feat(study): 이론↔문제 브리지 (Stage B) — 개념별 정답률·약점 개념 지도

이론공부 B→A→C 의 B. 완성된 문제풀이에 이론 연결(약점 구동).
- 마이그 382 study_concept_links(개념 doc↔기출, FK 없음) + 백필 SQL(임베딩 코사인 top-k=10·threshold 0.62 → 2362링크·284개념·964문항)
- concept_links 서비스(related_questions·weakness_map 롤업) + GET /concepts/{id}/questions·/concepts/weakness-map(라우트 순서=weakness-map 먼저)
- 리더 관련기출 섹션(정답률·문항 stub→문항상세) + 홈 약점개념 위젯
- 적대리뷰 반영: Promise.all 격리(weakness-map 실패→코어 대시보드 블랙아웃 방지)·q.subject null 폴백. 백필=배포 후 트랜잭션 래핑 실행. 문제풀이 무접촉

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

.gitignore

@@ -47,3 +47,6 @@ caddy_data/
 *.bak_*
 *.pre-*
 .pre-*/
+
+# SQLite 로컬 아티팩트 (Django/툴링 잔재)
+*.sqlite3

Caddyfile

@@ -12,6 +12,13 @@ http://document.hyungi.net {
     # 명시 Content-Type match — 기본 match 의 text/* 는 text/event-stream 까지 포함해
     # SSE(/api/eid/chat)의 첫 ~512B 를 gzip 버퍼링함. SSE 제외, 기존 압축 대상은 보존.
     # (응답 매처는 header <필드> <값> 한 쌍씩 — 여러 줄 = OR. 한 줄 다중 값은 파싱 에러)
+    # 2026-06-20 보안 헤더 (M: 클릭재킹·MIME 스니핑 방어). HSTS 는 TLS 종단 edge(home-caddy) 소관.
+    header {
+        X-Content-Type-Options nosniff
+        X-Frame-Options SAMEORIGIN
+        Referrer-Policy strict-origin-when-cross-origin
+        -Server
+    }
     encode {
         gzip
         match {

app/ai/client.py

@@ -1,5 +1,6 @@
 """AI 추상화 레이어 — 통합 클라이언트. 기본값은 항상 Qwen3.5."""
 
+import asyncio
 import json
 import re
 from pathlib import Path
@@ -188,6 +189,25 @@ def _load_prompt(name: str) -> str:
 CLASSIFY_PROMPT = _load_prompt("classify.txt") if (PROMPTS_DIR / "classify.txt").exists() else ""
 
 
+# 공유 httpx 클라이언트 — 호출마다 AsyncClient 를 새로 만들던 것(30+ 사이트, 연결풀 재사용 0)을
+# 일원화해 keep-alive 재사용. 이벤트루프 바인딩이라 루프 변경(pytest 격리 등) 시 재생성한다.
+# close() 는 공유 풀이라 no-op — 프로세스 종료 시 GC.
+_shared_http: httpx.AsyncClient | None = None
+_shared_http_loop: object | None = None
+
+
+def _get_shared_http() -> httpx.AsyncClient:
+    global _shared_http, _shared_http_loop
+    try:
+        loop: object | None = asyncio.get_running_loop()
+    except RuntimeError:
+        loop = None
+    if _shared_http is None or _shared_http.is_closed or _shared_http_loop is not loop:
+        _shared_http = httpx.AsyncClient(timeout=120)
+        _shared_http_loop = loop
+    return _shared_http
+
+
 class AIClient:
     """AI 모델 통합 클라이언트.
 
@@ -202,7 +222,7 @@ class AIClient:
 
     def __init__(self):
         self.ai = settings.ai
-        self._http = httpx.AsyncClient(timeout=120)
+        self._http = _get_shared_http()
 
     # ─── 3-tier routing (B-0) ───────────────────────────────────────────────
 
@@ -346,6 +366,10 @@ class AIClient:
                 payload["temperature"] = model_config.temperature
             if model_config.top_p is not None:
                 payload["top_p"] = model_config.top_p
+            if model_config.repetition_penalty is not None:
+                payload["repetition_penalty"] = model_config.repetition_penalty
+            if model_config.top_k is not None:
+                payload["top_k"] = model_config.top_k
             response = await self._http.post(
                 model_config.endpoint,
                 json=payload,
@@ -356,4 +380,5 @@ class AIClient:
             return data["choices"][0]["message"]["content"]
 
     async def close(self):
-        await self._http.aclose()
+        # 공유 풀(_get_shared_http) 이라 per-use close 안 함 — 연결 재사용. 프로세스 종료 시 GC.
+        return None

app/api/documents.py

@@ -28,7 +28,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from starlette.requests import ClientDisconnect
 
 from ai.client import AIClient, _load_prompt, parse_json_response
-from core.auth import get_current_user
+from core.auth import get_current_user, get_egress_class
 from core.config import settings
 from core.database import async_session, get_session
 from core.utils import file_hash
@@ -672,16 +672,101 @@ async def list_duplicates(
     )
 
 
+class ClauseHit(BaseModel):
+    doc_id: int
+    doc_title: str
+    section_title: str | None = None
+    char_start: int | None = None
+    chunk_id: int
+    node_type: str | None = None
+
+
+class ClauseLookupResponse(BaseModel):
+    label: str
+    hits: list[ClauseHit]
+
+
+# NOTE: '/{doc_id}' (int path param) 라우트보다 먼저 선언해야 '/clause-lookup' 이 doc_id 로
+# 잘못 매칭되지 않는다 (FastAPI 선언 순서 매칭). 이동 금지.
+@router.get("/clause-lookup", response_model=ClauseLookupResponse)
+async def clause_lookup(
+    label: str,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    """절 식별자(예: UG-79)로 크로스-doc 절 위치 조회 — 'UG-79 보여줘' 진입점 (U-1).
+
+    절(node_type=clause/clause_split)은 in_corpus=false(검색 비활성)라 의미검색으론 못 찾으므로,
+    라벨 prefix 정확매칭으로 (doc, char_start) 를 직접 해소해 읽기뷰 점프를 가능케 한다.
+    대부분 1건; 부록(A-/E-/F-) 등 doc 간 공유 라벨만 다중 반환(에디션 선택). /sections 와 동일하게
+    document_chunks 직접 조회 — corpus_chunks 우회는 retrieval 아닌 정확지목이므로 의도적 예외.
+    """
+    from sqlalchemy import text as sql_text
+
+    lab = (label or "").strip()
+    if not lab:
+        return ClauseLookupResponse(label=label, hits=[])
+    rows = (
+        await session.execute(
+            sql_text(
+                """
+                SELECT c.doc_id, d.title AS doc_title, c.section_title, c.char_start, c.node_type,
+                       -- 점프 타깃 = outline(/sections: is_leaf 또는 %_split)에 있는 chunk 여야 딥링크 동작.
+                       -- 자신이 그러면 자신, 아니면(컨테이너 절: 자식 heading 보유·is_leaf=false) 문서순서상
+                       -- 자신 이후 첫 딥링크 가능 chunk(=그 절 내용 시작)로 해소. 그래도 없으면 자신(폴백).
+                       COALESCE(
+                         CASE WHEN c.is_leaf = true OR c.node_type LIKE '%\\_split' ESCAPE '\\' THEN c.id END,
+                         (SELECT ch.id FROM document_chunks ch
+                          WHERE ch.doc_id = c.doc_id AND ch.source_type = 'hier_section'
+                            AND ch.chunk_index >= c.chunk_index
+                            AND (ch.is_leaf = true OR ch.node_type LIKE '%\\_split' ESCAPE '\\')
+                          ORDER BY ch.chunk_index LIMIT 1),
+                         c.id
+                       ) AS chunk_id
+                FROM document_chunks c
+                JOIN documents d ON d.id = c.doc_id
+                WHERE c.node_type IN ('clause', 'clause_split')
+                  AND (c.section_title ILIKE :lab_sp OR c.section_title ILIKE :lab_eq)
+                  AND d.deleted_at IS NULL
+                ORDER BY c.doc_id, c.char_start NULLS LAST
+                LIMIT 50
+                """
+            ).bindparams(lab_sp=lab + " %", lab_eq=lab)
+        )
+    ).mappings().all()
+    return ClauseLookupResponse(label=lab, hits=[ClauseHit(**dict(r)) for r in rows])
+
+
 @router.get("/{doc_id}", response_model=DocumentDetailResponse)
 async def get_document(
     doc_id: int,
     user: Annotated[User, Depends(get_current_user)],
     session: Annotated[AsyncSession, Depends(get_session)],
+    egress_class: Annotated[str, Depends(get_egress_class)],
 ):
-    """문서 단건 조회. 본문(extracted_text)·canonical markdown 동봉."""
+    """문서 단건 조회. 본문(extracted_text)·canonical markdown 동봉.
+
+    cloud egress(갭2): egress=cloud 토큰(예: Claude/MCP)은 search 와 동일한 cloud-eligibility
+    게이트를 통과한 문서만 열람 가능 — id 직접 fetch 로 비공개/인프라/개인/restricted 문서를
+    우회 열람하는 경로를 차단한다. 부적격은 404(존재 자체 비노출). local 토큰=게이트 미발동(무회귀).
+    """
+    from sqlalchemy import text as sql_text
+    from services.search.retrieval_service import cloud_eligible_doc_sql
+
     doc = await session.get(Document, doc_id)
     if not doc or doc.deleted_at is not None:
         raise HTTPException(status_code=404, detail="문서를 찾을 수 없습니다")
+    if egress_class == "cloud":
+        eligible = (
+            await session.execute(
+                sql_text(
+                    "SELECT 1 FROM documents WHERE id = :doc_id AND deleted_at IS NULL"
+                    + cloud_eligible_doc_sql("")
+                ).bindparams(doc_id=doc_id)
+            )
+        ).first()
+        if eligible is None:
+            raise HTTPException(status_code=404, detail="문서를 찾을 수 없습니다")
     return DocumentDetailResponse.model_validate(doc)
 
 
@@ -963,6 +1048,19 @@ async def get_document_image_raw(
             DocumentImage.image_key == image_key,
         )
     )
+    if img is None:
+        # clause-KB: 절-문서는 부모 표준 이미지를 공유(md_content=부모 슬라이스) → parent_id 폴백.
+        from sqlalchemy import text as sql_text
+        _par = (await session.execute(
+            sql_text("SELECT parent_id FROM documents WHERE id = :id").bindparams(id=doc_id)
+        )).scalar()
+        if _par is not None:
+            img = await session.scalar(
+                select(DocumentImage).where(
+                    DocumentImage.document_id == _par,
+                    DocumentImage.image_key == image_key,
+                )
+            )
     if img is None:
         raise HTTPException(status_code=404, detail="이미지를 찾을 수 없습니다")
 
@@ -1166,8 +1264,10 @@ async def upload_document(
             doc.duplicate_of = canonical.id
             canonical.duplicate_count = (canonical.duplicate_count or 0) + 1
 
-        # document + processing_queue 는 단일 트랜잭션으로 묶어 원자적 정리
-        await enqueue_stage(session, doc.id, "extract")
+        # document + processing_queue 는 단일 트랜잭션으로 묶어 원자적 정리.
+        # G2: 첫 stage=presegment (extract 前 번들 PDF 분할, 후보 A 검증완료 2026-06-18).
+        # 非PDF/단일은 presegment 가 무변 통과 → extract. 번들 PDF 만 N 자식 분할(worker-side gating).
+        await enqueue_stage(session, doc.id, "presegment")
         await session.commit()
     except Exception:
         # DB 예외 시 session 은 get_session 컨텍스트 종료로 자동 rollback.
@@ -1210,6 +1310,14 @@ async def update_document(
         if val is not None and val not in ("business", "knowledge"):
             raise HTTPException(status_code=400, detail="doc_purpose는 business 또는 knowledge만 가능")
 
+    # edit_url SSRF 가드 (2026-06-20 M1): 내부/메타데이터 주소 후속 fetch 차단 (news.py 동형 검증)
+    if update_data.get("edit_url"):
+        from core.url_validator import validate_feed_url
+        try:
+            await asyncio.to_thread(validate_feed_url, update_data["edit_url"])
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"edit_url 검증 실패: {e}")
+
     for field, value in update_data.items():
         setattr(doc, field, value)
     doc.updated_at = datetime.now(timezone.utc)
@@ -1490,7 +1598,7 @@ ANALYZE_PROMPT = (
 )
 
