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-sequence-to-sequence-models
+title: Sequence to Sequence Models
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [seq2seq, Encoder-Decoder, Sequence Modeling, Sequence-to-Sequence]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [architecture, nlp, transformer, encoder-decoder]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: PyTorch / Transformers
+---
+
+# Sequence to Sequence Models
+
+## 매 한 줄
+> **"매 input sequence → output sequence — 매 길이 다른 변환"**. 매 Sutskever (2014) RNN encoder-decoder → Bahdanau (2015) attention → Vaswani (2017) Transformer 의 진화. 매 2026: 거의 모든 generative LLM (GPT, Claude, Gemini) 이 매 decoder-only seq2seq, 매 T5/BART 같은 encoder-decoder 는 specific task (번역, summarization fine-tune) 에 잔존.
+
+## 매 핵심
+
+### 매 Architecture family
+- **RNN encoder-decoder** (2014): 매 historical, vanishing gradient, no attention.
+- **Attention seq2seq** (2015): 매 alignment 학습 — 번역 quality 점프.
+- **Transformer encoder-decoder** (2017): 매 self-attention, parallelizable. T5, BART, mT5.
+- **Decoder-only** (2018+): GPT family. 매 LLM 의 dominant pattern.
+- **Encoder-only** (BERT): classification/embedding, generation 아님.
+
+### 매 핵심 컴포넌트
+- Tokenizer (BPE, SentencePiece, tiktoken).
+- Embedding + positional encoding (RoPE, ALiBi 2026 표준).
+- Self-attention / cross-attention.
+- Teacher forcing for training, autoregressive decoding for inference.
+
+### 매 Decoding 전략
+- Greedy / Beam search — 매 deterministic task.
+- Sampling (temperature, top-p, top-k, min-p) — 매 creative.
+- Speculative / Medusa — 매 inference 가속.
+- Constrained / structured (JSON schema) — 매 tool use.
+
+### 매 응용
+1. Machine translation (NLLB, M2M-100).
+2. Summarization (BART, Pegasus).
+3. Code generation (Claude Code, Copilot).
+4. Speech (Whisper encoder + decoder).
+5. Image captioning, VQA (multimodal seq2seq).
+
+## 💻 패턴
+
+### Tiny Transformer encoder-decoder
+```python
+import torch.nn as nn
+class Seq2Seq(nn.Module):
+    def __init__(self, vocab, d=256, nhead=4, nl=4):
+        super().__init__()
+        self.emb_s = nn.Embedding(vocab, d)
+        self.emb_t = nn.Embedding(vocab, d)
+        self.tx = nn.Transformer(d, nhead, nl, nl, batch_first=True)
+        self.out = nn.Linear(d, vocab)
+    def forward(self, src, tgt):
+        return self.out(self.tx(self.emb_s(src), self.emb_t(tgt)))
+```
+
+### HF Transformers (T5)
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+tok = T5Tokenizer.from_pretrained("t5-base")
+m = T5ForConditionalGeneration.from_pretrained("t5-base")
+inp = tok("translate English to German: Hello world", return_tensors="pt").input_ids
+print(tok.decode(m.generate(inp)[0], skip_special_tokens=True))
+```
+
+### Decoder-only generation (Claude API)
+```python
+import anthropic
+c = anthropic.Anthropic()
+msg = c.messages.create(
+    model="claude-opus-4-7",
+    max_tokens=1024,
+    messages=[{"role": "user", "content": "Summarize: ..."}],
+)
+print(msg.content[0].text)
+```
+
+### Beam search decode
+```python
+out = model.generate(input_ids, num_beams=4, length_penalty=0.6,
+                     no_repeat_ngram_size=3, max_new_tokens=128)
+```
+
+### Streaming
+```python
+with c.messages.stream(model="claude-opus-4-7", max_tokens=512,
+                       messages=msgs) as s:
+    for text in s.text_stream:
+        print(text, end="", flush=True)
+```
+
+### KV cache reuse
+```python
+out = model(**inputs, use_cache=True, past_key_values=pkv)
+pkv = out.past_key_values  # 매 next step 에 재사용
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| General LLM task | decoder-only (Claude, GPT) |
+| Specific translation/summarization fine-tune | T5/BART encoder-decoder |
+| Embedding / classification | encoder-only (BERT family) |
+| Speech-to-text | Whisper-style enc-dec |
+| Long sequences, low cost | Mamba / Hybrid seq2seq |
+
+**기본값**: decoder-only LLM via API.
+
+## 🔗 Graph
+- 부모: [[Deep Learning]] · [[NLP]]
+- 변형: [[Transformer]] · [[Selective State Space Models (Mamba)]] · [[Encoder-Decoder]]
+- 응용: [[Summarization]] · [[Code-Generation]]
+- Adjacent: [[Attention Mechanism]] · [[Tokenization]]
+
+## 🤖 LLM 활용
+**언제**: input → output 변환 task 정의 가능. 매 API call 로 충분.
+**언제 X**: pure classification — encoder + head 가 매 더 cheap.
+
+## ❌ 안티패턴
+- **Greedy for creative**: repetition. 매 sampling 사용.
+- **No cache**: O(L²) inference. 매 KV cache 필수.
+- **Train from scratch**: 매 거의 항상 잘못된 선택. Fine-tune 또는 prompt.
+
+## 🧪 검증 / 중복
+- Verified (Sutskever 2014, Vaswani 2017, HF Transformers docs).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — full seq2seq family 2026 |