docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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+---
+id: wiki-2026-0508-feature-clamping-피처-고정
+title: Feature Clamping (피처 고정)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [feature clamping, activation clamping, SAE feature steering, mechanistic intervention]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.92
+verification_status: applied
+tags: [interpretability, mechanistic, sae, feature-clamping, anthropic, steering]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: TransformerLens / PyTorch / Anthropic SAE
+---
+
+# Feature Clamping (피처 고정)
+
+## 매 한 줄
+> **"매 SAE feature 의 activation 의 specific value 의 force"**. 매 model behavior 의 causally test. Anthropic 'Towards Monosemanticity' (2023), 'Golden Gate Claude' (2024). 매 mechanistic interpretability + 매 model steering 의 핵심 도구.
+
+## 매 핵심
+
+### 매 mechanism
+1. **SAE 학습**: 매 layer activation → 매 sparse feature.
+2. **Identify feature**: 매 interpret what feature represents.
+3. **Clamp**: 매 forward pass 의 의 의 feature value 의 fix.
+4. **Observe**: 매 output behavior change.
+
+### 매 응용
+- **Causal understanding**: 매 feature → behavior.
+- **Steering**: 매 desired behavior 의 amplify.
+- **Refusal modify**: 매 safety adjust (carefully).
+- **Concept editing**: 매 specific concept 의 emphasize.
+
+### 매 famous demo
+- **Golden Gate Claude** (Anthropic May 2024): 매 Golden Gate Bridge feature 의 high-clamp → 매 Claude 의 의 의 obsessed.
+- **Refusal feature**: 매 clamp = 매 always-refuse or never-refuse.
+- **Sycophancy feature**: 매 clamp 의 study.
+
+### 매 trade-off
+- ✅ Causal evidence.
+- ✅ Steering control.
+- ❌ Other capabilities 의 degrade (over-clamp).
+- ❌ SAE feature 의 monosemantic 의 X (often).
+
+## 💻 패턴
+
+### Basic SAE clamp
+```python
+import torch
+
+def clamp_feature(model, layer, sae, feature_idx, value):
+    """매 forward pass 시 의 feature 의 fix."""
+    def hook(module, input, output):
+        z = sae.encode(output)
+        z[:, :, feature_idx] = value  # 매 clamp
+        return sae.decode(z)
+    
+    handle = model.transformer.h[layer].register_forward_hook(hook)
+    return handle  # 매 unregister 시 handle.remove()
+```
+
+### Steered generation
+```python
+def generate_with_clamp(model, sae, layer, feature_idx, value, prompt, max_tokens=100):
+    handle = clamp_feature(model, layer, sae, feature_idx, value)
+    try:
+        return model.generate(prompt, max_new_tokens=max_tokens)
+    finally:
+        handle.remove()
+```
+
+### Multi-feature clamp
+```python
+def clamp_multi(model, layer, sae, features: dict):
+    def hook(module, input, output):
+        z = sae.encode(output)
+        for idx, val in features.items():
+            z[:, :, idx] = val
+        return sae.decode(z)
+    return model.transformer.h[layer].register_forward_hook(hook)
+
+# 매 example: amplify "kindness", suppress "hostility"
+features = {KINDNESS_FEATURE: 5.0, HOSTILITY_FEATURE: 0.0}
+```
+
+### Identify feature (find by activation)
+```python
+def find_feature_for_concept(sae, examples_of_concept):
+    """매 매 example 매 매 max-activating feature."""
+    activations = []
+    for ex in examples_of_concept:
+        with model.run_with_cache(ex) as cache:
+            z = sae.encode(cache[layer])
+            activations.append(z.max(dim=1).values)
+    
+    avg_activation = torch.stack(activations).mean(0)
+    return avg_activation.topk(10).indices
+```
+
+### Causal trace (clamp ablation)
+```python
+def ablation_study(model, sae, feature_idx, prompts):
+    """매 feature 의 ablate (=0) → 매 behavior shift."""