 ANALYZE_TEXT_LIMIT = 12000  # chars (15000 → 12000, 실측 timeout 빈발)
-ANALYZE_TIMEOUT_S = 60  # 15,000자 입력 + 4층 출력. 실측 7~45초, safety margin 포함
+ANALYZE_TIMEOUT_S = settings.llm_call_timeout_s  # 2026-06-20 config 단일소스 (구 60s=빠른 Gemma)
 ANALYZE_CACHE_TTL_S = 1800  # 30분
 ANALYZE_CACHE_MAXSIZE = 100
 ANALYZE_LAYER_MIN_CHARS = 50  # 이 미만이면 억지 채움으로 보고 제거
@@ -1726,3 +1834,305 @@ async def analyze_document(
             error_code=error_code,
             source=source,
         )
+
+
+# ─── ASME 절-지식베이스: 유기적 책 네비 (clause-KB, doc_kind='clause' 자식 문서 기반) ───
+class ClauseTocItem(BaseModel):
+    id: int
+    clause_code: str | None = None
+    clause_part: str | None = None
+    clause_order: int | None = None
+    title: str | None = None
+
+
+class ClauseBookResponse(BaseModel):
+    parent_id: int
+    parent_title: str | None = None
+    clauses: list[ClauseTocItem]
+
+
+@router.get("/{doc_id}/clauses", response_model=ClauseBookResponse)
+async def get_document_clauses(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    """부모 표준 doc 의 절-문서 목차(유기적 책 TOC). doc_kind='clause' 자식을 clause_order 순 반환.
+
+    절-문서는 in_corpus=false + doc_kind='clause'(검색 제외)라 일반 목록/검색엔 안 뜨지만,
+    이 책-내 네비는 부모 표준에서 자식 절로 진입하는 전용 경로다(ASME 2025판=한 권의 책).
+    """
+    from sqlalchemy import text as sql_text
+
+    parent = await session.get(Document, doc_id)
+    if not parent or parent.deleted_at is not None:
+        raise HTTPException(status_code=404, detail="문서를 찾을 수 없습니다")
+    rows = (
+        await session.execute(
+            sql_text(
+                """
+                SELECT id, clause_code, clause_part, clause_order, title
+                FROM documents
+                WHERE parent_id = :pid AND doc_kind = 'clause' AND deleted_at IS NULL
+                ORDER BY clause_order
+                """
+            ).bindparams(pid=doc_id)
+        )
+    ).mappings().all()
+    return ClauseBookResponse(
+        parent_id=doc_id,
+        parent_title=parent.title,
+        clauses=[ClauseTocItem(**dict(r)) for r in rows],
+    )
+
+
+class BacklinkRef(BaseModel):
+    code: str
+    doc_id: int | None = None   # 해소된 절-문서(같은 부모) — dangling 이면 None
+    title: str | None = None
+    anchor: str | None = None
+    ctx: str | None = None
+
+
+class BacklinksResponse(BaseModel):
+    doc_id: int
+    clause_code: str | None = None
+    parent_id: int | None = None
+    prev: ClauseTocItem | None = None
+    next: ClauseTocItem | None = None
+    forward: list[BacklinkRef]   # 이 절이 참조하는 절들
+    back: list[BacklinkRef]      # 이 절을 참조하는 절들
+
+
+@router.get("/{doc_id}/backlinks", response_model=BacklinksResponse)
+async def get_document_backlinks(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    """절-문서의 양방향 백링크 + 같은 부모 내 이전/다음 절(유기적 책 흐름)."""
+    from sqlalchemy import text as sql_text
+
+    doc = await session.get(Document, doc_id)
+    if not doc or doc.deleted_at is not None:
+        raise HTTPException(status_code=404, detail="문서를 찾을 수 없습니다")
+
+    _meta = (await session.execute(sql_text(
+        "SELECT parent_id, clause_code, clause_order FROM documents WHERE id = :id"
+    ).bindparams(id=doc_id))).mappings().first()
+    _parent_id = _meta["parent_id"] if _meta else None
+    _clause_code = _meta["clause_code"] if _meta else None
+    _clause_order = _meta["clause_order"] if _meta else None
+    forward = (
+        await session.execute(
+            sql_text(
+                """
+                SELECT cl.dst_code AS code, cl.dst_doc_id AS doc_id, cl.anchor, cl.ctx, d.title
+                FROM clause_links cl
+                LEFT JOIN documents d ON d.id = cl.dst_doc_id
+                WHERE cl.src_doc_id = :id
+                ORDER BY cl.char_off NULLS LAST
+                LIMIT 300
+                """
+            ).bindparams(id=doc_id)
+        )
+    ).mappings().all()
+    back = (
+        await session.execute(
+            sql_text(
+                """
+                SELECT s.clause_code AS code, cl.src_doc_id AS doc_id, s.title, cl.ctx
+                FROM clause_links cl
+                JOIN documents s ON s.id = cl.src_doc_id
+                WHERE cl.dst_doc_id = :id
+                ORDER BY s.clause_order NULLS LAST
+                LIMIT 300
+                """
+            ).bindparams(id=doc_id)
+        )
+    ).mappings().all()
+
+    prev = nxt = None
+    if _parent_id is not None and _clause_order is not None:
+        prow = (
+            await session.execute(
+                sql_text(
+                    """
+                    SELECT id, clause_code, clause_part, clause_order, title FROM documents
+                    WHERE parent_id = :pid AND doc_kind='clause' AND deleted_at IS NULL
+                      AND clause_order < :ord
+                    ORDER BY clause_order DESC LIMIT 1
+                    """
+                ).bindparams(pid=_parent_id, ord=_clause_order)
+            )
+        ).mappings().first()
+        nrow = (
+            await session.execute(
+                sql_text(
+                    """
+                    SELECT id, clause_code, clause_part, clause_order, title FROM documents
+                    WHERE parent_id = :pid AND doc_kind='clause' AND deleted_at IS NULL
+                      AND clause_order > :ord
+                    ORDER BY clause_order ASC LIMIT 1
+                    """
+                ).bindparams(pid=_parent_id, ord=_clause_order)
+            )
+        ).mappings().first()
+        prev = ClauseTocItem(**dict(prow)) if prow else None
+        nxt = ClauseTocItem(**dict(nrow)) if nrow else None
+
+    return BacklinksResponse(
+        doc_id=doc_id,
+        clause_code=_clause_code,
+        parent_id=_parent_id,
+        prev=prev,
+        next=nxt,
+        forward=[BacklinkRef(**dict(r)) for r in forward],
+        back=[BacklinkRef(**dict(r)) for r in back],
+    )
+
+
+# ─── 관련 문서 (유사도, on-demand pgvector KNN — 저부하·무저장) ───
+class RelatedItem(BaseModel):
+    id: int
+    title: str | None = None
+    ai_domain: str | None = None
+    material_type: str | None = None
+    year: int | None = None
+    sim: float | None = None
+
+
+class RelatedResponse(BaseModel):
+    doc_id: int
+    related: list[RelatedItem]
+
+
+@router.get("/{doc_id}/related", response_model=RelatedResponse)
+async def get_related_documents(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    limit: int = 8,
+    same_type: bool = True,
+):
+    """문서-레벨 임베딩 코사인 최근접 = '관련 문서'. on-demand(저장/배치 없음).
+
+    인용그래프가 부적합한 코퍼스(업계 기술기사=인용망 부재)의 대안 연결 레이어.
+    same_type=true면 같은 material_type 내, false면 전 코퍼스. doc_kind='clause'(절-문서)는 제외.
+    """
+    from sqlalchemy import text as sql_text
+
+    lim = max(1, min(limit, 30))
+    type_clause = "AND d.material_type = src.material_type" if same_type else ""
+    rows = (
+        await session.execute(
+            sql_text(
+                f"""
+                WITH src AS (
+                    SELECT embedding, material_type FROM documents WHERE id = :id
+                )
+                SELECT d.id, d.title, d.ai_domain, d.material_type, d.facet_year AS year,
+                       round((1 - (d.embedding <=> (SELECT embedding FROM src)))::numeric, 3) AS sim
+                FROM documents d, src
+                WHERE d.doc_kind = 'standard' AND d.deleted_at IS NULL
+                  AND d.id <> :id AND d.embedding IS NOT NULL
+                  AND (SELECT embedding FROM src) IS NOT NULL
+                  {type_clause}
+                ORDER BY d.embedding <=> (SELECT embedding FROM src)
+                LIMIT :lim
+                """
+            ).bindparams(id=doc_id, lim=lim)
+        )
+    ).mappings().all()
+    return RelatedResponse(
+        doc_id=doc_id,
+        related=[RelatedItem(**{k: r[k] for k in ("id", "title", "ai_domain", "material_type", "year")}, sim=float(r["sim"]) if r["sim"] is not None else None) for r in rows],
+    )
+
+
+# ─── 절 공부도구 (노트/형광펜/암기카드) — clause_study ───
+class StudyItem(BaseModel):
+    id: int
+    kind: str
+    payload: dict = {}
+    created_at: datetime | None = None
+
+
+class StudyListResponse(BaseModel):
+    doc_id: int
+    items: list[StudyItem]
+
+
+class StudyCreate(BaseModel):
+    kind: str           # note | highlight | card
+    payload: dict = {}
+
+
+def _parse_payload(p):
+    import json
+    if isinstance(p, str):
+        try:
+            return json.loads(p)
+        except Exception:
+            return {}
+    return p or {}
+
+
+@router.get("/{doc_id}/study", response_model=StudyListResponse)
+async def list_study(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    """절-문서의 공부도구 항목(노트/형광펜/암기카드) 목록."""
+    from sqlalchemy import text as sql_text
+    rows = (
+        await session.execute(
+            sql_text("SELECT id, kind, payload, created_at FROM clause_study "
+                     "WHERE doc_id = :id ORDER BY created_at DESC").bindparams(id=doc_id)
+        )
+    ).mappings().all()
+    return StudyListResponse(
+        doc_id=doc_id,
+        items=[StudyItem(id=r["id"], kind=r["kind"], payload=_parse_payload(r["payload"]),
+                         created_at=r["created_at"]) for r in rows],
+    )
+
+
+@router.post("/{doc_id}/study", response_model=StudyItem, status_code=201)
+async def add_study(
+    doc_id: int,
+    body: StudyCreate,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    """노트/형광펜/암기카드 1건 추가."""
+    import json
+    from sqlalchemy import text as sql_text
+    if body.kind not in ("note", "highlight", "card"):
+        raise HTTPException(status_code=400, detail="kind 는 note/highlight/card")
+    row = (
+        await session.execute(
+            sql_text("INSERT INTO clause_study(doc_id, kind, payload) "
+                     "VALUES (:d, :k, cast(:p AS jsonb)) RETURNING id, kind, payload, created_at")
+            .bindparams(d=doc_id, k=body.kind, p=json.dumps(body.payload, ensure_ascii=False))
+        )
+    ).mappings().first()
+    await session.commit()
+    return StudyItem(id=row["id"], kind=row["kind"], payload=_parse_payload(row["payload"]),
+                     created_at=row["created_at"])
+
+
+@router.delete("/{doc_id}/study/{study_id}", status_code=204)
+async def delete_study(
+    doc_id: int,
+    study_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+):
+    from sqlalchemy import text as sql_text
+    await session.execute(
+        sql_text("DELETE FROM clause_study WHERE id = :s AND doc_id = :d")
+        .bindparams(s=study_id, d=doc_id)
+    )
+    await session.commit()

app/api/ingest_study.py

@@ -0,0 +1,255 @@
+"""뷰어 write-back ingest (study-to-viewer P2) — 뷰어 로컬 풀이 세션을 DS 로 흘려 finalize 재생.
+
+흐름(plan study-to-viewer-slice1 P2, r2/r3 불변식):
+  뷰어 outbox → POST /ingest/study/attempts (Bearer VIEWER_SYNC_TOKEN, study_ingest_enabled gate)
+  → pub_id→published.source_id→StudyQuestion 해소(부재 graceful skip) → principal=question.user_id
+  → topic 별 그룹(뷰어 subject 퀴즈가 여러 DS topic 걸칠 수 있음) → topic 마다 DS quiz_session
+     (source='viewer', client_session_uuid) 생성 + attempt(derive_outcome=채점 단일 소스) + 세션 done
+  → finalize_session **무수정 재생**(SR/pattern/progress + 4-A/4-B enqueue) → finalized_at 마커
+  → 전부 1 트랜잭션(원자) 후 commit.
+
+멱등(r2 P2-2): client_session_uuid 로 기존 세션 있으면 이미 적재된 것 → 캐시 요약 반환(재실행 0).
+  원자 1-tx 라 'uuid 존재 ⟺ finalize 완료' → at-least-once outbox 재전송에도 SR 이중 advance 없음.
+user_id 리터럴 금지(r2): principal = 해소된 질문의 owner(단일, mixed 면 거부).
+"""
+
+from __future__ import annotations
+
+import hmac
+import logging
+from collections import defaultdict
+from datetime import datetime, timezone
+
+from fastapi import APIRouter, Depends, Header, HTTPException
+from pydantic import BaseModel
+from sqlalchemy import select
+from sqlalchemy.exc import IntegrityError
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from core.config import settings
+from core.database import async_session
+from models.published import Published
+from models.study_question import StudyQuestion, StudyQuestionAttempt
+from models.study_quiz_session import StudyQuizSession
+from services.study.outcome import derive_outcome
+from services.study.publish_projection import KIND_QUESTION
+from services.study.session_finalize import finalize_session
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+
+def _verify_token(authorization: str | None = Header(default=None)) -> None:
+    """뷰어↔DS 발행 채널 Bearer(read 와 동일 토큰, r3 단일토큰 수용). default-deny(미설정=503)."""
+    if not settings.viewer_sync_token:
+        raise HTTPException(status_code=503, detail="viewer_sync_token not configured")
+    if not authorization or not authorization.lower().startswith("bearer "):
+        raise HTTPException(status_code=401, detail="missing Bearer token")
+    token = authorization[7:].strip()
+    if not hmac.compare_digest(token, settings.viewer_sync_token):
+        raise HTTPException(status_code=403, detail="invalid token")
+
+
+async def _session() -> AsyncSession:
+    async with async_session() as s:
+        yield s
+
+
+class IngestAttempt(BaseModel):
+    question_pub_id: str
+    selected_choice: int | None = None
+    is_unsure: bool = False
+    answered_at: str | None = None  # 클라(오프라인) ISO 시각 — 미래 스큐 클램프, id 가 타이브레이커
+
+
+class IngestBody(BaseModel):
+    client_session_uuid: str
+    attempts: list[IngestAttempt]
+
+
+def _already_ingested(rows) -> dict:
+    """이미 적재된 세션들의 캐시 요약(멱등 응답). 최초 멱등체크 + 동시경합 흡수 양쪽에서 사용."""
+    return {
+        "status": "already_ingested",
+        "sessions": [
+            {
+                "topic_id": s.study_topic_id,
+                "correct": s.correct_count,
+                "wrong": s.wrong_count,
+                "unsure": s.unsure_count,
+            }
+            for s in rows
+        ],
+    }
+
+
+def _parse_answered_at(s: str | None, now: datetime) -> datetime:
+    if not s:
+        return now
+    try:
+        dt = datetime.fromisoformat(s.replace("Z", "+00:00"))
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        return min(dt, now)  # 미래 스큐는 now 로 클램프(클라 시계 오염 방지)
+    except Exception:
+        return now
+
+
+@router.post("/attempts")
+async def ingest_attempts(
+    body: IngestBody,
+    _auth: None = Depends(_verify_token),
+    session: AsyncSession = Depends(_session),
+):
+    if not settings.study_ingest_enabled:
+        raise HTTPException(status_code=503, detail="study_ingest not enabled")
+    if not body.client_session_uuid or not body.attempts:
+        raise HTTPException(status_code=400, detail="client_session_uuid 와 attempts 필요")
+
+    # 멱등: 이 uuid 로 이미 적재됐나(원자 1-tx 라 존재=완료). 있으면 캐시 요약 반환(재실행 0).
+    existing = (
+        await session.execute(
+            select(StudyQuizSession).where(
+                StudyQuizSession.client_session_uuid == body.client_session_uuid
+            )
+        )
+    ).scalars().all()
+    if existing:
+        return _already_ingested(existing)
+
+    # pub_id → source_id(내부 질문 id) 해소. deleted tombstone 제외.
+    pub_ids = list({a.question_pub_id for a in body.attempts})
+    pub_rows = (
+        await session.execute(
+            select(Published.pub_id, Published.source_id).where(
+                Published.kind == KIND_QUESTION,
+                Published.pub_id.in_(pub_ids),
+                Published.deleted.is_(False),
+            )
+        )
+    ).all()
+    src_by_pubid = {r.pub_id: r.source_id for r in pub_rows}
+
+    # 질문 fetch(미삭제). principal = owner(단일).
+    source_ids = list(set(src_by_pubid.values()))
+    q_rows = (
+        await session.execute(
+            select(StudyQuestion).where(
+                StudyQuestion.id.in_(source_ids), StudyQuestion.deleted_at.is_(None)
+            )
+        )
+    ).scalars().all()
+    q_by_id = {q.id: q for q in q_rows}
+    owners = {q.user_id for q in q_by_id.values()}
+    if len(owners) > 1:
+        raise HTTPException(status_code=400, detail="여러 사용자 소유 질문 혼재 — 단일 principal 위반")
+    if not owners:
+        raise HTTPException(status_code=404, detail="해소 가능한 질문 없음")
+    user_id = owners.pop()
+
+    now = datetime.now(timezone.utc)
+
+    # topic 별 그룹(해소 실패 attempt 는 graceful skip). 같은 (uuid, topic) 1 세션.
+    by_topic: dict[int, list[tuple[IngestAttempt, StudyQuestion]]] = defaultdict(list)
+    skipped: list[str] = []
+    for a in body.attempts:
+        src = src_by_pubid.get(a.question_pub_id)
+        q = q_by_id.get(src) if src is not None else None
+        if q is None:
+            skipped.append(a.question_pub_id)
+            continue
+        by_topic[q.study_topic_id].append((a, q))
+    if not by_topic:
+        raise HTTPException(status_code=404, detail="해소된 attempt 없음")
+
+    try:
+        summaries = []
+        for topic_id, items in by_topic.items():
+            qids = [q.id for (_, q) in items]
+            qs = StudyQuizSession(
+                user_id=user_id,
+                study_topic_id=topic_id,
+                question_ids=qids,
+                subject_distribution={},
+                status="done",
+                cursor=len(qids),
+                source="viewer",
+                client_session_uuid=body.client_session_uuid,
+                finished_at=now,
+                created_at=now,
+                updated_at=now,
+            )
+            session.add(qs)
+            await session.flush()  # qs.id
+
+            c = w = u = 0
+            for a, q in items:
+                try:
+                    sel, is_corr, outcome = derive_outcome(a.selected_choice, a.is_unsure, q.correct_choice)
+                except ValueError:
+                    skipped.append(a.question_pub_id)  # 선택 없고 unsure 아님 = 무효 → skip
+                    continue
+                if outcome == "correct":
+                    c += 1
+                elif outcome == "wrong":
+                    w += 1
+                elif outcome == "unsure":
+                    u += 1
+                session.add(
+                    StudyQuestionAttempt(
+                        user_id=user_id,
+                        study_question_id=q.id,
+                        study_topic_id=topic_id,
+                        selected_choice=sel,
+                        correct_choice=q.correct_choice,
+                        is_correct=is_corr,
+                        outcome=outcome,
+                        quiz_session_id=qs.id,
+                        answered_at=_parse_answered_at(a.answered_at, now),
+                    )
+                )
+            qs.correct_count, qs.wrong_count, qs.unsure_count = c, w, u
+            await session.flush()
+
+            # finalize 무수정 재생(progress/SR/pattern + 4-A/4-B enqueue). 그 후 멱등 마커.
+            summary = await finalize_session(
+                session, user_id=user_id, study_topic_id=topic_id, quiz_session_id=qs.id
+            )
+            qs.finalized_at = now
+            summaries.append(
+                {
+                    "topic_id": topic_id,
+                    "quiz_session_id": qs.id,
+                    "correct": summary.correct,
+                    "wrong": summary.wrong,
+                    "unsure": summary.unsure,
+                    "newly_correct": summary.newly_correct,
+                    "relapsed": summary.relapsed,
+                    "recovered": summary.recovered,
+                }
+            )
+
+        await session.commit()
+    except IntegrityError:
+        # 동시 같은 client_session_uuid 경합 — 상대가 먼저 commit → (client_session_uuid,
+        # study_topic_id) uq(mig376) 위반. 데이터는 안전(원자 1-tx 전체 롤백 → SR 이중 advance
+        # 없음). 승자 결과로 graceful 수렴(500 대신 already_ingested). uuid 경합이 아닌 진짜
+        # 무결성 오류면 재조회가 비어 → re-raise 로 표면화.
+        await session.rollback()
+        winner = (
+            await session.execute(
+                select(StudyQuizSession).where(
+                    StudyQuizSession.client_session_uuid == body.client_session_uuid
+                )
+            )
+        ).scalars().all()
+        if not winner:
+            raise
+        logger.info("study_ingest uuid=%s 동시경합 흡수 → already_ingested", body.client_session_uuid)
+        return _already_ingested(winner)
+
+    logger.info(
+        "study_ingest uuid=%s user=%s sessions=%s skipped=%s",
+        body.client_session_uuid, user_id, len(summaries), len(skipped),
+    )
+    return {"status": "ingested", "skipped": skipped, "sessions": summaries}

