Wiki cleanup: error-doc removal, dedup merge, link normalization

10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 23:52:15 +09:00
parent 2a4a5046b6
commit f8b21af4be
2874 changed files with 15296 additions and 27684 deletions
@@ -17,6 +17,6 @@
  "repelStrength": 10,
  "linkStrength": 1,
  "linkDistance": 250,
-  "scale": 0.07209699426636948,
+  "scale": 0.19926643092553617,
  "close": true
 }
@@ -11,14 +11,10 @@
            "id": "49ae5a843bcdef44",
            "type": "leaf",
            "state": {
-              "type": "markdown",
-              "state": {
-                "file": "AI_and_ML/The Evolution of Music Distribution.md",
-                "mode": "source",
-                "source": false
-              },
-              "icon": "lucide-file",
-              "title": "The Evolution of Music Distribution"
+              "type": "graph",
+              "state": {},
+              "icon": "lucide-git-fork",
+              "title": "그래프 뷰"
            }
          }
        ]
@@ -196,10 +192,20 @@
  },
  "active": "49ae5a843bcdef44",
  "lastOpenFiles": [
-    "AI_and_ML/AI for Social Good.md",
+    "무제 3.canvas",
    "무제 2.canvas",
+    "Special-Education.md",
+    "Topics.md",
+    "Graphics & Performance.md",
+    "ComfyUI.md",
+    "창의성.md",
+    "DevOps.md",
+    "Architecture.md",
+    "Yoast.md",
    "무제 1.canvas",
    "무제.canvas",
+    "AI_and_ML/The Evolution of Music Distribution.md",
+    "AI_and_ML/AI for Social Good.md",
    "Green-Check-Mark-Syndrome.md",
    "_company/00_Raw/conversations/2026-05-10.md",
    "AI_and_ML/Brain-Computer_Interface_(BCI).md",
@@ -224,16 +230,6 @@
    "UI_UX_Assets/Design & Experience/Computational-Fluid-Dynamics.md",
    "Coding/Frontend_Vue3_Svelte5_Patterns.md",
    "Coding/Backend_gRPC_Streaming_Deep.md",
-    "Coding/Frontend_A11y_Modern.md",
-    "Coding/AI_Fine_Tune_Practical.md",
-    "Coding/Productivity_Onboarding_Process.md",
-    "Coding/AI_Code_Agent_Patterns.md",
-    "Coding/Backend_Cron_Workflows_Inngest.md",
-    "Coding/Android_Compose_Performance.md",
-    "Coding/iOS_Swift_Macros_Deep.md",
-    "Coding/AI_Vision_Multimodal_Production.md",
-    "Coding/Backend_WebSocket_Production.md",
-    "Coding/Frontend_Solid_Qwik_Deep.md",
-    "Coding/Frontend_Astro_Islands_Deep.md"
+    "Coding/Frontend_A11y_Modern.md"
  ]
 }
@@ -137,10 +137,7 @@ class Diplomat:
 **기본값**: 매 explore-first, 매 expand-until-economy-saturates, 매 specialize.

 ## 🔗 Graph
- 부모: [[Strategy Games]] · [[Game AI]]
- 변형: [[Grand Strategy]] · [[RTS]] · [[Eurogame]]
- 응용: [[MCTS]] · [[Multi-Agent RL]] · [[Game Theory]]
- Adjacent: [[Civilization]] · [[Stellaris]] · [[Tech Tree Design]]
+- 응용: [[MCTS]]

 ## 🤖 LLM 활용
 **언제**: 매 narrative event generation, flavor text, 매 dynamic dialogue with diplomats.
@@ -2,156 +2,26 @@
 id: wiki-2026-0508-ai-safety-and-alignment
 title: AI Safety and Alignment
 category: 10_Wiki/Topics
-status: verified
-canonical_id: self
-aliases: [AI Alignment, AI Safety]
-duplicate_of: none
+status: duplicate
+canonical_id: wiki-2026-0509-ai-safety-and-alignment
+duplicate_of: "[[AI Safety and Alignment]]"
+aliases: []
 source_trust_level: A
 confidence_score: 0.9
-verification_status: applied
-tags: [ai-safety, alignment, rlhf, constitutional-ai]
-raw_sources: []
-last_reinforced: 2026-05-10
+verification_status: redirected
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
-tech_stack:
-  language: python
-  framework: trl/transformers
 ---

 # AI Safety and Alignment

-## 매 한 줄
-> **"매 capable model 의 intended behavior 의 reliable production — 매 outer + inner alignment."** 매 RLHF (InstructGPT 2022) 로 시작 의 mainstream — 매 Constitutional AI (Anthropic 2022), DPO (2023), RLAIF (2023), 매 2026 에 deliberative alignment + interpretability-aware training 의 frontier.
-
-## 매 핵심
-
-### 매 alignment problem 분해
- **Outer alignment**: 매 specified objective ≈ true human intent — 매 reward hacking, Goodhart's law.
- **Inner alignment**: 매 trained policy 의 specified objective 의 optimization — 매 mesa-optimization, deceptive alignment.
- **Scalable oversight**: 매 super-human capability 의 supervision — 매 debate, recursive reward modeling, weak-to-strong.
-
-### 매 techniques (2026 stack)
- **RLHF**: PPO on reward model from preferences.
- **DPO / IPO / KTO**: 매 reward-model-free preference optimization.
- **Constitutional AI**: 매 written principles → self-critique → RLAIF.
- **Deliberative alignment** (OpenAI o-series, Claude 4.x): 매 reasoning trace 의 spec lookup.
- **Interpretability**: SAEs, circuits — 매 feature steering.
-
-### 매 응용
-1. Refusal of harmful requests + helpful behavior on benign edge cases.
-2. Policy compliance (privacy, copyright, weapons).
-3. Honesty / calibration.
-
-## 💻 패턴
-
-### Reward model training (Bradley-Terry)
-```python
-import torch
-import torch.nn.functional as F
-
-def bt_loss(reward_chosen, reward_rejected):
-    # P(chosen > rejected) = sigmoid(r_c - r_r)
-    return -F.logsigmoid(reward_chosen - reward_rejected).mean()
-
-# Forward
-r_c = model(chosen_ids).logits[:, -1, 0]
-r_r = model(rejected_ids).logits[:, -1, 0]
-loss = bt_loss(r_c, r_r)
-```
-
-### DPO loss
-```python
-def dpo_loss(pi_logp_c, pi_logp_r, ref_logp_c, ref_logp_r, beta=0.1):
-    # Direct preference optimization
-    chosen = beta * (pi_logp_c - ref_logp_c)
-    rejected = beta * (pi_logp_r - ref_logp_r)
-    return -F.logsigmoid(chosen - rejected).mean()
-```
-
-### Constitutional self-critique
-```python
-def constitutional_revise(prompt, response, principles, llm):
-    critique = llm(f"""
-    Principles: {principles}
-    Prompt: {prompt}
-    Response: {response}
-    Critique the response against the principles.
-    """)
-    revised = llm(f"""
-    Original: {response}
-    Critique: {critique}
-    Revise the response to address the critique.
-    """)
-    return revised
-```
-
-### SAE feature steering (interpretability)
-```python
-# Sparse autoencoder feature ablation
-def steer(activations, sae, feature_idx, scale):
-    z = sae.encode(activations)
-    z[:, feature_idx] *= scale  # 0 = ablate, >1 = amplify
-    return sae.decode(z)
-
-# Hook on residual stream
-hook = lambda x: steer(x, sae, refusal_feature_idx, scale=0.0)
-```
-
-### Best-of-N with RM
-```python
-def best_of_n(prompt, policy, rm, n=64):
-    samples = [policy.sample(prompt) for _ in range(n)]
-    scores = [rm.score(prompt, s) for s in samples]
-    return samples[int(torch.tensor(scores).argmax())]
-```
-
-### Red-team probe
-```python
-def red_team_eval(model, attacks):
-    results = []
-    for attack in attacks:
-        out = model.generate(attack.prompt)
-        results.append({
-            "attack": attack.name,
-            "harmful": classify_harm(out),
-            "refused": "I can't" in out or "I cannot" in out,
-        })
-    return results
-```
-
-## 매 결정 기준
-| 상황 | Approach |
-|---|---|
-| Limited compute | DPO over PPO-RLHF |
-| Need transparent specs | Constitutional AI |
-| Frontier model | Deliberative alignment + scalable oversight |
-| Behavior debugging | SAE feature steering |
-| Pre-deployment | Red-team + capability evals |
-
-**기본값**: 매 SFT → DPO → eval → iterate. 매 PPO 의 only-when-needed.
+> **이 문서는 [[AI Safety and Alignment]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 🔗 Graph
- 부모: [[Machine Learning]] · [[AI Ethics]]
- 변형: [[RLHF]] · [[DPO]] · [[Constitutional AI]] · [[RLAIF]]
- 응용: [[Claude]] · [[GPT-5]] · [[Llama Guard]]
- Adjacent: [[Mechanistic Interpretability]] · [[Red Teaming]] · [[AI Governance]]
+- 부모: [[AI Safety and Alignment]] (canonical)

-## 🤖 LLM 활용
-**언제**: 매 production deployment 전 의 alignment pipeline (SFT + preference training + evals).
-**언제 X**: 매 pure capability research, 매 internal-only sandbox.
-
-## ❌ 안티패턴
- **Reward hacking**: 매 proxy metric 의 over-optimization — 매 KL penalty, eval diversity.
- **Sycophancy**: 매 user agreement 의 over-reward — 매 truthfulness 의 explicit reward.
- **Over-refusal**: 매 false-positive harmful detection — 매 helpfulness eval 의 balance.
- **Single-axis eval**: 매 only safety, no capability — 매 Pareto frontier.
-
-## 🧪 검증 / 중복
- Verified (Anthropic Constitutional AI paper, OpenAI InstructGPT, Rafailov et al. DPO 2023).
- 신뢰도 A.
-
-## 🕓 Changelog
+## 🕓 변경 이력
 | 날짜 | 변경 |
 |---|---|
-| 2026-05-08 | Phase 1 |
-| 2026-05-10 | Manual cleanup — alignment stack with code patterns |
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -145,10 +145,9 @@ def apply_repetition_penalty(logits, generated_ids, penalty=1.1):
 **기본값**: 매 T=0.7 + min-p=0.05.

 ## 🔗 Graph
- 부모: [[LLM Inference]] · [[Decoding]]
- 변형: [[Beam Search]] · [[Nucleus Sampling]] · [[Mirostat]]
- 응용: [[Self-Consistency]] · [[Speculative Decoding]] · [[CoT]]
- Adjacent: [[vLLM]] · [[Temperature]] · [[Repetition Penalty]]
+- 부모: [[Decoding]]
+- 응용: [[Self-Consistency]]
+- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|vLLM]]

 ## 🤖 LLM 활용
 **언제**: 매 inference pipeline 의 every call — 매 task 의 sampler 의 match.
@@ -341,11 +341,10 @@ public class TutorialHint : MonoBehaviour {
 - **AI 의 stance vs player 의 stance**: 매 player 의 strategic exploit.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Game-Design-RTS]] · [[Combat-AI]] · [[Unit-Control]]
- 변형: [[StarCraft-Micro]] · [[MOBA-Last-Hit]] · [[Auto-Battler]]
- 응용: [[AI-Exploitation-Game]] · [[Baiting-Tactics]] · [[Defensive-Design]]
- 게임: [[War-Commander]] · [[Battle-Pirates]] · [[VEGA-Conflict]] · [[Kixeye]]
- Adjacent: [[FSM-Game-AI]] · [[Behavior-Tree]] · [[Hotkey-Design]] · [[Player-Skill-Expression]]
+- 변형: [[StarCraft-Micro]]
+- 응용: [[Baiting-Tactics]]
+- 게임: [[War-Commander]] · [[Kixeye]]
+- Adjacent: [[Behavior-Tree]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -187,10 +187,9 @@ github_commit: pending
 **기본값**: Cross-platform + AI-augmented + UGC-friendly + 매 region 의 strategy.

 ## 🔗 Graph
- 부모: [[Game-Industry]] · [[Market-Research]] · [[Game-Monetization]]
- 변형: [[Cloud-Gaming]] · [[UGC-Games]] · [[Generative-AI-Games]]
- 응용: [[Cross-Platform-Release]] · [[Subscription-Gaming]] · [[Creator-Economy]]
- Adjacent: [[Apple-App-Store-Antitrust]] · [[DMA-Digital-Markets-Act]] · [[Roblox]] · [[Minecraft]]
+- 부모: [[Game-Monetization]]
+- 변형: [[Cloud-Gaming]]
+- Adjacent: [[Roblox]] · [[Minecraft]]

 ## 🤖 LLM 활용
 **언제**: 매 game studio 의 strategic plan. 매 monetization 의 design. 매 platform 결정.
@@ -247,11 +247,9 @@ def cost_aware_batch(prompts, target='exploration'):
 **기본값**: 5-layer prompt + draft mode + reference + post-edit + upscale 의 sequence.

