chore(wiki): dangling 링크 canonical 정규화 (768파일/1200건)

이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-08 12:24:15 +09:00
parent 2ddf30f8e4
commit d8a80f6272
768 changed files with 1085 additions and 1085 deletions
@@ -77,7 +77,7 @@ github_commit: ""
 *   [[Python]] - 백엔드 로직 작성을 위한 핵심 언어입니다.
 *   [[JavaScript]] - 클라이언트 측 확장을 위해 사용되는 언어입니다.
 *   [[torch.Tensor]] - 이미지 데이터 처리를 위한 기본 데이터 구조입니다.
-*   [[comfy-cli]] - 노드 스캐폴딩 및 관리를 위한 도구입니다.
+*   [[Comfy CLI]] - 노드 스캐폴딩 및 관리를 위한 도구입니다.
 ## 📝 변경 이력 (Change history)
 - 2026-05-20: Astra /wikify 로 https://docs.comfy.org/custom-nodes/walkthrough 본문에서 초안 생성.
@@ -25,7 +25,7 @@ github_commit: ""
 ComfyUI가 시작될 때 `custom_nodes` 디렉토리를 스캔하여 Python 모듈을 로드하고 커스텀 노드를 정의하는 라이프사이클 프로세스.
 ## 🧠 핵심 개념 (Core concepts)
-*   **[[custom_nodes]] 로딩**: ComfyUI 시작 시 `custom_nodes` 폴ের더를 스캔하여 Python 모듈을 찾아 로드함.
+*   **[[Custom Nodes]] 로딩**: ComfyUI 시작 시 `custom_nodes` 폴ের더를 스캔하여 Python 모듈을 찾아 로드함.
 *   **[[NODE_CLASS_MAPPINGS]]**: 모듈이 커스텀 노드로 인식되기 위해 반드시 내보내야(export) 하는 딕셔너리 매핑 정보.
 *   **[[__init__.py]] 역할**: 커스텀 노드 모듈의 진입점으로, 실행 시 `NODE_CLASS_MAPPINGS`를 포함하여 노드 정의를 ComfyUI에 가용하게 함.
 *   **[[WEB_DIRECTORY]] 설정**: 클라이언트 측 JavaScript 파일을 제공하기 위해 모듈 내 상대 경로를 지정하는 기능.
@@ -70,7 +70,7 @@ ComfyUI는 시작 시 `custom_nodes` 디렉토리를 스캔하여 Python 모듈
 - **중복 검사 결과:** 신규 생성 (New discovery)
 ## 🔗 관련 문서 링크 (Related document links)
-*   [[custom_nodes]] - 커스텀 노드가 로드되는 물리적 경로와 메커니즘 설명.
+*   [[Custom Nodes]] - 커스텀 노드가 로드되는 물리적 경로와 메커니즘 설명.
 *   [[NODE_CLASS_MAPPINGS]] - 노드 클래스를 식별하기 위한 핵심 매핑 구조.
 *   [[WEB_DIRECTORY]] - 클라이언트 사이드 JS 파일 배포를 위한 경로 설정법.
 *   [[__init__.py]] - Python 모듈의 초기화 및 커스텀 노드 로직 실행 지점.
@@ -342,7 +342,7 @@ public class TutorialHint : MonoBehaviour {
 ## 🔗 지식 연결 (Graph)
 - 변형: [[StarCraft-Micro]]
- 응용: [[Baiting-Tactics]]
+- 응용: [[Baiting Tactics]]
 - 게임: [[War-Commander]] · [[Kixeye]]
 - Adjacent: [[Behavior-Tree]]
@@ -374,7 +374,7 @@ public class TutorialHint : MonoBehaviour {
 - **검토 이유:** Manual cleanup. 매 specific game 의 historical. 매 design pattern 가 universal.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Exploitation]] (related), [[RTS-Combat-Design]] (parent), [[Game-AI-Design]] (parent).
+- **기존 유사 문서:** [[AI Exploitation (Game AI 공략)]] (related), [[RTS-Combat-Design]] (parent), [[Game-AI-Design]] (parent).
 - **처리 방식:** KEEP (specific historical case + transferable lesson).
 - **처리 이유:** 매 specific patch 의 historical record.
@@ -205,7 +205,7 @@ github_commit: pending
 ## 🧪 검증 / 중복
 - Applied (BCG 의 published report 2026).
 - 신뢰도 B (large industry survey).
- Related: [[Game-Industry-Trends]] · [[Mobile-AB-Testing]].
+- Related: [[Game-Industry-Trends]] · [[Mobile A/B Testing — Variant / Tracking / Cleanup]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -247,8 +247,8 @@ def cost_aware_batch(prompts, target='exploration'):
 **기본값**: 5-layer prompt + draft mode + reference + post-edit + upscale 의 sequence.
 ## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 부모: [[AI Image Generation]]
- 변형: [[Draft-Mode]] · [[Omni-Reference]]
+- 변형: [[Draft-Mode]] · [[Omni Reference]]
 - Tools: [[Midjourney-V7]] · [[Flux]]
 ## 🤖 LLM 활용
@@ -265,7 +265,7 @@ def cost_aware_batch(prompts, target='exploration'):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[AI 이미지 생성 (AI Image Generation)]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[Image-Workflow]].
+- Overlap with [[AI Image Generation]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[Image-Workflow]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -253,10 +253,10 @@ console.log({
 **기본값**: InstancedMesh2 + 5 optimization (frustum, LOD, animation throttle, GPU skin, atlas).
 ## 🔗 Graph
- 부모: [[Three-js-Performance]] · [[Skinned-Mesh]] · [[Instancing]]
+- 부모: [[Three-js-Performance]] · [[SkinnedMesh]] · [[Instancing]]
 - 변형: [[BatchedMesh]]
 - 응용: [[Crowd-Simulation]]
- 기술: [[GPU-Skinning]] · [[Frustum-Culling]] · [[Level-of-Detail]]
+- 기술: [[GPU-Skinning]] · [[Frustum Culling]] · [[Level-of-Detail]]
 ## 🤖 LLM 활용
 **언제**: 매 large character scene 의 design. 매 mobile / web 3D 의 performance.
@@ -226,7 +226,7 @@ viewer.addSplatScene('./scene.ply').then(() => {
 ## 🔗 지식 연결 (Graph)
 - Adjacent: [[Point-Cloud]]
 - Tools: gsplat · NeRF Studio · Brush · Splatfacto · Polycam · Luma AI
- Web: [[WebGPU]] · [[WebGL]] · [[Three.js]] · [[Babylon.js]]
+- Web: [[WebGPU]] · [[WebGL]] · [[Three.js]] · [[Babylonjs]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -258,8 +258,8 @@ def reward(state, action, next_state):
 - 응용: [[Habit-Formation]]
 - AI: [[Actor-Critic-Models]]
 - Game: [[Loot-Box-Mechanics]]
- 비판: [[Intrinsic-Motivation]]
+- 비판: [[Intrinsic Motivation]]
- Adjacent: [[Addiction-Neuroscience]] · [[Dopamine-Pathway]] · [[Behavioral-Economics]] · [[Nudge-Theory]]
+- Adjacent: [[Addiction Neuroscience]] · [[Dopamine-Pathway]] · [[Behavioral-Economics]] · [[Nudge Theory]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -378,7 +378,7 @@ function isAllowed(tool: string, args: any): boolean {
 ## 🔗 지식 연결 (Graph)
 - 부모: [[Agent-Architecture]] · [[Tool-Use-Function-Calling]] · [[Prompt_Engineering|Prompt-Engineering]]
 - 응용: [[Claude-Code]]
- Related: [[AI-Tool-Composition-Deep]] · [[AI-Anthropic-Skills-Patterns]] · [[AI-Multi-Agent-Coordination]]
+- Related: [[Tool Composition — agent 가 tool 사용 / chain]] · [[Anthropic Skills — modular agent capability]] · [[Multi-Agent Coordination — orchestrator / handoff]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -412,7 +412,7 @@ function isAllowed(tool: string, args: any): boolean {
 - **검토 이유:** Manual cleanup. ACI design 가 evolving. MCP 의 standardization 가 진행 중.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Tool-Composition-Deep]] (overlap), [[MCP-Server-Building]] (subset), [[AI-Anthropic-Skills-Patterns]] (related).
+- **기존 유사 문서:** [[Tool Composition — agent 가 tool 사용 / chain]] (overlap), [[MCP-Server-Building]] (subset), [[Anthropic Skills — modular agent capability]] (related).
 - **처리 방식:** KEEP (focused on interface design).
 - **처리 이유:** ACI 가 design discipline. Tool composition 가 algorithm. MCP 가 specific protocol.
@@ -144,7 +144,7 @@ export function activate(context: vscode.ExtensionContext) {
 **기본값**: 매 distinct domain = separate module.
 ## 🔗 지식 연결 (Graph)
- 부모: [[ADR-Architecture-Decision-Record]] · [[Modular-Design]] · [[Separation-of-Concerns]]
+- 부모: [[ADR (Architecture Decision Record)]] · [[Modular-Design]] · [[Separation of Concerns]]
 ## 🤖 LLM 활용 힌트
 **언제 사용**: 매 새 feature 의 architecture 의 결정. 매 modular boundary 의 example.
@@ -402,7 +402,7 @@ async function auditDataAccess(user: User, data: any, action: string) {
 - 변형: [[GDPR-Compliance]] · [[Data-Localization]] · [[Sovereign-Cloud]]
 - 기술: [[Federated-Learning]] · [[Differential-Privacy]] · [[Homomorphic-Encryption]]
 - 비판: [[Data-Colonialism]]
- 응용: [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[AI-Accountability]]
+- 응용: [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[AI Accountability]]
 - 정책: [[EU-AI-Act]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -434,7 +434,7 @@ async function auditDataAccess(user: User, data: any, action: string) {
 - **검토 이유:** Manual cleanup. Active regulation. 매 6 month review.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related), [[Privacy]] (parent), [[AI-Accountability]] (related).
+- **기존 유사 문서:** [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related), [[Privacy]] (parent), [[AI Accountability]] (related).
 - **처리 방식:** KEEP (sovereignty 의 specific lens).
 - **처리 이유:** Geopolitical + technical 의 intersection.
@@ -358,11 +358,11 @@ def predict(features):
 - **Cross-border**: 매 country 의 different regulation. AI 의 global → fragmented.
 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[Algorithmic-Fairness]]
+- 부모: [[AI-Ethics]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[Algorithmic Fairness]]
 - 변형: [[Explainable-AI-XAI]] · [[Model-Card]]
 - 기술: [[LIME]]
 - Adjacent: [[Responsibility-Gap]] · [[Human-in-the-Loop]]
- 응용: [[MLOps-Model-Monitoring]]
+- 응용: [[ML Monitoring — drift / quality / SLO]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -319,7 +319,7 @@ async function queryBrain(question: string): Promise<string[]> {
 - **검토 이유:** Manual cleanup. 매 architecture 의 implementation detail 가 ConnectAI repo 와 sync.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[Local-LLM-Inference]] (concept), [[VS-Code-Extension-Patterns]] (technical), [[AI-Code-Agent-Patterns]] (general).
+- **기존 유사 문서:** [[Local-LLM-Inference]] (concept), [[VS-Code-Extension-Patterns]] (technical), [[Code Agent — Devin / Cursor / Claude Code]] (general).
 - **처리 방식:** KEEP (specific tool 의 documentation).
 - **처리 이유:** ConnectAI 의 own design / architecture 가 distinct.
@@ -294,7 +294,7 @@ def chat(query):
 ## 🔗 지식 연결 (Graph)
 - 변형: [[LLM-as-Judge]]
 - Tools: lm-eval-harness · Promptfoo · LangSmith · Inspect (AISI) · Braintrust · Helicone · Langfuse
- Related: [[AI-Code-Agent-Patterns]]
+- Related: [[Code Agent — Devin / Cursor / Claude Code]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -383,7 +383,7 @@ public class AnomalyDetector {
 ## 🔗 지식 연결 (Graph)
 - 부모: [[Game-Mechanics]]
 - 변형: [[Behavior-Tree]]
- 응용: [[Baiting-Tactics]]
+- 응용: [[Baiting Tactics]]
 - 매 게임: [[War-Commander-Combat]]
 - Adjacent: [[Adaptive-Difficulty]]
@@ -284,8 +284,8 @@ class AITutor {
 ## 🔗 지식 연결 (Graph)
 - 부모: [[AI-Ethics]]
- 관련: [[AI-Literacy]] · [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[AI-Safety-and-Alignment]]
+- 관련: [[AI Literacy]] · [[AI Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[AI Safety and Alignment]]
- Adjacent: [[Universal-Basic-Income-UBI]]
+- Adjacent: [[Universal Basic Income (UBI)]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -317,7 +317,7 @@ class AITutor {
 - **검토 이유:** Manual cleanup. Philosophy 가 long-term 안정. 매 application 가 evolving.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Ethics]] (parent), [[AI-Literacy]] (subset), [[AI-for-Social-Good]] (related).