app/api/library.py

@@ -9,7 +9,7 @@ from sqlalchemy import func, select
 from sqlalchemy import text as sql_text
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from core.auth import get_current_user
+from core.auth import get_current_user, require_admin
 from core.database import get_session
 from core.library import LIBRARY_PREFIX, MAX_DEPTH, normalize_library_path
 from models.category import LibraryCategory
@@ -78,7 +78,7 @@ async def list_categories(
 @router.post("/categories", response_model=CategoryResponse, status_code=201)
 async def create_category(
     body: CategoryCreate,
-    user: Annotated[User, Depends(get_current_user)],
+    user: Annotated[User, Depends(require_admin)],
     session: Annotated[AsyncSession, Depends(get_session)],
 ):
     """카테고리 생성 (조상 자동 생성 포함)"""
@@ -133,7 +133,7 @@ async def create_category(
 @router.patch("/categories", response_model=CategoryResponse)
 async def rename_category(
     body: CategoryRename,
-    user: Annotated[User, Depends(get_current_user)],
+    user: Annotated[User, Depends(require_admin)],
     session: Annotated[AsyncSession, Depends(get_session)],
 ):
     """카테고리 이름 변경 (leaf only, path 기반 식별)"""
@@ -214,7 +214,7 @@ async def rename_category(
 @router.delete("/categories", status_code=204)
 async def delete_category(
     path: str = Query(..., description="삭제할 카테고리 경로"),
-    user: Annotated[User, Depends(get_current_user)] = None,
+    user: Annotated[User, Depends(require_admin)] = None,
     session: Annotated[AsyncSession, Depends(get_session)] = None,
 ):
     """카테고리 삭제 (leaf only, 문서 없는 경우만)"""
@@ -410,7 +410,7 @@ async def get_facet_values(
 @router.post("/facets", response_model=FacetValueResponse, status_code=201)
 async def add_facet_value(
     body: FacetValueResponse,
-    user: Annotated[User, Depends(get_current_user)],
+    user: Annotated[User, Depends(require_admin)],
     session: Annotated[AsyncSession, Depends(get_session)],
 ):
     """facet 사전에 새 값 추가"""

app/api/published.py

@@ -0,0 +1,254 @@
+"""발행 read API (docsrv-viewer-publish P0-2) — 뷰어가 pull-sync 로 당기는 feed.
+
+published 테이블(발행 워커가 rev 커밋순 gapless 부여)을 rev 커서로 페이지네이션해 반환.
+뷰어 = Bearer(settings.viewer_sync_token) 인증, default-deny. read-only(SELECT 만).
+  GET /published/feed?since={rev}&kind={kind}&limit={n}
+    rev > since 행을 rev ASC 로 limit 만큼. kind 옵션(study_question|study_explanation|... 후속).
+    tombstone(deleted=true)도 1급 이벤트로 포함 — 뷰어가 pub_id 로 로컬 삭제(stale 회피).
+
+rev 커서 안전성: 워커가 pg_advisory_xact_lock 단일 라이터로 배치 rev 를 한 트랜잭션에
+부여·커밋 → 리더는 rev N 을 N-1 없이 보지 못함(부분가시 0). 뷰어는 next_since 로 반복.
+
+엔벨로프 schema_version = 전송 계약 버전(payload 행별 schema_version 과 별개).
+미지원 버전 가시거부는 뷰어 책임(no-silent-fallback) — 여기선 행별 schema_version 그대로 전달.
+"""
+from __future__ import annotations
+
+import hmac
+import logging
+import logging
+from datetime import datetime, timezone
+
+from fastapi import APIRouter, Depends, Header, HTTPException, Query
+from pydantic import BaseModel
+from sqlalchemy import select, text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from core.config import settings
+from core.database import async_session
+from models.published import Published
+from models.published import Published
+from services.queue_overview import build_overview
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+# feed 엔벨로프(전송 계약) 버전 — payload schema_version 과 독립.
+FEED_SCHEMA_VERSION = 1
+DEFAULT_LIMIT = 200
+MAX_LIMIT = 500
+
+
+def _verify_token(authorization: str | None = Header(default=None)) -> None:
+    """뷰어↔DS 발행 채널 Bearer 인증. default-deny(미설정=503). 상수시간 비교(internal_study 정본).
+
+    이 토큰은 정답 포함 study payload 를 노출하므로 hmac.compare_digest 로 timing side-channel 차단.
+    """
+    if not settings.viewer_sync_token:
+        raise HTTPException(status_code=503, detail="viewer_sync_token not configured")
+    if not authorization or not authorization.lower().startswith("bearer "):
+        raise HTTPException(status_code=401, detail="missing Bearer token")
+    token = authorization[7:].strip()
+    if not hmac.compare_digest(token, settings.viewer_sync_token):
+        raise HTTPException(status_code=403, detail="invalid token")
+
+
+async def _session() -> AsyncSession:
+    async with async_session() as s:
+        yield s
+
+
+class FeedItem(BaseModel):
+    pub_id: str          # opaque+stable = 뷰어 dedup키 = progress키
+    kind: str
+    source_id: int       # DS 내부 소스 행 id (ingest write-back 역해소용, P2)
+    rev: int
+    deleted: bool        # tombstone — 뷰어 로컬 삭제 트리거
+    schema_version: int  # payload 모양 버전(뷰어 range 수용)
+    payload: dict        # render-ready projection (tombstone 이면 {})
+
+
+class FeedResponse(BaseModel):
+    schema_version: int       # 엔벨로프(전송 계약) 버전
+    items: list[FeedItem]
+    next_since: int           # 다음 호출 since (이 배치 max rev; 빈 배치면 입력 since 유지)
+    has_more: bool            # limit 가득 = 더 있을 수 있음(뷰어 반복)
+
+
+@router.get("/feed", response_model=FeedResponse)
+async def published_feed(
+    since: int = Query(0, ge=0),
+    kind: str | None = Query(None, max_length=40),
+    limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT),
+    _auth: None = Depends(_verify_token),
+    session: AsyncSession = Depends(_session),
+):
+    """rev > since 행을 rev ASC 로 limit 만큼 반환. 뷰어가 next_since 로 incremental pull."""
+    stmt = select(Published).where(Published.rev > since)
+    if kind:
+        stmt = stmt.where(Published.kind == kind)
+    stmt = stmt.order_by(Published.rev.asc()).limit(limit)
+    rows = (await session.execute(stmt)).scalars().all()
+
+    items = [
+        FeedItem(
+            pub_id=r.pub_id,
+            kind=r.kind,
+            source_id=r.source_id,
+            rev=r.rev,
+            deleted=r.deleted,
+            schema_version=r.schema_version,
+            payload=r.payload if r.payload is not None else {},
+        )
+        for r in rows
+    ]
+    next_since = items[-1].rev if items else since
+    has_more = len(rows) == limit
+    logger.info(
+        "published_feed since=%s kind=%s returned=%s next_since=%s has_more=%s",
+        since, kind, len(items), next_since, has_more,
+    )
+    return FeedResponse(
+        schema_version=FEED_SCHEMA_VERSION,
+        items=items,
+        next_since=next_since,
+        has_more=has_more,
+    )
+
+
+# ── P1-1: 뉴스/다이제스트 발행 read API (docsrv-viewer-publish) ────────────────────
+# global_digests(일간 컨테이너) + digest_topics(토픽 N, digest_id FK) -> render-ready
+# read-time projection. content-type 파라미터화(plan r2): version 커서=global_digests.id
+# (일간 단일 라이터라 gapless 불요·gap 무해) · pub_id=date-as-id(admin-gated feed 라 opacity
+# 불필요) · tombstone 없음(다이제스트 미삭제). 엔벨로프는 /feed 와 동일(FeedResponse)=뷰어 재사용.
+# scaffold-first: DIGEST_PUBLISH_ENABLED off(기본)=503(명시적 미가동, no-silent).
+DIGEST_PAYLOAD_SCHEMA_VERSION = 1
+
+
+@router.get("/digest", response_model=FeedResponse)
+async def published_digest(
+    since: int = Query(0, ge=0),
+    limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT),
+    _auth: None = Depends(_verify_token),
+    session: AsyncSession = Depends(_session),
+):
+    """global_digests.id > since 를 id ASC 로 limit 만큼. 각 digest 에 topics 조인해 render-ready 반환."""
+    if not settings.digest_publish_enabled:
+        raise HTTPException(status_code=503, detail="digest publish not enabled (scaffold)")
+
+    drows = (await session.execute(
+        text(
+            "SELECT id, digest_date, status, total_articles, total_topics, total_countries, created_at "
+            "FROM global_digests WHERE id > :since ORDER BY id ASC LIMIT :limit"
+        ),
+        {"since": since, "limit": limit},
+    )).mappings().all()
+
+    if not drows:
+        return FeedResponse(schema_version=FEED_SCHEMA_VERSION, items=[], next_since=since, has_more=False)
+
+    ids = [r["id"] for r in drows]
+    trows = (await session.execute(
+        text(
+            "SELECT digest_id, topic_rank, topic_label, summary, country, article_count, importance_score "
+            "FROM digest_topics WHERE digest_id = ANY(:ids) ORDER BY digest_id ASC, topic_rank ASC"
+        ),
+        {"ids": ids},
+    )).mappings().all()
+
+    topics_by_digest: dict[int, list[dict]] = {}
+    for t in trows:
+        topics_by_digest.setdefault(t["digest_id"], []).append({
+            "rank": t["topic_rank"],
+            "label": t["topic_label"],
+            "summary": t["summary"],
+            "country": t["country"],
+            "article_count": t["article_count"],
+            "importance": t["importance_score"],
+        })
+
+    items = []
+    for r in drows:
+        d_date = r["digest_date"].isoformat() if r["digest_date"] else None
+        items.append(FeedItem(
+            pub_id=f"digest:{d_date}",
+            kind="digest",
+            source_id=r["id"],
+            rev=r["id"],
+            deleted=False,
+            schema_version=DIGEST_PAYLOAD_SCHEMA_VERSION,
+            payload={
+                "digest_date": d_date,
+                "status": r["status"],
+                "total_articles": r["total_articles"],
+                "total_topics": r["total_topics"],
+                "total_countries": r["total_countries"],
+                "generated_at": r["created_at"].isoformat() if r["created_at"] else None,
+                "topics": topics_by_digest.get(r["id"], []),
+            },
+        ))
+    next_since = items[-1].rev
+    has_more = len(drows) == limit
+    logger.info(
+        "published_digest since=%s returned=%s next_since=%s has_more=%s",
+        since, len(items), next_since, has_more,
+    )
+    return FeedResponse(
+        schema_version=FEED_SCHEMA_VERSION,
+        items=items,
+        next_since=next_since,
+        has_more=has_more,
+    )
+
+
+# ── P1-2: 가공현황 라이브 스냅샷 API (+P1-4 점검 플래그) ──────────────────────────
+# 뷰어 리포트 '문서 가공현황' 섹션용. build_overview(기존 서비스) 재사용 + source_health
+# 조인 요약. pull-through(저장 X) — 라이브 수치라 캐시 없음, 소비자(뷰어)가 2~3s timeout 책임
+# (plan P1-2). P1-4: maintenance 플래그 동봉 — 소프트락/점검이 워커를 멈춰 수치가 정체로
+# 보일 때 뷰어가 '점검·실험 중' 배너로 구분(표면 != 데이터). read-only.
+@router.get("/processing-status")
+async def published_processing_status(
+    _auth: None = Depends(_verify_token),
+    session: AsyncSession = Depends(_session),
+):
+    """가공현황 스냅샷: queue overview + source_health 요약 + maintenance 플래그."""
+    overview = await build_overview(session)
+
+    sh_rows = (await session.execute(text(
+        "SELECT ns.name, ns.category, sh.circuit_state, sh.consecutive_failures, sh.empty_streak, "
+        "sh.last_success_at, sh.last_probe_ok "
+        "FROM source_health sh JOIN news_sources ns ON ns.id = sh.source_id "
+        "ORDER BY (sh.circuit_state <> 'closed') DESC, sh.consecutive_failures DESC"
+    ))).mappings().all()
+
+    by_state: dict[str, int] = {}
+    problems: list[dict] = []
+    for r in sh_rows:
+        st = r["circuit_state"]
+        by_state[st] = by_state.get(st, 0) + 1
+        if st != "closed":
+            problems.append({
+                "name": r["name"],
+                "category": r["category"],
+                "circuit_state": st,
+                "consecutive_failures": r["consecutive_failures"],
+                "empty_streak": r["empty_streak"],
+                "last_success_at": r["last_success_at"].isoformat() if r["last_success_at"] else None,
+                "last_probe_ok": r["last_probe_ok"],
+            })
+
+    return {
+        "schema_version": 1,
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "overview": overview,
+        "sources": {
+            "total": len(sh_rows),
+            "by_circuit_state": by_state,
+            "problems": problems,
+        },
+        "maintenance": {
+            "active": settings.maintenance_mode,
+            "note": settings.maintenance_note,
+        },
+    }