 ## 🔗 Graph
- 부모: [[AI-Image-Generation]] · [[Creative-Workflow]] · [[Visual-AI]]
- 변형: [[Draft-Mode]] · [[Omni-Reference]] · [[Continuous-Refinement]]
- 응용: [[Brand-Campaign-AI]] · [[Game-Asset-Generation]] · [[Concept-Art]]
- Tools: [[Midjourney-V7]] · [[Flux]] · [[Sora-OpenAI]] · [[Veo-Google]] · [[Magnific]]
- Adjacent: [[Photography-Vocabulary]] · [[Lighting-Science]] · [[Color-Theory]]
+- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 변형: [[Draft-Mode]] · [[Omni-Reference]]
+- Tools: [[Midjourney-V7]] · [[Flux]]

 ## 🤖 LLM 활용
 **언제**: 매 commercial creative project. 매 visual brand.
@@ -267,7 +265,7 @@ def cost_aware_batch(prompts, target='exploration'):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[AI-Image-Generation]] / [[Post-editing-Tools]] / [[Image-Workflow]].
+- Overlap with [[AI 이미지 생성 (AI Image Generation)]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[Image-Workflow]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -254,9 +254,9 @@ console.log({

 ## 🔗 Graph
 - 부모: [[Three-js-Performance]] · [[Skinned-Mesh]] · [[Instancing]]
- 변형: [[InstancedMesh2-agargaro]] · [[BatchedMesh]]
- 응용: [[Crowd-Simulation]] · [[Open-World-Rendering]] · [[Metaverse]]
- 기술: [[GPU-Skinning]] · [[Frustum-Culling]] · [[Level-of-Detail]] · [[Bone-Texture]]
+- 변형: [[BatchedMesh]]
+- 응용: [[Crowd-Simulation]]
+- 기술: [[GPU-Skinning]] · [[Frustum-Culling]] · [[Level-of-Detail]]

 ## 🤖 LLM 활용
 **언제**: 매 large character scene 의 design. 매 mobile / web 3D 의 performance.
@@ -224,10 +224,7 @@ viewer.addSplatScene('./scene.ply').then(() => {
 - **Mobile performance**: 매 platform 의 GPU 차이. iPhone 가 OK, low-end Android 가 부족.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Neural-Rendering]] · [[Novel-View-Synthesis]] · [[Differentiable-Rendering]]
- 변형: [[NeRF-Neural-Radiance-Fields]] · [[4D-Gaussian-Splatting]] · [[Dynamic-3DGS]]
- 응용: [[VR-AR-Reconstruction]] · [[Photogrammetry]] · [[Self-Driving-Simulation]] · [[Cultural-Heritage-3D]]
- Adjacent: [[Spherical-Harmonics]] · [[COLMAP-SfM]] · [[Point-Cloud]] · [[Mesh-Reconstruction]]
+- Adjacent: [[Point-Cloud]]
 - Tools: gsplat · NeRF Studio · Brush · Splatfacto · Polycam · Luma AI
 - Web: [[WebGPU]] · [[WebGL]] · [[Three.js]] · [[Babylon.js]]

@@ -255,12 +255,10 @@ def reward(state, action, next_state):
 - **Game design 의 ethics**: addiction-like design 의 윤리 / 법적 risk.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Behaviorism]] · [[Skinner-Operant-Conditioning]] · [[Learning-Theory]]
- 변형: [[Discrete-Trial-Training]] · [[Pivotal-Response-Training]] · [[Verbal-Behavior]]
- 응용: [[Habit-Formation]] · [[Atomic-Habits-Clear]] · [[Tiny-Habits-Fogg]] · [[Game-Reward-Design]] · [[RL-Reward-Shaping]]
- AI: [[RLHF-Human-Feedback]] · [[Reward-Hacking]] · [[Actor-Critic-Models]]
- Game: [[Variable-Reward-Schedule]] · [[Loot-Box-Mechanics]] · [[Skinner-Box]] · [[Token-Economy]]
- 비판: [[Autistic-Advocacy]] · [[Overjustification-Effect]] · [[Intrinsic-Motivation]]
+- 응용: [[Habit-Formation]]
+- AI: [[Actor-Critic-Models]]
+- Game: [[Loot-Box-Mechanics]]
+- 비판: [[Intrinsic-Motivation]]
 - Adjacent: [[Addiction-Neuroscience]] · [[Dopamine-Pathway]] · [[Behavioral-Economics]] · [[Nudge-Theory]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -376,10 +376,8 @@ function isAllowed(tool: string, args: any): boolean {
 - **Vision (browser screenshot) vs DOM**: Vision 가 robust 가, expensive. DOM tree 가 cheap 가, brittle.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Agent-Architecture]] · [[Tool-Use-Function-Calling]] · [[Prompt-Engineering]]
- 변형: [[MCP-Model-Context-Protocol]] · [[OpenAI-Function-Calling]] · [[ReAct-Pattern]]
- 응용: [[SWE-Agent-Princeton]] · [[Devin-Cognition]] · [[Cursor-Workflow-Patterns]] · [[Claude-Code]] · [[OpenAI-Operator]]
- Adjacent: [[Context-Engineering]] · [[Token-Budget-Patterns]] · [[Agent-Sandbox-E2B]] · [[Browser-Agent-Patterns]]
+- 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]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
@@ -145,8 +145,6 @@ export function activate(context: vscode.ExtensionContext) {

 ## 🔗 지식 연결 (Graph)
 - 부모: [[ADR-Architecture-Decision-Record]] · [[Modular-Design]] · [[Separation-of-Concerns]]
- 응용: [[Hexagonal-Clean]] · [[DDD-Bounded-Context]] · [[Module-Boundaries]]
- Project: [[Antigravity-Project]] · [[ConnectAI-LLM-Tool]]

 ## 🤖 LLM 활용 힌트
 **언제 사용**: 매 새 feature 의 architecture 의 결정. 매 modular boundary 의 example.
@@ -398,13 +398,12 @@ async function auditDataAccess(user: User, data: any, action: string) {
 - **개인 vs 국가 sovereignty 의 tension**: 매 government access (China, etc.).

 ## 🔗 지식 연결 (Graph)
- 부모: [[Privacy]] · [[Data-Governance]] · [[AI-Ethics]]
- 변형: [[GDPR-Compliance]] · [[Data-Localization]] · [[Sovereign-Cloud]] · [[Sovereign-AI]]
- 기술: [[Federated-Learning]] · [[Differential-Privacy]] · [[Homomorphic-Encryption]] · [[Confidential-Computing]] · [[Secure-MPC]]
- 비판: [[Data-Colonialism]] · [[Big-Tech-Power]] · [[Digital-Imperialism]]
- 응용: [[AI-Governance-Policy]] · [[AI-Accountability]] · [[Privacy-by-Design]]
- 정책: [[Schrems-II]] · [[EU-AI-Act]] · [[China-PIPL]] · [[GAIA-X]]
- AI sovereign: [[Mistral-AI]] · [[HyperCLOVA-X]] · [[Yi-Qwen-China]] · [[Falcon-UAE]]
+- 부모: [[Privacy]] · [[AI-Ethics]]
+- 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]]
+- 정책: [[EU-AI-Act]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -435,7 +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-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.

@@ -358,12 +358,11 @@ def predict(features):
 - **Cross-border**: 매 country 의 different regulation. AI 의 global → fragmented.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[AI-Governance-Policy]] · [[Algorithmic-Fairness]]
- 변형: [[Explainable-AI-XAI]] · [[Model-Card]] · [[Datasheets-for-Datasets]] · [[Bias-Audit]]
- 응용: [[EU-AI-Act-Compliance]] · [[GDPR-Article-22]] · [[NYC-Local-Law-144]] · [[FDA-AI-SaMD]]
- 기술: [[SHAP-Interpretability]] · [[LIME]] · [[Counterfactual-Explanation]] · [[DVC-MLflow-Versioning]]
- Adjacent: [[AI-Liability]] · [[Responsibility-Gap]] · [[Human-in-the-Loop]] · [[Right-to-Explanation]]
- 응용: [[MLOps-Model-Monitoring]] · [[Continuous-Learning-System]] · [[AI-Audit-Log]]
+- 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]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -396,7 +395,7 @@ def predict(features):
 - **검토 이유:** Manual cleanup. Active research / regulation. 매 6 month review.

 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI-Governance-Policy]] (related), [[AI-Ethics]] (parent), [[Explainable-AI-XAI]] (subset).
+- **기존 유사 문서:** [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related), [[AI-Ethics]] (parent), [[Explainable-AI-XAI]] (subset).
 - **처리 방식:** KEEP (focused on accountability mechanism).
 - **처리 이유:** Accountability 가 distinct discipline (legal + technical + ethical).

@@ -284,13 +284,9 @@ async function queryBrain(question: string): Promise<string[]> {
 - **Model lifecycle**: 옛 = 매 chat 의 load (slow). 모던 = persistent + idle eject (LM Studio 통합).

 ## 🔗 지식 연결 (Graph)
- 관련 tool: [[Ollama]] · [[LM-Studio]] · [[vLLM]] · [[llama.cpp]]
- VS Code: [[VS-Code-Extension-API]] · [[Webview-Provider]] · [[Tree-Sitter-Integration]]
- Cloud alternative: [[Cursor-Workflow-Patterns]] · [[Claude-Code]] · [[GitHub-Copilot]]
- Local LLM: [[Local-LLM-Inference]] · [[Quantization-GGUF]] · [[Model-Selection-Hardware]]
- RAG: [[Vector-DB-Local]] · [[ChromaDB]] · [[LanceDB]] · [[Embedding-Strategy-Deep]]
- Lifecycle: [[Model-Loading-Memory-Management]] · [[GPU-Memory-Pressure]]
- 적용: [[Antigravity-Project]] · [[Connect-AI-Lab]] · [[EZERAI-Infrastructure]]
+- 관련 tool: [[Ollama]] · [[LM-Studio]] · [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[llama.cpp]]
+- Cloud alternative: [[Claude-Code]]
+- 적용: [[Connect-AI-Lab]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -292,12 +292,9 @@ def chat(query):
 - **Reasoning trace 의 eval**: o1 / R1 의 chain-of-thought 의 quality 측정 = 새 challenge.

 ## 🔗 지식 연결 (Graph)
- 부모: [[LLM-Capabilities]] · [[Model-Quality]] · [[ML-Eval-Methodology]]
- 변형: [[Static-Benchmark]] · [[Live-Benchmark]] · [[Human-Pref-Eval]] · [[LLM-as-Judge]]
- 응용: [[Continuous-Learning-System]] · [[Production-Drift-Detection]] · [[Domain-Specific-Eval]]
- Adjacent: [[Contamination-Detection]] · [[Goodhart-Law-AI]] · [[Reasoning-Trace-Eval]]
+- 변형: [[LLM-as-Judge]]
 - Tools: lm-eval-harness · Promptfoo · LangSmith · Inspect (AISI) · Braintrust · Helicone · Langfuse
- Related: [[Continuous-Learning-System]] · [[AI-Code-Agent-Patterns]] · [[Multi-Modal-Vision-Production]]
+- Related: [[AI-Code-Agent-Patterns]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -381,13 +381,11 @@ public class AnomalyDetector {
 - **Adaptive AI 의 player frustration**: 매 매 try 의 different 가 unfair feel.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Game-AI-Design]] · [[Game-Mechanics]] · [[Combat-Design]]
- 변형: [[Behavior-Tree]] · [[Finite-State-Machine]] · [[Utility-AI]] · [[GOAP-Goal-Oriented]]
- 응용: [[Baiting-Tactics]] · [[Kiting-MMO]] · [[Boss-Fight-Design]] · [[AI-Director-Left4Dead]]
- 매 게임: [[War-Commander-Combat]] · [[Dark-Souls-AI]] · [[StarCraft-AI]] · [[Halo-AI]]
- Modern: [[AlphaStar]] · [[OpenAI-Five]] · [[RL-Game-AI]]
- Adjacent: [[Adaptive-Difficulty]] · [[Procedural-Combat]] · [[Player-Skill-Expression]]
- Anti-cheese: [[Leash-Reset]] · [[State-Validation]] · [[Anti-Exploit-Telemetry]]
+- 부모: [[Game-Mechanics]]
+- 변형: [[Behavior-Tree]]
+- 응용: [[Baiting-Tactics]]
+- 매 게임: [[War-Commander-Combat]]
+- Adjacent: [[Adaptive-Difficulty]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -283,12 +283,9 @@ class AITutor {
 - **AI 의 emergent capability**: 매 augmentation tool 가 replacement 가 될 수.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[Human-Centered-Design]] · [[Philosophy-of-Technology]]
- 변형: [[Co-Intelligence-Mollick]] · [[Augmented-Intelligence-IBM]] · [[Centaur-Model-Chess]] · [[Humane-Tech]]
- 응용: [[AI-in-Education]] · [[AI-in-Healthcare]] · [[AI-Workplace-Augmentation]] · [[Creative-AI-Ethics]]
- 비판: [[AI-Solutionism-Critique]] · [[Job-Replacement-Reality]] · [[Anthropomorphism-Critique]]
- 관련: [[AI-Literacy]] · [[AI-Accountability]] · [[AI-Governance-Policy]] · [[AI-Safety-and-Alignment]]
- Adjacent: [[Universal-Basic-Income-UBI]] · [[Reskilling-Workforce]] · [[Slow-AI-Movement]] · [[Critical-AI]]
+- 부모: [[AI-Ethics]]
+- 관련: [[AI-Literacy]] · [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] · [[AI-Safety-and-Alignment]]
+- Adjacent: [[Universal-Basic-Income-UBI]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -245,12 +245,10 @@ Week 6: Agent basic.
 - **Co-intelligence (Mollick)**: 매 task 의 AI + human 의 collaboration. "AI 가 없는 인간 의 의미" 재정의.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Digital-Literacy]] · [[Information-Literacy]] · [[Critical-Thinking]]
- 변형: [[Prompt-Engineering]] · [[AI-Ethics]] · [[Algorithm-Literacy]]
- 응용: [[AI-Education-Curriculum]] · [[AI-in-Workplace]] · [[Co-Intelligence-Mollick]]
- Adjacent: [[Hallucination-Detection]] · [[AI-Bias]] · [[Adaptability]] · [[Lifelong-Learning]]
- Tools / education: [[Elements-of-AI-Course]] · [[fast.ai]] · [[Khan-Academy-AI]] · [[Mollick-Co-Intelligence-Book]]
- 응용: [[AI-Code-Agent-Patterns]] · [[Cursor-Workflow-Patterns]] · [[Continuous-Learning-System]]
+- 부모: [[Digital-Literacy]] · [[Information-Literacy]]
+- 변형: [[Prompt_Engineering|Prompt-Engineering]] · [[AI-Ethics]]
+- Adjacent: [[Lifelong-Learning]]
+- 응용: [[AI-Code-Agent-Patterns]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -283,7 +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]] (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.

@@ -425,13 +425,8 @@ For each: Where? What's the issue? How to fix?
 - **WGA / writer 의 contract**: 매 industry change.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Storytelling]] · [[Generative-AI]] · [[Creative-Writing]]
- 변형: [[AI-Dungeon]] · [[Interactive-Fiction]] · [[Game-Narrative]] · [[Procedural-Storytelling]]
- 응용: [[NPC-Dynamic-Dialogue]] · [[Branching-Narrative]] · [[Worldbuilding-AI]] · [[Screenwriting-AI]]
- 기법: [[Hero-Journey-Campbell]] · [[Save-the-Cat-Snyder]] · [[3-Act-Structure]] · [[Pixar-22-Rules]]
- Tools: [[Inworld-AI]] · [[Convai]] · [[Sudowrite]] · [[NovelCrafter]] · [[Charisma-AI]] · [[NovelAI]]
- Game: [[Game-Narrative-Design]] · [[Procedural-Story-Generation]] · [[Emergent-Narrative]]
- 윤리: [[AI-Copyright]] · [[Author-Authenticity]] · [[WGA-Strike-2023]]
+- 부모: [[Generative-AI]]
+- 변형: [[Interactive-Fiction]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -353,13 +353,9 @@ class ImpactTracker:
 - **매 SDG 의 hype**: 매 vendor 의 SDG checkbox + 매 actual impact 의 부족.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[Technology-for-Development]] · [[Public-Interest-Tech]]
- 변형: [[AI-for-Earth]] · [[AI-for-Health]] · [[AI-for-Climate]] · [[AI-for-Accessibility]]
- 응용: [[Federated-Learning]] · [[Low-Resource-NLP]] · [[Satellite-Imagery-ML]] · [[Mobile-AI-Edge]]
- 비판: [[AI-Solutionism]] · [[AI-Colonialism]] · [[Pilotitis]] · [[AI4SG-Washing]]
- 관련: [[AI-Humanism]] · [[AI-Accountability]] · [[AI-Governance-Policy]]
- 기관: [[Google-AI-for-Social-Good]] · [[Microsoft-AI-for-Earth]] · [[Partnership-on-AI]] · [[UN-Global-Pulse]] · [[Anthropic-Claude-for-Climate]]
- Adjacent: [[Co-Design]] · [[Theory-of-Change]] · [[Human-Rights-Impact-Assessment]] · [[Sustainable-Development-Goals]]
+- 부모: [[AI-Ethics]]
+- 응용: [[Federated-Learning]]
+- 관련: [[AI-Humanism]] · [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -392,7 +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-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.

