20) serves as the central hub of the Structure Recognition Research Program. Research documentation, theory summaries, and AI collaboration workspace are maintained here.

Full theory documents: 4StructureRecognitionTheory

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 — areas increasingly relevant as AI tools become integral to academic inquiry.

Boolean Structure Studies (Papers 1–3)
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 — SRT (Paper 4)
A meta-theoretical framework investigating how and why humans detect explanatory significance in observed patterns. Current hypotheses (H1–H10) span structure discovery, attention filtering, the role of representation in research generation, and concept evolution as a generative mechanism (H8–H10, SRT v0.3 통합 확장 가설).

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.

Boolean Function Space Theory (Branch 8)
Active. Exploring how Boolean function spaces emerge from primitive operators and how these spaces can be recognized, classified, and transformed — extending the research program into applied mathematics via Karnaugh map geometry. Repository: 6BooleanFunctionSpaceTheory

The following sequence represents the actual historical development of the research program — not a logical reconstruction, but a record of discovery:

Pattern
 ↓  (1st observation: Karnaugh map checkerboards)
Layer Structure
 ↓  (Hamming Weight layers explain pattern positions)
Equivalence
 ↓  (functions with same pattern under different arrangements)
Structural Invariance
 ↓  (what is preserved across variable rearrangements?)
Structure Recognition Theory
 ↓  (why do certain structures become research-worthy?)
Human-AI Collaboration Model
 ↓  (what roles do humans and AI play in discovery?)
[Future: AI Education · Structure-Based Mathematics]

Difference → Attention → Structure → Meaning → Research Worthiness → Question → Research

Karnaugh map · Boolean function · structural invariance · structure recognition
symmetric Boolean function · variable rearrangement · Hamming weight
human-AI collaboration · AI-assisted research · knowledge transfer · context management
visual pattern analysis · research question generation · research program

Active work: Repository content improvement (Phase 4) · SEO meta tags (Phase 5) · Structure Recognition Theory development

Central hub: this repository (inha20)

"Understanding a structure is different from memorizing its result."