+    results = {'normal': [], 'ablated': []}
+    
+    # 매 normal
+    for p in prompts:
+        results['normal'].append(model.generate(p))
+    
+    # 매 ablated
+    handle = clamp_feature(model, LAYER, sae, feature_idx, 0)
+    for p in prompts:
+        results['ablated'].append(model.generate(p))
+    handle.remove()
+    
+    return results
+```
+
+### Saturation level (Golden Gate-style)
+```python
+def saturation_clamp(model, sae, layer, feature_idx, levels=[0, 1, 5, 10, 50]):
+    outputs = {}
+    for v in levels:
+        handle = clamp_feature(model, layer, sae, feature_idx, v)
+        outputs[v] = model.generate("Tell me about yourself.", max_new_tokens=100)
+        handle.remove()
+    return outputs
+```
+
+### Refusal feature
+```python
+# 매 careful — safety implication
+REFUSAL_FEATURES = [...]  # 매 from interpretability research
+
+def adjust_refusal(model, sae, level):
+    """매 level: 0 = unrestricted, 1 = normal, 2 = strict."""
+    if level == 0: value = -2.0  # 매 suppress
+    elif level == 1: value = None  # 매 don't clamp
+    else: value = 5.0  # 매 amplify
+    
+    if value is not None:
+        return clamp_feature(model, LAYER, sae, REFUSAL_FEATURES[0], value)
+    return None
+```
+
+### Train SAE
+```python
+class SparseAutoencoder(torch.nn.Module):
+    def __init__(self, d_model, d_sae, l1_coef=1e-3):
+        super().__init__()
+        self.W_enc = torch.nn.Linear(d_model, d_sae)
+        self.W_dec = torch.nn.Linear(d_sae, d_model, bias=False)
+        self.l1_coef = l1_coef
+    
+    def forward(self, x):
+        z = torch.relu(self.W_enc(x))
+        x_recon = self.W_dec(z)
+        loss = ((x - x_recon) ** 2).mean() + self.l1_coef * z.abs().sum(-1).mean()
+        return x_recon, z, loss
+```
+
+### Activation patching (alternative)
+```python
+def patch_activation(model, layer, source_run_cache, target_prompt):
+    def hook(module, input, output):
+        return source_run_cache[layer]
+    h = model.transformer.h[layer].register_forward_hook(hook)
+    out = model.generate(target_prompt)
+    h.remove()
+    return out
+```
+
+### Eval (specificity)
+```python
+def evaluate_clamp(model, sae, feature_idx, value, target_topic, neutral_topics):
+    """매 clamp 의 target 의 affect, 매 neutral 매 not."""
+    handle = clamp_feature(model, LAYER, sae, feature_idx, value)
+    target_outputs = [model.generate(p) for p in target_topic]
+    neutral_outputs = [model.generate(p) for p in neutral_topics]
+    handle.remove()
+    
+    return {
+        'target_affected': measure_topic_drift(target_outputs, target_topic),
+        'neutral_unchanged': measure_topic_drift(neutral_outputs, neutral_topics),
+    }
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Mechanistic study | Clamp + ablate |
+| Behavior steering | Clamp at moderate level |
+| Concept editing | Multi-feature clamp |
+| Safety study | Refusal feature (research only) |
+| Demo | Saturation clamp (Golden Gate) |
+
+**기본값**: 매 SAE 학습 → 매 feature 의 identify → 매 clamp + 매 ablate causal evidence + 매 specificity eval.
+
+## 🔗 Graph
+- 부모: [[Mechanistic-Interpretability]] · [[Explainable-AI-XAI]]
+- 변형: [[Activation-Patching]]
+- Adjacent: [[Sparse-Autoencoder]]
+
+## 🤖 LLM 활용
+**언제**: 매 interpretability research. 매 alignment study.
+**언제 X**: 매 production user-facing (unstable).
+
+## ❌ 안티패턴
+- **High clamp 의 production**: 매 capability degrade.
+- **Single-feature 의 trust**: 매 monosemanticity 의 false.
+- **No specificity eval**: 매 unwanted side effect.
+- **Refusal manipulation**: 매 safety 의 break.
+
+## 🧪 검증 / 중복
+- Verified (Anthropic Towards Monosemanticity 2023, Golden Gate Claude 2024).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-26 | CLAMP auto |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — clamp + 매 SAE / hook / ablation / saturation code |