+- **기존 유사 문서:** [[AI-Ethics]] (parent), [[AI Literacy]] (subset), [[AI for Social Good (AI4SG)]] (related).
 - **처리 방식:** KEEP (philosophical / design framework).
 - **처리 이유:** AI Humanism 가 distinct philosophy + design discipline.
@@ -248,7 +248,7 @@ Week 6: Agent basic.
 - 부모: [[Digital-Literacy]] · [[Information-Literacy]]
 - 변형: [[Prompt_Engineering|Prompt-Engineering]] · [[AI-Ethics]]
 - Adjacent: [[Lifelong-Learning]]
- 응용: [[AI-Code-Agent-Patterns]]
+- 응용: [[Code Agent — Devin / Cursor / Claude Code]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -281,7 +281,7 @@ Week 6: Agent basic.
 - **검토 이유:** Manual cleanup. 매 framework 가 active. 매 6 month review.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[Prompt_Engineering|Prompt-Engineering]] (subset), [[AI-Ethics]] (related), [[Critical-Thinking]] (parent).
+- **기존 유사 문서:** [[Prompt_Engineering|Prompt-Engineering]] (subset), [[AI-Ethics]] (related), [[Critical Thinking]] (parent).
 - **처리 방식:** KEEP (overall framework).
 - **처리 이유:** Literacy 가 holistic. 매 component 의 own document.
@@ -355,7 +355,7 @@ class ImpactTracker:
 ## 🔗 지식 연결 (Graph)
 - 부모: [[AI-Ethics]]
 - 응용: [[Federated-Learning]]
- 관련: [[AI-Humanism]] · [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]
+- 관련: [[AI Humanism]] · [[AI Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -388,7 +388,7 @@ class ImpactTracker:
 - **검토 이유:** Manual cleanup. 매 specific 프로그램 의 detail 가 evolving.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Humanism]] (related), [[AI-Ethics]] (parent), [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related).
+- **기존 유사 문서:** [[AI Humanism]] (related), [[AI-Ethics]] (parent), [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related).
 - **처리 방식:** KEEP (specific application focus).
 - **처리 이유:** AI4SG 가 distinct application area + methodology.
@@ -300,7 +300,7 @@ def audit_bias(model, test_set):
 - 부모: [[AI-Ethics]] · [[Risk_Management|Risk-Management]]
 - 응용: [[NIST-AI-RMF]] · [[ISO-42001]]
 - 기술: [[Model-Card]]
- 응용: [[AI-Literacy]] · [[AI-Accountability]]
+- 응용: [[AI Literacy]] · [[AI Accountability]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -334,7 +334,7 @@ def audit_bias(model, test_set):
 - **검토 이유:** Manual cleanup. Regulation 의 active update. 매 6 month review.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Ethics]] (parent), [[AI-Safety]] (related), [[AI-Accountability]] (subset).
+- **기존 유사 문서:** [[AI-Ethics]] (parent), [[AI Safety]] (related), [[AI Accountability]] (subset).
 - **처리 방식:** KEEP (organizational governance focus).
 - **처리 이유:** Policy 가 distinct discipline (ethics + compliance + ops).
@@ -480,12 +480,12 @@ async function processSuggestion(suggestion) {
 - **DORA metric 의 unclear improvement**: 매 study 의 mixed evidence.
 ## 🔗 지식 연결 (Graph)
- 부모: [[AI_코드_리뷰]] · [[Static-Analysis]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]]
+- 부모: [[AI_코드_리뷰]] · [[Static-Analysis]] · [[CI/CD Pipeline & IDE Security Integration|DevSecOps]]
 - 변형: [[CodeRabbit]] · [[Greptile]] · [[Cursor]] · [[Sonar]]
- 응용: [[Codebase-RAG]] · [[Code-Property-Graph]]
+- 응용: [[Codebase-RAG]] · [[Code Property Graph]]
 - 기술: [[AST]] · [[Semgrep]] · [[CodeQL]] · [[Joern]]
 - 응용: [[Behavioral-Code-Analysis]] · [[Technical_Debt|Technical-Debt]]
- Adjacent: [[AI-Code-Agent-Patterns]]
+- Adjacent: [[Code Agent — Devin / Cursor / Claude Code]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -425,7 +425,7 @@ with ThreadPoolExecutor(max_workers=4) as executor:
 - **Inpaint dedicated model vs general**: 매 dedicated 의 better.
 ## 🔗 지식 연결 (Graph)
- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Diffusion-Models]]
+- 부모: [[AI Image Generation]] · [[Diffusion-Models]]
 - 변형: [[Inpainting]] · [[Outpainting]] · [[Upscale]] · [[ControlNet]]
 - 응용: [[Midjourney-Vary-Region]]
 - Tool: [[IP-Adapter]]
@@ -461,7 +461,7 @@ with ThreadPoolExecutor(max_workers=4) as executor:
 - **검토 이유:** Manual cleanup. 매 platform 의 evolution.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI 이미지 생성 (AI Image Generation)]] (parent), [[AI 이미지 생성 및 편집 워크플로우]] (related), [[AI 이미지 품질 최적화]] (related).
+- **기존 유사 문서:** [[AI Image Generation]] (parent), [[AI 이미지 생성 및 편집 워크플로우]] (related), [[AI 이미지 품질 최적화]] (related).
 - **처리 방식:** KEEP (focused on post-editing tools).
 - **처리 이유:** Specific to refinement workflow.
@@ -284,10 +284,10 @@ I have reviewed:
 **기본값**: Type + lint + SAST + test + AI review + human review. 매 AI-heavy PR 의 enhanced.
 ## 🔗 Graph
- 부모: [[AI_코드_리뷰]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[Code-Quality]]
+- 부모: [[AI_코드_리뷰]] · [[CI/CD Pipeline & IDE Security Integration|DevSecOps]] · [[Code-Quality]]
 - 변형: [[SAST]] · [[LLM-as-Judge]]
 - 응용: [[CodeRabbit]] · [[Snyk-Code]] · [[Sonar]]
- Adjacent: [[AI-Code-Agent-Patterns]]
+- Adjacent: [[Code Agent — Devin / Cursor / Claude Code]]
 ## 🤖 LLM 활용
 **언제**: 매 team 의 AI tool 의 adoption + quality.
@@ -303,7 +303,7 @@ I have reviewed:
 ## 🧪 검증 / 중복
 - Verified (concept).
 - 신뢰도 B.
- Related: [[AI_코드_리뷰]], [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]].
+- Related: [[AI_코드_리뷰]], [[AI-Powered Code Analysis Tools]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -474,7 +474,7 @@ print(f"CLIP score: {similarity:.3f}")
 - 부모: [[Generative-AI]] · [[Diffusion-Models]] · [[Computer Vision|Computer-Vision]]
 - 변형: [[Stable-Diffusion]] · [[Flux]] · [[Midjourney]] · [[DALL-E]] · [[Imagen]]
 - 응용: [[ControlNet]] · [[LoRA]] · [[Inpainting]] · [[IP-Adapter]]
- 기법: [[Prompt_Engineering|Prompt-Engineering]] · [[Negative-Prompt]] · [[CFG-Scale]] · [[Sampling-Steps]]
+- 기법: [[Prompt_Engineering|Prompt-Engineering]] · [[Negative Prompt]] · [[CFG Scale]] · [[Sampling-Steps]]
 - Tools: [[ComfyUI]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -205,9 +205,9 @@ result = pipe(
 **기본값**: Draft 30 → Select 5 → Final + post-edit. 매 cost 의 80% saving + quality 의 maintain.
 ## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 부모: [[AI Image Generation]]
 - 변형: [[Iterative-Refinement]] · [[Draft-Mode]] · [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]]
- Adjacent: [[Style-Reference]] · [[ControlNet]]
+- Adjacent: [[Style Reference]] · [[ControlNet]]
 ## 🤖 LLM 활용
 **언제**: 매 commercial creative project. 매 brand campaign.
@@ -223,7 +223,7 @@ result = pipe(
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[AI 이미지 생성 (AI Image Generation)]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]].
+- Overlap with [[AI Image Generation]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -296,8 +296,8 @@ def llm_judge(image_url, prompt):
 **기본값**: Specific negative > generic. Inpaint > regenerate. ControlNet 의 anatomy. Detect → fix loop.
 ## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 부모: [[AI Image Generation]]
- 변형: [[Negative-Prompt]] · [[Inpainting]]
+- 변형: [[Negative Prompt]] · [[Inpainting]]
 - 응용: [[ControlNet]]
 - Adjacent: [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]]
@@ -315,7 +315,7 @@ def llm_judge(image_url, prompt):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[AI 이미지 생성 (AI Image Generation)]].
+- Overlap with [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[AI Image Generation]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -435,7 +435,7 @@ public class AntiCheese : MonoBehaviour {
 - **AI 의 squad coordination 의 cost**: 매 sophisticated 의 dev expensive.
 ## 🔗 지식 연결 (Graph)
- 응용: [[AI-Exploitation]] · [[Baiting-Tactics]]
+- 응용: [[AI Exploitation (Game AI 공략)]] · [[Baiting Tactics]]
 - 매 game: [[War-Commander]]
 - Adjacent: [[Behavior-Tree]]
@@ -468,7 +468,7 @@ public class AntiCheese : MonoBehaviour {
 - **검토 이유:** Manual cleanup. 매 specific game 의 implementation 가 design choice.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Exploitation]] (related), [[Game-AI-Behavior-Tree]] (parent), [[Combat-AI]] (parent).
+- **기존 유사 문서:** [[AI Exploitation (Game AI 공략)]] (related), [[Game-AI-Behavior-Tree]] (parent), [[Combat-AI]] (parent).
 - **처리 방식:** KEEP (specific focus on pursuit).
 - **처리 이유:** Pursuit logic 가 specific component.
@@ -261,10 +261,10 @@ repos:
 **기본값**: IDE + PR + pre-deploy 의 layered. 매 gate 의 different threshold.
 ## 🔗 Graph
- 부모: [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[AI_코드_리뷰]] · [[Security]]
+- 부모: [[CI/CD Pipeline & IDE Security Integration|DevSecOps]] · [[AI_코드_리뷰]] · [[Security]]
- 변형: [[SAST]] · [[보안_및_시스템_신뢰성_표준|DAST]] · [[Shift-Left-Security]]
+- 변형: [[SAST]] · [[보안 및 시스템 신뢰성 표준|DAST]] · [[Shift-Left-Security]]
 - 응용: [[CodeRabbit]] · [[Sonar]] · [[Semgrep]]
- 매 OWASP: [[OWASP-Top-10]]
+- 매 OWASP: [[OWASP Top 10]]
 - Adjacent: [[Dependency-Update]] · [[Secret-Detection]]
 ## 🤖 LLM 활용
@@ -197,7 +197,7 @@ inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
 ## 🔗 Graph
 - 부모: [[SEO]]
 - 변형: [[AI-Answer-Engine-Optimization]] · [[Generative-Engine-Optimization]]
- 응용: [[Core Web Vitals Optimization (INP, LCP 개선)|Core-Web-Vitals]]
+- 응용: [[Core Web Vitals Optimization (INP, LCP, CLS)|Core-Web-Vitals]]
 - Adjacent: [[Zero-Click-Search]] · [[Knowledge Graph|Knowledge-Graph]]
 ## 🤖 LLM 활용
@@ -249,7 +249,7 @@ async def monitor_articles():
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Related: [[AdSense-Revenue-Blog-Architecture]].
+- Related: [[AdSense Revenue Blog Architecture]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -53,7 +53,7 @@ github_commit: pending
 - Compositing.
