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-awards
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title: Awards (Recognition Systems)
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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: [상, awards, prize, recognition, Turing Award, Nobel, NeurIPS Best Paper, Kaggle]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.83
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verification_status: conceptual
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tags: [awards, recognition, motivation, scientific-community, prestige, ai-ethics, generative-ai]
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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: sociology / community
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applicable_to: [Research Strategy, Career Planning, Community Building]
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---
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# Awards
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## 📌 한 줄 통찰
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> **"매 우수 의 사회적 공인"**. 매 motivation + 매 standard 의 signal + 매 visibility. 매 modern 의 controversy: 매 AI generative 의 award 의 ethics. 매 traditional gatekeeping vs 매 community-driven.
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## 📖 핵심
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### 매 function
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1. **Validation**: 매 objective recognition.
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2. **Standard setting**: 매 community 의 value 의 signal.
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3. **Visibility**: 매 obscure talent 의 surface.
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4. **Motivation**: 매 future contribution 의 incentivize.
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5. **Network**: 매 winner 의 connect.
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### 매 AI / CS 의 award
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#### Lifetime achievement
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- **Turing Award** (ACM): 매 CS 의 Nobel.
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- **Nobel Prize** (Physics 2024 to Hinton).
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- **Lifetime Achievement** (학회).
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#### Paper / research
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- **NeurIPS / ICML / ICLR Best Paper**: 매 frontier 의 trend.
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- **NeurIPS Test of Time**: 매 10 year 의 enduring.
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- **CVPR / ECCV Best Paper**: 매 vision.
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#### Practical / applied
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- **Kaggle 우승**: 매 ML competition.
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- **Hackathon**: 매 rapid prototype.
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- **NeurIPS Datasets & Benchmarks**: 매 infra contribution.
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#### Industry
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- **Y Combinator** 선정: 매 startup recognition.
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- **Forbes 30 under 30**: 매 entrepreneur.
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### 매 trade-off
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- **Prestige vs accessibility**: 매 elite vs democratic.
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- **Quality vs popularity**: 매 expert vs vote.
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- **Innovation vs continuity**: 매 disruptive 의 reward 의 어려움.
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- **Individual vs team**: 매 large project 의 attribution.
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- **Disclosed methodology**: 매 transparent vs gatekeeping.
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### 매 modern issue
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#### Generative AI 와 award
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- 매 AI 생성 art 의 award (콜로라도 주 박람회 2022).
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- 매 photography contest 의 AI 의 ban.
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- 매 disclosure 의무.
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- 매 separate category (Adobe, Sony 의 시도).
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#### Bias
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- 매 reviewer demographic.
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- 매 ML conference 의 famous lab 의 favor.
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- 매 double-blind 의 effectiveness 의 limited.
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#### Replication crisis
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- 매 award winning 의 replicate 의 X.
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- 매 NeurIPS 의 reproducibility checklist.
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### 매 knowledge ecosystem 의 응용
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- **Best Paper**: 매 trend signal.
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- **Test of Time**: 매 enduring contribution.
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- **Citation count**: 매 long-term impact.
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- **GitHub stars / forks**: 매 community signal.
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### 매 alternative recognition
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- **Open access publication**.
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- **Replication studies**.
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- **Open-source contribution**.
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- **Mentorship recognition**.
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- **Public engagement**.
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## 💻 패턴 (응용 — community recognition system)
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### Reproducibility checklist (NeurIPS-style)
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```yaml
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- claims_match_results: true
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- code_available: https://github.com/...
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- data_available: true
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- compute_described: 8x A100, 36 hours
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- hyperparameter_searched: detailed in section 5
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- random_seed_disclosed: 42, 123, 456
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- statistical_significance: p < 0.01, n=10 seeds
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- error_bar: ± 1 std
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```
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### Award decision (multi-criteria)
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```python
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def evaluate_paper(paper, reviewers):
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scores = []
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for r in reviewers:
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scores.append({
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'novelty': r.score('novelty'),
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'rigor': r.score('rigor'),
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'impact': r.score('impact'),
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'clarity': r.score('clarity'),
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'reproducibility': r.score('reproducibility'),
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})
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# 매 inter-rater agreement check
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if max(scores, key=lambda s: sum(s.values()))[0] - min(scores, key=lambda s: sum(s.values()))[0] > 5:
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return 'discuss' # 매 disagreement 의 large
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# 매 multi-dim aggregate
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avg = {k: np.mean([s[k] for s in scores]) for k in scores[0]}
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return avg if all(v > 7 for v in avg.values()) else 'reject'
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```
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### Bias-aware reviewer matching
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```python
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def match_reviewers(paper, pool, n=3):
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# 매 author affiliation 의 conflict 회피
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pool = [r for r in pool if r.affiliation != paper.affiliation]
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# 매 expertise overlap (positive)
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by_expertise = sorted(pool, key=lambda r: -overlap(r.expertise, paper.topics))
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# 매 geographic / gender diversity
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selected = []
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for r in by_expertise:
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if any(s.affiliation == r.affiliation for s in selected): continue
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selected.append(r)
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if len(selected) == n: break
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return selected
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```
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### Generative AI disclosure
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```python
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class SubmissionPolicy:
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REQUIRES_DISCLOSURE = True
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def validate(self, submission):
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if not submission.has_disclosure_form():
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return 'rejected: missing AI disclosure'
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if submission.ai_use == 'generative_image' and \
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submission.category not in ['ai_art', 'experimental']:
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return 'rejected: wrong category for AI-generated work'
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return 'accepted'
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```
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### Test of Time (long-term impact)
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```python
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def test_of_time_score(paper, year=10):
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"""매 10 year 후 의 enduring impact."""
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return {
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'citations_per_year_5to10': paper.citations[5:10] / 5,
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'follow_up_papers': count_follow_ups(paper),
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'industry_adoption': industry_signals(paper),
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'curriculum_inclusion': in_textbook(paper),
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'reproductions': count_replications(paper),
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}
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```
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## 🤔 결정 기준
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| 상황 | Recognition |
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| Frontier research | Best Paper |
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| Long-term contribution | Test of Time |
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| Practical | Kaggle / hackathon |
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| Career milestone | Turing / Nobel |
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| Open science | Reproducibility / open-source |
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| Mentorship | Distinguished Mentor |
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| AI generative | Disclosed + separate category |
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**기본값**: 매 multi-dim + 매 disclosure + 매 reproducibility.
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## 🔗 Graph
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- 부모: [[Motivation]]
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- 변형: [[Turing-Award]] · [[NeurIPS-Best-Paper]]
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- 응용: [[Kaggle]]
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- Adjacent: [[Goodharts-Law]] · [[Authenticity]]
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## 🤖 LLM 활용
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**언제**: 매 award strategy. 매 community recognition design. 매 reviewer process.
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**언제 X**: 매 award 의 sole career goal (motivation 의 trap).
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## ❌ 안티패턴
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- **Single-criterion award**: 매 game.
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- **No reviewer diversity**: 매 echo chamber.
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- **No disclosure (AI)**: 매 trust violation.
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- **Award as goal** (Goodhart): 매 prestige farming.
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- **No reproducibility check**: 매 fake winner.
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- **Citation count 의 only**: 매 quantity > quality.
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## 🧪 검증 / 중복
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- Verified (NeurIPS / ICML reviewer guides, ACM Turing, generative AI policy debates).
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- 신뢰도 B.
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- Related: [[Benchmarks]] · [[Authenticity]] · [[Replication-Crisis]] · [[Goodharts-Law]].
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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 — function + AI/CS award + generative issue + 매 reviewer / disclosure code |
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