Independent researcher · 2nd-year student at Inha Technical College
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 eight papers in eleven repositories (Papers 1–8, Research-Portfolio, ANTIGRAVITY, and this profile repository). Papers 1–6 are archived, read-only early research notes 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), a methodological self-reflection on long-term human-AI research collaboration, and an early exploration of Boolean function spaces. The current work consists of two standalone papers: Paper 7 (group-theoretic classification of 4-variable Boolean function spaces on Karnaugh maps) and Paper 8 (structural thinking and human-AI research collaboration; SRT, HARCT, a single-case study, and educational implications). 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; Papers 5 and 6 are a methodology study and an early mathematical exploration. Papers 1–6 are archived early research notes (read-only); the current work is the two standalone Papers 7 and 8:

Layer 1 · Empirical Foundation
  Paper 1 — Karnaugh Map Structure Invariance
  Paper 2 — Symmetric Boolean Function Visual Patterns
  Paper 3 — Variable Rearrangement Invariance
           ↓
Layer 2 · Theoretical Integration
  Paper 4 — Structure Recognition Theory (SRT)
           ↓
Layer 3 · Methodological Meta-Layer
  Paper 5 — Human-AI Research Collaboration (HARCT)
           ↓
Layer 4 · Mathematical Extension
  Paper 6 — Boolean Function Space Theory (archived early exploration)
           ↓
Synthesis · Standalone Papers
  Paper 7 — Group-Theoretic Classification of 4-Variable Boolean Function Spaces
  Paper 8 — Structural Thinking and Human-AI Research Collaboration

Exploratory threads without a paper yet: AI Collaboration Education · Structure-Based Mathematics

Papers 1–6 are archived, read-only research notes; Papers 7 and 8 are the current papers.

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.

⚠️ 2026-06-21 구조 통합 안내 (Session 33): 이전에 program/, management/, theory/ 하위폴더로 분산되어 있던 프로그램 관리 문서들이 이 저장소 루트의 단일 파일들로 통합되었습니다. ProjectDashboard.md, Research_History.md는 v5.0 이후 SESSION_START.md로 완전 통합(Active Queue, Health 표, 세션 이력 포함).

전문 이론 문서 (H1–H10 전문, Level 0–8 구조, OP 전체): 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 (Paper 5, archived; continued in Paper 8)
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
Exploratory. How should collaboration with AI be taught? What skills distinguish productive human-AI inquiry?

Structure-Based Elementary Mathematics
Exploratory. Reinterpreting elementary mathematics through pattern, structure, and meaning.

Boolean Function Space Theory (Paper 6, archived)
Archived (early research notes, superseded by Paper 7). 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

Group-Theoretic Classification (Paper 7, current)
A standalone mathematics paper on the classification of 4-variable Boolean function spaces on Karnaugh maps, readable without the earlier papers. Repository: 7KarnaughGroupClassification (GitHub)

Structural Thinking and Human-AI Research Collaboration (Paper 8, current)
A standalone theory-proposal paper (SRT, HARCT) with a single-case study and educational implications, readable without the earlier papers. Repository: 8StructuralThinkingAndHumanAICollaboration (GitHub)

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?)
Standalone Papers 7 and 8 (current)
 ↓  (group-theoretic classification; structural thinking and human-AI research collaboration)
[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

Current work: the standalone Papers 7 (group-theoretic classification) and 8 (structural thinking and human-AI research collaboration). Papers 1–6 are archived early research notes (read-only).

• Paper 7 — Group-Theoretic Classification of 4-Variable Boolean Function Spaces on Karnaugh Maps: Pages · GitHub
• Paper 8 — Structural Thinking and Human-AI Research Collaboration in the Generative AI Era: Pages · GitHub

The program consists of 11 repositories: Papers 1–8, Research-Portfolio, ANTIGRAVITY, and this repository (inha20).

Central hub: this repository (inha20)

The account inha20 also holds repositories outside this research program: course notes, tools and personal projects (35 public repositories as of 2026-10-19). They are organized as public GitHub Lists in a folder-like tree. The list names below are shown as they appear on GitHub.

Only the 연구 group belongs to this program. The other groups are independent of it.

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