 ### Cross-reference
- See [[AI 이미지 생성 (AI Image Generation)]] (platform comparison).
+- See [[AI Image Generation]] (platform comparison).
 - See [[AI 이미지 생성 및 편집 워크플로우]] (continuous workflow).
 - See [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] (specific tools).
 - See [[AI 이미지 품질 최적화 및 디버깅]] (defect fix).
@@ -82,7 +82,7 @@ class Workflow:
 ```
 ## 🔗 Graph
- 매 detail 의 [[AI 이미지 생성 (AI Image Generation)]] · [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] ·  의 reference.
+- 매 detail 의 [[AI Image Generation]] · [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] ·  의 reference.
 ## 🤖 LLM 활용
 **언제**: 매 image gen 의 first overview.
@@ -259,10 +259,10 @@ def dashboard_metrics():
 **기본값**: SAST + LLM triage + autofix 의 confidence-based gating.
 ## 🔗 Graph
- 부모: [[AI_코드_리뷰]] · [[SAST]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]]
+- 부모: [[AI_코드_리뷰]] · [[SAST]] · [[CI/CD Pipeline & IDE Security Integration|DevSecOps]]
 - 변형: [[Snyk-Code]] · [[CodeRabbit]]
 - 응용: [[AI-Code-Assurance]]
- Adjacent: [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]]
+- Adjacent: [[AI-Powered Code Analysis Tools]]
 ## 🤖 LLM 활용
 **언제**: 매 large codebase 의 vulnerability triage. 매 alert fatigue 의 reduce.
@@ -278,7 +278,7 @@ def dashboard_metrics():
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Related: [[AI_코드_리뷰]] · [[AI-Code-Assurance]] · [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]].
+- Related: [[AI_코드_리뷰]] · [[AI-Code-Assurance]] · [[AI-Powered Code Analysis Tools]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -372,7 +372,7 @@ trainer.train()
 ## 🔗 지식 연결 (Graph)
 - 부모: [[AI-Ethics]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]
- 응용: [[AI-Accountability]] · [[AI-Literacy]]
+- 응용: [[AI Accountability]] · [[AI Literacy]]
 - Adjacent: [[AI_Safety_and_Alignment|Goal-Misgeneralization]] · [[Sparse-Autoencoder]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -43,7 +43,7 @@ github_commit: pending
 ## 🔗 Graph
 - 매 canonical 의 [[AI_Powered_Code_Analysis]].
- 매 related 의 [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]] · [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]] · .
+- 매 related 의 [[AI-Powered Code Analysis Tools]] · [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]] · .
 ## 🤖 LLM 활용
 **언제**: Korean documentation 의 reference. 매 main detail 은 [[AI_Powered_Code_Analysis]] 참고.
@@ -460,13 +460,13 @@ If NO critical issues, just say "LGTM 🎉".
 - **DORA metric 의 game-able**: 매 tool adoption ≠ outcome.
 ## 🔗 지식 연결 (Graph)
- 부모: [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[Static-Analysis]]
+- 부모: [[CI/CD Pipeline & IDE Security Integration|DevSecOps]] · [[Static-Analysis]]
 - 변형: [[CodeRabbit]] · [[Greptile]]
 - 응용: [[Snyk-Code]] · [[SonarQube]]
 - AI: [[Codebase-RAG]] · [[Auto-Fix]]
 - 응용: [[PR-Workflow]] · [[DORA-Metrics]]
 - Adjacent: [[Green-Check-Mark-Syndrome]]
- Related: [[AI-Code-Agent-Patterns]]
+- Related: [[Code Agent — Devin / Cursor / Claude Code]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -499,7 +499,7 @@ If NO critical issues, just say "LGTM 🎉".
 - **검토 이유:** Manual cleanup (extracted from messy auto-merged document). 매 tool 의 evolution.
 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[Code-Review-Modern]] (parent), [[AI-Powered-Code-Analysis]] (related), [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] (related).
+- **기존 유사 문서:** [[Code-Review-Modern]] (parent), [[AI-Powered Code Analysis (Autofix + Triage)]] (related), [[CI/CD Pipeline & IDE Security Integration|DevSecOps]] (related).
 - **처리 방식:** KEEP (focused on AI-augmented review).
 - **처리 이유:** 매 AI integration 의 specific.
@@ -215,7 +215,7 @@ npx biome lint .
 - 부모: [[Static-Analysis]] · [[Code-Quality]]
 - 변형: [[Biome]] · [[Oxlint]] ·  (CSS) ·  (Python) ·  (Rust)
 - 응용: [[AST]]
- Adjacent: [[AI_코드_리뷰]] · [[AI-Powered-Code-Analysis]]
+- Adjacent: [[AI_코드_리뷰]] · [[AI-Powered Code Analysis (Autofix + Triage)]]
 ## 🤖 LLM 활용
 **언제**: 매 codebase quality 의 setup. 매 team convention 의 enforce.
@@ -231,7 +231,7 @@ npx biome lint .
 ## 🧪 검증 / 중복
 - Verified (industry standard).
 - 신뢰도 B.
- Related: [[AI_코드_리뷰]] · [[AI-Powered-Code-Analysis]] · [[AST]].
+- Related: [[AI_코드_리뷰]] · [[AI-Powered Code Analysis (Autofix + Triage)]] · [[AST]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -314,7 +314,7 @@ test('login flow', () => {
 **기본값**: Discriminated union + useReducer. 매 complex flow 의 XState.
 ## 🔗 Graph
- 부모: [[State-Management]] · [[API-Design]]
+- 부모: [[State Management]] · [[API-Design]]
 - 변형: [[XState]]
 - 응용: [[FSM]]
 - Adjacent: [[Discriminated-Union]] · [[useReducer]] · [[Zustand]]
@@ -275,9 +275,9 @@ function useFetchUser(id: string) {
 **기본값**: Discriminated union + Result type + Zod validation.
 ## 🔗 Graph
- 부모: [[TypeScript]] · [[State-Management]] · [[API-Design]]
+- 부모: [[TypeScript]] · [[State Management]] · [[API-Design]]
- 변형: [[Discriminated-Union]] · [[Tagged-Union]] · [[Result-Type]]
+- 변형: [[Discriminated-Union]] · [[Tagged-Union]] · [[Result Type]]
- 응용: [[XState]] · [[Effect-TS]]
+- 응용: [[XState]] · [[Effect TS]]
 - Adjacent: [[Branded-Types]] · [[Exhaustive-Check]]
 ## 🤖 LLM 활용
@@ -154,7 +154,7 @@ ORDER BY rpm DESC LIMIT 50;
 ## 🔗 Graph
 - 부모: [[SEO]]
- 응용: [[Core Web Vitals Optimization (INP, LCP 개선)|Core Web Vitals]]
+- 응용: [[Core Web Vitals Optimization (INP, LCP, CLS)|Core Web Vitals]]
 ## 🤖 LLM 활용
 **언제**: 매 outline draft, 매 keyword cluster, 매 metadata generation, 매 internal-link suggestion.
@@ -173,7 +173,7 @@ function checkPurchase(monthlySpend: number, attemptedSpend: number) {
 - 부모: [[Reward-System]]
 - 관련 brain region: [[Dopamine-Pathway]] · [[Prefrontal-Cortex]]
 - 응용: [[Loot-Box-Mechanics]]
- Adjacent: [[Neuroplasticity]] · [[Reward-Prediction-Error]] · [[Operant-Conditioning]]
+- Adjacent: [[Neuroplasticity]] · [[Reward Prediction Error]] · [[Operant-Conditioning]]
 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -258,7 +258,7 @@ def counterfactual_test(model, instance, protected_attr='gender'):
 **기본값**: 4/5 rule check + per-group accuracy + counterfactual test + disclosure.
 ## 🔗 Graph
- 부모: [[AI-Ethics]] · [[AI-Accountability]]
+- 부모: [[AI-Ethics]] · [[AI Accountability]]
 - 변형: [[Group-Fairness]]
 ## 🤖 LLM 활용
@@ -275,7 +275,7 @@ def counterfactual_test(model, instance, protected_attr='gender'):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B (academic + industry consensus).
- Related: [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]].
+- Related: [[AI Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -299,10 +299,10 @@ function ChatHeader() {
 **기본값**: Disclosure + audit log + per-prediction explanation. 매 high-stakes 의 더 strict.
 ## 🔗 Graph
- 부모: [[AI-Ethics]] · [[AI-Accountability]]
+- 부모: [[AI-Ethics]] · [[AI Accountability]]
 - 변형: [[Explainable-AI-XAI]] · [[Model-Card]]
 - Tools: [[SHAP]] · [[LIME]]
- Adjacent: [[Algorithmic-Fairness]]
+- Adjacent: [[Algorithmic Fairness]]
 ## 🤖 LLM 활용
 **언제**: 매 production AI 의 transparency design. 매 user trust 의 build.
@@ -318,7 +318,7 @@ function ChatHeader() {
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Related: [[AI-Accountability]] · [[Algorithmic-Fairness]].
+- Related: [[AI Accountability]] · [[Algorithmic Fairness]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -143,9 +143,9 @@ sc.pl.umap(adata, color='leiden')
 **기본값**: BioPython + scanpy + AlphaFold (Colab) 의 entry stack.
 ## 🔗 Graph
- 부모: [[Bioinformatics]] · [[Computational-Biology]] · [[Systems-Biology]]
+- 부모: [[Bioinformatics]] · [[Computational-Biology]] · [[Systems Biology]]
 - 변형: [[AlphaFold]]
- Adjacent: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Cellular-Automata]] · [[Physics-Informed-Neural-Networks]]
+- Adjacent: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Cellular Automata]] · [[Physics-Informed-Neural-Networks]]
 ## 🤖 LLM 활용
 **언제**: 매 biological data 의 ML 적용. 매 protein / sequence / genome analysis. 매 drug discovery pipeline.
@@ -194,7 +194,7 @@ class Order {
 - 부모: [[Domain-Driven-Design]]
 - 변형: [[Transaction-Script]]
 - 응용: [[Aggregate-Root]] · [[Value-Object]] · [[Domain-Event]]
- Adjacent: [[Bounded-Context]] · [[CQRS]] · [[Event-Sourcing]] · [[Hexagonal-Architecture]]
+- Adjacent: [[Bounded Context]] · [[CQRS]] · [[Event Sourcing]] · [[Hexagonal Architecture]]
 ## 🤖 LLM 활용
 **언제**: 매 backend service design 의 review. 매 DDD 의 적용 결정. 매 legacy 의 refactor.
@@ -125,7 +125,7 @@ def query_with_consciousness(prompt, model='gpt-4'):
 ## 🔗 Graph
 - 부모: [[Anarchism]]
 - 변형: [[Rewilding]]
- Adjacent: [[Sustainability]] · [[Addiction-Neuroscience]]
+- Adjacent: [[Sustainability]] · [[Addiction Neuroscience]]
 ## 🤖 LLM 활용
 **언제**: 매 tech 의 ethics review. 매 sustainability decision. 매 digital wellness design.
@@ -144,7 +144,7 @@ const avatar = generateStylized('friendly cartoon robot, blue palette');
 ## 🔗 Graph
 - 부모: [[Psychology]] · [[HCI]] · [[AI-Ethics]]
 - 변형: [[ELIZA-Effect]] · [[Agent-Personality]]
- Adjacent: [[EU-AI-Act]] · [[Addiction-Neuroscience]]
+- Adjacent: [[EU-AI-Act]] · [[Addiction Neuroscience]]
 ## 🤖 LLM 활용
 **언제**: 매 AI agent persona design. 매 chatbot UX. 매 robot 의 social acceptability.
@@ -161,7 +161,7 @@ const avatar = generateStylized('friendly cartoon robot, blue palette');
 ## 🧪 검증 / 중복
 - Verified (Epley et al., Mori, EU AI Act).
 - 신뢰도 B.
- Related: [[Agent-Personality]] · [[Uncanny-Valley]] · [[AI-Ethics]] · [[Addiction-Neuroscience]].
