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-encoder-decoder-inconsistency
+title: Encoder Decoder Inconsistency
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [encoder-decoder-mismatch, seq2seq-inconsistency]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [nlp, transformer, training, decoding]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: pytorch
+---
+
+# Encoder Decoder Inconsistency
+
+## 매 한 줄
+> **"매 encoder가 본 분포 ≠ decoder가 생성하는 분포"**. Seq2seq training 시 encoder는 ground-truth context를 보지만 decoder는 inference에서 자기 prediction을 다시 입력으로 받기 때문에 train/inference 간 distribution shift가 발생한다. 매 exposure bias 의 근본 원인.
+
+## 매 핵심
+
+### 매 정의
+- **Train**: decoder input = teacher-forced ground truth.
+- **Inference**: decoder input = previously generated token.
+- **Gap**: 매 error compounds along sequence — early mistake → later tokens conditioned on out-of-distribution prefix.
+
+### 매 표현
+- Exposure bias (Ranzato 2016).
+- Schedule sampling 의 motivation.
+- Hallucination 의 한 원인 (특히 long-form generation).
+
+### 매 응용
+1. NMT (Neural Machine Translation) — 매 long sentence translation degradation.
+2. Summarization — repetition / drift.
+3. Speech recognition — RNN-T vs CTC trade-off.
+4. Code generation — 매 long completion 의 syntax break.
+
+## 💻 패턴
+
+### Scheduled Sampling
+```python
+import torch
+import torch.nn.functional as F
+
+def scheduled_sampling_step(decoder, prev_token, hidden, gt_token, p_use_gt: float):
+    """p_use_gt 의 확률로 ground-truth, 아니면 model prediction 의 사용."""
+    if torch.rand(1).item() < p_use_gt:
+        input_tok = gt_token
+    else:
+        with torch.no_grad():
+            logits, _ = decoder(prev_token, hidden)
+            input_tok = logits.argmax(dim=-1)
+    out_logits, hidden = decoder(input_tok, hidden)
+    return out_logits, hidden
+```
+
+### Minimum Risk Training
+```python
+def mrt_loss(model, src, refs, n_samples=8):
+    """매 sequence-level loss 의 — 매 sampled hypotheses 에 대해 risk minimize."""
+    hyps = [model.sample(src) for _ in range(n_samples)]
+    risks = torch.tensor([1 - bleu(h, refs) for h in hyps])
+    log_probs = torch.stack([model.log_prob(h, src) for h in hyps])
+    weights = F.softmax(log_probs, dim=0)
+    return (weights * risks).sum()
+```
+
+### Self-distillation Fix
+```python
+def self_distill(student, teacher, src, T=2.0):
+    """매 teacher 가 자기 생성한 sequence 의 사용 — 매 train/inference gap 축소."""
+    with torch.no_grad():
+        gen = teacher.generate(src, do_sample=True, top_p=0.9)
+        teacher_logits = teacher(src, gen).logits
+    student_logits = student(src, gen).logits
+    return F.kl_div(
+        F.log_softmax(student_logits / T, dim=-1),
+        F.softmax(teacher_logits / T, dim=-1),
+        reduction="batchmean",
+    ) * T * T
+```
+
+### Beam Search with Length Penalty
+```python
+def length_penalty(score, length, alpha=0.7):
+    """GNMT length penalty — 매 short hypothesis 의 bias 보정."""
+    return score / ((5 + length) ** alpha / (5 + 1) ** alpha)
+```
+
+### Contrastive Decoding
+```python
+def contrastive_decode(big, small, prompt, alpha=0.5):
+    """매 large model logit − small model logit — 매 expert/amateur gap 의 강조."""
+    big_logits = big(prompt).logits[:, -1]
+    small_logits = small(prompt).logits[:, -1]
+    return big_logits - alpha * small_logits
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Short sequence (<32) | Teacher forcing 충분 |
+| Long sequence | Scheduled sampling / MRT |
+| Production NMT | Beam + length penalty + coverage |
+| LLM long-form | Contrastive decoding / self-distillation |
+
+**기본값**: teacher forcing + 1k step warmup 이후 scheduled sampling.
+
+## 🔗 Graph
+- 부모: [[Sequence-to-Sequence]] · [[Transformer]]
+- Adjacent: [[Hallucination]]
+
+## 🤖 LLM 활용
+**언제**: long-form generation 의 quality issue 분석 시. Train/eval BLEU gap 의 진단.
+**언제 X**: 매 short classification — 매 inconsistency 의 무관.
+
+## ❌ 안티패턴
+- **Pure teacher forcing forever**: 매 inference distribution 의 미본 채 deploy.
+- **Greedy decoding only**: 매 early mistake 의 lock-in.
+- **No length normalization**: beam 의 short hypothesis bias.
+
+## 🧪 검증 / 중복
+- Verified (Ranzato et al. 2016, Bengio et al. 2015 scheduled sampling).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — encoder/decoder distribution shift + scheduled sampling/MRT/contrastive decoding |