app/api/search.py

@@ -3,42 +3,28 @@
 실제 검색 파이프라인(retrieval → fusion → rerank → diversity → confidence)
 은 `services/search/search_pipeline.py::run_search()` 로 분리되어 있다.
 이 파일은 다음만 담당:
-  - Pydantic 스키마 (SearchResult / SearchResponse / SearchDebug / DebugCandidate
-    / Citation / AskResponse / AskDebug)
+  - Pydantic 스키마 (SearchResult / SearchResponse / SearchDebug / DebugCandidate)
   - `/search` endpoint wrapper (run_search 호출 + logger + telemetry + 직렬화)
-  - `/ask` endpoint wrapper (Phase 3.3 에서 추가)
 """
 
-import asyncio
-import hmac
-import time
 from datetime import date
-from typing import Annotated, Literal
+from typing import Annotated
 
-from fastapi import APIRouter, BackgroundTasks, Depends, Header, Query
+from fastapi import APIRouter, BackgroundTasks, Depends, Query
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from core.auth import get_current_user
-from core.config import settings
+from core.auth import get_current_user, get_egress_class
 from core.database import get_session
 from core.utils import setup_logger
 from models.user import User
-from services.document_telemetry import sanitize_source
-from services.search.classifier_service import ClassifierResult, classify
-from services.search.evidence_service import EvidenceItem, extract_evidence
 from services.search.fusion_service import DEFAULT_FUSION
-from services.search.grounding_check import check as grounding_check
-from services.search.refusal_gate import RefusalDecision, decide as refusal_decide
 from services.search import query_rewriter
 from services.search.retrieval_service import AxisFilter
 from services.search.result_decorate import compute_facets, decorate_version_status
 from services.search.search_pipeline import PipelineResult, run_search
-from services.search.synthesis_service import SynthesisResult, synthesize
-from services.search.verifier_service import VerifierResult, verify
-from services.prompt_versions import ASK_PROMPT_VERSION, resolve_primary_model
-from services.search_telemetry import record_ask_event, record_search_event
+from services.search_telemetry import record_search_event
 