+- Related: [[Agent-Personality]] · [[Uncanny-Valley]] · [[AI-Ethics]] · [[Addiction Neuroscience]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -196,7 +196,7 @@ def optimal_attack_mix(defender, available_units, budget):
 ## 🔗 Graph
 - 부모: [[War-Commander]] · [[Balance-Patch]]
 - 변형: [[Platform-Specialization]] · [[Mixed-Platoon-Tactics]] · [[Defensive-Architecture]]
- 응용: [[Operation-Western-Sun]]
+- 응용: [[Operation - Western Sun]]
 - Adjacent: [[Damage-Type]]
 ## 🤖 LLM 활용
@@ -191,10 +191,10 @@ def detect_distributed_monolith(services, traces):
 **기본값**: 매 ADR + architecture test + tracing + postmortem.
 ## 🔗 Graph
- 부모: [[Architecture-Styles]]
+- 부모: [[Software Architecture Styles]]
- 변형: [[Big-Ball-of-Mud]] · [[Distributed-Monolith]] · [[Anaemic-Domain-Model]] · [[God-Object]]
+- 변형: [[Big-Ball-of-Mud]] · [[Distributed-Monolith]] · [[Anaemic Domain Model]] · [[God-Object]]
- 응용: [[Circuit-Breaker]] · [[ADR]] · [[Bounded-Context]]
+- 응용: [[Circuit-Breaker]] · [[ADR]] · [[Bounded Context]]
- Adjacent: [[Technical_Debt|Technical-Debt]] · [[Code-Smells]] · [[Refactoring_Best_Practices|Refactoring]] · [[Postmortem]]
+- Adjacent: [[Technical_Debt|Technical-Debt]] · [[Code Smells]] · [[Refactoring_Best_Practices|Refactoring]] · [[Postmortem]]
 ## 🤖 LLM 활용
 **언제**: 매 architecture review. 매 design decision. 매 incident 의 root cause analysis.
@@ -210,7 +210,7 @@ def detect_distributed_monolith(services, traces):
 ## 🧪 검증 / 중복
 - Verified (Mark Richards, Sam Newman, AWS Well-Architected).
 - 신뢰도 B.
- Related: [[Architecture-Styles]] · [[Anaemic-Domain-Model]] · [[Circuit-Breaker]].
+- Related: [[Software Architecture Styles]] · [[Anaemic Domain Model]] · [[Circuit-Breaker]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -199,10 +199,10 @@ module.exports = {
 ## 🔗 Graph
 - 부모: [[System-Design]]
- 변형: [[Layered-Architecture]] · [[Clean-Architecture]] · [[Hexagonal-Architecture]] · [[Domain-Driven-Design]] · [[Microservices]] · [[Event-Driven-Architecture]]
+- 변형: [[Layered-Architecture]] · [[Clean-Architecture]] · [[Hexagonal Architecture]] · [[Domain-Driven-Design]] · [[Microservices]] · [[Event-Driven-Architecture]]
- 응용: [[CQRS]] · [[Event-Sourcing]] · [[Serverless]] · [[Strangler-Fig]]
+- 응용: [[CQRS]] · [[Event Sourcing]] · [[Serverless]] · [[Strangler-Fig]]
- 검증: [[ADR]] · [[C4-Model]] · [[Dependency-Cruiser]] · [[ArchUnit]]
+- 검증: [[ADR]] · [[C4 Model (Architecture Documentation)]] · [[Dependency-Cruiser]] · [[ArchUnit]]
- Adjacent: [[SOLID]] · [[Design-Patterns]] · [[Anaemic-Domain-Model]]
+- Adjacent: [[SOLID]] · [[Design-Patterns]] · [[Anaemic Domain Model]]
 ## 🤖 LLM 활용
 **언제**: 매 system design review. 매 codebase 의 first read. 매 architecture decision.
@@ -27,7 +27,7 @@ github_commit: pending
 ## 🔗 Graph
 - 부모: [[Artificial-Intelligence]] (canonical)
- Adjacent: [[Machine-Learning]] · [[LLM]] · [[Deep-Learning]] · [[AGI]]
+- Adjacent: [[Machine-Learning]] · [[LLM]] · [[Deep Learning]] · [[AGI]]
 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -204,9 +204,9 @@ result = agent.invoke({'input': 'What is 2026 + 1, and search what happened then
 ## 🔗 Graph
 - 부모: [[Statistics]]
- 변형: [[Machine-Learning]] · [[Deep-Learning]] · [[Reinforcement-Learning]] · [[NLP]] · [[Computer Vision|Computer-Vision]]
+- 변형: [[Machine-Learning]] · [[Deep Learning]] · [[Reinforcement-Learning]] · [[NLP]] · [[Computer Vision|Computer-Vision]]
 - 응용: [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Agent]] · [[RAG]] · [[Foundation-Model]]
- 비판: [[AI-Safety]] · [[AI-Ethics]] · [[AI_Safety_and_Alignment|AI-Alignment]] · [[Hallucination]]
+- 비판: [[AI Safety]] · [[AI-Ethics]] · [[AI_Safety_and_Alignment|AI-Alignment]] · [[Hallucination]]
 - Adjacent: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]] · [[AGI]] · [[Scaling-Laws]]
 ## 🤖 LLM 활용
@@ -224,7 +224,7 @@ result = agent.invoke({'input': 'What is 2026 + 1, and search what happened then
 ## 🧪 검증 / 중복
 - Verified (Russell-Norvig, Goodfellow DL, Sutton RL, OpenAI / DeepMind / Anthropic papers).
 - 신뢰도 A.
- Related: [[Machine-Learning]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Deep-Learning]] · [[AGI]] · [[Bitter-Lesson]].
+- Related: [[Machine-Learning]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Deep Learning]] · [[AGI]] · [[Bitter-Lesson]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -171,7 +171,7 @@ def map_elites(grid_size, generations=1000):
 ## 🔗 Graph
 - 부모: [[Complexity_Theory|Complexity-Theory]] · [[Emergence]] · [[Multi-agent-System|Multi-Agent-Systems]]
- 변형: [[Cellular-Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]]
+- 변형: [[Cellular Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]]
 - 응용: [[NEAT]] · [[Procedural-Generation]]
 - Adjacent: [[Reinforcement-Learning]] · [[Self-Organization]] · [[Algorithmic-Biology]]
@@ -189,7 +189,7 @@ def map_elites(grid_size, generations=1000):
 ## 🧪 검증 / 중복
 - Verified (Langton, Reynolds, Sims, Stanley).
 - 신뢰도 B.
- Related: [[Cellular-Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]].
+- Related: [[Cellular Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -226,7 +226,7 @@ function ArtworkCard({ artwork }: { artwork: Artwork }) {
 - 부모: [[Aesthetic-Value]] · [[Creativity]]
 - 변형: [[Generative-Art]] · [[AI-Art]]
 - 응용: [[Stable-Diffusion]] · [[Midjourney]] · [[ControlNet]] · [[ComfyUI]]
- Adjacent: [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[AI 이미지 생성 (AI Image Generation)]]
+- Adjacent: [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[AI Image Generation]]
 ## 🤖 LLM 활용
 **언제**: 매 generative art workflow. 매 brand asset. 매 game art pipeline. 매 hybrid creative.
@@ -243,7 +243,7 @@ function ArtworkCard({ artwork }: { artwork: Artwork }) {
 ## 🧪 검증 / 중복
 - Verified (C2PA spec, ongoing copyright cases, art community discourse).
 - 신뢰도 B (rapidly evolving).
- Related: [[AI 이미지 생성 (AI Image Generation)]] · [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze]].
+- Related: [[AI Image Generation]] · [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -247,7 +247,7 @@ def evaluate_model(model, eval_set):
 - 부모: [[Evaluation]]
 - 응용: [[Rubric]]
 - ML parallel: [[ML-Evaluation]] · [[Benchmarks]] · [[LLM-as-Judge]] · [[Bias-Correction-Algorithm]]
- Adjacent: [[Algorithmic-Fairness]] · [[Validity]] · [[Reliability]]
+- Adjacent: [[Algorithmic Fairness]] · [[Validity]] · [[Reliability]]
 ## 🤖 LLM 활용
 **언제**: 매 educational system design. 매 ML evaluation suite. 매 performance review framework. 매 rubric 작성.
@@ -265,7 +265,7 @@ def evaluate_model(model, eval_set):
 ## 🧪 검증 / 중복
 - Verified (educational psychology + ML evaluation literature).
 - 신뢰도 B.
- Related: [[Benchmarks]] · [[Bias-Correction-Algorithm]] · [[Algorithmic-Fairness]] · [[LLM-as-Judge]].
+- Related: [[Benchmarks]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[LLM-as-Judge]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -185,8 +185,8 @@ def listen():
 **기본값**: 매 edge-first + 매 user control + 매 minimum data.
 ## 🔗 Graph
- 부모: [[Ubiquitous-Computing]] · [[HCI]] · [[클라우드_인프라_및_IaC_운영_표준|IoT]]
+- 부모: [[Ubiquitous-Computing]] · [[HCI]] · [[클라우드 인프라 및 IaC 운영 표준|IoT]]
- 변형: [[Spatial-Computing]] · [[Zero-UI]]
+- 변형: [[Spatial Computing]] · [[Zero-UI]]
 - 응용: [[Edge-AI]]
 - Adjacent: [[Privacy]] · [[Federated-Learning]] · [[Differential-Privacy]]
@@ -194,7 +194,7 @@ while not gym.is_complete():
 **기본값**: 매 critical = Lean / Coq + Z3. 매 distributed = TLA+. 매 smart contract = K / Move.
 ## 🔗 Graph
- 부모: [[Formal-Methods]] · [[Logic]] · [[Type-Theory]]
+- 부모: [[Formal Methods]] · [[Logic]] · [[Type Theory]]
 - 변형: [[Proof-Assistant]] · [[Model-Checking]]
 - 응용: [[CompCert]]
 - AI hybrid: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]]
@@ -262,8 +262,8 @@ if (ret[0].Score > 0.7) {
 **기본값**: Visual SLAM = ORB-SLAM3. LiDAR = LIO-SAM. Photoreal = Gaussian Splatting.
 ## 🔗 Graph
- 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]] · [[Spatial-Computing]]
+- 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]] · [[Spatial Computing]]
- 응용: [[Autonomous-Vehicles]] · [[HD-Map]]
+- 응용: [[Autonomous Vehicles]] · [[HD-Map]]
 - Modern: [[NeRF]] · [[Gaussian-Splatting]]
 - Adjacent: [[Bundle-Adjustment]] · [[Loop-Closure]] · [[Bayesian-Brain-Hypothesis]]
@@ -282,7 +282,7 @@ if (ret[0].Score > 0.7) {
 ## 🧪 검증 / 중복
 - Verified (ORB-SLAM3, FAST-LIO, NeRF, 3DGS papers).
 - 신뢰도 A.
- Related: [[Autonomous-Vehicles]] · [[Computer Vision|Computer-Vision]] · [[Robotics]] · [[NeRF]] · [[Gaussian-Splatting]].
+- Related: [[Autonomous Vehicles]] · [[Computer Vision|Computer-Vision]] · [[Robotics]] · [[NeRF]] · [[Gaussian-Splatting]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -237,7 +237,7 @@ def mpc_step(x_current, x_ref, horizon=10, dt=0.1):
 - 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]]
 - 변형: [[Tesla-FSD]] · [[Waymo]]
 - 응용: [[SLAM]] · [[End-to-End-Driving]]
- Adjacent: [[Reinforcement-Learning]] · [[AI-Safety]]
+- Adjacent: [[Reinforcement-Learning]] · [[AI Safety]]
 ## 🤖 LLM 활용
 **언제**: 매 AV system architecture review. 매 ADAS feature design. 매 simulation scenario. 매 sensor fusion debug.
@@ -254,7 +254,7 @@ def mpc_step(x_current, x_ref, horizon=10, dt=0.1):
 ## 🧪 검증 / 중복
 - Verified (SAE J3016, Waymo / Tesla papers, ISO 26262).
 - 신뢰도 A.
- Related: [[Tesla-FSD]] · [[Waymo]] · [[SLAM]] · [[End-to-End-Driving]] · [[AI-Safety]].
