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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Antigravity Agent
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---
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id: wiki-2026-0508-test-time-compute-scaling-추론-시간-
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title: Test Time Compute Scaling (추론 시간 계산 스케일링)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Test-Time Compute, Inference-Time Scaling, Reasoning Models]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [llm, reasoning, scaling, test-time-compute]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: vLLM / Anthropic SDK / OpenAI SDK
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---
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# Test Time Compute Scaling (추론 시간 계산 스케일링)
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## 매 한 줄
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> **"매 think longer, get smarter"**. Test-time compute scaling 매 inference 시 더 많은 compute (매 longer chain-of-thought, sampling, search) 로 quality 의 trade off. OpenAI o1 (2024-09) → o3 / DeepSeek-R1 (2025-01) → Claude 4.x extended thinking (2025+) 의 paradigm. 매 training-time scaling laws 의 보완.
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## 매 핵심
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### 매 두 axes
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- **More thinking (long CoT)** — 매 single sample 안 더 긴 reasoning trace. o1, R1, Claude extended thinking.
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- **Search / sampling** — 매 multiple samples + verifier (best-of-N, MCTS, beam). AlphaCode, ReST, MathShepherd.
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### 매 modern (2025-2026)
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- **RL on reasoning** — 매 RLHF + RL on verifiable rewards (math, code) → 매 long CoT 의 emerge. R1-zero, R1.
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- **Extended thinking budgets** — 매 Claude 의 `thinking_budget` parameter, OpenAI 의 `reasoning_effort`.
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- **Scaling law** — 매 log compute ↔ accuracy linear (Snell 2024, OpenAI o-series chart).
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- **Cost shift** — 매 training 1x 의 inference Nx — 매 economics 의 reshape.
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### 매 응용
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1. Math (AIME, IMO).
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2. Code (SWE-bench, competition).
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3. Agentic planning (deep tool-use chains).
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4. Scientific reasoning (GPQA).
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## 💻 패턴
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### Claude extended thinking
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```python
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from anthropic import Anthropic
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client = Anthropic()
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=8000,
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thinking={"type": "enabled", "budget_tokens": 16000},
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messages=[{"role": "user", "content": "Solve: ..."}],
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)
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for block in resp.content:
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if block.type == "thinking":
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print("THINK:", block.thinking[:200])
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elif block.type == "text":
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print("ANS:", block.text)
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```
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### OpenAI reasoning effort
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```python
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from openai import OpenAI
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client = OpenAI()
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resp = client.responses.create(
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model="o3",
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input="Prove the AM-GM inequality.",
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reasoning={"effort": "high"}, # low / medium / high
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)
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print(resp.output_text)
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```
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### Best-of-N + verifier
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```python
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def best_of_n(prompt, n=8, verifier=None):
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samples = [client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2000,
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temperature=0.8,
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messages=[{"role": "user", "content": prompt}],
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).content[0].text for _ in range(n)]
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return max(samples, key=verifier) # 매 verifier: unit test pass count, etc.
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```
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### Self-consistency (majority vote)
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```python
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from collections import Counter
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answers = [extract_answer(s) for s in samples]
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final = Counter(answers).most_common(1)[0][0]
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```
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### MCTS-style search (sketch)
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```python
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def expand(node):
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children = [llm.continue_from(node.partial, temp=0.9) for _ in range(k)]
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return [Node(c, score=verifier(c)) for c in children]
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def search(root, depth=4):
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frontier = [root]
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for _ in range(depth):
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candidates = sum((expand(n) for n in frontier), [])
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frontier = sorted(candidates, key=lambda n: -n.score)[:beam]
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return max(frontier, key=lambda n: n.score)
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```
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### Budget controller
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```python
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def adaptive_thinking(prompt, easy_budget=2000, hard_budget=32000):
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# 매 difficulty classifier 의 first
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diff = client.messages.create(model="claude-haiku-4", ...).content[0].text
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budget = hard_budget if "hard" in diff else easy_budget
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return client.messages.create(
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model="claude-opus-4-7",
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thinking={"type": "enabled", "budget_tokens": budget},
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messages=[{"role": "user", "content": prompt}],
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)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Math / code with verifier | RL-trained reasoning model (o3, R1) + search |
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| Open-ended reasoning | Extended thinking (Claude 4.x) |
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| Latency-critical | Skip — use small fast model |
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| Cost-critical batch | Self-consistency 4-8 samples |
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| Search exploitable | Best-of-N + verifier |
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| Fuzzy quality | Reasoning model > base model |
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**기본값**: 매 reasoning model (o3 / Claude extended thinking) 매 hard task, base model 매 easy task — 매 difficulty router 로 split.
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## 🔗 Graph
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- 부모: [[Scaling-Laws]]
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- 변형: [[Chain-of-Thought]] · [[Self-Consistency]] · [[Best-of-N]] · [[MCTS]]
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- 응용: [[Code-Generation]]
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- Adjacent: [[RLHF]]
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## 🤖 LLM 활용
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**언제**: 매 hard reasoning task, verifiable output (math/code), agent planning, quality > latency.
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**언제 X**: 매 simple lookup / chat — 매 thinking 매 cost waste.
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## ❌ 안티패턴
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- **Always max thinking budget**: 매 easy task 의 32k thinking 매 cost burn — 매 router 사용.
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- **No verifier in best-of-N**: 매 random sample 매 noise — 매 verifier (unit test, math check) 의 essential.
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- **Stream thinking to user**: 매 thinking content 매 internal — 매 user UI 에 final text 만.
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- **Caching invalidation**: 매 thinking budget 변경 시 cache miss — 매 stable budget 권장.
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## 🧪 검증 / 중복
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- Verified (OpenAI o1/o3 system cards, DeepSeek-R1 paper 2025-01, Anthropic extended thinking docs, Snell et al. 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — o-series / R1 / Claude extended thinking patterns |
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