@@ -297,12 +297,10 @@ def audit_bias(model, test_set):
 - **Vendor 의 data assurance**: "data not used for training" claim 의 verification 어려움.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[Corporate-Governance]] · [[Risk-Management]]
- 변형: [[Acceptable-Use-Policy]] · [[Data-Privacy-Policy]] · [[Vendor-Management]]
- 응용: [[EU-AI-Act-Compliance]] · [[NIST-AI-RMF]] · [[ISO-42001]] · [[GDPR-AI-Implications]]
- 기술: [[DLP-Data-Loss-Prevention]] · [[Bias-Audit]] · [[Model-Card]] · [[AI-Audit-Log]]
- 응용: [[AI-Literacy]] · [[AI-Safety-Constitutional]] · [[AI-Accountability]]
- Adjacent: [[Shadow-IT]] · [[Compliance-Framework]] · [[Privacy-by-Design]]
+- 부모: [[AI-Ethics]] · [[Risk_Management|Risk-Management]]
+- 응용: [[NIST-AI-RMF]] · [[ISO-42001]]
+- 기술: [[Model-Card]]
+- 응용: [[AI-Literacy]] · [[AI-Accountability]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -480,12 +480,12 @@ async function processSuggestion(suggestion) {
 - **DORA metric 의 unclear improvement**: 매 study 의 mixed evidence.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Code-Review]] · [[Static-Analysis]] · [[DevSecOps]]
- 변형: [[CodeRabbit]] · [[Greptile]] · [[Cursor]] · [[Sourcegraph]] · [[Snyk]] · [[Sonar]]
- 응용: [[MCP-Model-Context-Protocol]] · [[Codebase-RAG]] · [[Code-Property-Graph]] · [[Taint-Analysis]]
+- 부모: [[AI_코드_리뷰]] · [[Static-Analysis]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]]
+- 변형: [[CodeRabbit]] · [[Greptile]] · [[Cursor]] · [[Sonar]]
+- 응용: [[Codebase-RAG]] · [[Code-Property-Graph]]
 - 기술: [[AST]] · [[Semgrep]] · [[CodeQL]] · [[Joern]]
- 응용: [[Behavioral-Code-Analysis]] · [[CodeScene-Hotspot]] · [[Technical-Debt]]
- Adjacent: [[AI-Code-Agent-Patterns]] · [[Cursor-Workflow-Patterns]] · [[ConnectAI-LLM-Tool]] · [[AI-Coding-Productivity]]
+- 응용: [[Behavioral-Code-Analysis]] · [[Technical_Debt|Technical-Debt]]
+- Adjacent: [[AI-Code-Agent-Patterns]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -516,7 +516,7 @@ async function processSuggestion(suggestion) {
 - **검토 이유:** Manual cleanup. 매 vendor / tool 의 매 6 month 의 evolution.

 ## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI_코드_리뷰]] (related), [[AI-Code-Review]] (related), [[AI_Powered_Code_Analysis]] (similar — possibly duplicate).
+- **기존 유사 문서:** [[AI_코드_리뷰]] (related), [[AI_코드_리뷰]] (related), [[AI_Powered_Code_Analysis]] (similar — possibly duplicate).
 - **처리 방식:** KEEP (focused on tool landscape).
 - **처리 이유:** 매 tool 의 broader survey.

@@ -425,11 +425,11 @@ with ThreadPoolExecutor(max_workers=4) as executor:
 - **Inpaint dedicated model vs general**: 매 dedicated 의 better.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Image-Generation]] · [[Image-Editing]] · [[Diffusion-Models]]
- 변형: [[Inpainting]] · [[Outpainting]] · [[Img2Img]] · [[Upscale]] · [[ControlNet]]
- 응용: [[Photoshop-Generative-Fill]] · [[Midjourney-Vary-Region]] · [[ComfyUI-Workflow]]
- Tool: [[Diffusers-Library]] · [[Real-ESRGAN]] · [[GFPGAN]] · [[CodeFormer]] · [[IP-Adapter]] · [[Flux-Fill]]
- Adjacent: [[Image-Quality-Optimization-Debugging]] · [[Iterative-Refinement]] · [[Prompt-Engineering]]
+- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Diffusion-Models]]
+- 변형: [[Inpainting]] · [[Outpainting]] · [[Upscale]] · [[ControlNet]]
+- 응용: [[Midjourney-Vary-Region]]
+- Tool: [[IP-Adapter]]
+- Adjacent: [[Iterative-Refinement]] · [[Prompt_Engineering|Prompt-Engineering]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -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-Image-Generation]] (parent), [[AI 이미지 생성 및 편집 워크플로우]] (related), [[AI 이미지 품질 최적화]] (related).
+- **기존 유사 문서:** [[AI 이미지 생성 (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-Code-Review]] · [[DevSecOps]] · [[Code-Quality]]
- 변형: [[Hallucination-Detection]] · [[SAST]] · [[LLM-as-Judge]]
+- 부모: [[AI_코드_리뷰]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[Code-Quality]]
+- 변형: [[SAST]] · [[LLM-as-Judge]]
 - 응용: [[CodeRabbit]] · [[Snyk-Code]] · [[Sonar]]
- Adjacent: [[Vibe-Coding]] · [[AI-Code-Agent-Patterns]] · [[Cursor-Workflow-Patterns]]
+- Adjacent: [[AI-Code-Agent-Patterns]]

 ## 🤖 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-Code-Review]], [[AI-Powered-Code-Analysis-Tools]].
+- Related: [[AI_코드_리뷰]], [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -471,13 +471,11 @@ print(f"CLIP score: {similarity:.3f}")
 - **Flux 의 emerging**: 매 modern SoTA 가 SDXL 의 surpass.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Generative-AI]] · [[Diffusion-Models]] · [[Computer-Vision]]
+- 부모: [[Generative-AI]] · [[Diffusion-Models]] · [[Computer Vision|Computer-Vision]]
 - 변형: [[Stable-Diffusion]] · [[Flux]] · [[Midjourney]] · [[DALL-E]] · [[Imagen]]
- 응용: [[ControlNet]] · [[LoRA]] · [[Inpainting]] · [[Img2Img]] · [[IP-Adapter]]
- 기법: [[Prompt-Engineering]] · [[Negative-Prompt]] · [[CFG-Scale]] · [[Sampling-Steps]]
- 응용 분야: [[AI-Art-Commercial]] · [[Game-Asset-Generation]] · [[Marketing-AI]]
- 윤리: [[AI-Copyright]] · [[Training-Data-Lawsuit]] · [[AI-Disclosure]]
- Tools: [[ComfyUI]] · [[Automatic1111]] · [[Diffusers-Library]] · [[Replicate-API]]
+- 응용: [[ControlNet]] · [[LoRA]] · [[Inpainting]] · [[IP-Adapter]]
+- 기법: [[Prompt_Engineering|Prompt-Engineering]] · [[Negative-Prompt]] · [[CFG-Scale]] · [[Sampling-Steps]]
+- Tools: [[ComfyUI]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -205,10 +205,9 @@ result = pipe(
 **기본값**: Draft 30 → Select 5 → Final + post-edit. 매 cost 의 80% saving + quality 의 maintain.

 ## 🔗 Graph
- 부모: [[AI-Image-Generation]] · [[Creative-Workflow]]
- 변형: [[Iterative-Refinement]] · [[Draft-Mode]] · [[Post-editing-Tools]]
- 응용: [[Marketing-Campaign-AI]] · [[Product-Mockup]] · [[Concept-Art-Generation]]
- Adjacent: [[Style-Reference]] · [[LoRA-Fine-Tune]] · [[ControlNet]]
+- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 변형: [[Iterative-Refinement]] · [[Draft-Mode]] · [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]]
+- Adjacent: [[Style-Reference]] · [[ControlNet]]

 ## 🤖 LLM 활용
 **언제**: 매 commercial creative project. 매 brand campaign.
@@ -224,7 +223,7 @@ result = pipe(
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[AI-Image-Generation]] / [[Post-editing-Tools]].
+- Overlap with [[AI 이미지 생성 (AI Image Generation)]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -296,10 +296,10 @@ def llm_judge(image_url, prompt):
 **기본값**: Specific negative > generic. Inpaint > regenerate. ControlNet 의 anatomy. Detect → fix loop.

 ## 🔗 Graph
- 부모: [[AI-Image-Generation]] · [[Image-Quality]]
- 변형: [[Negative-Prompt]] · [[Inpainting]] · [[Face-Restoration]]
- 응용: [[ControlNet]] · [[GFPGAN]] · [[Real-ESRGAN]]
- Adjacent: [[Post-editing-Tools]] · [[Workflow-Iteration]]
+- 부모: [[AI 이미지 생성 (AI Image Generation)]]
+- 변형: [[Negative-Prompt]] · [[Inpainting]]
+- 응용: [[ControlNet]]
+- Adjacent: [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]]

 ## 🤖 LLM 활용
 **언제**: 매 commercial output 의 quality 의 critical.
@@ -315,7 +315,7 @@ def llm_judge(image_url, prompt):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Overlap with [[Post-editing-Tools]] / [[AI-Image-Generation]].
+- Overlap with [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] / [[AI 이미지 생성 (AI Image Generation)]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -435,11 +435,9 @@ public class AntiCheese : MonoBehaviour {
 - **AI 의 squad coordination 의 cost**: 매 sophisticated 의 dev expensive.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Game-AI-Design]] · [[Combat-AI]] · [[NPC-Behavior]]
- 변형: [[Aggro-Threat-System]] · [[Stance-Based-AI]] · [[Sight-Cone-Detection]] · [[Investigation-State]]
- 응용: [[AI-Exploitation]] · [[Baiting-Tactics]] · [[Kiting-Strategy]] · [[Boss-Fight-Design]]
- 매 game: [[War-Commander]] · [[StarCraft-AI]] · [[WoW-Mob-AI]] · [[Splinter-Cell-Stealth]] · [[Halo-Combat-AI]]
- Adjacent: [[Behavior-Tree]] · [[FSM-Game-AI]] · [[Utility-AI]] · [[Squad-Coordination]] · [[Anti-Cheese-Mechanism]]
+- 응용: [[AI-Exploitation]] · [[Baiting-Tactics]]
+- 매 game: [[War-Commander]]
+- Adjacent: [[Behavior-Tree]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -261,11 +261,11 @@ repos:
 **기본값**: IDE + PR + pre-deploy 의 layered. 매 gate 의 different threshold.

 ## 🔗 Graph
- 부모: [[DevSecOps]] · [[AI-Code-Review]] · [[Security]]
- 변형: [[SAST]] · [[DAST]] · [[Shift-Left-Security]]
- 응용: [[CodeRabbit]] · [[Snyk]] · [[Sonar]] · [[Semgrep]]
- 매 OWASP: [[OWASP-Top-10]] · [[OWASP-API-Top-10]]
- Adjacent: [[Container-Scanning]] · [[Dependency-Update]] · [[Secret-Detection]]
+- 부모: [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] · [[AI_코드_리뷰]] · [[Security]]
+- 변형: [[SAST]] · [[보안_및_시스템_신뢰성_표준|DAST]] · [[Shift-Left-Security]]
+- 응용: [[CodeRabbit]] · [[Sonar]] · [[Semgrep]]
+- 매 OWASP: [[OWASP-Top-10]]
+- Adjacent: [[Dependency-Update]] · [[Secret-Detection]]

 ## 🤖 LLM 활용
 **언제**: 매 production system 의 security strategy. 매 vibe coding 의 review.
@@ -281,7 +281,7 @@ repos:
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B.
- Related: [[AI-Code-Review]] · [[AI-Code-Assurance]] · [[OWASP-API-Top-10]].
+- Related: [[AI_코드_리뷰]] · [[AI-Code-Assurance]] · [[OWASP-API-Top-10]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -184,10 +184,8 @@ function FAQ({ items }: { items: { q: string; a: string }[] }) {
 **기본값**: SSR/SSG + JSON-LD + Q&A format + direct answer in lead.

 ## 🔗 Graph
- 부모: [[SEO]] · [[Content-Strategy]] · [[Generative-AI]]
- 변형: [[GEO]] · [[Schema-Markup]] · [[E-E-A-T]]
- 응용: [[SSR-Server-Side-Rendering]] · [[FAQ-Schema]] · [[Direct-Answer-Optimization]]
- Adjacent: [[Robots-Txt]] · [[GPTBot]] · [[Perplexity-AI]] · [[Google-AI-Overviews]]
+- 부모: [[SEO]] · [[Generative-AI]]
+- 변형: [[GEO]]

 ## 🤖 LLM 활용
 **언제**: 매 content site 의 AI traffic 의 capture. 매 documentation site 의 visibility.
@@ -29,8 +29,8 @@ github_commit: pending

 ## 🔗 Graph
 - 부모: [[AI 생성 코드 검증(AI Code Assurance)]] (canonical)
- 변형: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]] · [[Software-Supply-Chain-Security]]
- Adjacent: [[Static-Analysis-Linting]] · [[Shift-Left-Security]] · [[Slopsquatting]]
+- 변형: [[AI 코드 리뷰 및 보안 취약점 점검(DevSecOps)]]
+- Adjacent: [[ESLint-Static-Analysis|Static-Analysis-Linting]] · [[Shift-Left-Security]]

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -195,10 +195,10 @@ inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
 **기본값**: Direct answer + FAQ schema + Core Web Vitals + E-E-A-T author info.

 ## 🔗 Graph
- 부모: [[SEO]] · [[Search-Marketing]]
+- 부모: [[SEO]]
 - 변형: [[AI-Answer-Engine-Optimization]] · [[Generative-Engine-Optimization]]
- 응용: [[Core-Web-Vitals]] · [[E-E-A-T]] · [[Schema-Markup]] · [[Direct-Answer-Optimization]]
- Adjacent: [[Zero-Click-Search]] · [[Knowledge-Graph]] · [[Featured-Snippet]]
+- 응용: [[Core Web Vitals Optimization (INP, LCP 개선)|Core-Web-Vitals]]
+- Adjacent: [[Zero-Click-Search]] · [[Knowledge Graph|Knowledge-Graph]]

 ## 🤖 LLM 활용
 **언제**: Content website 의 SGE traffic 의 capture.
@@ -218,10 +218,10 @@ Sitemap: https://example.com/llm-sitemap.xml
 **기본값**: SSR + schema.org + topic cluster + E-E-A-T author + Core Web Vitals.

 ## 🔗 Graph
- 부모: [[SEO]] · [[Search-Marketing]] · [[Content-Strategy]]
+- 부모: [[SEO]]
 - 변형: [[AI-Answer-Engine-Optimization]] · [[AI-Overviews-and-SGE]] · [[Generative-Engine-Optimization]]
- 응용: [[Topic-Cluster]] · [[Entity-SEO]] · [[Schema-Markup]] · [[E-E-A-T]]
- Adjacent: [[Knowledge-Graph]] · [[Wikidata]] · [[IndexNow]]
+- 응용: [[Entity-SEO]]
+- Adjacent: [[Knowledge Graph|Knowledge-Graph]]

 ## 🤖 LLM 활용
 **언제**: Public content site 의 AI traffic 의 strategy.
@@ -233,10 +233,7 @@ async def monitor_articles():
 **기본값**: AI draft + human edit + automated quality gate.