+- Related: [[Tesla-FSD]] · [[Waymo]] · [[SLAM]] · [[End-to-End-Driving]] · [[AI Safety]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -215,7 +215,7 @@ def is_real_improvement(metric_before, metric_after, vanity_metric_change):
 ## 🔗 Graph
 - 부모: [[DevOps]] · [[SRE]]
 - 변형: [[DORA-Metrics]] · [[Value-Stream-Mapping]] · [[Engineering-Metrics]]
- Adjacent: [[CI-CD]] · [[Branching-Strategies]] · [[Code-Review]] · [[Goodharts-Law]]
+- Adjacent: [[CI CD]] · [[Git Branching Strategies]] · [[Code-Review]] · [[Goodharts-Law]]
 ## 🤖 LLM 활용
 **언제**: 매 engineering metrics 의 design. 매 AI tool 도입 의 ROI 의 measure. 매 productivity dashboard.
@@ -232,7 +232,7 @@ def is_real_improvement(metric_before, metric_after, vanity_metric_change):
 ## 🧪 검증 / 중복
 - Verified (DORA "Accelerate" book, Axify docs).
 - 신뢰도 B.
- Related: [[Quality_Code_Review_Modern]] · [[Branching-Strategies]] · [[Goodharts-Law]] · [[CI-CD]].
+- Related: [[Quality_Code_Review_Modern]] · [[Git Branching Strategies]] · [[Goodharts-Law]] · [[CI CD]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -177,7 +177,7 @@ for topic_idx, topic in enumerate(lda.components_):
 **기본값**: 매 baseline = TF-IDF + LinearSVC. 매 result 의 transformer 와 비교.
 ## 🔗 Graph
- 부모: [[NLP]] · [[Information-Retrieval]]
+- 부모: [[NLP]] · [[Information Retrieval]]
 - 변형: [[TF-IDF]] · [[N-gram]] · [[BM25]]
 - 응용: [[LDA]]
 - 대체: [[Sentence-Transformers]] · [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Embedding]]
@@ -208,7 +208,7 @@ az.plot_ppc(az.from_pymc3(posterior_predictive=ppc, model=model))
 **기본값**: PyMC + NUTS 의 baseline. 매 scale 가 NumPyro / VI.
 ## 🔗 Graph
- 부모: [[Statistics]] · [[Probability-Theory]]
+- 부모: [[Statistics]] · [[Probability Theory]]
 - 변형: [[MCMC]] · [[Variational-Inference]] · [[Bayesian-Network]]
 - 응용: [[Bayesian-Optimization]] · [[LDA]] · [[SLAM]]
 - Tool: [[PyMC]] · [[Stan]]
@@ -181,7 +181,7 @@ class WorldModel(nn.Module):
 **기본값**: 매 perception = predictive coding. 매 action = active inference (sparse reward) or RL (dense).
 ## 🔗 Graph
- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Bayesian-Inference]]
+- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Bayesian Inference]]
 - 변형: [[Predictive-Coding]] · [[Free-Energy-Principle]] · [[Active-Inference]]
 - 응용: [[World-Model]] · [[VAE]]
 - 사상가: [[Karl-Friston]]
@@ -197,7 +197,7 @@ def detect_confirmation_bias(query, results, user_belief):
 - 부모: [[Epistemology]]
 - 변형: [[Knowledge]] · [[Bayesian-Belief]] · [[Doxastic-Logic]]
 - 응용: [[POMDP]]
- 비판: [[Confirmation-Bias]]
+- 비판: [[Confirmation Bias]]
 - Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Multi-agent-System|Multi-Agent-Systems]]
 ## 🤖 LLM 활용
@@ -215,7 +215,7 @@ def detect_confirmation_bias(query, results, user_belief):
 ## 🧪 검증 / 중복
 - Verified (Plato JTB, Gettier, AGM postulates, Bayesian).
 - 신뢰도 B.
- Related: [[Bayesian-Statistics]] · [[Bayesian-Brain-Hypothesis]] · [[Confirmation-Bias]] · [[POMDP]].
+- Related: [[Bayesian Statistics]] · [[Bayesian-Brain-Hypothesis]] · [[Confirmation Bias]] · [[POMDP]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -185,7 +185,7 @@ print(f'{unfair_count}/{len(X_test)} 의 prediction 의 protected attr 의 depen
 **기본값**: 매 audit 먼저. 매 metric 의 stakeholder 의 align. 매 reductions (Fairlearn) 의 baseline.
 ## 🔗 Graph
- 부모: [[Algorithmic-Fairness]]
+- 부모: [[Algorithmic Fairness]]
 - 변형: [[Demographic-Parity]] · [[Equalized-Odds]]
 - 응용: [[AIF360]]
 - Adjacent: [[EU-AI-Act]]
@@ -205,7 +205,7 @@ print(f'{unfair_count}/{len(X_test)} 의 prediction 의 protected attr 의 depen
 ## 🧪 검증 / 중복
 - Verified (AIF360 docs, Fairlearn, Kleinberg paper).
 - 신뢰도 A.
- Related: [[Algorithmic-Fairness]] · [[Algorithmic-Bias]] · [[AI-Governance]] · [[EU-AI-Act]].
+- Related: [[Algorithmic Fairness]] · [[Algorithmic-Bias]] · [[AI-Governance]] · [[EU-AI-Act]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -217,7 +217,7 @@ def active_inference(belief, action_space, world_model):
 ## 🔗 Graph
 - 부모: [[Evolution]]
- 변형: [[Embodied-Cognition]] · [[Bayesian-Brain-Hypothesis]] · [[Free-Energy-Principle]]
+- 변형: [[Embodied Cognition]] · [[Bayesian-Brain-Hypothesis]] · [[Free-Energy-Principle]]
 - 응용: [[Neuromorphic-Computing]] · [[Brain-Computer_Interface_(BCI)]] · [[Bioenergetics]]
 - Adjacent: [[Reinforcement-Learning]] · [[Active-Inference]] · [[Predictive-Coding]]
@@ -235,7 +235,7 @@ def active_inference(belief, action_space, world_model):
 ## 🧪 검증 / 중복
 - Verified (Kandel neuroscience, Friston FEP, Lakoff embodied).
 - 신뢰도 B.
- Related: [[Neuromorphic-Computing]] · [[Bayesian-Brain-Hypothesis]] · [[Bioenergetics]] · [[Brain-Computer_Interface_(BCI)]] · [[Embodied-Cognition]].
+- Related: [[Neuromorphic-Computing]] · [[Bayesian-Brain-Hypothesis]] · [[Bioenergetics]] · [[Brain-Computer_Interface_(BCI)]] · [[Embodied Cognition]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -258,10 +258,10 @@ CatBoostClassifier(task_type='GPU', devices='0')
 **기본값**: LightGBM 의 baseline. 매 categorical 가 CatBoost. 매 ensemble 의 stack.
 ## 🔗 Graph
- 부모: [[Ensemble-Methods]] · [[Decision-Tree]] · [[데이터_사이언스_및_ML_엔지니어링|Gradient-Descent]]
+- 부모: [[Ensemble-Methods]] · [[Decision Tree]] · [[데이터 사이언스 및 ML 엔지니어링|Gradient-Descent]]
 - 변형: [[XGBoost]] · [[LightGBM]] · [[CatBoost]] · [[AdaBoost]] · [[GBM]]
 - 응용: [[Kaggle]] · [[SHAP]] · [[Stacking]]
- Adjacent: [[Random-Forest]] · [[Bagging]] · [[Bias-vs-Variance]] · [[Optuna]]
+- Adjacent: [[Random-Forest]] · [[Bagging]] · [[Bias vs Variance Trade-off]] · [[Optuna]]
 ## 🤖 LLM 활용
 **언제**: 매 tabular task. 매 fraud detection. 매 Kaggle. 매 risk scoring. 매 conversion prediction.
@@ -279,7 +279,7 @@ CatBoostClassifier(task_type='GPU', devices='0')
 ## 🧪 검증 / 중복
 - Verified (Chen XGBoost, Ke LightGBM, Prokhorenkova CatBoost, Kaggle dominance).
 - 신뢰도 A.
- Related: [[XGBoost]] · [[LightGBM]] · [[CatBoost]] · [[Random-Forest]] · [[Bias-vs-Variance]].
+- Related: [[XGBoost]] · [[LightGBM]] · [[CatBoost]] · [[Random-Forest]] · [[Bias vs Variance Trade-off]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -246,8 +246,8 @@ def critical_path(tasks):
 ## 🔗 Graph
 - 부모: [[System-Design]]
 - 변형: [[CPU-Bound]]
- 응용: [[Theory-of-Constraints]] · [[Amdahls Law (암달의 법칙)]] · [[Critical-Path]]
+- 응용: [[Theory-of-Constraints]] · [[Amdahl's Law]] · [[Critical-Path]]
- Tool: [[Profiling]] · [[Flame-Graph]] · [[Distributed-Tracing]]
+- Tool: [[Profiling]] · [[Flame-Graph]] · [[Distributed Tracing]]
 - Adjacent: [[Optimization]] · [[Scalability]] · [[DORA-Metrics]]
 ## 🤖 LLM 활용
@@ -265,7 +265,7 @@ def critical_path(tasks):
 ## 🧪 검증 / 중복
 - Verified (Goldratt TOC, Knuth premature optimization, Brendan Gregg Systems Performance).
 - 신뢰도 A.
- Related: [[Amdahls Law (암달의 법칙)]] · [[Theory-of-Constraints]] · [[Profiling]] · [[Critical-Path]].
+- Related: [[Amdahl's Law]] · [[Theory-of-Constraints]] · [[Profiling]] · [[Critical-Path]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -243,9 +243,9 @@ bounded_contexts:
 ## 🔗 Graph
 - 부모: [[Domain-Driven-Design]] · [[Software-Architecture]]
- 변형: [[Ubiquitous-Language]] · [[Context-Map]] · [[Anti-Corruption-Layer]]
+- 변형: [[Ubiquitous Language]] · [[Context-Map]] · [[Anti-Corruption-Layer]]
- 응용: [[Microservices]] · [[Modular-Monolith]] · [[Event-Storming]] · [[Strangler-Fig]]
+- 응용: [[Microservices]] · [[Modular Monolith]] · [[Event Storming]] · [[Strangler-Fig]]
- Adjacent: [[Aggregate-Root]] · [[Team-Topologies]] · [[Anaemic-Domain-Model]]
+- Adjacent: [[Aggregate-Root]] · [[Team Topologies]] · [[Anaemic Domain Model]]
 ## 🤖 LLM 활용
 **언제**: 매 large system design. 매 microservice boundary. 매 legacy refactor planning. 매 team-structure align.
@@ -263,7 +263,7 @@ bounded_contexts:
 ## 🧪 검증 / 중복
 - Verified (Eric Evans DDD, Vaughn Vernon Implementing DDD, Sam Newman Microservices).
 - 신뢰도 A.
- Related: [[Domain-Driven-Design]] · [[Microservices]] · [[Event-Storming]] · [[Conway-Law]] · [[Architecture-Styles]].
+- Related: [[Domain-Driven-Design]] · [[Microservices]] · [[Event Storming]] · [[Conway-Law]] · [[Software Architecture Styles]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -225,7 +225,7 @@ def recognition_heuristic(a, b, recognized_set):
 **기본값**: 매 aspiration level + 매 anytime + 매 default fallback.
 ## 🔗 Graph
- 부모: [[Decision-Theory]] · [[Behavioral-Economics]]
+- 부모: [[Decision Theory]] · [[Behavioral-Economics]]
 - 변형: [[Satisficing]] · [[Heuristic]] · [[Fast-and-Frugal]] · [[Ecological-Rationality]]
 - 사상가: [[Herbert-Simon]] · [[Tversky-Kahneman]]
 - 응용: [[MCTS]] · [[POMDP]] · [[Receding-Horizon-Control]]
@@ -246,7 +246,7 @@ def recognition_heuristic(a, b, recognized_set):
 ## 🧪 검증 / 중복
 - Verified (Simon Nobel lecture, Gigerenzer Simple Heuristics, Kahneman Thinking).
 - 신뢰도 A.
- Related: [[Antifragility]] · [[Heuristic]] · [[MCTS]] · [[Decision-Theory]] · [[Beliefs]].
+- Related: [[Antifragility]] · [[Heuristic]] · [[MCTS]] · [[Decision Theory]] · [[Beliefs]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -252,7 +252,7 @@ print(f"mAP@50: {result['map_50']:.4f}")
 ## 🔗 Graph
 - 부모: [[Object-Detection]] · [[Computer Vision|Computer-Vision]]
 - 변형: [[YOLO]] · [[Faster-R-CNN]] · [[DETR]] · [[SAM]]
- 응용: [[Autonomous-Vehicles]]
+- 응용: [[Autonomous Vehicles]]
 - Loss: [[Focal-Loss]]
 - Adjacent: [[Anchor-Box]] · [[NMS]] · [[mAP]]
@@ -271,7 +271,7 @@ print(f"mAP@50: {result['map_50']:.4f}")
 ## 🧪 검증 / 중복
 - Verified (Faster R-CNN, YOLO papers, DETR, SAM).