 # logs/search.log + stdout 동시 출력 (Phase 0.4)
 logger = setup_logger("search")
@@ -153,6 +139,7 @@ def _build_search_debug(pr: PipelineResult) -> SearchDebug:
 async def search(
     q: str,
     user: Annotated[User, Depends(get_current_user)],
+    egress_class: Annotated[str, Depends(get_egress_class)],
     session: Annotated[AsyncSession, Depends(get_session)],
     background_tasks: BackgroundTasks,
     mode: str = Query("hybrid", pattern="^(fts|trgm|vector|hybrid)$"),
@@ -225,6 +212,8 @@ async def search(
         None, description="안전 자료실 C-1: 관할 필터 (KR/US/EU/JP/GB/INT)"),
     year_from: int | None = Query(None, ge=1900, le=2100, description="published_date 연도 하한 (NULL=created_at fallback)"),
     year_to: int | None = Query(None, ge=1900, le=2100, description="published_date 연도 상한"),
+    domain_bucket: str | None = Query(None, description="377: domain_bucket 스코프 CSV (Safety,Engineering,Law,Philosophy,Programming,General,News). domain_bucket = ANY"),
+    exclude_bucket: str | None = Query(None, description="377: domain_bucket 제외 CSV (예: News). 지식질의 시 News 기본제외용"),
     facets: bool = Query(False, description="안전 자료실 C-1 후속: top-K 결과 분류 축 분포(material_type/jurisdiction/version_status)를 응답 facets 에 집계. 미지정=계산/노출 0"),
 ):
     """문서 검색 — FTS + ILIKE + 벡터 결합 (Phase 3.1 이후 run_search wrapper)"""
@@ -235,6 +224,9 @@ async def search(
             jurisdiction=jurisdiction,
             year_from=year_from,
             year_to=year_to,
+            domain_buckets=[b.strip() for b in domain_bucket.split(",") if b.strip()] if domain_bucket else None,
+            exclude_buckets=[b.strip() for b in exclude_bucket.split(",") if b.strip()] if exclude_bucket else None,
+            cloud_egress=(egress_class == "cloud"),
         )
         pr = await run_search(
             session,
@@ -354,836 +346,3 @@ async def search(
         debug=debug_obj,
         facets=facets_obj,
     )
-
-
-# ═══════════════════════════════════════════════════════════
-# Phase 3.3: /api/search/ask — Evidence + Grounded Synthesis
-# ═══════════════════════════════════════════════════════════
-
-
-class Citation(BaseModel):
-    """answer 본문의 [n] 에 해당하는 근거 단일 행."""
-
-    n: int
-    chunk_id: int | None
-    doc_id: int
-    title: str | None
-    section_title: str | None
-    span_text: str  # evidence LLM 이 추출한 50~300자
-    full_snippet: str  # 원본 800자 (citation 원문 보기 전용)
-    relevance: float
-    rerank_score: float
-
-
-class ConfirmedItem(BaseModel):
-    """Partial answer 의 개별 aspect 답변."""
-
-    aspect: str
-    text: str
-    citations: list[int]
-
-
-class AskDebug(BaseModel):
-    """`/ask?debug=true` 응답 확장."""
-
-    timing_ms: dict[str, float]
-    search_notes: list[str]
-    query_analysis: dict | None = None
-    confidence_signal: float
-    evidence_candidate_count: int
-    evidence_kept_count: int
-    evidence_skip_reason: str | None
-    synthesis_cache_hit: bool
-    synthesis_prompt_preview: str | None = None
-    synthesis_raw_preview: str | None = None
-    hallucination_flags: list[str] = []
-    # Phase 3.5a: per-layer defense 로깅
-    defense_layers: dict | None = None
-
-
-class AskResponse(BaseModel):
-    """`/ask` 응답. Phase 3.5a: completeness + aspects 추가."""
-
-    results: list[SearchResult]
-    ai_answer: str | None
-    citations: list[Citation]
-    synthesis_status: Literal[
-        "completed", "timeout", "skipped", "no_evidence", "parse_failed", "llm_error",
-        # PR-MacBook-RAG-Backend-1: 200 응답에는 등장하지 않음 (해당 status 는 503 분기).
-        # Literal 호환성 위해 포함.
-        "backend_unavailable",
-    ]
-    synthesis_ms: float
-    confidence: Literal["high", "medium", "low"] | None
-    refused: bool
-    no_results_reason: str | None
-    query: str
-    total: int
-    # Phase 3.5a
-    completeness: Literal["full", "partial", "insufficient"] = "full"
-    covered_aspects: list[str] | None = None
-    missing_aspects: list[str] | None = None
-    confirmed_items: list[ConfirmedItem] | None = None
-    # PR-MacBook-RAG-Backend-1: backend dispatcher metadata.
-    # backend 미지정 호출은 둘 다 None 으로 유지 (기존 호출자 호환 — Hermes docsrv_ask /
-    # voice-memo-bot 응답 형식 변동 0). 명시 opt-in 시만 채워짐.
-    backend_requested: str | None = None
-    backend_used: str | None = None
-    debug: AskDebug | None = None
-
-
-def _map_no_results_reason(
-    pr: PipelineResult,
-    evidence: list[EvidenceItem],
-    ev_skip: str | None,
-    sr: SynthesisResult,
-) -> str | None:
-    """사용자에게 보여줄 한국어 메시지 매핑.
-
-    Failure mode 표 (plan §Failure Modes) 기반.
-    """
-    # LLM 자가 refused → 모델이 준 사유 그대로
-    if sr.refused and sr.refuse_reason:
-        return sr.refuse_reason
-
-    # synthesis 상태 우선
-    if sr.status == "no_evidence":
-        if not pr.results:
-            return "검색 결과가 없습니다."
-        return "관련도 높은 근거를 찾지 못했습니다."
-    if sr.status == "skipped":
-        return "검색 결과가 없습니다."
-    if sr.status == "timeout":
-        return "답변 생성이 지연되어 생략했습니다. 검색 결과를 확인해 주세요."
-    if sr.status == "parse_failed":
-        return "답변 형식 오류로 생략했습니다."
-    if sr.status == "llm_error":
-        return "AI 서버에 일시적 문제가 있습니다."
-
-    # evidence 단계 실패는 fallback 을 탔더라도 notes 용
-    if ev_skip == "all_low_rerank":
-        return "관련도 높은 근거를 찾지 못했습니다."
-    if ev_skip == "empty_retrieval":
-        return "검색 결과가 없습니다."
-
-    return None
-
-
-def _build_citations(
-    evidence: list[EvidenceItem], used_citations: list[int]
-) -> list[Citation]:
-    """answer 본문에 실제로 등장한 n 만 Citation 으로 변환."""
-    by_n = {e.n: e for e in evidence}
-    out: list[Citation] = []
-    for n in used_citations:
-        e = by_n.get(n)
-        if e is None:
-            continue
-        out.append(
-            Citation(
-                n=e.n,
-                chunk_id=e.chunk_id,
-                doc_id=e.doc_id,
-                title=e.title,
-                section_title=e.section_title,
-                span_text=e.span_text,
-                full_snippet=e.full_snippet,
-                relevance=e.relevance,
-                rerank_score=e.rerank_score,
-            )
-        )
-    return out
-
-
-def _build_ask_debug(
-    pr: PipelineResult,
-    evidence: list[EvidenceItem],
-    ev_skip: str | None,
-    sr: SynthesisResult,
-    ev_ms: float,
-    synth_ms: float,
-    total_ms: float,
-) -> AskDebug:
-    timing: dict[str, float] = dict(pr.timing_ms)
-    timing["evidence_ms"] = ev_ms
-    timing["synthesis_ms"] = synth_ms
-    timing["ask_total_ms"] = total_ms
-
-    # candidate count 는 rule filter 통과한 수 (recomputable from results)
-    # 엄밀히는 evidence_service 내부 숫자인데, evidence 길이 ≈ kept, candidate
-    # 는 관측이 어려움 → kept 는 evidence 길이, candidate 는 별도 필드 없음.
-    # 단순화: candidate_count = len(evidence) 를 상한 근사로 둠 (debug 전용).
-    return AskDebug(
-        timing_ms=timing,
-        search_notes=pr.notes,
-        query_analysis=pr.query_analysis,
-        confidence_signal=pr.confidence_signal,
-        evidence_candidate_count=len(evidence),
-        evidence_kept_count=len(evidence),
-        evidence_skip_reason=ev_skip,
-        synthesis_cache_hit=sr.cache_hit,
-        synthesis_prompt_preview=None,  # 현재 synthesis_service 에서 노출 안 함
-        synthesis_raw_preview=sr.raw_preview,
-        hallucination_flags=sr.hallucination_flags,
-    )
-
-
-def _detect_synthesis_failure(sr: SynthesisResult) -> str | None:
-    """Synthesis 가 유효한 답을 못 냈으면 re_gate 라벨, 아니면 None.
-
-    판정 우선순위 (Phase 3.5 fix3):
-      1) sr.refused → LLM self-refuse (status="completed") 또는 mechanical fail 후 refused 전파
-         - status=="completed" + refused=True → "synthesis_self_refuse"
-         - 그 외 → f"synthesis_failed({status})"
-      2) sr.status ∈ {timeout, parse_failed, llm_error} → f"synthesis_failed({status})"
-      3) answer 공백 → f"synthesis_failed({status})"
-      4) 유효 → None
-    """
-    if sr.refused:
-        if sr.status == "completed":
-            return "synthesis_self_refuse"
-        return f"synthesis_failed({sr.status})"
-    if sr.status in ("timeout", "parse_failed", "llm_error"):
-        return f"synthesis_failed({sr.status})"
-    if not (sr.answer or "").strip():
-        return f"synthesis_failed({sr.status})"
-    return None
-
-
-def _resolve_eval_identity(
-    x_source: str | None,
-    x_eval_case_id: str | None,
-    x_eval_token: str | None,
-) -> tuple[str, str | None]:
-    """X-Source/X-Eval-Case-Id 신뢰 검증 (Phase 3.5 fix2).
-
-    규칙:
-      - 기본값: source='document_server', eval_case_id=None
-      - X-Source=eval 또는 X-Eval-Case-Id 가 들어왔다면 eval claim 으로 간주
-      - eval claim 은 X-Eval-Token == settings.eval_runner_token 일 때만 수용
-        (constant-time compare, env 미설정 시 항상 거부)
-      - 거부 시: 헤더 무시 + warning log + source=sanitize(non-eval) / eval_case_id=None
-      - 통과 시: source='eval', eval_case_id=x_eval_case_id
-
-    반환: (source, eval_case_id)
-    """
-    claimed_source = sanitize_source(x_source)
-    is_eval_claim = (claimed_source == "eval") or bool(x_eval_case_id)
-    if not is_eval_claim:
-        # 일반 호출 — eval_case_id 강제 None (source != 'eval' 이면 case_id 의미 없음)
-        return claimed_source, None
-
-    # eval claim — token 검증
-    expected = settings.eval_runner_token
-    presented = x_eval_token or ""
-    token_valid = bool(expected) and hmac.compare_digest(presented, expected)
-    if not token_valid:
-        logger.warning(
-            "eval header rejected: source=%s case_id=%s token_present=%s expected_set=%s",
-            x_source, x_eval_case_id, bool(x_eval_token), bool(expected),
-        )
-        # 일반 호출로 강등 — source='eval' 주장은 무시, case_id 도 무시
-        # claimed_source 가 'eval' 이면 default 'document_server' 로
-        if claimed_source == "eval":
-            return "document_server", None
-        return claimed_source, None
-
-    # token OK — eval 라벨 수용
-    return "eval", x_eval_case_id
-
-
-@router.get("/ask", response_model=AskResponse)
-async def ask(
-    q: str,
-    user: Annotated[User, Depends(get_current_user)],
-    session: Annotated[AsyncSession, Depends(get_session)],
-    background_tasks: BackgroundTasks,
-    limit: int = Query(10, ge=1, le=20, description="synthesis 입력 상한"),
-    debug: bool = Query(False, description="evidence/synthesis 중간 상태 노출"),
-    backend: Annotated[
-        str | None,
-        Query(
-            pattern="^(qwen-macbook|gemma-macmini|mac-mini-default|claude-cloud|auto)$",
-            description=(
-                "PR-2 of DS AI routing policy (2026-05-23) — 명시 backend opt-in via llm-router. "
-                "미지정 = mac-mini-default (gemma-macmini alias, default). "
-                "'mac-mini-default' = router 가 tier_b (Mac mini gemma-4-26b). "
-                "'qwen-macbook' = router 가 named upstream (M5 Max Qwen 3.6 27B). "
-                "'claude-cloud' = router 가 503 provider_not_configured (활성화 별 PR). "
-                "'auto' = router 의 rule + LLM triage. "
-                "backend unavailable 시 503 + error_reason=macbook_unavailable / router_* "
-                "(자동 fallback 없음 — 다시 호출하거나 backend 인자 제거 후 재시도)."
-            ),
-        ),
-    ] = None,
-    corpus_variant: str | None = Query(
-        None,
-        pattern=r"^(prehier|hier_sim_raw|hier_sim_clean)$",
-        description=(
-            "⚠️ EVAL-ONLY (Hier-PassageRAG-Diagnose-1). evidence retrieval 의 chunk leg 를 측정 뷰로 "
-            "교체 — prehier(legacy) | hier_sim_raw | hier_sim_clean. 운영 UI 미사용. "
-            "미지정 = production corpus_chunks (기존 /ask 동작 동일)."
-        ),
-    ),
-    exact_knn: bool = Query(
-        False,
-        description=(
-            "⚠️ EVAL-ONLY (Hier-PassageRAG-Diagnose-1). vector leg exact KNN (ivfflat 근사 제거). "
-            "passage 변종 공정 비교용. 운영 미사용. 미지정(false) = 기존 /ask 동작 동일."
-        ),
-    ),
-    x_source: Annotated[str | None, Header(alias="X-Source")] = None,
-    x_eval_case_id: Annotated[str | None, Header(alias="X-Eval-Case-Id")] = None,
-    x_eval_token: Annotated[str | None, Header(alias="X-Eval-Token")] = None,
-):
-    """근거 기반 AI 답변 (Phase 3.5a).
-
-    Phase 3.3 기반 + classifier parallel + refusal gate + grounding re-gate.
-    실패 경로에서도 `results` 는 항상 반환.
-
-    Phase 3.5 calibration trust boundary (fix2):
-      - X-Source / X-Eval-Case-Id 는 X-Eval-Token 이 EVAL_RUNNER_TOKEN 와 일치하는
-        trusted internal eval runner 에서만 수용된다.
-      - 일반 client 의 X-Source=eval 시도는 무시되고 source='document_server' 로 강제.
-      - source != 'eval' 이면 eval_case_id 항상 None.
-    """
-    t_total = time.perf_counter()
-    defense_log: dict = {}  # per-layer flag snapshot
-    source, eval_case_id = _resolve_eval_identity(x_source, x_eval_case_id, x_eval_token)
-
-    # 1. 검색 파이프라인 (corpus_variant/exact_knn = EVAL-ONLY, 미지정 시 기존 동작 동일)
-    pr = await run_search(
-        session, q, mode="hybrid", limit=limit,
-        fusion=DEFAULT_FUSION, rerank=True, analyze=True,
-        corpus_variant=corpus_variant, exact_knn=exact_knn,
-    )
-
-    # 1.5. ask_includable=false 문서를 evidence 입력에서 제외
-    # 검색 결과 자체는 유지 (사용자에게 보여줌), evidence만 필터
-    if pr.results:
-        from sqlalchemy import select as sa_select
-        from models.document import Document as DocModel
-        ask_doc_ids = set()
-        excluded_ids = {r.id for r in pr.results}
-        rows = await session.execute(
-            sa_select(DocModel.id, DocModel.ask_includable).where(
-                DocModel.id.in_(excluded_ids)
-            )
-        )
-        for doc_id, includable in rows:
-            if includable is False:
-                ask_doc_ids.add(doc_id)
-        evidence_results = [r for r in pr.results if r.id not in ask_doc_ids]
-    else:
-        evidence_results = pr.results
-
-    # 2. Evidence + Classifier 병렬
-    t_ev = time.perf_counter()
-    evidence_task = asyncio.create_task(extract_evidence(q, evidence_results))
-
-    # classifier input: top 3 chunks meta + rerank scores
-    top_chunks = [
-        {
-            "title": r.title or "",
-            "section": r.section_title or "",
-            "snippet": (r.snippet or "")[:200],
-        }
-        for r in pr.results[:3]
-    ]
-    rerank_scores_top = [
-        r.rerank_score if r.rerank_score is not None else r.score
-        for r in pr.results[:3]
-    ]
-    classifier_task = asyncio.create_task(
-        classify(q, top_chunks, rerank_scores_top)
-    )
-
-    evidence, ev_skip = await evidence_task
-    ev_ms = (time.perf_counter() - t_ev) * 1000
-
-    # classifier await (timeout 보호 — classifier_service 내부에도 있지만 여기서 이중 보호)
-    # 2026-05-17: 6s outer wrapper 가 classifier_service.LLM_TIMEOUT_MS (30s) 를 override → 동시 부하 시
-    # 거의 모든 classifier 호출 timeout → conservative_refuse(no_classifier) 경로. 15s 로 상향 — classifier
-    # 가 실제 작동하도록 (단, ask 전체 응답 시간 상한 영향: ev_ms + max(classifier_wait, evidence_extract) +
-    # synth_ms + verifier 누적).
-    # 2026-05-17 B-3: 15s 도 동시 부하 시 부족 (classifier_service LLM_TIMEOUT_MS 30s 와 misalign).
-    # 30s 로 align → classifier 동작 안정. ask 응답 latency 상한 ↑ 의도.
-    try:
-        classifier_result = await asyncio.wait_for(classifier_task, timeout=30.0)
-    except asyncio.CancelledError:
-        raise  # 요청 취소는 전파 — broad except 가 삼키지 않게 명시 (R3)
-    except Exception:
-        classifier_result = ClassifierResult("timeout", None, [], [], 0.0)
-
-    defense_log["classifier"] = {
-        "status": classifier_result.status,
-        "verdict": classifier_result.verdict,
-        "covered_aspects": classifier_result.covered_aspects,
-        "missing_aspects": classifier_result.missing_aspects,
-        "elapsed_ms": classifier_result.elapsed_ms,
-    }
-
-    # 3. Refusal gate (multi-signal fusion)
-    all_rerank_scores = [
-        e.rerank_score for e in evidence
-    ] if evidence else rerank_scores_top
-    decision = refusal_decide(all_rerank_scores, classifier_result)
-
-    defense_log["score_gate"] = {
-        "max": max(all_rerank_scores) if all_rerank_scores else 0.0,
-        "agg_top3": sum(sorted(all_rerank_scores, reverse=True)[:3]),
-    }
-    defense_log["refusal"] = {
-        "refused": decision.refused,
-        "rule_triggered": decision.rule_triggered,
-    }
-
-    if decision.refused:
-        total_ms = (time.perf_counter() - t_total) * 1000
-        no_reason = "관련 근거를 찾지 못했습니다."
-        if not pr.results:
-            no_reason = "검색 결과가 없습니다."
-        logger.info(
-            "ask REFUSED query=%r rule=%s max_score=%.2f total=%.0f",
-            q[:80], decision.rule_triggered,
-            max(all_rerank_scores) if all_rerank_scores else 0.0, total_ms,
-        )
-        # telemetry — search + ask_events 두 경로 동시
-        background_tasks.add_task(
-            record_search_event, q, user.id, pr.results, "hybrid",
-            pr.confidence_signal, pr.analyzer_confidence,
-        )
-        # input_snapshot (디버깅/재현용)
-        defense_log["input_snapshot"] = {
-            "query": q,
-            "top_chunks_preview": [
-                {"title": c.get("title", ""), "snippet": c.get("snippet", "")[:100]}
-                for c in top_chunks[:3]
-            ],
-            "answer_preview": None,
-        }
-        background_tasks.add_task(
-            record_ask_event,
-            q, user.id, "insufficient", "skipped", None,