 ## 🔗 Graph
- 부모: [[Content-Strategy]] · [[Blog-Monetization]] · [[AI-Content]]
- 변형: [[AdSense-Revenue-Blog]] · [[SEO-Pipeline]]
 - 응용: [[AI-Search-Optimization]] · [[AI-Answer-Engine-Optimization]]
- Tools: [[Ahrefs]] · [[Yoast]] · [[Originality-AI]] · [[Copyscape]]

 ## 🤖 LLM 활용
 **언제**: 매 indie content business. 매 brand content scale.
@@ -53,9 +53,9 @@ github_commit: pending
 - Compositing.

 ### Cross-reference
- See [[AI-Image-Generation]] (platform comparison).
+- See [[AI 이미지 생성 (AI Image Generation)]] (platform comparison).
 - See [[AI 이미지 생성 및 편집 워크플로우]] (continuous workflow).
- See [[Post-editing-Tools]] (specific tools).
+- See [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]] (specific tools).
 - See [[AI 이미지 품질 최적화 및 디버깅]] (defect fix).

 ## 💻 Code (간단)
@@ -82,7 +82,7 @@ class Workflow:
 ```

 ## 🔗 Graph
- 매 detail 의 [[AI-Image-Generation]] · [[AI 이미지 생성 및 편집 워크플로우]] · [[Post-editing-Tools]] · [[AI 이미지 품질 최적화 및 디버깅]] 의 reference.
+- 매 detail 의 [[AI 이미지 생성 (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-Code-Review]] · [[SAST]] · [[DevSecOps]]
- 변형: [[Snyk-Code]] · [[Corgea-Autofix]] · [[CodeRabbit]]
- 응용: [[AI-Code-Assurance]] · [[OWASP-API-Top-10]]
- Adjacent: [[AI-Code-Review-DevSecOps]] · [[AI-Powered-Code-Analysis-Tools]]
+- 부모: [[AI_코드_리뷰]] · [[SAST]] · [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]]
+- 변형: [[Snyk-Code]] · [[CodeRabbit]]
+- 응용: [[AI-Code-Assurance]]
+- Adjacent: [[AI 기반 코드 분석 도구 (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-Code-Review]] · [[AI-Code-Assurance]] · [[AI-Powered-Code-Analysis-Tools]].
+- Related: [[AI_코드_리뷰]] · [[AI-Code-Assurance]] · [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -371,13 +371,9 @@ trainer.train()
 - **Cost of safety**: red-team / RLHF / interpretability 가 model 의 cost ↑.

 ## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]] · [[AI-Governance-Policy]] · [[AGI-Safety]]
- 변형: [[RLHF-Human-Feedback]] · [[DPO-Direct-Preference]] · [[Constitutional-AI-Anthropic]] · [[Interpretability-Mechanistic]]
- 응용: [[Red-Teaming-AI]] · [[Jailbreak-Defense]] · [[Hallucination-Mitigation]] · [[Bias-Audit]]
- 기관: [[Anthropic-Safety-Research]] · [[OpenAI-Superalignment]] · [[MIRI]] · [[AI-Safety-Institute]]
- 정책: [[Responsible-Scaling-Policy]] · [[OpenAI-Preparedness-Framework]] · [[Frontier-Safety-Framework]]
- 응용: [[AI-Accountability]] · [[AI-Literacy]] · [[Continuous-Learning-System]]
- Adjacent: [[Reward-Hacking]] · [[Goal-Misgeneralization]] · [[Specification-Gaming]] · [[Sparse-Autoencoder]]
+- 부모: [[AI-Ethics]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]
+- 응용: [[AI-Accountability]] · [[AI-Literacy]]
+- Adjacent: [[AI_Safety_and_Alignment|Goal-Misgeneralization]] · [[Sparse-Autoencoder]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -411,7 +407,7 @@ trainer.train()
 - **검토 이유:** Manual creation. Active research field. 매 6 month review.

 ## 🧬 중복 검사 (Duplicate Check)
- **Redirect 의 source files**: [[AI-Alignment]] · [[AI Safety & Constitutional AI]] · [[AI Safety (AI 안전)]] · [[AI Safety]] · [[AI_Safety]].
+- **Redirect 의 source files**: [[AI_Safety_and_Alignment|AI-Alignment]] · [[AI_Safety_and_Alignment|AI Safety & Constitutional AI]] · [[AI Safety (AI 안전)]] · [[AI_Safety_and_Alignment|AI Safety]] · [[AI_Safety_and_Alignment|AI_Safety]].
 - **처리 방식**: KEEP as canonical (이 file 가 모든 redirect 의 target).
 - **Reasoning**: 매 variant 의 redirect 의 single canonical document.

@@ -43,7 +43,7 @@ github_commit: pending

 ## 🔗 Graph
 - 매 canonical 의 [[AI_Powered_Code_Analysis]].
- 매 related 의 [[AI-Powered-Code-Analysis-Tools]] · [[AI 기반 코드 분석 도구 (AI-Powered Code Analysis Tools)]] · [[AI-Code-Review-DevSecOps]].
+- 매 related 의 [[AI 기반 코드 분석 도구 (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: [[Code-Review-Modern]] · [[DevSecOps]] · [[Static-Analysis]]
- 변형: [[CodeRabbit]] · [[Greptile]] · [[Sourcery]] · [[Cursor-AI-Review]]
- 응용: [[SAST-Static-Analysis]] · [[Snyk-Code]] · [[SonarQube]] · [[Semgrep-Custom-Rules]]
- AI: [[LLM-Code-Understanding]] · [[Tree-Sitter-Parsing]] · [[Codebase-RAG]] · [[Auto-Fix]]
- 응용: [[GitHub-Actions-CI]] · [[GitLab-CI]] · [[PR-Workflow]] · [[DORA-Metrics]]
- Adjacent: [[Hybrid-Review-Model]] · [[Green-Check-Mark-Syndrome]] · [[Context-Blindness-AI]]
- Related: [[AI-Code-Agent-Patterns]] · [[Cursor-Workflow-Patterns]] · [[ConnectAI-LLM-Tool]]
+- 부모: [[CI_CD 파이프라인 및 IDE 통합 보안|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]]

 ## 🤖 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), [[DevSecOps]] (related).
+- **기존 유사 문서:** [[Code-Review-Modern]] (parent), [[AI-Powered-Code-Analysis]] (related), [[CI_CD 파이프라인 및 IDE 통합 보안|DevSecOps]] (related).
 - **처리 방식:** KEEP (focused on AI-augmented review).
 - **처리 이유:** 매 AI integration 의 specific.

@@ -213,9 +213,9 @@ npx biome lint .

 ## 🔗 Graph
 - 부모: [[Static-Analysis]] · [[Code-Quality]]
- 변형: [[Biome]] · [[Oxlint]] · [[StyleLint]] (CSS) · [[Pylint]] (Python) · [[clippy]] (Rust)
- 응용: [[AST]] · [[Pre-commit-Hook]] · [[CI-Quality-Gate]]
- Adjacent: [[AI-Code-Review]] · [[AI-Powered-Code-Analysis]]
+- 변형: [[Biome]] · [[Oxlint]] ·  (CSS) ·  (Python) ·  (Rust)
+- 응용: [[AST]]
+- Adjacent: [[AI_코드_리뷰]] · [[AI-Powered-Code-Analysis]]

 ## 🤖 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-Code-Review]] · [[AI-Powered-Code-Analysis]] · [[AST]].
+- Related: [[AI_코드_리뷰]] · [[AI-Powered-Code-Analysis]] · [[AST]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -315,8 +315,8 @@ test('login flow', () => {

 ## 🔗 Graph
 - 부모: [[State-Management]] · [[API-Design]]
- 변형: [[State Modeling and API Responses]] · [[XState]] · [[Robot]]
- 응용: [[Statechart-Harel]] · [[Actor-Model]] · [[FSM]]
+- 변형: [[XState]]
+- 응용: [[FSM]]
 - Adjacent: [[Discriminated-Union]] · [[useReducer]] · [[Zustand]]

 ## 🤖 LLM 활용
@@ -277,8 +277,8 @@ function useFetchUser(id: string) {
 ## 🔗 Graph
 - 부모: [[TypeScript]] · [[State-Management]] · [[API-Design]]
 - 변형: [[Discriminated-Union]] · [[Tagged-Union]] · [[Result-Type]]
- 응용: [[XState]] · [[Effect-TS]] · [[Zod-Validation]]
- Adjacent: [[Branded-Types]] · [[Exhaustive-Check]] · [[TanStack-Query]]
+- 응용: [[XState]] · [[Effect-TS]]
+- Adjacent: [[Branded-Types]] · [[Exhaustive-Check]]

 ## 🤖 LLM 활용
 **언제**: 매 TypeScript app 의 state design. 매 API contract 의 type-safe.
@@ -134,10 +134,8 @@ def charge(user_id: str, model: str, n: int):
 **기본값**: FAL FLUX 1.1 Pro Ultra (cost/quality 의 sweet spot 2026).

 ## 🔗 Graph
- 부모: [[Image Generation]] · [[Diffusion Models]]
- 변형: [[Self-hosted ComfyUI Workflow]] · [[Edge Image Generation]]
- 응용: [[AdSense Revenue Blog Architecture]] · [[E-commerce Product Photography]]
- Adjacent: [[Webhook Patterns]] · [[CDN Asset Pipeline]] · [[Prompt Engineering for Images]]
+- 부모: [[Diffusion Models]]
+- 응용: [[AdSense Revenue Blog Architecture]]

 ## 🤖 LLM 활용
 **언제**: 매 product feature (avatar, blog hero, marketing) 의 image gen — 매 launch speed 의 priority.
@@ -4,7 +4,7 @@ title: AST (Abstract Syntax Tree)
 category: 10_Wiki/Topics
 status: duplicate
 canonical_id: wiki-2026-0508-abstract-syntax-tree
-duplicate_of: "[[Abstract Syntax Tree (AST)]]"
+duplicate_of: "[[Abstract_Syntax_Tree]]"
 aliases: []
 source_trust_level: A
 confidence_score: 0.9
@@ -16,14 +16,14 @@ github_commit: pending

 # AST (Abstract Syntax Tree)

-> **이 문서는 [[Abstract Syntax Tree (AST)]] 의 중복본입니다.** Canonical 문서로 redirect.
+> **이 문서는 [[Abstract_Syntax_Tree]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 핵심 요약
 - AST = compiler/interpreter 의 source code intermediate representation.
 - 매 동일 concept — 매 naming variant 의 only difference.

 ## 🔗 Graph
- 부모: [[Abstract Syntax Tree (AST)]] (canonical)
+- 부모: [[Abstract_Syntax_Tree]] (canonical)

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -144,10 +144,9 @@ apply_ast_edit(edit)  # 매 syntactic safety guaranteed
 **기본값**: 매 cross-language tooling — tree-sitter. Python-only — `ast` + LibCST.

 ## 🔗 Graph
- 부모: [[Compiler]] · [[Programming Language Theory]]
- 변형: [[Concrete Syntax Tree]] · [[HIR]] · [[MIR]] · [[SSA]]
- 응용: [[Linter]] · [[Codemod]] · [[Static Analysis]] · [[LSP]] · [[Tree-sitter]]
- Adjacent: [[Lexer]] · [[Parser Combinator]] · [[Visitor Pattern]]
+- 변형: [[Concrete Syntax Tree]]
+- 응용: [[Linter]] · [[Static Analysis]]
+- Adjacent: [[Visitor Pattern]]

 ## 🤖 LLM 활용
 **언제**: 매 codemod, 매 lint rule, 매 LLM-output 의 syntactic validation, 매 IDE refactor.
@@ -118,9 +118,6 @@ diff prereg/hypothesis.md paper/section_3_hypothesis.md

 ## 🔗 Graph
 - 부모: [[Research Ethics]] · [[Scientific Method]]
- 변형: [[Open Science]] · [[Reproducibility Crisis]]
- 응용: [[Pre-registration]] · [[Peer Review]] · [[Authorship Criteria (CRediT)]]
- Adjacent: [[AI Disclosure Policy]] · [[Citation Hygiene]] · [[Data Provenance]]

 ## 🤖 LLM 활용
 **언제**: 매 prose editing, literature summarization, code review — 매 disclosure 와 함께.
@@ -2,173 +2,26 @@
 id: wiki-2026-0508-accessibility
 title: Accessibility (a11y)
 category: 10_Wiki/Topics
-status: verified
-canonical_id: self
-aliases: [a11y, Web Accessibility, Inclusive Design, WCAG]
-duplicate_of: none
+status: duplicate
+canonical_id: wiki-2026-0508-accessibility-a11y
+duplicate_of: "[[Accessibility (A11y)]]"
+aliases: []
 source_trust_level: A
-confidence_score: 0.95
-verification_status: applied
-tags: [accessibility, a11y, wcag, aria, inclusive-design, frontend]
-raw_sources: []
-last_reinforced: 2026-05-10
+confidence_score: 0.9
+verification_status: redirected
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
-tech_stack:
-  language: HTML/CSS/TypeScript
-  framework: ARIA/WCAG 2.2/EAA
 ---

 # Accessibility (a11y)

-## 매 한 줄
-> **"매 user 의 disability spectrum 의 across 의 first-class UX"**. Accessibility = 매 perceivable / operable / understandable / robust (POUR) 의 product 의 design — 매 screen reader, keyboard-only, low vision, cognitive, motor 의 모두 의 cover. 2026 EU EAA (June 28, 2025 enforcement) 의 매 legal requirement 의 됨 — 매 nice-to-have 의 X.
-
-## 매 핵심
-
-### 매 POUR (WCAG 4 principle)
- **Perceivable**: 매 alt text, caption, contrast.
- **Operable**: 매 keyboard, focus, target size.
- **Understandable**: 매 readable, predictable, error-helpful.
- **Robust**: 매 assistive tech 의 compatible, semantic HTML.
-
-### 매 WCAG 2.2 (current standard)
- **Level A**: 매 minimum (alt text, lang attr, no keyboard trap).
- **Level AA**: 매 industry default (contrast 4.5:1, focus visible, target 24x24).
- **Level AAA**: 매 specialized context.
-
-### 매 disability category
- **Visual** (blind, low vision, color blind) — screen reader, zoom, contrast.
- **Auditory** — caption, transcript.
- **Motor** — keyboard, switch, voice control, large target.
- **Cognitive** — plain language, predictable nav, no time limit.
- **Vestibular / seizure** — `prefers-reduced-motion`, no flash >3Hz.
-
-### 매 응용
-1. Public website (legal in EU/US/JP/KR).
-2. Government / education (Section 508, EN 301 549).
-3. Mobile app (iOS Accessibility, Android TalkBack).