 - 신뢰도 A.
- Related: [[YOLO]] · [[DETR]] · [[Object-Detection]] · [[SAM]] · [[Autonomous-Vehicles]].
+- Related: [[YOLO]] · [[DETR]] · [[Object-Detection]] · [[SAM]] · [[Autonomous Vehicles]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -194,9 +194,9 @@ def burnout_risk(week_log):
 ## 🔗 Graph
 - 부모: [[Neurotrophin]]
- 변형: [[Synaptic-Plasticity]] · [[Cognitive-Reserve]]
+- 변형: [[Synaptic-Plasticity]] · [[Cognitive Reserve]]
 - 응용: [[Productivity]]
- Adjacent: [[Dopamine]] · [[Bioenergetics]] · [[Addiction-Neuroscience]]
+- Adjacent: [[Dopamine]] · [[Bioenergetics]] · [[Addiction Neuroscience]]
 ## 🤖 LLM 활용
 **언제**: 매 productivity routine design. 매 cognitive worker advice. 매 burnout prevention. 매 learning strategy.
@@ -213,7 +213,7 @@ def burnout_risk(week_log):
 ## 🧪 검증 / 중복
 - Verified (Cotman 2002, Vaynman exercise BDNF, Duman 2014 depression-BDNF).
 - 신뢰도 A.
- Related: [[Bioenergetics]] · [[Addiction-Neuroscience]] · [[Bayesian-Brain-Hypothesis]] · [[Burnout]].
+- Related: [[Bioenergetics]] · [[Addiction Neuroscience]] · [[Bayesian-Brain-Hypothesis]] · [[Burnout]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -237,7 +237,7 @@ gh pr create --label urgent
 - 부모: [[Version-Control]] · [[DevOps]]
 - 변형: [[Trunk-Based-Development]] · [[GitHub-Flow]] · [[Git-Flow]] · [[Feature-Branch]]
 - 응용: [[Feature-Flag]] · [[Continuous-Deployment]] · [[Branch-by-Abstraction]] · [[Conventional-Commits]]
- Adjacent: [[CI-CD]] · [[Code-Review]] · [[Semver]]
+- Adjacent: [[CI CD]] · [[Code-Review]] · [[Semver]]
 ## 🤖 LLM 활용
 **언제**: 매 team workflow 의 design. 매 Git strategy decision. 매 release process.
@@ -255,7 +255,7 @@ gh pr create --label urgent
 ## 🧪 검증 / 중복
 - Verified (Atlassian docs, Trunk-Based Development website, Driessen 의 Git Flow retraction).
 - 신뢰도 A.
- Related: [[Trunk-Based-Development]] · [[Feature-Flag]] · [[CI-CD]] · [[Conventional-Commits]] · [[Quality_Code_Review_Modern]].
+- Related: [[Trunk-Based-Development]] · [[Feature-Flag]] · [[CI CD]] · [[Conventional-Commits]] · [[Quality_Code_Review_Modern]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -236,8 +236,8 @@ def brand_consistency_check(reference, generated, threshold=0.7):
 **기본값**: 매 sref / IP-Adapter 의 baseline. 매 character = LoRA. 매 face = InstantID.
 ## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Marketing]]
+- 부모: [[AI Image Generation]] · [[Marketing]]
- 변형: [[Style-Reference]] · [[Character-Reference]] · [[IP-Adapter]] · [[ControlNet]]
+- 변형: [[Style Reference]] · [[Character Reference]] · [[IP-Adapter]] · [[ControlNet]]
 - 응용: [[LoRA]] · [[InstantID]] · [[PhotoMaker]] · [[Midjourney]]
 - Adjacent: [[CFG 스케일(Classifier-Free Guidance Scale)]] · [[Stable-Diffusion]] · [[Flux]] · [[Authenticity]] · [[Arts]]
@@ -256,7 +256,7 @@ def brand_consistency_check(reference, generated, threshold=0.7):
 ## 🧪 검증 / 중복
 - Verified (Midjourney docs, IP-Adapter paper, InstantID).
 - 신뢰도 B.
- Related: [[CFG 스케일(Classifier-Free Guidance Scale)]] · [[AI 이미지 생성 (AI Image Generation)]] · [[LoRA]] · [[Authenticity]] · [[Stable-Diffusion]].
+- Related: [[CFG 스케일(Classifier-Free Guidance Scale)]] · [[AI Image Generation]] · [[LoRA]] · [[Authenticity]] · [[Stable-Diffusion]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -241,7 +241,7 @@ await icepanel.upsertContainer({
 - 부모: [[Architecture-Documentation]] · [[System-Design]]
 - 변형: [[Living-Documentation]]
 - 응용: [[Structurizr]] · [[PlantUML]] · [[Mermaid]] · [[IcePanel]]
- Adjacent: [[ADR]] · [[arc42]] · [[Architecture-Styles]] · [[Bounded-Contexts]] · [[Domain-Driven-Design]]
+- Adjacent: [[ADR]] · [[arc42]] · [[Software Architecture Styles]] · [[Bounded Contexts (DDD)]] · [[Domain-Driven-Design]]
 ## 🤖 LLM 활용
 **언제**: 매 architecture documentation. 매 onboarding. 매 review. 매 refactor planning.
@@ -259,7 +259,7 @@ await icepanel.upsertContainer({
 ## 🧪 검증 / 중복
 - Verified (Simon Brown's c4model.com, Structurizr docs).
 - 신뢰도 A.
- Related: [[Architecture-Styles]] · [[ADR]] · [[Bounded-Contexts]] · [[Architecture-as-Code]].
+- Related: [[Software Architecture Styles]] · [[ADR]] · [[Bounded Contexts (DDD)]] · [[Architecture-as-Code]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -269,7 +269,7 @@ class HybridStore:
 - 변형: [[PACELC]] · [[BASE]] · [[ACID]]
 - 응용: [[Raft]] · [[Paxos]] · [[CRDT]] · [[Quorum]] · [[Spanner]]
 - DB: [[Cassandra]] · [[Postgres]] · [[Etcd]]
- Adjacent: [[Availability-and-Persistence]] · [[Eventual-Consistency]] · [[Linearizability]]
+- Adjacent: [[Availability-and-Persistence]] · [[Eventual Consistency]] · [[Linearizability]]
 ## 🤖 LLM 활용
 **언제**: 매 distributed system design. 매 database choice. 매 multi-region architecture. 매 consistency model decision.
@@ -287,7 +287,7 @@ class HybridStore:
 ## 🧪 검증 / 중복
 - Verified (Brewer 2000, Gilbert-Lynch 2002 proof, Abadi PACELC).
 - 신뢰도 A.
- Related: [[Availability-and-Persistence]] · [[Database-Theory]] · [[Raft]] · [[CRDT]] · [[Architecture-Styles]].
+- Related: [[Availability-and-Persistence]] · [[Database-Theory]] · [[Raft]] · [[CRDT]] · [[Software Architecture Styles]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -210,7 +210,7 @@ image = pipe(prompt=prompt, negative_prompt=neg, guidance_scale=7).images[0]
 ## 🔗 Graph
 - 부모: [[Diffusion-Models]] · [[AI 이미지 생성 (AI Image Generation)|Image-Generation]]
 - 응용: [[Stable-Diffusion]] · [[Flux]] · [[ComfyUI]]
- Adjacent: [[Negative-Prompt]]
+- Adjacent: [[Negative Prompt]]
 ## 🤖 LLM 활용
 **언제**: 매 image generation tuning. 매 SD / Flux pipeline. 매 quality vs prompt-fidelity trade-off.
@@ -226,7 +226,7 @@ image = pipe(prompt=prompt, negative_prompt=neg, guidance_scale=7).images[0]
 ## 🧪 검증 / 중복
 - Verified (Ho & Salimans CFG paper, Flux docs, Diffusers).
 - 신뢰도 A.
- Related: [[Stable-Diffusion]] · [[Flux]] · [[Negative-Prompt]] · [[DPM-Solver]] · [[AI 이미지 생성 (AI Image Generation)]].
+- Related: [[Stable-Diffusion]] · [[Flux]] · [[Negative Prompt]] · [[DPM-Solver]] · [[AI Image Generation]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -27,7 +27,7 @@ github_commit: pending
 ## 🔗 Graph
 - 부모: [[CFG 스케일(Classifier-Free Guidance Scale)]] (canonical)
- Adjacent: [[Stable-Diffusion]] · [[Flux]] · [[Negative-Prompt]] · [[Diffusion-Models]]
+- Adjacent: [[Stable-Diffusion]] · [[Flux]] · [[Negative Prompt]] · [[Diffusion-Models]]
 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -290,10 +290,10 @@ def prioritize_findings(findings):
 **기본값**: 매 4 layer 의 모두 + 매 quality gate + 매 SLSA Level 2+.
 ## 🔗 Graph
- 부모: [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[CI-CD]]
+- 부모: [[CI/CD Pipeline & IDE Security Integration|DevSecOps]] · [[CI CD]]
 - 변형: [[Shift-Left-Security]] · [[Quality-Gate]] · [[SLSA]] · [[SBOM]]
 - 응용: [[SonarQube]] · [[Semgrep]] · [[CodeQL]] · [[Trivy]] · [[Cosign]]
- Adjacent: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]] · [[OWASP-Top-10]] · [[Branching-Strategies]] · [[Quality_Code_Review_Modern]]
+- Adjacent: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]] · [[OWASP Top 10]] · [[Git Branching Strategies]] · [[Quality_Code_Review_Modern]]
 ## 🤖 LLM 활용
 **언제**: 매 DevSecOps pipeline design. 매 security policy. 매 compliance audit. 매 vulnerability triage.
@@ -311,7 +311,7 @@ def prioritize_findings(findings):
 ## 🧪 검증 / 중복
 - Verified (NIST SSDF, OWASP, SLSA spec).
 - 신뢰도 A.
- Related: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]] · [[Software-Supply-Chain-Security]] · [[OWASP-Top-10]] · [[Branching-Strategies]].
+- Related: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]] · [[Software-Supply-Chain-Security]] · [[OWASP Top 10]] · [[Git Branching Strategies]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -217,7 +217,7 @@ def predict_with_bias_check(features, model, bias_audit):
 - 부모: [[Security]]
 - 변형: [[Defensible-Space]] · [[Broken-Windows]]
 - 사상가: [[Oscar-Newman]]
- Adjacent: [[Atmospheric-Intelligence]] · [[Algorithmic-Fairness]]
+- Adjacent: [[Atmospheric-Intelligence]] · [[Algorithmic Fairness]]
 ## 🤖 LLM 활용
 **언제**: 매 urban planning. 매 building design. 매 smart city. 매 community safety initiative.
@@ -234,7 +234,7 @@ def predict_with_bias_check(features, model, bias_audit):
 ## 🧪 검증 / 중복
 - Verified (Jacobs 1961, Newman 1972, Wilson-Kelling 1982).
 - 신뢰도 A.
- Related: [[Smart-City]] · [[Atmospheric-Intelligence]] · [[Surveillance-Capitalism]] · [[Algorithmic-Fairness]].
+- Related: [[Smart-City]] · [[Atmospheric-Intelligence]] · [[Surveillance-Capitalism]] · [[Algorithmic Fairness]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -304,7 +304,7 @@ import { motion, AnimatePresence } from 'framer-motion';
 - 부모: [[Web-Performance]] · [[Frontend]]
 - 변형: [[Transform]] · [[Will-Change]] · [[View-Transitions]]
 - 응용: [[Web-Animations-API]] · [[Framer-Motion]] · [[GSAP]]
- Adjacent: [[Reflow-Repaint]] · [[GPU-Acceleration]] · [[Accessibility (A11y)|Accessibility]] · [[Baseline-Project]]
+- Adjacent: [[Reflow   Repaint]] · [[GPU-Acceleration]] · [[Accessibility (A11y)|Accessibility]] · [[Baseline (Web Platform Features)]]
 ## 🤖 LLM 활용
 **언제**: 매 frontend animation. 매 micro-interaction. 매 UX polish. 매 accessibility audit.
@@ -322,7 +322,7 @@ import { motion, AnimatePresence } from 'framer-motion';
 ## 🧪 검증 / 중복
 - Verified (web.dev animation, MDN, Paul Lewis 의 Aerotwist).