-            True, classifier_result.verdict,
-            max(all_rerank_scores) if all_rerank_scores else 0.0,
-            sum(sorted(all_rerank_scores, reverse=True)[:3]),
-            [], len(evidence), 0,
-            defense_log, int(total_ms),
-            # Phase E.1 측정 필드
-            answer_length=0,
-            covered_aspects=classifier_result.covered_aspects or None,
-            missing_aspects=classifier_result.missing_aspects or None,
-            model_name=resolve_primary_model(),
-            prompt_version=ASK_PROMPT_VERSION,
-            # Phase 3.5 calibration
-            source=source,
-            eval_case_id=eval_case_id,
-        )
-        debug_obj = None
-        if debug:
-            debug_obj = AskDebug(
-                timing_ms={**pr.timing_ms, "evidence_ms": ev_ms, "ask_total_ms": total_ms},
-                search_notes=pr.notes,
-                confidence_signal=pr.confidence_signal,
-                evidence_candidate_count=len(evidence),
-                evidence_kept_count=len(evidence),
-                evidence_skip_reason=ev_skip,
-                synthesis_cache_hit=False,
-                hallucination_flags=[],
-                defense_layers=defense_log,
-            )
-        return AskResponse(
-            results=pr.results,
-            ai_answer=None,
-            citations=[],
-            synthesis_status="skipped",
-            synthesis_ms=0.0,
-            confidence=None,
-            refused=True,
-            no_results_reason=no_reason,
-            query=q,
-            total=len(pr.results),
-            completeness="insufficient",
-            covered_aspects=classifier_result.covered_aspects or None,
-            missing_aspects=classifier_result.missing_aspects or None,
-            # refusal gate 단계에서는 backend 호출 자체가 일어나지 않음 →
-            # backend_used = None. backend_requested 는 호출자 의도 표시용.
-            backend_requested=backend,
-            backend_used=None,
-            debug=debug_obj,
-        )
-
-    # 4. Synthesis (backend dispatcher 적용 — PR-MacBook-RAG-Backend-1)
-    t_synth = time.perf_counter()
-    sr = await synthesize(q, evidence, debug=debug, backend=backend)
-    synth_ms = (time.perf_counter() - t_synth) * 1000
-
-    # 4.1. backend_unavailable → 503 fail-fast (자동 fallback 금지)
-    # 명시 opt-in backend (예: qwen-macbook) 가 비가용일 때만 발생. /ask wrapper 는
-    # 절대 다른 backend 로 재시도하지 않음. 사용자가 backend 인자 제거 또는 wake 후 재시도.
-    if sr.status == "backend_unavailable":
-        backend_requested_val = backend or "gemma-macmini"
-        total_ms = (time.perf_counter() - t_total) * 1000
-        logger.warning(
-            "ask backend_unavailable backend=%s query=%r total_ms=%.0f flags=%s",
-            backend_requested_val, q[:80], total_ms,
-            ",".join(sr.hallucination_flags) if sr.hallucination_flags else "-",
-        )
-        # error_reason 명명 — macbook_unavailable 만 정착 (자동 fallback 부재).
-        error_reason = (
-            "macbook_unavailable"
-            if backend_requested_val == "qwen-macbook"
-            else "backend_unavailable"
-        )
-        # telemetry — search 만 기록 (ask_events 는 200 응답 path 전용)
-        background_tasks.add_task(
-            record_search_event, q, user.id, pr.results, "hybrid",
-            pr.confidence_signal, pr.analyzer_confidence,
-        )
-        return JSONResponse(
-            status_code=503,
-            content={
-                "error": "backend_unavailable",
-                "error_reason": error_reason,
-                "backend_requested": backend_requested_val,
-                "backend_used": None,
-                "query": q,
-                "detail": (
-                    "명시 선택한 backend 가 일시적으로 응답할 수 없습니다. "
-                    "MacBook 깨우거나 backend 인자를 제거하고 (기본 Gemma) 다시 호출하세요."
-                ),
-            },
-        )
-
-    # 5. Grounding check + Verifier (조건부 병렬) + re-gate (Phase 3.5b)
-    grounding = grounding_check(q, sr.answer or "", evidence)
-
-    # verifier skip: grounding strong 2+ OR retrieval 자체가 망함
-    grounding_only_strong = [
-        f for f in grounding.strong_flags if not f.startswith("verifier_")
-    ]
-    max_rerank = max(all_rerank_scores, default=0.0)
-    if len(grounding_only_strong) >= 2 or max_rerank < 0.2:
-        verifier_result = VerifierResult("skipped", [], 0.0)
-    else:
-        verifier_task = asyncio.create_task(
-            verify(q, sr.answer or "", evidence)
-        )
-        # 2026-05-17 B-3: 4s outer wait_for 가 verifier_service LLM_TIMEOUT_MS (10s) 를 override
-        # → classifier 와 동일 패턴 (search.py:522 가 6s→15s swap 했던 case). 10s 로 align.
-        try:
-            verifier_result = await asyncio.wait_for(verifier_task, timeout=10.0)
-        except asyncio.CancelledError:
-            raise  # 요청 취소는 전파 — broad except 가 삼키지 않게 명시 (R3)
-        except Exception:
-            verifier_result = VerifierResult("timeout", [], 0.0)
-
-    # Verifier contradictions → grounding flags 머지 (prefix 로 구분, severity 3단계)
-    for c in verifier_result.contradictions:
-        if c.severity == "strong":
-            grounding.strong_flags.append(f"verifier_{c.type}:{c.claim[:30]}")
-        elif c.severity == "medium":
-            grounding.weak_flags.append(f"verifier_{c.type}_medium:{c.claim[:30]}")
-        else:
-            grounding.weak_flags.append(f"verifier_{c.type}:{c.claim[:30]}")
-
-    defense_log["evidence"] = {
-        "skip_reason": ev_skip,
-        "kept_count": len(evidence),
-    }
-    defense_log["grounding"] = {
-        "strong": grounding.strong_flags,
-        "weak": grounding.weak_flags,
-    }
-    defense_log["verifier"] = {
-        "status": verifier_result.status,
-        "contradictions_count": len(verifier_result.contradictions),
-        "strong_count": sum(1 for c in verifier_result.contradictions if c.severity == "strong"),
-        "medium_count": sum(1 for c in verifier_result.contradictions if c.severity == "medium"),
-        "elapsed_ms": verifier_result.elapsed_ms,
-    }
-
-    # ── Re-gate: 7-tier completeness 결정 (Phase 3.5 B2 — Tier 4 신규 삽입, 재번호) ──
-    # 기존 6-tier (3.5b 4차 리뷰) + Tier 4(g_strong + v_strong_numeric + low_conf → refuse).
-    # 호환성: defense_layers["re_gate"] 의 string literal 들은 기존 그대로 유지.
-    # 신규 "refuse(grounding+verifier_numeric)" 만 추가.
-    completeness: Literal["full", "partial", "insufficient"] = "full"
-    covered_aspects = classifier_result.covered_aspects or None
-    missing_aspects = classifier_result.missing_aspects or None
-    confirmed_items: list[ConfirmedItem] | None = None
-
-    # verifier/grounding strong 구분
-    g_strong = [f for f in grounding.strong_flags if not f.startswith("verifier_")]
-    v_strong = [f for f in grounding.strong_flags if f.startswith("verifier_")]
-    v_medium = [f for f in grounding.weak_flags if f.startswith("verifier_") and "_medium:" in f]
-    has_direct_negation = any("direct_negation" in f for f in v_strong)
-    # Phase 3.5 B2: verifier strong flags 중 numeric_conflict 만 카운트.
-    # promote(VERIFIER_NUMERIC_PROMOTE=1) 활성 시 critical numeric_conflict 가 strong 으로 승격되며
-    # 여기 카운트에 잡힘. promote off 면 항상 0 → Tier 4 활성 안 됨 (기존 동작 유지).
-    v_strong_numeric = sum(
-        1 for f in v_strong if f.startswith("verifier_numeric_conflict")
-    )
-
-    # ── Tier 0 (Phase 3.5 fix3): synthesis 자체 실패 처리 ──
-    # LLM self-refuse, 메커니즘 실패(timeout/parse_failed/llm_error), answer 공백.
-    # 빈 답에 대해 grounding/verifier flag 가 0건이라 기존 체인이 "else clean" 으로 빠지며
-    # completeness="full" 초기값이 보존되던 모순을 여기서 일관되게 차단.
-    # 과거 baseline(v1-400char) 에서 20(self-refuse)+4(timeout) = 24/223 (10.8%) 해당.
-    tier0_label = _detect_synthesis_failure(sr)
-    if tier0_label:
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = tier0_label
-    elif len(g_strong) >= 2:
-        # Tier 1: grounding strong 2+ → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding_2+strong)"
-    elif g_strong and has_direct_negation:
-        # Tier 2: grounding strong + verifier direct_negation → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+direct_negation)"
-    elif g_strong and sr.confidence == "low" and max_rerank < 0.25:
-        # Tier 3: grounding strong 1 + (low confidence AND weak evidence) → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+low_conf+weak_ev)"
-    elif g_strong and v_strong_numeric >= 1 and sr.confidence == "low":
-        # Tier 4 (B2 신규): grounding strong + verifier numeric_conflict strong + low conf → refuse.
-        # verifier strong 단독 refuse 금지 원칙 유지 — g_strong 교차 필수.
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+verifier_numeric)"
-    elif g_strong or has_direct_negation:
-        # Tier 5 (기존 4): grounding strong 1 또는 verifier direct_negation 단독 → partial
-        completeness = "partial"
-        sr.confidence = "low"
-        defense_log["re_gate"] = "partial(strong_or_negation)"
-    elif v_medium:
-        # Tier 6 (기존 5): verifier medium 누적 → count 기반 confidence 하향
-        medium_count = len(v_medium)
-        if medium_count >= 3:
-            sr.confidence = "low"
-            defense_log["re_gate"] = f"conf_low(medium_x{medium_count})"
-        elif medium_count == 2 and sr.confidence == "high":
-            sr.confidence = "medium"
-            defense_log["re_gate"] = "conf_cap_medium(medium_x2)"
-        else:
-            defense_log["re_gate"] = f"medium_x{medium_count}(no_action)"
-    elif grounding.weak_flags:
-        # Tier 7 (기존 6): weak → confidence 한 단계 하향
-        if sr.confidence == "high":
-            sr.confidence = "medium"
-        defense_log["re_gate"] = "conf_lower(weak)"
-    else:
-        defense_log["re_gate"] = "clean"
-
-    # Confidence cap from refusal gate (classifier 부재 시 conservative)
-    if decision.confidence_cap and sr.confidence:
-        conf_rank = {"low": 0, "medium": 1, "high": 2}
-        if conf_rank.get(sr.confidence, 0) > conf_rank.get(decision.confidence_cap, 2):
-            sr.confidence = decision.confidence_cap
-
-    # Partial 이면 max confidence = medium
-    if completeness == "partial" and sr.confidence == "high":
-        sr.confidence = "medium"
-
-    sr.hallucination_flags.extend(
-        [f"strong:{f}" for f in grounding.strong_flags]
-        + [f"weak:{f}" for f in grounding.weak_flags]
-    )
-
-    total_ms = (time.perf_counter() - t_total) * 1000
-
-    # 6. 응답 구성
-    citations = _build_citations(evidence, sr.used_citations)
-    no_reason = _map_no_results_reason(pr, evidence, ev_skip, sr)
-    if completeness == "insufficient" and not no_reason:
-        # Tier 0 경로: synthesis self-refuse 는 LLM 이 준 사유가 가장 정확.
-        if sr.refused and sr.refuse_reason:
-            no_reason = sr.refuse_reason
-        else:
-            no_reason = "답변 검증에서 복수 오류 감지"
-
-    logger.info(
-        "ask query=%r results=%d evidence=%d cite=%d synth=%s conf=%s completeness=%s "
-        "refused=%s grounding_strong=%d grounding_weak=%d ev_ms=%.0f synth_ms=%.0f total=%.0f",
-        q[:80], len(pr.results), len(evidence), len(citations),
-        sr.status, sr.confidence or "-", completeness,
-        sr.refused, len(grounding.strong_flags), len(grounding.weak_flags),
-        ev_ms, synth_ms, total_ms,
-    )
-
-    # 7. telemetry — search + ask_events 두 경로 동시
-    background_tasks.add_task(
-        record_search_event, q, user.id, pr.results, "hybrid",
-        pr.confidence_signal, pr.analyzer_confidence,
-    )
-    # input_snapshot (디버깅/재현용)
-    defense_log["input_snapshot"] = {
-        "query": q,
-        "top_chunks_preview": [
-            {"title": (r.title or "")[:50], "snippet": (r.snippet or "")[:100]}
-            for r in pr.results[:3]
-        ],
-        "answer_preview": (sr.answer or "")[:200],
-    }
-    background_tasks.add_task(
-        record_ask_event,
-        q, user.id, completeness, sr.status, sr.confidence,
-        sr.refused, classifier_result.verdict,
-        max(all_rerank_scores) if all_rerank_scores else 0.0,
-        sum(sorted(all_rerank_scores, reverse=True)[:3]),
-        sr.hallucination_flags, len(evidence), len(citations),
-        defense_log, int(total_ms),
-        # Phase E.1 측정 필드
-        answer_length=len(sr.answer or ""),
-        covered_aspects=covered_aspects,
-        missing_aspects=missing_aspects,
-        model_name=resolve_primary_model(),
-        prompt_version=ASK_PROMPT_VERSION,
-        # Phase 3.5 calibration
-        source=source,
-        eval_case_id=eval_case_id,
-    )
-
-    debug_obj = None
-    if debug:
-        timing = dict(pr.timing_ms)
-        timing["evidence_ms"] = ev_ms
-        timing["synthesis_ms"] = synth_ms
-        timing["ask_total_ms"] = total_ms
-        debug_obj = AskDebug(
-            timing_ms=timing,
-            search_notes=pr.notes,
-            query_analysis=pr.query_analysis,
-            confidence_signal=pr.confidence_signal,
-            evidence_candidate_count=len(evidence),
-            evidence_kept_count=len(evidence),
-            evidence_skip_reason=ev_skip,
-            synthesis_cache_hit=sr.cache_hit,
-            synthesis_raw_preview=sr.raw_preview,
-            hallucination_flags=sr.hallucination_flags,
-            defense_layers=defense_log,
-        )
-
-    # backend_used: synthesize 가 실제 호출한 backend (backend 인자 그대로 신뢰 OK —
-    # backend_unavailable 은 위 503 분기에서 이미 return 됨).
-    backend_used_val = backend or "gemma-macmini"
-
-    return AskResponse(
-        results=pr.results,
-        ai_answer=sr.answer,
-        citations=citations,
-        synthesis_status=sr.status,
-        synthesis_ms=sr.elapsed_ms,
-        confidence=sr.confidence,
-        refused=sr.refused,
-        no_results_reason=no_reason,
-        query=q,
-        total=len(pr.results),
-        completeness=completeness,
-        covered_aspects=covered_aspects,
-        missing_aspects=missing_aspects,
-        confirmed_items=confirmed_items,
-        backend_requested=backend,
-        backend_used=backend_used_val,
-        debug=debug_obj,
-    )
-
-
-# ─── PR-DocSrv-Ask-ToolCalling-ReAct-1 ────────────────────────────────────
-# /api/search/ask/react — Qwen native tool calling 로 ReAct loop.
-# 본 endpoint 는 qwen-macbook only (endpoint 자체가 implicit opt-in).
-# MacBook unavailable 시 503 + error_reason=macbook_unavailable. Gemma 자동 fallback X.
-# G0-2 counter semantics: max_tool_rounds=2, max LLM calls=3, search exec ≤ 2.
-# G0-3 trace exposure: default response 의 debug_trace=None, debug=True 시만 채움.
-
-
-class AskReactRequest(BaseModel):
-    query: str
-    debug: bool = False
-
-
-class AskReactResponse(BaseModel):
-    final_answer: str
-    iterations: int
-    partial: bool
-    sources: list[dict]
-    debug_trace: list[dict] | None = None
-
-
-@router.post("/ask/react", response_model=AskReactResponse)
-async def ask_react(
-    payload: AskReactRequest,
-    user: Annotated[User, Depends(get_current_user)],
-    session: Annotated[AsyncSession, Depends(get_session)],
-):
-    """ReAct loop endpoint (qwen-macbook only, no fallback).
-
-    호출자가 명시 opt-in 한 endpoint. MacBook 가 sleep / unreachable / 5xx 시
-    HTTP 503 + body `{error_reason: "macbook_unavailable", backend: "qwen-macbook"}`
-    를 반환한다. Gemma Mac mini 로 자동 fallback 하지 않는다 (정정 4 의 연장).
-
-    request body:
-      - query: str (사용자 원본 질의)
-      - debug: bool (default false; true 시 응답 `debug_trace` 채움)
-
-    response body (성공 200):
-      - final_answer: str (Qwen 종합문, partial 일 수 있음)
-      - iterations: int (실제 진행된 tool round 수)
-      - partial: bool (max_tool_rounds 도달 후 LLM content 비었을 때 true)
-      - sources: list[dict] (검색에서 모인 evidence 메타, id-기준 dedup)
-      - debug_trace: list[dict] | null (debug=true 시 round 별 trace)
-    """
-    # 지연 import — 순환 의존성 회피 (react_loop 가 api.search.SearchResult 사용 안 함)
-    from services.llm.backends import BackendUnavailable, get_backend
-    from services.search.react_loop import agentic_ask_loop
-
-    backend_inst = get_backend("qwen-macbook")
-    # PR-2 of DS AI routing policy: backend_inst may be RouterBackend (default)
-    # or QwenMacBookBackend (DS_BACKENDS_VIA_ROUTER=false rollback). Both
-    # implement generate_with_tools so the ReAct loop is identical.
-    assert hasattr(backend_inst, "generate_with_tools")
-
-    try:
-        result = await agentic_ask_loop(
-            session,
-            payload.query,
-            backend=backend_inst,
-            debug=payload.debug,
-        )
-    except BackendUnavailable as exc:
-        logger.warning(
-            "ask_react backend unavailable backend=%s reason=%s",
-            exc.backend_name, exc.reason,
-        )
-        return JSONResponse(
-            status_code=503,
-            content={
-                "error_reason": "macbook_unavailable",
-                "backend_requested": "qwen-macbook",
-                "backend_used": None,
-                "detail": exc.reason,
-            },
-        )
-
-    return AskReactResponse(
-        final_answer=result.final_answer,
-        iterations=result.iterations,
-        partial=result.partial,
-        sources=result.sources,
-        debug_trace=result.debug_trace,
-    )