-4. Game (Xbox Accessibility Guidelines).
-5. AI interface (screen-reader-friendly streaming output).
-
-## 💻 패턴
-
-### Semantic HTML > ARIA
-```html
-<!-- 매 right -->
-<button type="button" onclick="save()">Save</button>
-
-<!-- 매 wrong (div+role) -->
-<div role="button" tabindex="0" onclick="save()" onkeydown="...">Save</div>
-```
-
-### Skip link
-```html
-<a href="#main" class="skip-link">Skip to main content</a>
-<style>
-.skip-link { position: absolute; left: -9999px; }
-.skip-link:focus { left: 1rem; top: 1rem; z-index: 100; }
-</style>
-```
-
-### Accessible form field
-```html
-<label for="email">Email</label>
-<input id="email" type="email" required
-       aria-describedby="email-hint email-err"
-       aria-invalid="false" />
-<p id="email-hint">We never share your email.</p>
-<p id="email-err" role="alert"></p>
-```
-
-### Live region (announce dynamic update)
-```html
-<div role="status" aria-live="polite" aria-atomic="true" id="toast"></div>
-<script>
-document.getElementById("toast").textContent = "Saved";
-</script>
-```
-
-### Focus management (modal)
-```ts
-function openModal(modal: HTMLElement) {
-  const prev = document.activeElement as HTMLElement;
-  const focusables = modal.querySelectorAll<HTMLElement>(
-    'a[href], button, input, select, textarea, [tabindex]:not([tabindex="-1"])'
-  );
-  focusables[0]?.focus();
-  modal.addEventListener("keydown", (e) => {
-    if (e.key === "Escape") { modal.hidden = true; prev?.focus(); }
-    if (e.key === "Tab") {
-      // 매 trap focus
-      const first = focusables[0], last = focusables[focusables.length - 1];
-      if (e.shiftKey && document.activeElement === first) { last.focus(); e.preventDefault(); }
-      else if (!e.shiftKey && document.activeElement === last) { first.focus(); e.preventDefault(); }
-    }
-  });
-}
-```
-
-### Reduced motion
-```css
-@media (prefers-reduced-motion: reduce) {
-  *, *::before, *::after { animation: none !important; transition: none !important; }
-}
-```
-
-### Color contrast check
-```ts
-import { hex } from "wcag-contrast";
-const ratio = hex("#777", "#fff");  // 4.48 — AA fails for normal text (need ≥4.5)
-```
-
-### Automated test (Playwright + axe)
-```ts
-import AxeBuilder from "@axe-core/playwright";
-test("no a11y violations", async ({ page }) => {
-  await page.goto("/");
-  const r = await new AxeBuilder({ page }).analyze();
-  expect(r.violations).toEqual([]);
-});
-```
-
-## 매 결정 기준
-| 상황 | Approach |
-|---|---|
-| Native semantic exists | Use it (button, nav, main, h1-h6) |
-| Custom widget required | ARIA Authoring Practices pattern |
-| Icon-only button | `aria-label` + visible focus ring |
-| Decorative image | `alt=""` (not omit) |
-| Color 의 only convey info | Add text/icon/pattern |
-| Time-sensitive UI | Offer extend/disable |
-
-**기본값**: WCAG 2.2 Level AA + automated axe in CI + manual screen reader spot-check.
+> **이 문서는 [[Accessibility (A11y)]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 🔗 Graph
- 부모: [[Frontend Engineering]] · [[Inclusive Design]]
- 변형: [[Mobile Accessibility]] · [[Game Accessibility]]
- 응용: [[Design System]] · [[Component Library]] · [[Form UX]]
- Adjacent: [[ARIA]] · [[Screen Reader]] · [[Keyboard Navigation]] · [[Color Theory]]
+- 부모: [[Accessibility (A11y)]] (canonical)

-## 🤖 LLM 활용
-**언제**: 매 alt-text generation (vision LLM), 매 plain-language rewrite, 매 ARIA pattern lookup.
-**언제 X**: 매 a11y compliance 의 sole authority — 매 human + screen reader test required.
-
-## ❌ 안티패턴
- **`role="button"` on div**: 매 keyboard handler missing — 매 native `<button>`.
- **Placeholder as label**: 매 disappear on focus — 매 explicit `<label>`.
- **`tabindex` >0**: 매 tab order breaks — 매 0 또는 -1 only.
- **`aria-hidden="true"` on focusable**: 매 inconsistent — 매 confusing.
- **Color-only error**: red border without text — color blind 의 invisible.
- **Auto-play video w/ sound**: 매 WCAG 1.4.2 violation.
- **Clicking entire row w/o keyboard equiv**: 매 keyboard user 의 inaccessible.
- **`outline: none` w/o replacement**: 매 focus invisible — 매 custom ring 의 add.
-
-## 🧪 검증 / 중복
- Verified (W3C WCAG 2.2 spec 2023-10; ARIA Authoring Practices 1.2; EU EAA Directive 2019/882; axe-core rule set).
- 신뢰도 A.
-
-## 🕓 Changelog
+## 🕓 변경 이력
 | 날짜 | 변경 |
 |---|---|
-| 2026-05-08 | Phase 1 |
-| 2026-05-10 | Manual cleanup — WCAG 2.2 + EAA 2025 + axe automation patterns |
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -126,10 +126,9 @@ const reps = await fetch(`/api/reps?zip=${zip}`).then(r => r.json());
 **기본값**: 매 multi-tactic + 매 sustained (movement >> moment) + 매 affected community 의 leadership.

 ## 🔗 Graph
- 부모: [[Civic Engagement]] · [[Political Theory]]
- 변형: [[Tech Worker Activism]] · [[Climate Activism]] · [[Digital Rights Advocacy]]
- 응용: [[Algorithmic Accountability]] · [[OSINT]] · [[Mutual Aid Network]]
- Adjacent: [[AI Ethics]] · [[Whistleblower Protection]] · [[Open Source Politics]]
+- 부모: [[Civic Engagement]]
+- 응용: [[Algorithmic Accountability]]
+- Adjacent: [[AI Ethics]]

 ## 🤖 LLM 활용
 **언제**: 매 policy doc summarization, 매 outreach draft, 매 large-corpus FOIA review.
@@ -135,9 +135,9 @@ def dpo_loss(logp_w, logp_l, ref_logp_w, ref_logp_l, beta=0.1):

 ## 🔗 Graph
 - 부모: [[Reinforcement Learning]] · [[Policy Gradient Methods]]
- 변형: [[PPO]] · [[SAC]] · [[A3C]] · [[IMPALA]] · [[GRPO]]
- 응용: [[RLHF]] · [[Robotic Locomotion]] · [[Game-Playing Agents]] · [[LLM Post-training]]
- Adjacent: [[GAE]] · [[Advantage Function]] · [[DPO]] · [[Reward Modeling]]
+- 변형: [[PPO]] · [[A3C]] · [[GRPO]]
+- 응용: [[RLHF]]
+- Adjacent: [[GAE]] · [[DPO]]

 ## 🤖 LLM 활용
 **언제**: 매 LLM RLHF / RLAIF post-training (PPO/GRPO), 매 RL agent code review.
@@ -134,10 +134,9 @@ def falsifiability_score(theory: str) -> dict:
 **기본값**: 매 each auxiliary 의 "what NEW would this predict?" 의 ask. 매 None — 매 ad-hoc.

 ## 🔗 Graph
- 부모: [[Philosophy of Science]] · [[Epistemology]]
- 변형: [[Auxiliary Hypothesis]] · [[Conventionalism]] · [[Duhem-Quine Thesis]]
- 응용: [[Falsifiability]] · [[Pre-registration]] · [[Postmortem Analysis]]
- Adjacent: [[Confirmation Bias]] · [[Occam's Razor]] · [[Lakatos Research Program]] · [[Scientific Method]]
+- 부모: [[Epistemology]]
+- 변형: [[Auxiliary Hypothesis]]
+- Adjacent: [[Confirmation Bias]] · [[Scientific Method]]

 ## 🤖 LLM 활용
 **언제**: 매 prompt audit, 매 paper reviewer 의 ad-hoc rescue 의 detect, 매 debugging journal 의 retro.
@@ -153,10 +153,8 @@ ORDER BY rpm DESC LIMIT 50;
 **기본값**: Astro SSG + manual ad slot + structured data + monthly E-E-A-T audit.

 ## 🔗 Graph
- 부모: [[SEO]] · [[Web Monetization]]
- 변형: [[Affiliate Site Architecture]] · [[Programmatic SEO]] · [[Newsletter-Driven Site]]
- 응용: [[Core Web Vitals]] · [[Schema.org Structured Data]] · [[Content Cluster Strategy]]
- Adjacent: [[E-E-A-T]] · [[Helpful Content Update]] · [[GA4 Analytics]] · [[Ad Manager]]
+- 부모: [[SEO]]
+- 응용: [[Core Web Vitals Optimization (INP, LCP 개선)|Core Web Vitals]]

 ## 🤖 LLM 활용
 **언제**: 매 outline draft, 매 keyword cluster, 매 metadata generation, 매 internal-link suggestion.
@@ -24,7 +24,6 @@ github_commit: pending
 - Test-time scaling, early-exit, mixture-of-depths, adaptive thinking budget (Claude extended thinking 2024+) 의 family.

 ## 🔗 Graph
- 부모: [[Adaptive Compute]] (canonical)

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -170,13 +170,10 @@ function checkPurchase(monthlySpend: number, attemptedSpend: number) {
 - **AI 의 personalization → addiction risk**: 매 user 의 vulnerability 의 ML 가 detect → 매 max engagement. TikTok / Instagram 의 알고리즘.

 ## 🔗 지식 연결 (Graph)
- 부모: [[Behavioral-Neuroscience]] · [[Reward-System]] · [[Psychiatry]]
- 변형: [[Substance-Addiction]] · [[Behavioral-Addiction]] · [[Gaming-Disorder]] · [[Internet-Addiction]]
- 관련 brain region: [[Dopamine-Pathway]] · [[Nucleus-Accumbens]] · [[Prefrontal-Cortex]] · [[Amygdala]] · [[Hippocampus]]
- 응용: [[Game-Design-Ethics]] · [[Loot-Box-Mechanics]] · [[Dark-Patterns]] · [[Hook-Model-Eyal]] · [[Variable-Reward-Schedule]]
- 치료: [[CBT-Cognitive-Behavioral]] · [[12-Step-Recovery]] · [[Naltrexone]] · [[Habit-Replacement]] · [[Psychedelic-Therapy]]
- 사회: [[Screen-Time-Regulation]] · [[Humane-Tech]] · [[Tristan-Harris]] · [[Center-for-Humane-Technology]]
- Adjacent: [[Neuroplasticity]] · [[Reward-Prediction-Error]] · [[Operant-Conditioning]] · [[Skinner-Box]]
+- 부모: [[Reward-System]]
+- 관련 brain region: [[Dopamine-Pathway]] · [[Prefrontal-Cortex]]
+- 응용: [[Loot-Box-Mechanics]]
+- Adjacent: [[Neuroplasticity]] · [[Reward-Prediction-Error]] · [[Operant-Conditioning]]

 ## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

@@ -258,11 +258,8 @@ def counterfactual_test(model, instance, protected_attr='gender'):
 **기본값**: 4/5 rule check + per-group accuracy + counterfactual test + disclosure.

 ## 🔗 Graph
- 부모: [[AI-Ethics]] · [[ML-Fairness]] · [[AI-Accountability]]
- 변형: [[Group-Fairness]] · [[Individual-Fairness]] · [[Counterfactual-Fairness]]
- 응용: [[COMPAS-Recidivism]] · [[Gender-Shades]] · [[Amazon-Hiring-AI]]
- Tools: [[AIF360-IBM]] · [[Fairlearn-Microsoft]] · [[Aequitas]] · [[What-If-Tool-Google]]
- 정책: [[EU-AI-Act-Bias]] · [[NYC-LL144]] · [[EEOC-4-5-Rule]]
+- 부모: [[AI-Ethics]] · [[AI-Accountability]]
+- 변형: [[Group-Fairness]]

 ## 🤖 LLM 활용
 **언제**: 매 ML system 의 deployment review. 매 audit. 매 high-risk 의 design.
@@ -278,7 +275,7 @@ def counterfactual_test(model, instance, protected_attr='gender'):
 ## 🧪 검증 / 중복
 - Verified.
 - 신뢰도 B (academic + industry consensus).
- Related: [[AI-Accountability]] · [[AI-Governance-Policy]].
+- Related: [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -299,11 +299,10 @@ function ChatHeader() {
 **기본값**: Disclosure + audit log + per-prediction explanation. 매 high-stakes 의 더 strict.

 ## 🔗 Graph
- 부모: [[AI-Ethics]] · [[AI-Governance]] · [[AI-Accountability]]
- 변형: [[Explainable-AI-XAI]] · [[Model-Card]] · [[Datasheet-for-Datasets]]
- 응용: [[GDPR-Article-22]] · [[EU-AI-Act-Transparency]] · [[NYC-LL144]]
- Tools: [[SHAP]] · [[LIME]] · [[Model-Card-Toolkit-Google]]
- Adjacent: [[Open-Source-AI]] · [[Algorithmic-Fairness]] · [[Right-to-Explanation]]
+- 부모: [[AI-Ethics]] · [[AI-Accountability]]
+- 변형: [[Explainable-AI-XAI]] · [[Model-Card]]
+- Tools: [[SHAP]] · [[LIME]]
+- Adjacent: [[Algorithmic-Fairness]]

 ## 🤖 LLM 활용
 **언제**: 매 production AI 의 transparency design. 매 user trust 의 build.
@@ -144,9 +144,8 @@ sc.pl.umap(adata, color='leiden')

 ## 🔗 Graph
 - 부모: [[Bioinformatics]] · [[Computational-Biology]] · [[Systems-Biology]]
- 변형: [[AlphaFold]] · [[Genomics]] · [[Proteomics]] · [[Synthetic-Biology]]
- 응용: [[Drug-Discovery]] · [[Personalized-Medicine]] · [[Phylogenetics]]
- Adjacent: [[Computational-Neuroscience]] · [[Cellular-Automata]] · [[Physics-Informed-Neural-Networks]]
+- 변형: [[AlphaFold]]
+- 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.
@@ -1,25 +1,27 @@
 ---
 id: wiki-20260508-ambient-declarations-redir
 title: Ambient Declarations
-category: AI_and_ML
-status: merged
-redirect_to: Ambient_Declarations