 - 신뢰도 A.
- Related: [[Web-Performance]] · [[Accessibility (A11y)|Accessibility]] · [[Baseline-Project]] · [[FLIP-Technique]].
+- Related: [[Web-Performance]] · [[Accessibility (A11y)|Accessibility]] · [[Baseline (Web Platform Features)]] · [[FLIP-Technique]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -28,7 +28,7 @@ github_commit: pending
 ## 🔗 Graph
 - 부모: [[CSS Animations]] (canonical)
- Adjacent: [[Web-Performance]] · [[Accessibility]] · [[Baseline-Project]] · [[GPU-Acceleration]]
+- Adjacent: [[Web-Performance]] · [[Accessibility]] · [[Baseline (Web Platform Features)]] · [[GPU-Acceleration]]
 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -264,7 +264,7 @@ def adaptive_mix(epoch, real_data, synthetic_data):
 ## 🔗 Graph
 - 부모: [[Computer Vision|Computer-Vision]] · [[Synthetic-Data]] · [[Simulation]]
 - 변형: [[Sim2Real]] · [[Domain-Adaptation]] · [[CycleGAN]]
- 응용: [[Autonomous-Vehicles]] · [[Robotics]] · [[Unity-Perception]]
+- 응용: [[Autonomous Vehicles]] · [[Robotics]] · [[Unity-Perception]]
 - Adjacent: [[Diffusion-Models]] · [[ControlNet]] · [[Model-Collapse]] · [[Algorithmic-Biology]]
 ## 🤖 LLM 활용
@@ -281,7 +281,7 @@ def adaptive_mix(epoch, real_data, synthetic_data):
 ## 🧪 검증 / 중복
 - Verified (Tobin domain randomization 2017, Tremblay 2018, NVIDIA Omniverse).
 - 신뢰도 A.
- Related: [[Autonomous-Vehicles]] · [[Diffusion-Models]] · [[Robotics]] · [[Algorithmic-Biology]] · [[Model-Collapse]].
+- Related: [[Autonomous Vehicles]] · [[Diffusion-Models]] · [[Robotics]] · [[Algorithmic-Biology]] · [[Model-Collapse]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -245,7 +245,7 @@ Transcript:
 ## 🔗 Graph
 - 부모: [[Problem_Solving|Problem-Solving]]
- 변형: [[MECE]] · [[Pyramid-Principle]] · [[Hypothesis-Driven]]
+- 변형: [[MECE]] · [[Pyramid Principle]] · [[Hypothesis-Driven]]
 - Adjacent: [[Articulateness]] · [[Be-Detailed]] · [[Beliefs]] · [[Bounded_Rationality|Bounded-Rationality]]
 ## 🤖 LLM 활용
@@ -264,7 +264,7 @@ Transcript:
 ## 🧪 검증 / 중복
 - Verified (Cosentino "Case in Point", Cheng's "Case Interview Secrets", MBB own materials).
 - 신뢰도 B.
- Related: [[Articulateness]] · [[Be-Detailed]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Pyramid-Principle]].
+- Related: [[Articulateness]] · [[Be-Detailed]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Pyramid Principle]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -234,7 +234,7 @@ ci:
 - 부모: [[E-commerce]] · [[Web-Performance]]
 - 변형: [[PWA]] · [[Service-Worker]] · [[CRO]]
 - 응용: [[Workbox]] · [[Web-Vitals]] · [[Lighthouse]]
- Adjacent: [[CSS Animations]] · [[Baseline-Project]] · [[Authenticity]] · [[Brand Consistency Maintenance]]
+- Adjacent: [[CSS Animations]] · [[Baseline (Web Platform Features)]] · [[Authenticity]] · [[Brand Consistency Maintenance]]
 ## 🤖 LLM 활용
 **언제**: 매 e-commerce redesign. 매 PWA migration. 매 conversion strategy. 매 brand-value integration.
@@ -250,7 +250,7 @@ ci:
 ## 🧪 검증 / 중복
 - Verified (Allbirds public case study, web.dev PWA pattern).
 - 신뢰도 B.
- Related: [[CSS Animations]] · [[Baseline-Project]] · [[Brand Consistency Maintenance]] · [[Authenticity]] · [[Web-Vitals]].
+- Related: [[CSS Animations]] · [[Baseline (Web Platform Features)]] · [[Brand Consistency Maintenance]] · [[Authenticity]] · [[Web-Vitals]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -285,7 +285,7 @@ def detect_drift(base_model, finetuned_model, eval_set):
 - 부모: [[Continual-Learning]]
 - 변형: [[EWC]] · [[Replay-Buffer]]
 - 응용: [[LoRA]] · [[Adapter]] · [[Mixture-of-Experts]]
- Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Biological-Intelligence]] · [[Bias-vs-Variance]] · [[Auto-Encoding]]
+- Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Biological-Intelligence]] · [[Bias vs Variance Trade-off]] · [[Auto-Encoding]]
 ## 🤖 LLM 활용
 **언제**: 매 sequential task. 매 LLM domain adapt. 매 streaming data. 매 lifelong agent.
@@ -277,9 +277,9 @@ def detect_simpson(df, x_col, y_col, group_col):
 **기본값**: 매 RCT first. 매 observational 가 DoWhy + sensitivity refute.
 ## 🔗 Graph
- 부모: [[Statistics]] · [[Decision-Theory]]
+- 부모: [[Statistics]] · [[Decision Theory]]
 - 변형: [[DAG]] · [[Do-Calculus]] · [[Counterfactual]]
- Adjacent: [[Bayesian-Statistics]] · [[Anthropic-Principle]] · [[Beliefs]] · [[Algorithmic-Fairness]]
+- Adjacent: [[Bayesian Statistics]] · [[Anthropic-Principle]] · [[Beliefs]] · [[Algorithmic Fairness]]
 ## 🤖 LLM 활용
 **언제**: 매 policy decision. 매 marketing attribution. 매 medical treatment. 매 root cause analysis. 매 fairness counterfactual.
@@ -297,7 +297,7 @@ def detect_simpson(df, x_col, y_col, group_col):
 ## 🧪 검증 / 중복
 - Verified (Pearl "Book of Why", Hernán "Causal Inference: What If", DoWhy paper).
 - 신뢰도 A.
- Related: [[Bayesian-Statistics]] · [[Algorithmic-Fairness]] · [[Bias-Correction-Algorithm]] · [[A-B-Testing]] · [[Anthropic-Principle]].
+- Related: [[Bayesian Statistics]] · [[Algorithmic Fairness]] · [[Bias-Correction-Algorithm]] · [[A/B Testing]] · [[Anthropic-Principle]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -213,7 +213,7 @@ for p in prompts:
 **기본값**: ChatGPT 의 explore. 매 production 의 direct API + 매 verbatim prompt.
 ## 🔗 Graph
- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[AI 이미지 생성 (AI Image Generation)]]
+- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[AI Image Generation]]
 - 변형: [[DALL-E]]
 - 응용: [[ChatGPT_Emoticon_Prompt_Engineering]] · [[Brand Consistency Maintenance]]
 - Adjacent: [[CFG 스케일(Classifier-Free Guidance Scale)]] · [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]] · [[Be-Detailed]]
@@ -221,7 +221,7 @@ def split_grid(grid_image, rows, cols):
 **기본값**: 매 ChatGPT (DALL-E 3) 의 entry. 매 advanced = Flux + IP-Adapter.
 ## 🔗 Graph
- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[AI 이미지 생성 (AI Image Generation)]]
+- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[AI Image Generation]]
 - 변형: [[Brand Consistency Maintenance]] · [[Brand Consistency Maintenance|Character Consistency]] · [[Sticker-Generation]]
 - 응용: [[DALL-E]] · [[Midjourney]] · [[Flux]] · [[InstantID]] · [[IP-Adapter]]
 - Adjacent: [[CFG 스케일(Classifier-Free Guidance Scale)]] · [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]] · [[Authenticity]]
@@ -241,7 +241,7 @@ def split_grid(grid_image, rows, cols):
 ## 🧪 검증 / 중복
 - Verified (blog samples + Midjourney / DALL-E community).
 - 신뢰도 B.
- Related: [[Brand Consistency Maintenance]] · [[Brand Consistency Maintenance|Character Consistency]] · [[AI 이미지 생성 (AI Image Generation)]] · [[Be-Detailed]] · [[Articulateness]].
+- Related: [[Brand Consistency Maintenance]] · [[Brand Consistency Maintenance|Character Consistency]] · [[AI Image Generation]] · [[Be-Detailed]] · [[Articulateness]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -289,8 +289,8 @@ async function detectLeak() {
 **기본값**: 매 heap snapshot 3-shot + Comparison + Retainer trace.
 ## 🔗 Graph
- 부모: [[Web-Performance]] · [[Browser]] · [[Memory-Management]]
+- 부모: [[Web-Performance]] · [[Browser]] · [[Memory Management]]
- 변형: [[Heap-Snapshot]] · [[Allocation-Timeline]] · [[Garbage-Collection]] · [[V8]]
+- 변형: [[Heap Snapshot]] · [[Allocation Timeline]] · [[Garbage Collection]] · [[V8]]
 - 응용: [[Memory-Leak-Detection]] · [[WeakRef]] · [[Lighthouse]]
 - Adjacent: [[Bottlenecks]] · [[CSS Animations]] · [[Frontend-Performance]]
@@ -309,7 +309,7 @@ async function detectLeak() {
 ## 🧪 검증 / 중복
 - Verified (Chrome DevTools docs, V8 blog).
 - 신뢰도 A.
- Related: [[Web-Performance]] · [[Bottlenecks]] · [[CSS Animations]] · [[Baseline-Project]].
+- Related: [[Web-Performance]] · [[Bottlenecks]] · [[CSS Animations]] · [[Baseline (Web Platform Features)]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -268,7 +268,7 @@ milestones:
 ## 🔗 Graph
 - 변형: [[Central-Sensitization]] · [[Biopsychosocial]] · [[Pain-Neuroscience-Education]] · [[GMI]]
 - 응용: [[CBT]] · [[Yoga]]
- Adjacent: [[Bioenergetics]] · [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Addiction-Neuroscience]] (opioid risk)
+- Adjacent: [[Bioenergetics]] · [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Addiction Neuroscience]] (opioid risk)
 ## 🤖 LLM 활용
 **언제**: 매 productivity wellness routine. 매 cognitive worker burnout. 매 multidisciplinary protocol design.
@@ -286,7 +286,7 @@ milestones:
 ## 🧪 검증 / 중복
 - Verified (Moseley PNE, Engel biopsychosocial 1977, CDC opioid guideline 2022).
 - 신뢰도 B.
- Related: [[Bioenergetics]] · [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Addiction-Neuroscience]] · [[Bayesian-Brain-Hypothesis]].
+- Related: [[Bioenergetics]] · [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Addiction Neuroscience]] · [[Bayesian-Brain-Hypothesis]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -269,7 +269,7 @@ def find_max_activating(sae, feature_idx, dataset, top_k=10):
 **기본값**: TransformerLens + activation patching 의 baseline. 매 large model = SAE.
 ## 🔗 Graph
- 부모: [[Interpretability]] · [[AI-Safety]] · [[Mechanistic-Interpretability]]
+- 부모: [[Interpretability]] · [[AI Safety]] · [[Mechanistic-Interpretability]]
 - 변형: [[Activation-Patching]] · [[Path-Patching]] · [[ACDC]]
 - 응용: [[Steering]] · [[Induction-Head]]
 - Adjacent: [[Anthropic]] · [[AI_Safety_and_Alignment|AI-Alignment]]
@@ -289,7 +289,7 @@ def find_max_activating(sae, feature_idx, dataset, top_k=10):
 ## 🧪 검증 / 중복
 - Verified (Anthropic transformer-circuits.pub, Olsson induction heads, Wang IOI, ACDC paper).
 - 신뢰도 A.