app/api/study_cards.py

@@ -21,12 +21,14 @@ from sqlalchemy.exc import IntegrityError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from core.auth import get_current_user
+from core.config import settings
 from core.database import get_session
 from models.study_memo_card import StudyMemoCard, StudyMemoCardEvidence, record_card_view
 from models.study_memo_card_progress import StudyMemoCardProgress, rate_card
 from models.study_question import StudyQuestion
 from models.user import User
 from services.study.card_normalize import compute_dedup_hash
+from services.study.publish_enqueue import enqueue_card_progress_publish, enqueue_card_publish
 
 router = APIRouter()
 
@@ -248,9 +250,18 @@ async def approve_batch(
             StudyMemoCard.needs_review,
         )
         .values(needs_review=False, flagged_by=None, flagged_at=None)
+        .returning(StudyMemoCard.id)
     )
+    approved_ids = list(result.scalars().all())
+    # 방금 검수완료된 카드 발행(같은 tx, flag off 면 no-op). S-2.
+    if settings.study_publish_enabled and approved_ids:
+        cards = (
+            await session.execute(select(StudyMemoCard).where(StudyMemoCard.id.in_(approved_ids)))
+        ).scalars().all()
+        for c in cards:
+            await enqueue_card_publish(session, c)
     await session.commit()
-    return {"approved": result.rowcount or 0}
+    return {"approved": len(approved_ids)}
 
 
 # ─── 복습(SR) 트랙 ───
@@ -310,6 +321,9 @@ async def rate(
     if outcome is None:
         raise HTTPException(status_code=422, detail=f"invalid outcome: {body.outcome!r}")
     progress = await rate_card(session, card=card, outcome=outcome, now=datetime.now(timezone.utc))
+    # 카드 SR 상태 발행(같은 tx, flag off=no-op) — ALL row(sentinel/terminal 포함). S-4.
+    if settings.study_publish_enabled:
+        await enqueue_card_progress_publish(session, progress)
     await session.commit()
     return RateResult(
         card_id=card.id, outcome=outcome, review_stage=progress.review_stage, due_at=progress.due_at
@@ -392,6 +406,9 @@ async def update_card(
             card.flagged_by = None
             card.flagged_at = None
 
+    # 발행 재투영/tombstone(같은 tx) — 검수완료=발행·검수대기복귀=tombstone(상태 기반). S-2.
+    if settings.study_publish_enabled:
+        await enqueue_card_publish(session, card)
     try:
         await session.commit()
     except IntegrityError:
@@ -414,4 +431,7 @@ async def delete_card(
     card = await session.get(StudyMemoCard, card_id)
     card = _verify_card(card, user)
     card.deleted_at = datetime.now(timezone.utc)
+    # 발행 tombstone(같은 tx) — 삭제는 feed 1급 이벤트. S-2.
+    if settings.study_publish_enabled:
+        await enqueue_card_publish(session, card)
     await session.commit()

app/api/study_concepts.py

@@ -0,0 +1,94 @@
+"""study_concepts API — 이론공부 홈(오늘의 개념 · 진도 · 회독 SR). prefix = /api/study.
+
+문제풀이 표면 무접촉. 개념문서(가스기사 태그) 읽기 집계 + 회독 SR write 만. 단일 토픽(가스기사=4).
+경로: GET /curriculum · GET /today-concepts · POST /concepts/{doc_id}/read.
+"""
+
+from __future__ import annotations
+
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from core.auth import get_current_user
+from core.database import get_session
+from models.user import User
+from services.study import concept_curriculum as cc
+from services.study import concept_links as cl
+
+router = APIRouter()
+
+# 가스기사 단일 토픽 운영(현행). 다토픽 확장 시 쿼리 파라미터로 승격.
+DEFAULT_TOPIC_ID = 4
+
+
+@router.get("/curriculum")
+async def get_curriculum(
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    topic_id: int = DEFAULT_TOPIC_ID,
+):
+    """과목별 회독 진도 + 개념/문항 복습 due 요약."""
+    return await cc.curriculum(session, user.id, topic_id)
+
+
+@router.get("/today-concepts")
+async def get_today_concepts(
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    topic_id: int = DEFAULT_TOPIC_ID,
+    limit: int = 6,
+):
+    """오늘 공부할 개념(재복습 → 미독 빈출순)."""
+    return await cc.today_concepts(session, user.id, topic_id, limit)
+
+
+@router.get("/concepts/weakness-map")
+async def get_weakness_map(
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    topic_id: int = DEFAULT_TOPIC_ID,
+    limit: int = 12,
+):
+    """개념 약점 지도 — 링크된 기출 정답률로 약점 개념(정답률<60%) 우선(이론↔문제)."""
+    name = await cc._topic_name(session, topic_id)
+    if not name:
+        return {"weak": [], "weak_total": 0, "evaluated_total": 0}
+    return await cl.weakness_map(session, user.id, name, limit)
+
+
+@router.get("/concepts/{doc_id}")
+async def get_concept_detail(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    topic_id: int = DEFAULT_TOPIC_ID,
+):
+    """개념 리더 재료 — 구조 파싱(요약/본문/빈출/관련) + 백링크 해소 + 회독/SR + 이전/다음."""
+    detail = await cc.concept_detail(session, user.id, topic_id, doc_id)
+    if detail is None:
+        raise HTTPException(status_code=404, detail="concept not found")
+    return detail
+
+
+@router.get("/concepts/{doc_id}/questions")
+async def get_concept_questions(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    limit: int = 20,
+):
+    """개념 관련 기출 + 내 정답률 (이론↔문제 브리지)."""
+    return await cl.related_questions(session, user.id, doc_id, limit)
+
+
+@router.post("/concepts/{doc_id}/read")
+async def post_concept_read(
+    doc_id: int,
+    user: Annotated[User, Depends(get_current_user)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    topic_id: int = DEFAULT_TOPIC_ID,
+):
+    """개념 회독 처리 → 회독 플래그 + SR 입고/전진."""
+    return await cc.mark_read(session, user.id, topic_id, doc_id)

app/api/study_questions.py

@@ -39,6 +39,9 @@ from services.study.explanation_rag import (
     gather_explanation_context,
     render_evidence_block,
 )
+from services.study.publish_enqueue import enqueue_publish, enqueue_question_publish
+from services.study.publish_projection import KIND_CARD, KIND_EXPLANATION, KIND_QUESTION
+from services.study.outcome import derive_outcome
 
 logger = logging.getLogger(__name__)
 router = APIRouter()
@@ -543,6 +546,9 @@ async def create_question_in_topic(
     )
     session.add(q)
     await session.flush()
+    # 발행 outbox 적재(같은 tx, flag off 면 no-op) — 신규 문항 발행. P0-1b.
+    if settings.study_publish_enabled:
+        await enqueue_question_publish(session, q)
     await session.commit()
 
     stats = QuestionAttemptStats(attempt_count=0, correct_count=0, wrong_count=0)
@@ -905,9 +911,16 @@ async def update_question(
     # 카드는 '구' ai_explanation 에서 추출됐으므로 정정 후 stale 가능 — 즉시 가시화 플래그.
     # 최종 stale 정리는 card_extract 워커의 supersede 가 책임(새 버전 추출 시 구버전 retire).
     if AI_STALE_TRIGGER & fields_set:
-        await flag_cards_for_source(session, source_question_id=q.id, reason="source_changed")
+        flagged_card_ids = await flag_cards_for_source(session, source_question_id=q.id, reason="source_changed")
+        # 발행 자격 잃은(검수대기 복귀) 파생 카드 tombstone(같은 tx). S-2.
+        if settings.study_publish_enabled:
+            for cid in flagged_card_ids:
+                await enqueue_publish(session, kind=KIND_CARD, source_id=cid, payload=None, deleted=True)
 
     q.updated_at = datetime.now(timezone.utc)
+    # 발행 재투영(같은 tx) — 문항 갱신 반영. 해설은 ready 일 때만 동봉, stale→tombstone 은 P1-3. P0-1b.
+    if settings.study_publish_enabled:
+        await enqueue_question_publish(session, q)
     await session.commit()
 
     stats = await _attempt_stats(session, user.id, question_id)
@@ -970,7 +983,16 @@ async def soft_delete_question(
     )
     # 공부 암기노트: 소스 문제 삭제 시 파생 암기카드를 검토 대기로 마킹(source_deleted).
     # study_questions 는 soft-delete 만이라 카드 FK CASCADE 는 미발동 — 이 훅이 실 경로.
-    await flag_cards_for_source(session, source_question_id=q.id, reason="source_deleted")
+    flagged_card_ids = await flag_cards_for_source(session, source_question_id=q.id, reason="source_deleted")
+    # 발행 자격 잃은 파생 카드 tombstone(같은 tx). S-2.
+    if settings.study_publish_enabled:
+        for cid in flagged_card_ids:
+            await enqueue_publish(session, kind=KIND_CARD, source_id=cid, payload=None, deleted=True)
+    # 발행 tombstone(같은 tx) — 삭제는 feed 1급 이벤트(raw DELETE 금지·워커 경유). 해설 본문 있으면 그 kind 도. P0-1b.
+    if settings.study_publish_enabled:
+        await enqueue_publish(session, kind=KIND_QUESTION, source_id=q.id, payload=None, deleted=True)
+        if q.ai_explanation:
+            await enqueue_publish(session, kind=KIND_EXPLANATION, source_id=q.id, payload=None, deleted=True)
     await session.commit()
 
 
@@ -992,19 +1014,13 @@ async def submit_attempt(
     q = await session.get(StudyQuestion, question_id)
     q = _verify_question_ownership(q, user)
 
-    if body.is_unsure:
-        selected = None
-        is_correct = False
-        outcome = "unsure"
-    elif body.selected_choice is None:
-        raise HTTPException(
-            status_code=422,
-            detail="selected_choice (1~4) 또는 is_unsure=true 가 필요합니다",
+    # 채점 단일 소스 — 뷰어 ingest 와 동일 함수(P2). 선택 없고 unsure 아니면 422.
+    try:
+        selected, is_correct, outcome = derive_outcome(
+            body.selected_choice, body.is_unsure, q.correct_choice
         )
-    else:
-        selected = body.selected_choice
-        is_correct = selected == q.correct_choice
-        outcome = "correct" if is_correct else "wrong"
+    except ValueError as e:
+        raise HTTPException(status_code=422, detail=str(e))
 
     # PR-10: 세션 연동. 기본은 None.
     quiz_session: StudyQuizSession | None = None
@@ -1543,8 +1559,8 @@ async def delete_question_image(
 
 # ─── PR-3: AI 풀이 생성 엔드포인트 ───
 
-# MLX 호출 timeout (초). MLX gate + 26B 추론 평균 ~10s, 안전 마진.
-LLM_TIMEOUT_S = 30.0
+# 2026-06-20: config 단일소스 (구 하드코딩 30s = 빠른 Gemma 기준).
+LLM_TIMEOUT_S = settings.llm_call_timeout_s
 # 프롬프트 템플릿 lazy load
 _PROMPT_PATH = "study_question_explanation.txt"
 _prompt_cache: str | None = None
@@ -1713,6 +1729,9 @@ async def generate_ai_explanation(
     primary_name = ai_client.ai.primary.model if hasattr(ai_client.ai.primary, "model") else "primary"
     q.ai_explanation_model = f"mlx:{primary_name}"
     q.updated_at = q.ai_explanation_generated_at
+    # 발행 재투영(같은 tx) — 실시간 해설 ready → 문항+해설 발행. P0-1b.
+    if settings.study_publish_enabled:
+        await enqueue_question_publish(session, q)
     await session.commit()
 
     return AIExplanationResponse(

app/api/study_topics.py

@@ -33,6 +33,7 @@ from ai.client import AIClient, strip_thinking
 from eid.ai import EidAIClient
 from eid.compose import compose
 from core.auth import get_current_user
+from core.config import settings
 from core.database import get_session
 from core.library import LIBRARY_PREFIX, normalize_library_path
 from models.document import Document
@@ -46,6 +47,8 @@ from models.eid_study_weakness import EidStudyWeakness
 from models.eid_review_set_draft import EidReviewSetDraft
 from models.user import User
 from services.search.llm_gate import Priority, acquire_mlx_gate
+from services.study.publish_enqueue import enqueue_publish, enqueue_topic_publish
+from services.study.publish_projection import KIND_TOPIC
 from services.study.subject_note_rag import (
     SubjectNoteContext,
     gather_subject_note_context,
@@ -466,6 +469,9 @@ async def create_study_topic(
     session.add(topic)
     try:
         await session.flush()
+        # 발행 outbox 적재(같은 tx, flag off 면 no-op) — 신규 주제 발행. S-1.
+        if settings.study_publish_enabled:
+            await enqueue_topic_publish(session, topic)
         await session.commit()
     except IntegrityError:
         await session.rollback()
@@ -695,6 +701,10 @@ async def update_study_topic(
         topic.focused_at = datetime.now(timezone.utc) if body.focused else None
 
     topic.updated_at = datetime.now(timezone.utc)
+    # 발행 재투영(같은 tx) — 주제 메타 갱신 반영. payload(name·exam_round_size) 무변경(focused 등)
+    # 은 워커 (payload_hash, deleted) 디둡이 rev 안 올리고 흡수 = churn 없음. S-1.
+    if settings.study_publish_enabled:
+        await enqueue_topic_publish(session, topic)
     try:
         await session.commit()
     except IntegrityError:
@@ -770,6 +780,9 @@ async def delete_study_topic(
     )
 
     topic.deleted_at = datetime.now(timezone.utc)
+    # 발행 tombstone(같은 tx) — 삭제는 feed 1급 이벤트(raw DELETE 금지·워커 경유). S-1.
+    if settings.study_publish_enabled:
+        await enqueue_publish(session, kind=KIND_TOPIC, source_id=topic.id, payload=None, deleted=True)
     await session.commit()
 
