refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-assumptions-vs-facts
+title: Assumptions vs Facts
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Fact-Assumption Distinction, Premise vs Evidence]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [reasoning, epistemology, decision-making, critical-thinking]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: na
+---
+
+# Assumptions vs Facts
+
+## 매 한 줄
+> **"매 fact 는 매 verifiable observation, 매 assumption 은 매 unverified premise"**. 매 둘 의 conflation 매 most decision failure 의 root. 매 military intelligence (CIA Tradecraft Primer), 매 software engineering (RFC, design doc), 매 LLM agent reasoning (chain-of-thought 매 assumption 명시) 모두 의 핵심 discipline.
+
+## 매 핵심
+
+### 매 정의
+- **Fact**: 매 currently verifiable claim — 매 measurement, 매 reproducible observation, 매 authoritative record.
+- **Assumption**: 매 not verified, 매 taken as true 매 reasoning 진행 위해. 매 implicit / explicit.
+- **Inference**: 매 fact + assumption → 매 conclusion.
+
+### 매 Verification spectrum
+- **Hard fact**: 매 measurement (e.g., latency = 142ms p95).
+- **Soft fact**: 매 expert testimony / consensus (e.g., "FDA-approved").
+- **Reasonable assumption**: 매 base rate / 매 prior (e.g., "user 매 attention < 10s").
+- **Speculative assumption**: 매 untested premise (e.g., "competitor 매 Q4 launch").
+
+### 매 응용
+1. **Design doc**: 매 "Assumptions" section 별도 — 매 reviewer 검증.
+2. **Intelligence analysis**: 매 ACH (Analysis of Competing Hypotheses).
+3. **Postmortem**: 매 implicit assumption 적출 — 매 next-time fact 로 verify.
+4. **LLM CoT**: 매 reasoning chain 에서 매 assumption 의 explicit tag.
+
+## 💻 패턴
+
+### Pattern 1: 매 Design doc template
+```markdown
+## Facts
+- 매 current p95 latency: 240ms (verified via 매 grafana 2026-05-09).
+- 매 user count: 1.2M MAU (analytics dashboard).
+
+## Assumptions
+- [A1] 매 traffic grow 30% YoY (prior: 2024-2025 trend).
+- [A2] 매 redis cluster 매 horizontal scale 가능 (vendor docs, untested at our scale).
+
+## Inferences
+- A1 + Facts → 매 Q4 capacity = 1.56M MAU.
+- A2 + Facts → 매 cache layer 매 bottleneck 의 X.
+
+## Validation plan
+- A1: 매 monthly reforecast.
+- A2: 매 Q3 load-test 8x current.
+```
+
+### Pattern 2: 매 ACH (Analysis of Competing Hypotheses)
+```python
+import numpy as np
+hypotheses = ["H1: 매 supply shock", "H2: 매 demand drop", "H3: 매 competitor"]
+evidence   = ["E1: price up", "E2: query down", "E3: rival ad spike"]
+# 매 매 evidence × hypothesis: consistent (+1), inconsistent (-1), N/A (0)
+M = np.array([
+    # E1, E2, E3
+    [+1,  0,  0],   # H1
+    [-1, +1,  0],   # H2
+    [ 0, +1, +1],   # H3
+])
+scores = M.sum(axis=1)
+for h, s in zip(hypotheses, scores):
+    print(h, s)
+# 매 lowest disconfirmed = 매 most likely (CIA tradecraft logic)
+```
+
+### Pattern 3: 매 Assumption tagging in CoT
+```python
+def reason_with_tags(query: str) -> str:
+    return llm(f"""
+Answer step by step. For every claim:
+- Tag [FACT: source] if verifiable.
+- Tag [ASSUMP: confidence 0-1] if untested.
+- Tag [INFER] if derived.
+
+Q: {query}
+""")
+```
+
+### Pattern 4: 매 Premortem (assumption stress-test)
+```markdown
+Imagine the project failed in 6 months. List the 5 most likely
+failed assumptions. For each, design a 2-week experiment to test
+it now.
+```
+
+### Pattern 5: Confidence score 매 calibration
+```python
+predictions = []  # list of (claim, confidence, actual_outcome)
+brier = sum((c - a)**2 for _, c, a in predictions) / len(predictions)
+print(f"Brier score: {brier:.3f}")  # 매 lower = better calibration
+```
+
+## 매 결정 기준
+| 상황 | Treat as |
+|---|---|
+| 매 metric in current dashboard | Fact (with date) |
+| 매 vendor capability claim | Soft fact, 매 verify if critical |
+| 매 future user behavior | Assumption — 매 explicit |
+| 매 "everyone knows" | 매 strong assumption — 매 challenge |
+| 매 LLM output | Assumption until cross-checked |
+
+**기본값**: 매 reasoning 시작 시 매 explicit "Facts" / "Assumptions" 분리. 매 implicit assumption 의 surface — 매 brittle.
+
+## 🔗 Graph
+- 부모: [[Belief-Revision]] · [[Bayesian-Updating]]
+- 변형: [[Bayes-Theorem]] · [[Hypostatic-Abstraction]]
+- 응용: [[Problem Solving Process]] · [[Process_Reflection_Template]]
+- Adjacent: [[Big-Picture]] · [[Outside-Thinking]] · [[Anticipation]]
+
+## 🤖 LLM 활용
+**언제**: 매 agent design — 매 [FACT]/[ASSUMP] tagging 매 hallucination detection 도움. 매 reasoning trace audit.
+**언제 X**: 매 creative ideation — 매 over-tagging 매 flow 방해.
+
+## ❌ 안티패턴
+- **Implicit assumption**: 매 unmentioned premise — 매 reviewer 못 catch.
+- **Fact inflation**: 매 weak evidence 의 hard fact 처럼 표현.
+- **Confidence theater**: 매 "obviously" / "clearly" — 매 hidden assumption marker.
+- **Single-source fact**: 매 1 source = 매 still soft. 매 triangulate.
+- **Stale fact**: 매 6개월 전 metric — 매 currently fact 인지 재검증.
+
+## 🧪 검증 / 중복
+- Verified (CIA Tradecraft Primer 2009, Heuer *Psychology of Intelligence Analysis*, Tetlock *Superforecasting*).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — ACH + 매 design-doc pattern + LLM CoT tagging |