docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동
최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
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Antigravity Agent
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---
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id: wiki-2026-0508-research
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title: Research
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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: [Research-Methodology, Literature-Review, Deep-Research]
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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: [research, methodology, literature, ai-aided]
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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: anthropic-sdk
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---
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# Research
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## 매 한 줄
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> **"매 모든 답은 누군가 이미 reformulated"**. Research는 매 question → literature → synthesis → novel contribution의 매 disciplined loop — 2026 의 매 AI-aided synthesis (Claude Opus 4.7 deep research, GPT-5 with browsing, Elicit, Consensus, undermind.ai) 가 매 weeks of work 를 매 hours로 단축.
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## 매 핵심
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### 매 Phases
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1. **Question framing** — vague curiosity → specific testable question (PICO, FINER criteria).
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2. **Literature scoping** — keywords, citation graph (forward/backward), Connected Papers / Litmaps.
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3. **Reading & extraction** — structured notes (Zettelkasten, claim-evidence-source).
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4. **Synthesis** — themes, gaps, contradictions.
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5. **Hypothesis / contribution** — what novel claim this work adds.
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6. **Validation** — experiment / proof / case study.
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7. **Communication** — paper, blog, talk.
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### 매 Modern toolchain (2026)
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- **Search**: Semantic Scholar API, Google Scholar, OpenAlex.
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- **Discovery**: Connected Papers, Litmaps, Inciteful (citation graph viz).
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- **AI synthesis**: Claude Opus 4.7 deep-research mode, GPT-5 deep research, Elicit (extracts data per paper), Consensus (claim-level), undermind.ai (deep retrieval).
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- **Notes**: Obsidian + Zotero integration; Logseq; Reflect.
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- **Reproducibility**: Quarto, Jupyter Book, Code Ocean.
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### 매 AI-aided literature review pattern
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1. Seed papers (3–5 known relevant) → Connected Papers graph.
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2. Snowball (citations both ways) → ~100 candidates.
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3. LLM screen abstracts: relevance score 0–10.
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4. Top 30 → full-text PDF → AI structured extraction (claim, method, evidence, limitations).
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5. AI cluster into themes; human reviews + writes synthesis.
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### 매 안전장치 (필수)
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- 매 hallucination 의 적: AI 의 매 fake citation 매 흔함 → DOI 의 매 verify 의 must.
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- 매 echo chamber: AI synthesis 의 매 popular sources 매 over-weight → manually 의 매 deliberate diverse sampling.
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- 매 confirmation bias: AI 의 매 user의 매 hypothesis 매 align — 매 explicit "steelman opposite" prompt.
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### 매 응용
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1. PhD literature review.
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2. Industry tech radar / market research.
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3. Due diligence (M&A, investment).
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4. Pre-implementation prior-art search (patents, OSS).
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## 💻 패턴
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### Claude deep-research synthesis (verify-first)
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```python
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from anthropic import Anthropic
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import httpx
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client = Anthropic()
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def synthesize(question: str, papers: list[dict]) -> str:
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"""papers: [{title, abstract, doi, year}]"""
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corpus = "\n\n".join(
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f"[{i}] {p['title']} ({p['year']}, doi:{p['doi']})\n{p['abstract']}"
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for i, p in enumerate(papers)
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)
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msg = client.messages.create(
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model="claude-opus-4-7", max_tokens=4096,
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system=("Synthesize evidence. Cite EVERY claim with [index]. "
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"If evidence is weak/contradictory, say so explicitly. "
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"Never fabricate citations."),
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messages=[{"role": "user", "content": f"Q: {question}\n\nPapers:\n{corpus}"}],
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)
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return msg.content[0].text
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def verify_dois(text: str, papers: list[dict]) -> list[str]:
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"""Hallucination check — every cited DOI must exist in our set."""