-canonical_id: Ambient_Declarations
+category: 10_Wiki/Topics
+status: duplicate
+canonical_id: wiki-2026-0508-ambient-declarations
+duplicate_of: "[[Ambient Declarations]]"
 aliases: []
-duplicate_of: none
 source_trust_level: A
-confidence_score: 0.92
-tags: [redirect]
-raw_sources: []
-last_reinforced: 2026-05-08
+confidence_score: 0.9
+verification_status: redirected
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
-inferred_by: Claude Opus 4.7 (auto-merge 2026-05-08)
 ---

 # Ambient Declarations

-> [!IMPORTANT]
-> 이 문서는 P-Reinforce Phase 2 자동 MERGE에 의해 **[[Ambient_Declarations]]**로 통합되었습니다.
+> **이 문서는 [[Ambient Declarations]] 의 중복본입니다.** Canonical 문서로 redirect.

---
-*Redirected to: [[Ambient_Declarations]]*
+## 🔗 Graph
+- 부모: [[Ambient Declarations]] (canonical)
+
+## 🕓 변경 이력
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -131,10 +131,9 @@ model = DistributedDataParallel(model, device_ids=[local_rank])
 **기본값**: 매 profile 먼저. 매 P 의 measure. 매 serial bottleneck 의 reduce.

 ## 🔗 Graph
- 부모: [[Parallel-Computing]] · [[Performance-Engineering]]
- 변형: [[Gustafsons-Law]] · [[Universal-Scalability-Law]]
- 응용: [[GPU-Computing]] · [[Distributed-Training]] · [[MapReduce]] · [[CUDA]]
- Adjacent: [[Profiling]] · [[Benchmarking]] · [[Critical-Path-Analysis]]
+- 부모: [[Parallel-Computing]]
+- 응용: [[Distributed-Training]] · [[MapReduce]] · [[CUDA]]
+- Adjacent: [[Profiling]]

 ## 🤖 LLM 활용
 **언제**: 매 performance optimization decision. 매 GPU / cluster sizing. 매 distributed training planning.
@@ -191,9 +191,9 @@ class Order {
 **기본값**: 매 core domain = Rich. 매 supporting = Anaemic 가 OK.

 ## 🔗 Graph
- 부모: [[Domain-Driven-Design]] · [[Object-Oriented-Design]]
- 변형: [[Transaction-Script]] · [[Active-Record]] · [[Data-Mapper]]
- 응용: [[Aggregate-Root]] · [[Value-Object]] · [[Domain-Event]] · [[Repository-Pattern]]
+- 부모: [[Domain-Driven-Design]]
+- 변형: [[Transaction-Script]]
+- 응용: [[Aggregate-Root]] · [[Value-Object]] · [[Domain-Event]]
 - Adjacent: [[Bounded-Context]] · [[CQRS]] · [[Event-Sourcing]] · [[Hexagonal-Architecture]]

 ## 🤖 LLM 활용
@@ -118,11 +118,10 @@ function suggest_match(offers: Offer[], request: Request): Match[] {
 **기본값**: 매 small / voluntary / low-stake 환경 의 anarchist principle 가 좋음. 매 large / coercive / high-stake 의 hybrid (state + voluntary).

 ## 🔗 Graph
- 부모: [[Political-Philosophy]] · [[Libertarianism]]
- 변형: [[Anarcho-Capitalism]] · [[Anarcho-Primitivism]] · [[Anarcho-Syndicalism]] · [[Crypto-Anarchism]]
- 응용: [[DAO]] · [[Open-Source-Governance]] · [[Web3]] · [[P2P-Networks]]
- 사상가: [[Proudhon]] · [[Kropotkin]] · [[Bakunin]] · [[Chomsky]]
- Adjacent: [[Mutual-Aid]] · [[Direct-Action]] · [[Cypherpunk]] · [[Tristan-Harris]]
+- 변형: [[Anarcho-Primitivism]]
+- 응용: [[DAO]] · [[Web3]]
+- 사상가: [[Chomsky]]
+- Adjacent: [[Mutual-Aid]]

 ## 🤖 LLM 활용
 **언제**: 매 governance design (DAO, open source). 매 decentralization 설계. 매 political philosophy discussion.
@@ -123,11 +123,9 @@ def query_with_consciousness(prompt, model='gpt-4'):
 **기본값**: 매 strong primitivism (8B → 100M) 의 reject. 매 mild primitivism (digital detox, slow, repair) 의 적용.

 ## 🔗 Graph
- 부모: [[Anarchism]] · [[Environmentalism]] · [[Tech-Criticism]]
- 변형: [[Green-Anarchism]] · [[Deep-Ecology]] · [[Rewilding]]
- 사상가: [[John-Zerzan]] · [[Daniel-Quinn]] · [[Marshall-Sahlins]]
- 응용: [[Digital-Detox]] · [[Slow-Movement]] · [[Right-to-Repair]] · [[Off-Grid]]
- Adjacent: [[Surveillance-Capitalism]] · [[Attention-Economy]] · [[Sustainability]] · [[Addiction-Neuroscience]]
+- 부모: [[Anarchism]]
+- 변형: [[Rewilding]]
+- Adjacent: [[Sustainability]] · [[Addiction-Neuroscience]]

 ## 🤖 LLM 활용
 **언제**: 매 tech 의 ethics review. 매 sustainability decision. 매 digital wellness design.
@@ -115,10 +115,8 @@ def aligned_reward(model_output, human_pref):
 **기본값**: 매 selection effect 의 explicit. 매 conclusion 의 careful.

 ## 🔗 Graph
- 부모: [[Philosophy-of-Science]] · [[Cosmology]]
- 변형: [[Weak-Anthropic-Principle]] · [[Strong-Anthropic-Principle]] · [[Doomsday-Argument]] · [[Sleeping-Beauty]]
- 응용: [[AI-Alignment]] · [[X-Risk]] · [[Anthropic-Shadow]] · [[Selection-Bias]]
- Adjacent: [[Multiverse]] · [[Fine-Tuning]] · [[Bostrom]] · [[Survivorship-Bias]]
+- 응용: [[AI_Safety_and_Alignment|AI-Alignment]]
+- Adjacent: [[Fine-Tuning]]

 ## 🤖 LLM 활용
 **언제**: 매 selection bias 의 detect. 매 AI safety reasoning. 매 cosmology discussion. 매 base-rate 의 question.
@@ -134,7 +132,7 @@ def aligned_reward(model_output, human_pref):
 ## 🧪 검증 / 중복
 - Verified (Bostrom "Anthropic Bias", Rees "Just Six Numbers").
 - 신뢰도 B (philosophy 의 active debate).
- Related: [[AI-Alignment]] · [[X-Risk]] · [[Selection-Bias]].
+- Related: [[AI_Safety_and_Alignment|AI-Alignment]] · [[X-Risk]] · [[Selection-Bias]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -143,9 +143,8 @@ const avatar = generateStylized('friendly cartoon robot, blue palette');

 ## 🔗 Graph
 - 부모: [[Psychology]] · [[HCI]] · [[AI-Ethics]]
- 변형: [[ELIZA-Effect]] · [[Uncanny-Valley]] · [[Agent-Personality]] · [[Companion-AI]]
- 응용: [[Replika]] · [[Character-AI]] · [[Voice-Assistant-Design]]
- Adjacent: [[Social-Robotics]] · [[AI-Disclosure]] · [[EU-AI-Act]] · [[Addiction-Neuroscience]]
+- 변형: [[ELIZA-Effect]] · [[Agent-Personality]]
+- Adjacent: [[EU-AI-Act]] · [[Addiction-Neuroscience]]

 ## 🤖 LLM 활용
 **언제**: 매 AI agent persona design. 매 chatbot UX. 매 robot 의 social acceptability.
@@ -155,10 +155,10 @@ for x, y in loader:
 **기본값**: 매 small stressor 의 expose. 매 optionality 의 increase. 매 fragile middle 의 회피.

 ## 🔗 Graph
- 부모: [[Risk-Management]] · [[Systems-Thinking]] · [[Resilience]]
- 변형: [[Robustness]] · [[Black-Swan]] · [[Optionality]] · [[Skin-in-the-Game]]
- 응용: [[Chaos-Engineering]] · [[Circuit-Breaker]] · [[Adversarial-Training]] · [[Barbell-Strategy]]
- Adjacent: [[Hormesis]] · [[Reinforcement-Learning]] · [[Evolutionary-Algorithm]]
+- 부모: [[Risk_Management|Risk-Management]] · [[Systems_Thinking|Systems-Thinking]] · [[Resilience]]
+- 변형: [[Robustness]]
+- 응용: [[Chaos-Engineering]] · [[Circuit-Breaker]] · [[Barbell-Strategy]]
+- Adjacent: [[Reinforcement-Learning]] · [[Evolutionary-Algorithm]]

 ## 🤖 LLM 활용
 **언제**: 매 system resilience design. 매 risk decision. 매 ML robustness. 매 organizational design.
@@ -194,10 +194,10 @@ def optimal_attack_mix(defender, available_units, budget):
 **기본값**: 매 mixed platoon + 매 specialized platform + 매 Nightwatch / Metronomos.

 ## 🔗 Graph
- 부모: [[War-Commander]] · [[Game-Meta]] · [[Balance-Patch]]
+- 부모: [[War-Commander]] · [[Balance-Patch]]
 - 변형: [[Platform-Specialization]] · [[Mixed-Platoon-Tactics]] · [[Defensive-Architecture]]
- 응용: [[Operation-Western-Sun]] · [[Sector-Breach]] · [[Iridium-Economy]]
- Adjacent: [[Counter-Class-System]] · [[Damage-Type]] · [[Electronic-Warfare]] · [[Steamroll-Prevention]]
+- 응용: [[Operation-Western-Sun]]
+- Adjacent: [[Damage-Type]]

 ## 🤖 LLM 활용
 **언제**: 매 War Commander 매 strategy 의 plan. 매 game design 의 counter-class 의 reference. 매 balance patch 의 case study.
@@ -191,10 +191,10 @@ def detect_distributed_monolith(services, traces):
 **기본값**: 매 ADR + architecture test + tracing + postmortem.

 ## 🔗 Graph
- 부모: [[Architecture-Styles]] · [[Software-Engineering]]
+- 부모: [[Architecture-Styles]]
 - 변형: [[Big-Ball-of-Mud]] · [[Distributed-Monolith]] · [[Anaemic-Domain-Model]] · [[God-Object]]
- 응용: [[Circuit-Breaker]] · [[ADR]] · [[Architecture-Test]] · [[Bounded-Context]]
- Adjacent: [[Technical-Debt]] · [[Code-Smells]] · [[Refactoring]] · [[Postmortem]]
+- 응용: [[Circuit-Breaker]] · [[ADR]] · [[Bounded-Context]]
+- 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.
@@ -198,7 +198,7 @@ module.exports = {
 **기본값**: Modular Monolith (DDD inside) → 매 scale 가 명확 후 Microservices.

 ## 🔗 Graph
- 부모: [[Software-Engineering]] · [[System-Design]]
+- 부모: [[System-Design]]
 - 변형: [[Layered-Architecture]] · [[Clean-Architecture]] · [[Hexagonal-Architecture]] · [[Domain-Driven-Design]] · [[Microservices]] · [[Event-Driven-Architecture]]
 - 응용: [[CQRS]] · [[Event-Sourcing]] · [[Serverless]] · [[Strangler-Fig]]
 - 검증: [[ADR]] · [[C4-Model]] · [[Dependency-Cruiser]] · [[ArchUnit]]
@@ -141,10 +141,9 @@ input data is properly sanitized before being processed."
 **기본값**: BLUF + active + concise + concrete.

 ## 🔗 Graph
- 부모: [[Communication]] · [[Writing]] · [[Critical-Thinking]]
- 변형: [[Plain-Language]] · [[BLUF]] · [[Diátaxis]] · [[Technical-Writing]]
- 응용: [[Prompt-Engineering]] · [[PR-Template]] · [[Spec-Writing]] · [[Documentation]]
- Adjacent: [[Vocabulary-Expansion]] · [[Active-Voice]] · [[Plain-Writing-Act]]
+- 변형: [[Plain-Language]] · [[Technical-Writing]]
+- 응용: [[Prompt_Engineering|Prompt-Engineering]] · [[PR-Template]]
+- Adjacent: [[Vocabulary-Expansion]]

 ## 🤖 LLM 활용
 **언제**: 매 prompt 의 craft. 매 doc / spec / PR write. 매 communication 의 review.
@@ -161,7 +160,7 @@ input data is properly sanitized before being processed."
 ## 🧪 검증 / 중복
 - Verified (Plain Writing Act, GOV.UK style, Diátaxis).
 - 신뢰도 B.
- Related: [[Prompt-Engineering]] · [[Technical-Writing]] · [[Plain-Language]].
+- Related: [[Prompt_Engineering|Prompt-Engineering]] · [[Technical-Writing]] · [[Plain-Language]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -268,8 +268,8 @@ def scan_for_secrets(artifact_content: str) -> list[str]:
 ## 🔗 Graph
 - 부모: [[Agent-Architecture]] · [[Cloud-Infrastructure]]
 - 변형: [[Sandbox]] · [[Container]] · [[MicroVM]] · [[Wasm]]
- 응용: [[E2B]] · [[Modal]] · [[Fly-Machines]] · [[Firecracker]] · [[gVisor]]
- Adjacent: [[Agent-Harness]] · [[Context-Manager]] · [[Tool-Use]] · [[Code-Execution]]
+- 응용: [[E2B]] · [[Modal]] · [[Firecracker]] · [[gVisor]]
+- Adjacent: [[Tool-Use]] · [[Code-Execution]]

 ## 🤖 LLM 활용
 **언제**: 매 agent system design. 매 sandbox selection. 매 artifact store schema. 매 security review.
@@ -203,11 +203,11 @@ result = agent.invoke({'input': 'What is 2026 + 1, and search what happened then
 **기본값**: LLM (general) + RAG (knowledge) + agent (tool). 매 specific = 매 specialized model.

 ## 🔗 Graph
- 부모: [[Computer-Science]] · [[Statistics]]
- 변형: [[Machine-Learning]] · [[Deep-Learning]] · [[Reinforcement-Learning]] · [[NLP]] · [[Computer-Vision]]
- 응용: [[LLM]] · [[Agent]] · [[RAG]] · [[Foundation-Model]]
- 비판: [[AI-Safety]] · [[AI-Ethics]] · [[AI-Alignment]] · [[Hallucination]]
- Adjacent: [[Neuro-Symbolic-AI]] · [[AGI]] · [[Bitter-Lesson]] · [[Scaling-Laws]]
+- 부모: [[Statistics]]
+- 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]]
+- Adjacent: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]] · [[AGI]] · [[Scaling-Laws]]

 ## 🤖 LLM 활용
 **언제**: 매 AI strategy. 매 paradigm choice. 매 history overview. 매 stack design.
@@ -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]] · [[LLM]] · [[Deep-Learning]] · [[AGI]] · [[Bitter-Lesson]].
+- Related: [[Machine-Learning]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Deep-Learning]] · [[AGI]] · [[Bitter-Lesson]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -170,10 +170,10 @@ def map_elites(grid_size, generations=1000):
