독립 연구자 · 인하공업전문대학 2학년 재학
Independent Researcher · 2nd-Year Student at Inha Technical College
🔬 Structure Recognition Research Program
A long-term academic investigation into how humans discover structure, assign meaning, and systematically generate research questions — spanning Boolean function spaces, visual invariance, and Human-AI collaboration.
This portfolio documents a long-term research program exploring the cognitive and mathematical mechanisms of research generation. Beginning with specific observations of Karnaugh map visual patterns, the program developed through three empirical case studies into an integrated theoretical framework: Structure Recognition Theory (SRT).
The program adheres to Architecture B (Empirical Foundation Model): three empirical papers ground the theoretical integration in Paper 4, with Paper 5 addressing human-AI collaboration as a methodological meta-layer for distributed research continuity.
Papers 1–6 are archived early research notes (read-only since 2026-10-18). Their content has been consolidated and corrected in the two current standalone papers: Paper 7, a group-theoretic classification of 4-variable Boolean function spaces on Karnaugh maps, and Paper 8, a framework proposal on structural thinking and human-AI research collaboration. Each of the two can be read without the earlier papers.
The research is structured into four cohesive layers, ensuring empirical grounding feeds directly into theoretical formalization and meta-methodology.
Explore the peer repositories constituting the program. Filter by domain or copy citations directly. Papers 1–6 are archived early research notes (read-only); Papers 7 and 8 are the current papers.
Investigation of structural invariance in Karnaugh map visual patterns. Early observational note on checkerboard XOR/XNOR configurations. The 4-variable XOR/XNOR picture is the same in all 24 variable arrangements; it changes only with the axis label order.
Pattern structure analysis of symmetric Boolean functions, identifying Hamming-weight layer invariants and concentric ring representations in geometric function spaces.
Observational study of how Karnaugh map pictures behave under variable rearrangement. The classification of the 24 arrangements into 3 classes is shown there and proved in Paper 7.
The core theoretical framework synthesizing Papers 1–3 into 10 exploratory hypotheses (H1–H10) on how humans recognize structure and convert patterns into rigorous research inquiries.
Human-AI Research Continuity Theory (HARCT). Formulates goal preservation, reconstruction cost, and externalized distributed memory in multi-session autonomous AI research.
Mathematical exploration of Boolean clone theory, Post's lattice, gate universality, and closed subspaces generated by elementary logic operators.
Group-theoretic classification of 4-variable Boolean function spaces on Karnaugh maps, readable without the earlier papers: variable-arrangement classes, S₄ orbits (3,984 in total), and the signed permutation group B₄ of order 384 (402 orbits).
Framework proposal, readable without the earlier papers: Structure Recognition Theory (SRT, hypotheses H1–H10, not yet empirically tested), Human-AI Research Continuity Theory (HARCT), a single-case study, and educational implications for the generative AI era.
Central coordinating program repository managing research logs, concept genealogies, summer roadmaps, and cross-repository synchronization.
The primary public entry point and GitHub researcher profile showcasing research statements, master handovers, and academic trajectory.
AI collaboration workspace of the program: it preserves research context, documents decisions, and keeps work continuous across AI-assisted sessions. It holds operational infrastructure, not research papers.
How simple visual observations evolved into an interconnected theoretical program.