- Related: [[AI_Safety_and_Alignment|AI-Alignment]] · [[AI-Safety]] · [[Anthropic-Principle]] · [[Sparse-Autoencoder]].
+- Related: [[AI_Safety_and_Alignment|AI-Alignment]] · [[AI Safety]] · [[Anthropic-Principle]] · [[Sparse-Autoencoder]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -237,8 +237,8 @@ def pr_smell_check(diff):
 - 부모: [[Refactoring_Best_Practices|Refactoring]]
 - 변형: [[SOLID]] · [[DRY]] · [[KISS]] · [[YAGNI]]
 - 응용: [[Code_Smells]] · [[Refactoring_Best_Practices|Refactoring]] · [[Clean-Architecture]]
- 비판: [[Anaemic-Domain-Model]]
+- 비판: [[Anaemic Domain Model]]
- Adjacent: [[Architecture-Anti-Patterns]] · [[Quality_Code_Review_Modern]] · [[Architecture-Styles]]
+- Adjacent: [[Architecture Anti-patterns]] · [[Quality_Code_Review_Modern]] · [[Software Architecture Styles]]
 ## 🤖 LLM 활용
 **언제**: 매 review. 매 refactor. 매 onboarding. 매 AI-generated code 의 quality check.
@@ -255,7 +255,7 @@ def pr_smell_check(diff):
 ## 🧪 검증 / 중복
 - Verified (Robert Martin "Clean Code", Fowler "Refactoring", Beck "Implementation Patterns").
 - 신뢰도 A (with caveats — 매 not gospel).
- Related: [[Code_Smells]] · [[SOLID]] · [[Architecture-Styles]] · [[Refactoring_Best_Practices|Refactoring]].
+- Related: [[Code_Smells]] · [[SOLID]] · [[Software Architecture Styles]] · [[Refactoring_Best_Practices|Refactoring]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -289,9 +289,9 @@ app.get('/health', (req, res) => {
 **기본값**: REST + 3-tier. 매 internal = gRPC. 매 real-time = WebSocket.
 ## 🔗 Graph
- 부모: [[Architecture-Styles]] · [[Distributed-Systems]]
+- 부모: [[Software Architecture Styles]] · [[Distributed-Systems]]
 - 변형: [[REST]] · [[gRPC]] · [[MQTT]] · [[WebRTC]]
- 응용: [[BFF]] · [[API-Gateway]] · [[Service-Mesh]] · [[Edge Computing|Edge-Computing]]
+- 응용: [[BFF]] · [[API-Gateway]] · [[Service Mesh]] · [[Edge Computing|Edge-Computing]]
 - 대안: [[Local-First]] · [[Serverless]] · [[CRDT]]
 - Adjacent: [[CAP-Theorem]] · [[Availability-and-Persistence]] · [[Microservices]]
@@ -311,7 +311,7 @@ app.get('/health', (req, res) => {
 ## 🧪 검증 / 중복
 - Verified (Roy Fielding REST thesis, gRPC docs, BFF pattern Sam Newman).
 - 신뢰도 A.
- Related: [[Architecture-Styles]] · [[Microservices]] · [[CAP-Theorem]] · [[Bounded-Contexts]] · [[Availability-and-Persistence]].
+- Related: [[Software Architecture Styles]] · [[Microservices]] · [[CAP-Theorem]] · [[Bounded Contexts (DDD)]] · [[Availability-and-Persistence]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -231,7 +231,7 @@ def refactor_dashboard(repo):
 - 부모: [[Code-Quality]] · [[Refactoring_Best_Practices|Refactoring]]
 - 변형: [[Behavioral-Code-Analysis]] · [[Hotspot-Detection]] · [[Code-Health]]
 - 응용: [[Quality-Gate]]
- Adjacent: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[SonarQube]] · [[Architecture-Anti-Patterns]]
+- Adjacent: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[SonarQube]] · [[Architecture Anti-patterns]]
 ## 🤖 LLM 활용
 **언제**: 매 refactor priority. 매 onboarding. 매 architecture review. 매 incident prevention.
@@ -247,7 +247,7 @@ def refactor_dashboard(repo):
 ## 🧪 검증 / 중복
 - Verified (Adam Tornhill "Your Code as a Crime Scene", CodeScene docs).
 - 신뢰도 B.
- Related: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[Architecture-Anti-Patterns]] · [[Asset-Specific-Knowledge]].
+- Related: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[Architecture Anti-patterns]] · [[Asset-Specific-Knowledge]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -253,8 +253,8 @@ def visit(file):
 - 부모: [[Refactoring_Best_Practices|Refactoring]] · [[Code-Quality]]
 - 변형: [[Bloater]] · [[Coupler]] · [[Dispensable]]
 - 응용: [[Replace-Conditional-with-Polymorphism]]
- 비판: [[Anaemic-Domain-Model]] (Data Class)
+- 비판: [[Anaemic Domain Model]] (Data Class)
- Adjacent: [[Architecture-Anti-Patterns]] · [[Quality_Code_Review_Modern]] · [[Clean-Code-Principles]] · [[SOLID]]
+- Adjacent: [[Architecture Anti-patterns]] · [[Quality_Code_Review_Modern]] · [[Clean-Code-Principles]] · [[SOLID]]
 ## 🤖 LLM 활용
 **언제**: 매 code review. 매 refactor planning. 매 onboarding (smell 의 catalog 인지).
@@ -271,7 +271,7 @@ def visit(file):
 ## 🧪 검증 / 중복
 - Verified (Fowler "Refactoring", Beck "Implementation Patterns", SonarQube rules).
 - 신뢰도 A.
- Related: [[Architecture-Anti-Patterns]] · [[Anaemic-Domain-Model]] · [[Quality_Code_Review_Modern]] · [[Clean-Code-Principles]].
+- Related: [[Architecture Anti-patterns]] · [[Anaemic Domain Model]] · [[Quality_Code_Review_Modern]] · [[Clean-Code-Principles]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -61,7 +61,7 @@ tech_stack:
 ## 🔗 Graph
 - 부모: [[Codebase_Onboarding_Guide]] · [[C4_Model]]
- Adjacent: [[Asset-Specific-Knowledge]] · [[CodeScene]] · [[Bounded-Contexts]]
+- Adjacent: [[Asset-Specific-Knowledge]] · [[CodeScene]] · [[Bounded Contexts (DDD)]]
 ## 🕓 변경 이력 (Changelog)
@@ -61,7 +61,7 @@ tech_stack:
 **언제 X**: 매 single-script project.
 ## 🔗 지식 연결
- Adjacent: [[Asset-Specific-Knowledge]] · [[C4_Model]] · [[Architecture-Styles]] · [[Bounded-Contexts]] · [[CodeScene]]
+- Adjacent: [[Asset-Specific-Knowledge]] · [[C4_Model]] · [[Software Architecture Styles]] · [[Bounded Contexts (DDD)]] · [[CodeScene]]
 ## 💻 패턴
@@ -233,7 +233,7 @@ def premortem(plan):
 **기본값**: 매 weekly cycle. 매 quarterly deep review. 매 daily small adjustment.
 ## 🔗 Graph
- 부모: [[Strategy]] · [[Decision-Theory]]
+- 부모: [[Strategy]] · [[Decision Theory]]
 - 변형: [[Resilience]]
 - 응용: [[Strategic-Agility]]
 - Adjacent: [[Bounded_Rationality|Bounded-Rationality]] · [[Antifragility]] · [[Articulateness]] · [[Be-Detailed]]
@@ -253,7 +253,7 @@ def premortem(plan):
 ## 🧪 검증 / 중복
 - Verified (Korean business literature, Boyd OODA, VUCA US Army War College).
 - 신뢰도 B.
- Related: [[OODA-Loop]] · [[Antifragility]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Premortem]] · [[Case Interviews]].
+- Related: [[OODA-Loop]] · [[Antifragility]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Pre-Mortem]] · [[Case Interviews]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -106,7 +106,7 @@ tech_stack:
 - 매 confirm-shaming.
 - 매 forced continuity.
 - 매 misdirection.
- 매 [[Addiction-Neuroscience]] 참조.
+- 매 [[Addiction Neuroscience]] 참조.
 ### 매 debiasing 기법
 1. **Premortem** (Klein): 매 imagine failure.
@@ -294,10 +294,10 @@ def survivorship_audit(success_set, full_set):
 **기본값**: 매 explicit slow-down + 매 system 2 의 invoke + 매 evidence-based.
 ## 🔗 Graph
- 부모: [[Psychology]] · [[Decision-Theory]] · [[Behavioral-Economics]]
+- 부모: [[Psychology]] · [[Decision Theory]] · [[Behavioral-Economics]]
- 변형: [[Confirmation-Bias]] · [[Loss-Aversion]]
+- 변형: [[Confirmation Bias]] · [[Loss-Aversion]]
 - 응용: [[Nudge]] · [[Debiasing]]
- Adjacent: [[Bounded_Rationality|Bounded-Rationality]] · [[Bias-Correction-Algorithm]] · [[Algorithmic-Fairness]] · [[Beliefs]] · [[Addiction-Neuroscience]] (dark pattern)
+- Adjacent: [[Bounded_Rationality|Bounded-Rationality]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[Beliefs]] · [[Addiction Neuroscience]] (dark pattern)
 - 사상가: [[Kahneman]]
 ## 🤖 LLM 활용
@@ -315,7 +315,7 @@ def survivorship_audit(success_set, full_set):
 ## 🧪 검증 / 중복
 - Verified (Tversky-Kahneman, Kahneman "Thinking", Cialdini "Influence", Thaler "Nudge").
 - 신뢰도 A.
- Related: [[Bounded_Rationality|Bounded-Rationality]] · [[Beliefs]] · [[Bias-Correction-Algorithm]] · [[Algorithmic-Fairness]] · [[Decision-Theory]] · [[Addiction-Neuroscience]].
+- Related: [[Bounded_Rationality|Bounded-Rationality]] · [[Beliefs]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[Decision Theory]] · [[Addiction Neuroscience]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -269,9 +269,9 @@ def classify_capability(capability):
 ## 🔗 Graph
 - 부모: [[Software-Architecture]]
- 변형: [[Team-Topologies]] · [[Inverse-Conway]] · [[Cognitive Load Theory|Cognitive-Load-Theory]]
+- 변형: [[Team Topologies]] · [[Inverse-Conway]] · [[Cognitive Load Theory|Cognitive-Load-Theory]]
- 응용: [[Bounded-Contexts]] · [[Microservices]] · [[Platform-Engineering]]
+- 응용: [[Bounded Contexts (DDD)]] · [[Microservices]] · [[Platform-Engineering]]
- Adjacent: [[Architecture-Styles]] · [[Architecture-Anti-Patterns]] · [[Bottlenecks]] · [[Asset-Specific-Knowledge]]
+- Adjacent: [[Software Architecture Styles]] · [[Architecture Anti-patterns]] · [[Bottlenecks]] · [[Asset-Specific-Knowledge]]
 ## 🤖 LLM 활용
 **언제**: 매 org redesign. 매 team boundary. 매 platform strategy. 매 onboarding optimization.
@@ -288,7 +288,7 @@ def classify_capability(capability):
 ## 🧪 검증 / 중복
 - Verified (Conway 1967, Skelton-Pais 2019, Sweller cognitive load).
 - 신뢰도 A.
- Related: [[Architecture-Styles]] · [[Bounded-Contexts]] · [[Architecture-Anti-Patterns]] · [[Codebase_Onboarding_Guide]] · [[Asset-Specific-Knowledge]].
+- Related: [[Software Architecture Styles]] · [[Bounded Contexts (DDD)]] · [[Architecture Anti-patterns]] · [[Codebase_Onboarding_Guide]] · [[Asset-Specific-Knowledge]].
 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -301,7 +301,7 @@ Explain in plain language.""")
 - 부모: [[Agent-Architecture]] · [[AI]]
 - Classical: [[SOAR]] · [[ACT-R]]
 - Modern: [[ReAct]] · [[LangGraph]] · [[AutoGen]] · [[Reflexion]]
- 응용: [[Memory-Hierarchy]] · [[Working-Memory]]
+- 응용: [[Memory-Hierarchy]] · [[Working Memory]]
 - Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Biological-Intelligence]] · [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]] · [[Multi-agent-System|Multi-Agent-Systems]]
 ## 🤖 LLM 활용
--- a/Show More
+++ b/Show More