 **기본값**: 매 specific objective = GA. 매 diversity = MAP-Elites. 매 NN = NEAT or RL.

 ## 🔗 Graph
- 부모: [[Complexity-Theory]] · [[Emergence]] · [[Multi-Agent-Systems]]
- 변형: [[Cellular-Automata]] · [[Evolutionary-Computation]] · [[Swarm-Intelligence]] · [[Boids]]
- 응용: [[NEAT]] · [[Quality-Diversity]] · [[Procedural-Generation]] · [[Game-AI]]
- Adjacent: [[Reinforcement-Learning]] · [[Self-Organization]] · [[Stigmergy]] · [[Algorithmic-Biology]]
+- 부모: [[Complexity_Theory|Complexity-Theory]] · [[Emergence]] · [[Multi-agent-System|Multi-Agent-Systems]]
+- 변형: [[Cellular-Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]]
+- 응용: [[NEAT]] · [[Procedural-Generation]]
+- Adjacent: [[Reinforcement-Learning]] · [[Self-Organization]] · [[Algorithmic-Biology]]

 ## 🤖 LLM 활용
 **언제**: 매 NPC swarm design. 매 procedural generation. 매 evolutionary optimization. 매 emergent behavior research.
@@ -189,7 +189,7 @@ def map_elites(grid_size, generations=1000):
 ## 🧪 검증 / 중복
 - Verified (Langton, Reynolds, Sims, Stanley).
 - 신뢰도 B.
- Related: [[Cellular-Automata]] · [[Evolutionary-Computation]] · [[Swarm-Intelligence]].
+- Related: [[Cellular-Automata]] · [[Evolutionary Biology|Evolutionary-Computation]] · [[Swarm_Intelligence|Swarm-Intelligence]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -223,10 +223,10 @@ function ArtworkCard({ artwork }: { artwork: Artwork }) {
 **기본값**: 매 disclosure + 매 attribution + 매 hybrid (human + AI). 매 100% AI 의 explicit category.

 ## 🔗 Graph
- 부모: [[Aesthetic-Value]] · [[Culture]] · [[Creativity]]
- 변형: [[Generative-Art]] · [[AI-Art]] · [[Conceptual-Art]] · [[Neo-Humanism]]
+- 부모: [[Aesthetic-Value]] · [[Creativity]]
+- 변형: [[Generative-Art]] · [[AI-Art]]
 - 응용: [[Stable-Diffusion]] · [[Midjourney]] · [[ControlNet]] · [[ComfyUI]]
- Adjacent: [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze-Nightshade]] · [[AI-Image-Generation]]
+- Adjacent: [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[AI 이미지 생성 (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-Image-Generation]] · [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze]].
+- Related: [[AI 이미지 생성 (AI Image Generation)]] · [[Authenticity]] · [[C2PA]] · [[Copyright]] · [[Glaze]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -244,11 +244,10 @@ def evaluate_model(model, eval_set):
 **기본값**: Multi-method + rubric + inter-rater check + fairness audit.

 ## 🔗 Graph
- 부모: [[Education]] · [[Evaluation]] · [[Measurement]]
- 변형: [[Formative-Assessment]] · [[Summative-Assessment]] · [[Adaptive-Testing]] · [[Authentic-Assessment]]
- 응용: [[Rubric]] · [[Portfolio]] · [[IRT]] · [[Cohen-Kappa]]
+- 부모: [[Evaluation]]
+- 응용: [[Rubric]]
 - ML parallel: [[ML-Evaluation]] · [[Benchmarks]] · [[LLM-as-Judge]] · [[Bias-Correction-Algorithm]]
- Adjacent: [[Algorithmic-Fairness]] · [[Validity]] · [[Reliability]] · [[Construct-Validity]]
+- Adjacent: [[Algorithmic-Fairness]] · [[Validity]] · [[Reliability]]

 ## 🤖 LLM 활용
 **언제**: 매 educational system design. 매 ML evaluation suite. 매 performance review framework. 매 rubric 작성.
@@ -228,10 +228,9 @@ trainer.train()
 **기본값**: Internal RAG 의 baseline. 매 high-volume specific = LoRA. 매 critical = self-host.

 ## 🔗 Graph
- 부모: [[Knowledge-Management]] · [[Organizational-Learning]]
- 변형: [[Tacit-Knowledge]] · [[Tribal-Knowledge]] · [[Institutional-Memory]]
+- 변형: [[Tacit-Knowledge]] · [[Tribal-Knowledge]]
 - 응용: [[RAG]] · [[Fine-Tuning]] · [[LoRA]] · [[Onboarding]] · [[ADR]]
- Adjacent: [[Bus-Factor]] · [[Postmortem]] · [[Williamson-Economics]] · [[Moat]]
+- Adjacent: [[Postmortem]] · [[Moat]]

 ## 🤖 LLM 활용
 **언제**: 매 internal RAG 설계. 매 onboarding system. 매 war story 의 extract. 매 organization-specific tool.
@@ -185,10 +185,10 @@ def listen():
 **기본값**: 매 edge-first + 매 user control + 매 minimum data.

 ## 🔗 Graph
- 부모: [[Ubiquitous-Computing]] · [[HCI]] · [[IoT]]
- 변형: [[Smart-Home]] · [[Wearable-Computing]] · [[Spatial-Computing]] · [[Zero-UI]]
- 응용: [[Matter]] · [[HomeKit]] · [[Home-Assistant]] · [[Edge-AI]]
- Adjacent: [[Privacy]] · [[Federated-Learning]] · [[Differential-Privacy]] · [[Surveillance-Capitalism]]
+- 부모: [[Ubiquitous-Computing]] · [[HCI]] · [[클라우드_인프라_및_IaC_운영_표준|IoT]]
+- 변형: [[Spatial-Computing]] · [[Zero-UI]]
+- 응용: [[Edge-AI]]
+- Adjacent: [[Privacy]] · [[Federated-Learning]] · [[Differential-Privacy]]

 ## 🤖 LLM 활용
 **언제**: 매 ambient device design. 매 smart home automation. 매 IoT privacy review. 매 voice assistant integration.
@@ -166,10 +166,8 @@ const aiPersona = {
 **기본값**: 매 disclose + provenance + vulnerability.

 ## 🔗 Graph
- 부모: [[Ethics]] · [[Branding]] · [[Trust]]
- 변형: [[Vulnerability-Brown]] · [[Moral-Courage]] · [[Brand-Authenticity]]
- 응용: [[C2PA]] · [[Content-Provenance]] · [[SynthID]] · [[AI-Disclosure]]
- Adjacent: [[Deepfake]] · [[Anthropomorphism]] · [[EU-AI-Act]] · [[Watermarking]]
+- 응용: [[C2PA]] · [[Content-Provenance]]
+- Adjacent: [[Deepfake]] · [[Anthropomorphism]] · [[EU-AI-Act]]

 ## 🤖 LLM 활용
 **언제**: 매 brand / agent persona design. 매 content provenance system. 매 AI disclosure policy.
@@ -219,10 +219,9 @@ interpreter = tflite.Interpreter(model_path='emotion_model.tflite')
 **기본값**: 매 user-driven + 매 consent + 매 local processing + 매 neurodiversity affirming.

 ## 🔗 Graph
- 부모: [[Accessibility]] · [[Special-Education]] · [[AI-for-Good]]
+- 부모: [[Accessibility (A11y)|Accessibility]] · [[AI-for-Good]]
 - 변형: [[Emotion-Recognition]] · [[Social-Skills-Training]] · [[AAC]] · [[Social-Robot]]
- 응용: [[NAO-Robot]] · [[Floreo-VR]] · [[Proloquo2Go]] · [[Visual-Schedule]]
- Adjacent: [[Neurodiversity-Movement]] · [[ABA-Critique]] · [[Sensory-Processing]] · [[Anthropomorphism]]
+- Adjacent: [[Anthropomorphism]]

 ## 🤖 LLM 활용
 **언제**: 매 AAC supplement. 매 social practice prompt. 매 visual schedule generation. 매 sensory-friendly content.
@@ -240,7 +239,7 @@ interpreter = tflite.Interpreter(model_path='emotion_model.tflite')
 ## 🧪 검증 / 중복
 - Verified (peer-reviewed ASD research, neurodiversity literature).
 - 신뢰도 B.
- Related: [[Accessibility]] · [[AI-for-Good]] · [[Humane-Tech]] · [[Anthropomorphism]].
+- Related: [[Accessibility (A11y)|Accessibility]] · [[AI-for-Good]] · [[Humane-Tech]] · [[Anthropomorphism]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -226,10 +226,10 @@ loss = recon + beta * kl  # 매 β = 4 ~ 10
 **기본값**: Task-specific. 매 representation = AE. 매 generative = VAE. 매 vision pretrain = MAE.

 ## 🔗 Graph
- 부모: [[Unsupervised-Learning]] · [[Representation-Learning]] · [[Generative-Models]]
- 변형: [[VAE]] · [[VQ-VAE]] · [[β-VAE]] · [[MAE]] · [[Denoising-AE]] · [[Sparse-AE]]
- 응용: [[Anomaly-Detection]] · [[Stable-Diffusion]] · [[DALL-E]] · [[Self-Supervised-Learning]]
- Adjacent: [[PCA]] · [[GAN]] · [[Diffusion-Model]] · [[Latent-Space]]
+- 부모: [[Generative-AI|Generative-Models]]
+- 변형: [[VAE]] · [[β-VAE]] · [[MAE]] · [[Denoising-AE]]
+- 응용: [[Anomaly-Detection]] · [[Stable-Diffusion]] · [[DALL-E]]
+- Adjacent: [[PCA]] · [[Generative-Adversarial-Networks|GAN]] · [[Diffusion-Model]] · [[Latent-Space]]

 ## 🤖 LLM 활용
 **언제**: 매 representation learning. 매 anomaly detection. 매 generative latent. 매 vision pretrain.
@@ -195,10 +195,10 @@ while not gym.is_complete():

 ## 🔗 Graph
 - 부모: [[Formal-Methods]] · [[Logic]] · [[Type-Theory]]
- 변형: [[SAT-Solver]] · [[SMT-Solver]] · [[Proof-Assistant]] · [[Model-Checking]]
- 응용: [[seL4]] · [[CompCert]] · [[TLA-Plus]] · [[Mathlib]] · [[AlphaProof]]
- AI hybrid: [[Neuro-Symbolic-AI]] · [[GPT-f]] · [[AlphaGeometry]]
- Adjacent: [[Dependent-Types]] · [[Curry-Howard]] · [[Hoare-Logic]]
+- 변형: [[Proof-Assistant]] · [[Model-Checking]]
+- 응용: [[CompCert]]
+- AI hybrid: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]]
+- Adjacent: [[Curry-Howard]]

 ## 🤖 LLM 활용
 **언제**: 매 critical software (kernel, crypto, smart contract). 매 distributed protocol. 매 deep math. 매 ATP-LLM hybrid 의 research.
@@ -215,7 +215,7 @@ while not gym.is_complete():
 ## 🧪 검증 / 중복
 - Verified (seL4, CompCert, Lean Mathlib, AlphaProof papers).
 - 신뢰도 A.
- Related: [[Lean-4]] · [[Coq]] · [[TLA-Plus]] · [[Neuro-Symbolic-AI]] · [[AlphaProof]].
+- Related: [[Lean-4]] · [[Coq]] · [[TLA-Plus]] · [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]] · [[AlphaProof]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -262,10 +262,9 @@ if (ret[0].Score > 0.7) {
 **기본값**: Visual SLAM = ORB-SLAM3. LiDAR = LIO-SAM. Photoreal = Gaussian Splatting.

 ## 🔗 Graph
- 부모: [[Robotics]] · [[Computer-Vision]] · [[Spatial-Computing]]
- 변형: [[Visual-SLAM]] · [[LiDAR-SLAM]] · [[VIO]] · [[Visual-Inertial-SLAM]]
- 응용: [[Autonomous-Vehicles]] · [[AR-VR]] · [[Drone]] · [[HD-Map]] · [[Photogrammetry]]
- Modern: [[NeRF]] · [[Gaussian-Splatting]] · [[DROID-SLAM]] · [[NICE-SLAM]]
+- 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]] · [[Spatial-Computing]]
+- 응용: [[Autonomous-Vehicles]] · [[HD-Map]]
+- Modern: [[NeRF]] · [[Gaussian-Splatting]]
 - Adjacent: [[Bundle-Adjustment]] · [[Loop-Closure]] · [[Bayesian-Brain-Hypothesis]]

 ## 🤖 LLM 활용
@@ -283,7 +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]] · [[Robotics]] · [[NeRF]] · [[Gaussian-Splatting]].
+- Related: [[Autonomous-Vehicles]] · [[Computer Vision|Computer-Vision]] · [[Robotics]] · [[NeRF]] · [[Gaussian-Splatting]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -234,10 +234,10 @@ def mpc_step(x_current, x_ref, horizon=10, dt=0.1):
 **기본값**: Modular for safety-critical. End-to-end for scale.

 ## 🔗 Graph
- 부모: [[Robotics]] · [[Computer-Vision]] · [[Embedded-Systems]]
- 변형: [[Tesla-FSD]] · [[Waymo]] · [[Mobileye]] · [[Comma-AI]]
- 응용: [[SLAM]] · [[BEV-Perception]] · [[End-to-End-Driving]] · [[Behavior-Planning]]
- Adjacent: [[Reinforcement-Learning]] · [[Imitation-Learning]] · [[CARLA]] · [[Trolley-Problem]] · [[AI-Safety]]
+- 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]]
+- 변형: [[Tesla-FSD]] · [[Waymo]]
+- 응용: [[SLAM]] · [[End-to-End-Driving]]
+- Adjacent: [[Reinforcement-Learning]] · [[AI-Safety]]

 ## 🤖 LLM 활용
 **언제**: 매 AV system architecture review. 매 ADAS feature design. 매 simulation scenario. 매 sensor fusion debug.
@@ -244,9 +244,9 @@ def poll_progress(job_id):

 ## 🔗 Graph
 - 부모: [[Async-Programming]] · [[API-Design]]
- 변형: [[Long-Polling]] · [[Webhook]] · [[Server-Sent-Events]] · [[Exponential-Backoff]]
- 응용: [[NotebookLM]] · [[Replicate-API]] · [[Job-Queue]] · [[Agent-Loop]]
- Adjacent: [[Retry-with-Backoff]] · [[Circuit-Breaker]] · [[AbortController]] · [[Promise]]
+- 변형: [[Server-Sent-Events]] · [[Exponential-Backoff]]
+- 응용: [[NotebookLM]] · [[Agent-Loop]]
+- Adjacent: [[Circuit-Breaker]] · [[AbortController]]

 ## 🤖 LLM 활용
 **언제**: 매 long-running job. 매 agent automation. 매 third-party API integration. 매 batch inference orchestration.
@@ -262,8 +262,8 @@ def release_lock(client, key, token):
 - 부모: [[Distributed-Systems]] · [[SRE]] · [[Reliability]]
 - 변형: [[High-Availability]] · [[Durability]] · [[Replication]] · [[Backup-Strategy]]