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import re
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cited = re.findall(r"doi:(10\.\d+/\S+)", text)
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valid = {p["doi"] for p in papers}
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return [d for d in cited if d not in valid] # offenders
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```
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### Semantic Scholar fetch
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```python
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def search_s2(query: str, limit: int = 50) -> list[dict]:
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r = httpx.get(
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"https://api.semanticscholar.org/graph/v1/paper/search",
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params={"query": query, "limit": limit,
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"fields": "title,abstract,year,citationCount,externalIds"},
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).json()
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return [{"title": p["title"], "abstract": p.get("abstract") or "",
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"year": p.get("year"), "doi": p.get("externalIds", {}).get("DOI"),
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"cites": p["citationCount"]}
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for p in r["data"]]
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```
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### Snowball expansion
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```python
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def snowball(seed_ids: list[str], depth: int = 2) -> set[str]:
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frontier, seen = set(seed_ids), set(seed_ids)
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for _ in range(depth):
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next_frontier = set()
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for pid in frontier:
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r = httpx.get(f"https://api.semanticscholar.org/graph/v1/paper/{pid}/references",
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params={"fields": "paperId", "limit": 100}).json()
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next_frontier.update(ref["citedPaper"]["paperId"]
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for ref in r.get("data", [])
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if ref["citedPaper"].get("paperId"))
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frontier = next_frontier - seen
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seen.update(frontier)
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return seen
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```
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### Structured extraction prompt
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```python
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EXTRACT_PROMPT = """Extract from this paper as JSON:
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{
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"claim": "main thesis in one sentence",
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"method": "how they tested it",
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"evidence": "key result with numbers",
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"n": "sample size",
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"limitations": ["limit1", "limit2"],
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"novelty": "what this adds vs prior work"
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}
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If field unknown, use null. Don't invent."""
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```
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### Steelman opposite (debias)
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```python
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def steelman(claim: str) -> str:
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return client.messages.create(
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model="claude-opus-4-7", max_tokens=1024,
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messages=[{"role": "user", "content":
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f"Claim: {claim}\n\nWrite the strongest argument AGAINST this, "
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f"citing actual contrary evidence. Be a hostile reviewer."}],
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).content[0].text
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```
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### Zettelkasten note (atomic)
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```markdown
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---
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id: 2026-05-10-1432
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tags: [retrieval, rag]
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source: [[Lewis-2020-RAG]]
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---
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# Dense retrieval beats BM25 only when query-doc lexical overlap is low
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In Lewis 2020 (Table 3), DPR > BM25 on NaturalQuestions (+6 EM)
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but BM25 ≥ DPR on TriviaQA where queries copy doc tokens.
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→ Hybrid search is robust: pick BM25 for lexical, dense for paraphrase.
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Connects to: [[Hybrid Search]] · [[BM25]] · [[Dense-Retrieval]]
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 매 빠른 scan (1h) | Elicit / Consensus / Claude deep-research |
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| 매 deep dive (1주) | Manual snowball + AI extraction |
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| Systematic review (PRISMA) | PRISMA flow + Covidence + AI screening |
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| 매 cutting-edge (preprints) | arXiv-sanity + Twitter/Bluesky + Semantic Scholar alerts |
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| 매 industry / OSS | GitHub trending + State of X reports + AI synthesis |
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**기본값**: Connected Papers seed → S2 snowball → AI extract → manual synthesis with steelman.
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## 🔗 Graph
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- 부모: [[Scientific Method]]
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- 변형: [[Literature-Review]] · [[Tech-Radar]]
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- Adjacent: [[Hallucination]] · [[RAG]]
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## 🤖 LLM 활용
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**언제**: literature scan, abstract screening, structured extraction, synthesis draft, steelmanning.
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**언제 X**: novelty claim 의 매 final assertion (LLM 의 매 ground truth 의 X), 매 quantitative meta-analysis (use proper stats software), 매 citation 의 verify 없이.
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## ❌ 안티패턴
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- **Cite-without-verify**: AI 의 매 만들어낸 fake DOI.
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- **Single-source synthesis**: 매 한 paper 의 매 truth로 취급 — 매 replication 의 무시.
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- **Recency bias**: 매 latest preprint 만 → 매 foundational work 의 무지.
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- **No gap analysis**: literature dump 의 매 only — 매 "what's missing" 의 부재 → contribution 의 unclear.
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- **Hypothesis fishing**: 매 data 부터 → 매 post-hoc theory (HARKing).
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## 🧪 검증 / 중복
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- Verified (PRISMA 2020 statement, Semantic Scholar API docs, Claude Opus 4.7 deep research, Elicit methodology).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full rewrite covering methodology + AI-aided synthesis pipeline |
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