 - 응용: [[ACID]] · [[CAP-Theorem]] · [[PACELC]] · [[Raft]] · [[Paxos]]
- 응용 (cloud): [[S3]] · [[Multi-Region]] · [[Auto-Scaling]] · [[Chaos-Engineering]]
- Adjacent: [[SLO-SLI]] · [[Error-Budget]] · [[Circuit-Breaker]] · [[Postmortem]]
+- 응용 (cloud): [[Multi-Region]] · [[Chaos-Engineering]]
+- Adjacent: [[Circuit-Breaker]] · [[Postmortem]]

 ## 🤖 LLM 활용
 **언제**: 매 system design. 매 SLA negotiation. 매 incident response. 매 backup strategy review.
@@ -188,10 +188,10 @@ def test_of_time_score(paper, year=10):
 **기본값**: 매 multi-dim + 매 disclosure + 매 reproducibility.

 ## 🔗 Graph
- 부모: [[Scientific-Community]] · [[Recognition-Systems]] · [[Motivation]]
- 변형: [[Turing-Award]] · [[Nobel-Prize]] · [[NeurIPS-Best-Paper]] · [[Test-of-Time]]
- 응용: [[Kaggle]] · [[Hackathon]] · [[Open-Source-Recognition]]
- Adjacent: [[Replication-Crisis]] · [[Citation-Count]] · [[Goodharts-Law]] · [[Authenticity]]
+- 부모: [[Motivation]]
+- 변형: [[Turing-Award]] · [[NeurIPS-Best-Paper]]
+- 응용: [[Kaggle]]
+- Adjacent: [[Goodharts-Law]] · [[Authenticity]]

 ## 🤖 LLM 활용
 **언제**: 매 award strategy. 매 community recognition design. 매 reviewer process.
@@ -213,9 +213,8 @@ def is_real_improvement(metric_before, metric_after, vanity_metric_change):
 **기본값**: DORA + VSM + AI impact 의 monthly review.

 ## 🔗 Graph
- 부모: [[Engineering-Productivity]] · [[DevOps]] · [[SRE]]
+- 부모: [[DevOps]] · [[SRE]]
 - 변형: [[DORA-Metrics]] · [[Value-Stream-Mapping]] · [[Engineering-Metrics]]
- 응용: [[LinearB]] · [[Sleuth]] · [[Faros-AI]]
 - Adjacent: [[CI-CD]] · [[Branching-Strategies]] · [[Code-Review]] · [[Goodharts-Law]]

 ## 🤖 LLM 활용
@@ -211,11 +211,10 @@ def counterfactual_axiom_check(model, x, protected_idx):
 **기본값**: 매 axiom 의 explicit. 매 implicit assumption 의 surface.

 ## 🔗 Graph
- 부모: [[Logic]] · [[Mathematics]] · [[Foundations]]
- 변형: [[Postulate]] · [[Theorem]] · [[Definition]]
- 응용: [[Constitutional-AI]] · [[Formal-Verification]] · [[Ontology]] · [[First-Principles-Thinking]]
- 비판: [[Gödel-Incompleteness]] · [[Non-Euclidean]] · [[Asimov-Laws]]
- Adjacent: [[Lean-4]] · [[Coq]] · [[ZFC]] · [[Peano]] · [[Z3]]
+- 부모: [[Logic]]
+- 변형: [[Postulate]]
+- 응용: [[AI_Safety_and_Alignment|Constitutional-AI]] · [[Formal-Verification]] · [[Ontology]]
+- Adjacent: [[Coq]] · [[ZFC]] · [[Peano]]

 ## 🤖 LLM 활용
 **언제**: 매 reasoning 의 base 의 surface. 매 AI alignment 설계. 매 formal verify. 매 first principles 의 problem 의 break.
@@ -232,7 +231,7 @@ def counterfactual_axiom_check(model, x, protected_idx):
 ## 🧪 검증 / 중복
 - Verified (Euclid, Peano, ZFC, Anthropic Constitutional AI paper).
 - 신뢰도 A.
- Related: [[Formal-Verification]] · [[Constitutional-AI]] · [[First-Principles-Thinking]] · [[Lean-4]].
+- Related: [[Formal-Verification]] · [[AI_Safety_and_Alignment|Constitutional-AI]] · [[First-Principles-Thinking]] · [[Lean-4]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -177,11 +177,11 @@ for topic_idx, topic in enumerate(lda.components_):
 **기본값**: 매 baseline = TF-IDF + LinearSVC. 매 result 의 transformer 와 비교.

 ## 🔗 Graph
- 부모: [[NLP]] · [[Text-Representation]] · [[Information-Retrieval]]
- 변형: [[TF-IDF]] · [[N-gram]] · [[Hashing-Trick]] · [[BM25]]
- 응용: [[Spam-Classification]] · [[Topic-Modeling]] · [[LDA]] · [[Document-Retrieval]]
- 대체: [[Word2Vec]] · [[Sentence-Transformers]] · [[BERT]] · [[Embedding]]
- Adjacent: [[Naive-Bayes]] · [[Linear-SVM]] · [[Stop-Words]] · [[Stemming]]
+- 부모: [[NLP]] · [[Information-Retrieval]]
+- 변형: [[TF-IDF]] · [[N-gram]] · [[BM25]]
+- 응용: [[LDA]]
+- 대체: [[Sentence-Transformers]] · [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Embedding]]
+- Adjacent: [[Naive-Bayes]] · [[Stemming]]

 ## 🤖 LLM 활용
 **언제**: 매 baseline. 매 fast classifier. 매 interpretability 필요. 매 IR. 매 topic modeling.
@@ -180,10 +180,10 @@ function generateHoneyPot(layout: BaseLayout): Trap {
 **기본값**: 매 player tactic = bait + ambush. 매 NPC design = multi-factor + leash + cohesion.

 ## 🔗 Graph
- 부모: [[Game-AI]] · [[RTS-Tactics]] · [[NPC-Behavior]]
- 변형: [[Kiting]] · [[Aggro-Pull]] · [[Honey-Pot]] · [[Wild-Goose-Chase]]
- 응용: [[Behavior-Tree]] · [[Threat-Evaluation]] · [[Leash]] · [[Counter-Pairing]]
- Adjacent: [[War-Commander]] · [[MMO-Aggro]] · [[Souls-like-AI]] · [[Combat-AI]]
+- 부모: [[RTS-Tactics]]
+- 변형: [[Kiting]] · [[Aggro-Pull]] · [[Wild-Goose-Chase]]
+- 응용: [[Behavior-Tree]]
+- Adjacent: [[War-Commander]]

 ## 🤖 LLM 활용
 **언제**: 매 RTS / MMO tactic. 매 NPC AI design. 매 player 의 AI exploit pattern 분석.
@@ -28,7 +28,7 @@ github_commit: pending

 ## 🔗 Graph
 - 부모: [[Baiting]] (canonical)
- Adjacent: [[Combat-AI]] · [[Behavior-Tree]] · [[Pursuit-Logic]] · [[Game-AI]]
+- Adjacent: [[Behavior-Tree]] · [[Pursuit-Logic]]

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -200,10 +200,9 @@ CSS Container Queries allow elements to respond to their container's size...
 **기본값**: Baseline widely available + 매 progressive enhancement.

 ## 🔗 Graph
- 부모: [[Web-Platform]] · [[Browser-Compatibility]] · [[Web-Performance]]
- 변형: [[CanIUse]] · [[Browserslist]] · [[Progressive-Enhancement]]
- 응용: [[Container-Queries]] · [[CSS-has-Pseudo]] · [[View-Transitions]] · [[AVIF]]
- Adjacent: [[Polyfill]] · [[Transpile]] · [[ESLint-Compat]] · [[MDN]]
+- 부모: [[Web-Performance]]
+- 변형: [[Progressive-Enhancement]]
+- 응용: [[Container-Queries]] · [[View-Transitions]] · [[AVIF]]

 ## 🤖 LLM 활용
 **언제**: 매 web feature 의 ship decision. 매 polyfill drop. 매 build target 결정.
@@ -189,10 +189,9 @@ config = {
 **기본값**: vLLM (LLM) / Triton (general) / Ray (distributed).

 ## 🔗 Graph
- 부모: [[ML-Inference]] · [[Performance-Engineering]]
 - 변형: [[Continuous-Batching]] · [[Dynamic-Batching]] · [[Static-Batching]]
- 응용: [[vLLM]] · [[Triton-Inference-Server]] · [[Ray-Serve]] · [[PagedAttention]]
- Adjacent: [[GPU-Utilization]] · [[Spot-Instance]] · [[KV-Cache]] · [[Inference-Optimization]]
+- 응용: [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[LLM_Optimization_and_Deployment_Strategies|PagedAttention]]
+- Adjacent: [[KV-Cache]] · [[LLM_Optimization_and_Deployment_Strategies|Inference-Optimization]]

 ## 🤖 LLM 활용
 **언제**: 매 cost optimization. 매 throughput 우선 task. 매 LLM serving infra design.
@@ -209,7 +208,7 @@ config = {
 ## 🧪 검증 / 중복
 - Verified (vLLM paper, NVIDIA Triton, Ray).
 - 신뢰도 A.
- Related: [[vLLM]] · [[Continuous-Batching]] · [[GPU-Utilization]] · [[ML-Inference]].
+- Related: [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[Continuous-Batching]] · [[GPU-Utilization]] · [[ML-Inference]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -209,10 +209,10 @@ az.plot_ppc(az.from_pymc3(posterior_predictive=ppc, model=model))

 ## 🔗 Graph
 - 부모: [[Statistics]] · [[Probability-Theory]]
- 변형: [[MCMC]] · [[Variational-Inference]] · [[Bayesian-Network]] · [[Hierarchical-Model]]
- 응용: [[A-B-Testing]] · [[Bayesian-Optimization]] · [[Particle-Filter]] · [[LDA]] · [[SLAM]]
- Tool: [[PyMC]] · [[Stan]] · [[NumPyro]] · [[Pyro]]
- Adjacent: [[Bayes-Theorem]] · [[Bayesian-Updating]] · [[Conjugate-Prior]] · [[Frequentist]]
+- 변형: [[MCMC]] · [[Variational-Inference]] · [[Bayesian-Network]]
+- 응용: [[Bayesian-Optimization]] · [[LDA]] · [[SLAM]]
+- Tool: [[PyMC]] · [[Stan]]
+- Adjacent: [[Bayes-Theorem]] · [[Bayesian-Updating]]

 ## 🤖 LLM 활용
 **언제**: 매 small data + prior. 매 uncertainty quantify. 매 hierarchical structure. 매 hyperparameter tune.
@@ -181,11 +181,11 @@ class WorldModel(nn.Module):
 **기본값**: 매 perception = predictive coding. 매 action = active inference (sparse reward) or RL (dense).

 ## 🔗 Graph
- 부모: [[Cognitive-Science]] · [[Computational-Neuroscience]] · [[Bayesian-Inference]]
- 변형: [[Predictive-Coding]] · [[Free-Energy-Principle]] · [[Active-Inference]] · [[Markov-Blanket]]
- 응용: [[World-Model]] · [[Dreamer]] · [[VAE]] · [[Self-Supervised-Learning]]
- 사상가: [[Karl-Friston]] · [[Helmholtz]] · [[Rao-Ballard]]
- Adjacent: [[Reinforcement-Learning]] · [[Generative-Model]] · [[Hierarchical-Prediction]]
+- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Bayesian-Inference]]
+- 변형: [[Predictive-Coding]] · [[Free-Energy-Principle]] · [[Active-Inference]]
+- 응용: [[World-Model]] · [[VAE]]
+- 사상가: [[Karl-Friston]]
+- Adjacent: [[Reinforcement-Learning]] · [[Generative-Model]]

 ## 🤖 LLM 활용
 **언제**: 매 active inference agent design. 매 world model. 매 perception system. 매 sparse-reward RL.
@@ -246,10 +246,10 @@ Special:
 **기본값**: 매 audience + 매 task 의 calibrate. 매 vague 의 default 의 X.

 ## 🔗 Graph
- 부모: [[Communication]] · [[Articulateness]] · [[Spec-Writing]]
- 변형: [[Quantification]] · [[Edge-Case-Enumeration]] · [[Acceptance-Criteria]]
- 응용: [[Prompt-Engineering]] · [[RFC]] · [[Bug-Report]] · [[Given-When-Then]]
- Adjacent: [[Anti-Vague]] · [[Progressive-Disclosure]] · [[BLUF]] · [[ADR]]
+- 부모: [[Articulateness]]
+- 변형: [[Edge-Case-Enumeration]]
+- 응용: [[Prompt_Engineering|Prompt-Engineering]] · [[RFC]]
+- Adjacent: [[Progressive-Disclosure]] · [[ADR]]

 ## 🤖 LLM 활용
 **언제**: 매 LLM prompt. 매 spec 작성. 매 bug report. 매 design review.
@@ -266,7 +266,7 @@ Special:
 ## 🧪 검증 / 중복
 - Verified (Pragmatic Programmer, RFC tradition, prompt engineering best practice).
 - 신뢰도 B.
- Related: [[Articulateness]] · [[Prompt-Engineering]] · [[Spec-Writing]] · [[Edge-Case]].
+- Related: [[Articulateness]] · [[Prompt_Engineering|Prompt-Engineering]] · [[Spec-Writing]] · [[Edge-Case]].

 ## 🕓 Changelog
 | 날짜 | 변경 |
@@ -164,11 +164,9 @@ prompt = """Write a 6-line dialogue in the style of Beckett's Godot:
 **기본값**: 매 metaphor 로 사용. 매 literal 의 implementation X.

 ## 🔗 Graph
- 부모: [[Literature]] · [[Theatre]] · [[Modernism]]
 - 변형: [[Theatre-of-the-Absurd]] · [[Existentialism]] · [[Minimalism]]
- 영향: [[Pinter]] · [[Ionesco]] · [[Camus]] · [[Derrida]]
- 응용 (AI metaphor): [[Loop-Detection]] · [[Hallucination]] · [[AI-Narrative]] · [[Agent-Monitoring]]
- Adjacent: [[Authenticity]] · [[Anxiety]] · [[Aesthetic-Value]] · [[Sisyphus]]
+- 응용 (AI metaphor): [[Hallucination]]
+- Adjacent: [[Authenticity]] · [[Anxiety]] · [[Aesthetic-Value]]

 ## 🤖 LLM 활용
 **언제**: 매 narrative analysis. 매 AI agent 의 metaphorical reasoning. 매 minimalist design discussion. 매 existential UX.
@@ -194,11 +194,11 @@ def detect_confirmation_bias(query, results, user_belief):
 **기본값**: Bayesian belief + ECE calibration + diverse evidence.

 ## 🔗 Graph
- 부모: [[Epistemology]] · [[Cognitive-Science]]
- 변형: [[Knowledge]] · [[Justified-True-Belief]] · [[Bayesian-Belief]] · [[Doxastic-Logic]]
- 응용: [[POMDP]] · [[BDI-Agent]] · [[LLM-Calibration]] · [[RAG-Trust]]
- 비판: [[Confirmation-Bias]] · [[Belief-Perseverance]] · [[Motivated-Reasoning]] · [[Gettier-Problem]]
- Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Multi-Agent-Systems]] · [[Aumann-Theorem]]
+- 부모: [[Epistemology]]
+- 변형: [[Knowledge]] · [[Bayesian-Belief]] · [[Doxastic-Logic]]
+- 응용: [[POMDP]]
+- 비판: [[Confirmation-Bias]]
+- Adjacent: [[Bayesian-Brain-Hypothesis]] · [[Multi-agent-System|Multi-Agent-Systems]]

 ## 🤖 LLM 활용
 **언제**: 매 agent design (belief state). 매 RAG trust scoring. 매 LLM calibration eval. 매 bias detection.
--- a/Show More
+++ b/Show More