 
@@ -1015,7 +1028,7 @@ async def detach_session_from_topic(
 
 # ─── PR-9: 분야 설명 (study_topic_subject_notes) ───
 
-SUBJECT_NOTE_TIMEOUT_S = 30.0
+SUBJECT_NOTE_TIMEOUT_S = settings.llm_call_timeout_s
 _SUBJECT_NOTE_PROMPT_PATH = "study_subject_note.txt"
 _subject_note_prompt_cache: str | None = None
 
@@ -1242,7 +1255,7 @@ async def generate_subject_note(
 # 워커(study_weakness)가 산출한 최신 eid_study_weakness 스냅샷을 '학습 진단 코치'(study overlay)
 # 로 번역. 약점/태도 '판정'은 코드 derived(스냅샷) — LLM 은 스냅샷 블록 값만 인용(환각 약점 차단).
 # compose("study_diagnosis") = persona+rules+study overlay(+{placeholder}) → 표면이 블록 substitute.
-DIAGNOSIS_TIMEOUT_S = 40.0
+DIAGNOSIS_TIMEOUT_S = settings.llm_call_timeout_s
 
 
 class StudyDiagnosisResponse(BaseModel):

app/core/auth.py

@@ -31,11 +31,11 @@ def hash_password(password: str) -> str:
     return bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()
 
 
-def create_access_token(subject: str, expires_minutes: int | None = None) -> str:
+def create_access_token(subject: str, expires_minutes: int | None = None, egress: str = "local") -> str:
     minutes = expires_minutes if expires_minutes is not None else ACCESS_TOKEN_EXPIRE_MINUTES
     now = datetime.now(timezone.utc)
     expire = now + timedelta(minutes=minutes)
-    payload = {"sub": subject, "exp": expire, "iat": int(now.timestamp()), "type": "access"}
+    payload = {"sub": subject, "exp": expire, "iat": int(now.timestamp()), "type": "access", "egress": egress}
     return jwt.encode(payload, settings.jwt_secret, algorithm=ALGORITHM)
 
 
@@ -100,6 +100,15 @@ def verify_totp(code: str, secret: str | None = None) -> bool:
     return totp.verify(code)
 
 
+async def get_egress_class(
+    credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)],
+) -> str:
+    """토큰 egress claim -> 'cloud'|'local' (갭2 cloud-egress allowlist). claim 부재=local
+    (비파괴; 기존 토큰=신뢰/로컬). 쿼리파라미터 아님 -> 호출자가 끌 수 없음(우회 차단)."""
+    payload = decode_token(credentials.credentials)
+    return (payload or {}).get("egress", "local")
+
+
 async def get_current_user(
     credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)],
     session: Annotated[AsyncSession, Depends(get_session)],

app/core/config.py

@@ -30,6 +30,11 @@ class AIModelConfig(BaseModel):
     # None = MLX/OpenAI server default. Anthropic branch 는 미적용 (별 plan 범위).
     temperature: float | None = None
     top_p: float | None = None
+    # mlx 네이티브 샘플링 — 한국어 장문 코드스위칭(CJK/라틴 누수)·반복루프 억제용.
+    # Qwen3 권장: top_k=20, repetition_penalty 1.05~1.1. None = 서버 기본값(주입 안 함).
+    # OpenAI 호환 분기(mlx)만 적용 — Anthropic 분기는 미적용(별 범위).
+    repetition_penalty: float | None = None
+    top_k: int | None = None
 
 
 class DeepSummaryBacklogConfig(BaseModel):
@@ -176,16 +181,29 @@ class Settings(BaseModel):
     digest_llm_timeout_s: int = 200
     digest_llm_attempts: int = 2
     digest_pipeline_hard_cap_s: int = 1800
+    # 2026-06-20: study/analyze 단일 primary-call 타임아웃 (구 하드코딩 30~60s = 빠른 Gemma 기준,
+    # Qwen 27B 교체 sweep 누락 → 사용자 대면 504 + 워커 영구 stuck). digest 와 동형 단일소스.
+    llm_call_timeout_s: int = 200
 
     # PR-MacMini-Derived-Worker-1: study explanation owner = Mac mini
     # GPU 측은 false 로 설정 (.env), explanation 분기 skip guard 트리거.
     study_explanation_enabled: bool = True
     # 공부 암기노트 Phase 1: card_extract 폴러/consumer 게이트. owner 분리 시 false 로.
     study_card_extract_enabled: bool = True
+    # 발행 레이어(docsrv-viewer-publish): publish_outbox 워커 게이트. 저자/4-A enqueue 결선(P0-1b) 후 true.
+    study_publish_enabled: bool = False
+    digest_publish_enabled: bool = False  # docsrv-viewer-publish P1-1 (뉴스/다이제스트 발행 feed gate)
+    maintenance_mode: bool = False  # P1-4: 점검/실험 중 = 가공현황 배너(표면 != 데이터)
+    maintenance_note: str = ""
+    # 뷰어 write-back ingest(study-to-viewer P2) 게이트. /ingest/study/attempts 활성. 기본 false=inert(503).
+    study_ingest_enabled: bool = False
 
     # internal endpoint Bearer token (Mac mini derived-worker 호출용)
     internal_worker_token: str = ""
 
+    # 뷰어↔DS 발행 채널 Bearer token (publish read API P0-2 + ingest P2). Mac mini 토큰과 분리(폭발반경 격리).
+    viewer_sync_token: str = ""
+
 
 def load_settings() -> Settings:
     """config.yaml + 환경변수에서 설정 로딩"""
@@ -193,7 +211,13 @@ def load_settings() -> Settings:
     database_url = os.getenv("DATABASE_URL", "")
     study_explanation_enabled = os.getenv("STUDY_EXPLANATION_ENABLED", "true").lower() in ("1", "true", "yes")
     study_card_extract_enabled = os.getenv("STUDY_CARD_EXTRACT_ENABLED", "true").lower() in ("1", "true", "yes")
+    study_publish_enabled = os.getenv("STUDY_PUBLISH_ENABLED", "false").lower() in ("1", "true", "yes")
+    digest_publish_enabled = os.getenv("DIGEST_PUBLISH_ENABLED", "false").lower() in ("1", "true", "yes")
+    maintenance_mode = os.getenv("MAINTENANCE_MODE", "false").lower() in ("1", "true", "yes")
+    maintenance_note = os.getenv("MAINTENANCE_NOTE", "")
+    study_ingest_enabled = os.getenv("STUDY_INGEST_ENABLED", "false").lower() in ("1", "true", "yes")
     internal_worker_token = os.getenv("INTERNAL_WORKER_TOKEN", "")
+    viewer_sync_token = os.getenv("VIEWER_SYNC_TOKEN", "")
     jwt_secret = os.getenv("JWT_SECRET", "")
     totp_secret = os.getenv("TOTP_SECRET", "")
     eval_runner_token = os.getenv("EVAL_RUNNER_TOKEN", "")
@@ -268,6 +292,7 @@ def load_settings() -> Settings:
     digest_llm_timeout_s = 200
     digest_llm_attempts = 2
     digest_pipeline_hard_cap_s = 1800
+    llm_call_timeout_s = 200
     if config_path.exists() and raw and "pipeline" in raw:
         held_raw = (raw.get("pipeline") or {}).get("held_stages") or []
         # 스칼라(문자열) 오기입 시 char-split 방지 — 단일 항목 리스트로 수용.
@@ -293,6 +318,10 @@ def load_settings() -> Settings:
             digest_pipeline_hard_cap_s = max(60, int(_pl.get("digest_pipeline_hard_cap_s", 1800)))
         except (TypeError, ValueError):
             digest_pipeline_hard_cap_s = 1800
+        try:
+            llm_call_timeout_s = max(1, int(_pl.get("llm_call_timeout_s", 200)))
+        except (TypeError, ValueError):
+            llm_call_timeout_s = 200
 
     taxonomy = raw.get("taxonomy", {}) if config_path.exists() and raw else {}
     document_types = raw.get("document_types", []) if config_path.exists() and raw else []
@@ -321,12 +350,19 @@ def load_settings() -> Settings:
         upload=upload_cfg,
         study_explanation_enabled=study_explanation_enabled,
         study_card_extract_enabled=study_card_extract_enabled,
+        study_publish_enabled=study_publish_enabled,
+        digest_publish_enabled=digest_publish_enabled,
+        maintenance_mode=maintenance_mode,
+        maintenance_note=maintenance_note,
+        study_ingest_enabled=study_ingest_enabled,
         internal_worker_token=internal_worker_token,
+        viewer_sync_token=viewer_sync_token,
         pipeline_held_stages=pipeline_held_stages,
         mlx_gate_concurrency=mlx_gate_concurrency,
         digest_llm_timeout_s=digest_llm_timeout_s,
         digest_llm_attempts=digest_llm_attempts,
         digest_pipeline_hard_cap_s=digest_pipeline_hard_cap_s,
+        llm_call_timeout_s=llm_call_timeout_s,
     )
 
 

app/core/database.py

@@ -57,12 +57,12 @@ def _parse_migration_files(migrations_dir: Path) -> list[tuple[int, str, Path]]:
 
 def _validate_sql_content(name: str, sql: str) -> None:
     """migration SQL에 BEGIN/COMMIT이 포함되어 있으면 에러 (외부 트랜잭션 깨짐 방지)"""
-    # 주석(-- ...) 라인 제거 후 검사
-    lines = [
-        line for line in sql.splitlines()
-        if not line.strip().startswith("--")
-    ]
-    stripped = "\n".join(lines).upper()
+    # 주석(전체 줄 + 인라인 `-- ...`) 제거 후 검사. ★인라인 주석을 안 지우면 설명 주석의
+    # 'commit/begin' 단어(예 365_scan_jobs 의 `-- commit 시 documents.title 로 전파`)를
+    # 트랜잭션 제어문으로 false-positive 로 잡아 fresh DB/DR 부트스트랩이 깨진다(verification
+    # 실측 2026-06). 줄별로 `--` 이후를 잘라 주석 텍스트를 검사에서 제외.
+    cleaned = [re.sub(r"--.*$", "", line) for line in sql.splitlines()]
+    stripped = "\n".join(cleaned).upper()
     for keyword in ("BEGIN", "COMMIT", "ROLLBACK"):
         # 단어 경계로 매칭 (예: BEGIN_SOMETHING은 제외)
         if re.search(rf"\b{keyword}\b", stripped):
@@ -70,6 +70,13 @@ def _validate_sql_content(name: str, sql: str) -> None:
                 f"migration {name}에 {keyword} 포함됨 — "
                 f"migration SQL에는 트랜잭션 제어문을 넣지 마세요"
             )
+    # schema_migrations 수정 금지 (runner 가 스탬프 관리) — 주석 제외(stripped) 검사.
+    # (구: _run_migrations 의 raw `"schema_migrations" in sql.lower()` 가 주석 미제외라
+    #  365 의 '-- ... schema_migrations 를 건드리지 않음' 주석을 false-positive 로 잡았음.)
+    if "SCHEMA_MIGRATIONS" in stripped:
+        raise RuntimeError(
+            f"Migration {name} must not modify schema_migrations table"
+        )
 
 
 # R1: baseline 스냅샷이 대표하는 마지막 마이그레이션 버전 (이하 버전은 baseline 에 포함).
@@ -167,16 +174,15 @@ async def _run_migrations(conn) -> None:
 
     for version, name, path in pending:
         sql = path.read_text(encoding="utf-8")
-        _validate_sql_content(name, sql)
-        if "schema_migrations" in sql.lower():
-            raise ValueError(
-                f"Migration {name} must not modify schema_migrations table"
-            )
+        _validate_sql_content(name, sql)  # BEGIN/COMMIT + schema_migrations 검사(주석 제외)
         logger.info(f"[migration] {name} 실행 중...")
-        # raw driver SQL 사용 — text() 의 :name bind parameter 해석으로
-        # SQL 주석/literal 에 콜론이 들어가면 InvalidRequestError 발생.
-        # exec_driver_sql 은 SQL 을 driver(asyncpg) 에 그대로 전달.
-        await conn.exec_driver_sql(sql)
+        # raw asyncpg simple 프로토콜로 실행 — baseline 적재(_load_baseline_if_fresh)와 동일.
+        # ★exec_driver_sql 은 prepared 프로토콜이라 multi-statement 불허("cannot insert multiple
+        #  commands into a prepared statement"). 365_scan_jobs 처럼 테이블+시드+인덱스를 한 파일에
+        #  담은 마이그(컨벤션상 1-statement 권장이나 이미 prod 적재)도 fresh DB/DR replay 되게
+        #  simple execute 사용. text() :name 콜론-binding 이슈도 동일하게 회피(raw 전달).
+        raw = await conn.get_raw_connection()
+        await raw.driver_connection.execute(sql)
         await conn.execute(
             text("INSERT INTO schema_migrations (version, name) VALUES (:v, :n)"),
             {"v": version, "n": name},

app/core/utils.py

@@ -2,6 +2,7 @@
 
 import hashlib
 import logging
+from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 
@@ -13,7 +14,9 @@ def setup_logger(name: str, log_dir: str = "logs") -> logging.Logger:
 
     if not logger.handlers:
         # 파일 핸들러
-        fh = logging.FileHandler(f"{log_dir}/{name}.log", encoding="utf-8")
+        fh = RotatingFileHandler(
+            f"{log_dir}/{name}.log", maxBytes=10 * 1024 * 1024, backupCount=3, encoding="utf-8"
+        )
         fh.setFormatter(logging.Formatter(
             "%(asctime)s [%(levelname)s] %(message)s",
             datefmt="%Y-%m-%d %H:%M:%S"

app/main.py

@@ -9,6 +9,8 @@ from sqlalchemy import func, select, text
 from api.audio import router as audio_router
 from api.internal_study import router as internal_study_router
 from api.internal_worker import router as internal_worker_router
+from api.published import router as published_router
+from api.ingest_study import router as ingest_study_router
 from api.auth import router as auth_router
 from api.briefing import router as briefing_router
 from api.config import router as config_router
@@ -31,6 +33,7 @@ from api.study_sessions import router as study_sessions_router
 from api.study_topics import router as study_topics_router
 from api.study_reminders import router as study_reminders_router
 from api.study_cards import router as study_cards_router
+from api.study_concepts import router as study_concepts_router
 from api.video import router as video_router
 from core.config import settings
 from core.database import async_session, engine, init_db
@@ -70,6 +73,7 @@ async def lifespan(app: FastAPI):
     from workers.study_session_queue_consumer import consume_study_session_queue
     from workers.study_memo_card_jobs_consumer import consume_study_memo_card_queue
     from workers.study_card_enqueue import run as study_card_enqueue_run
+    from workers.study_publish_worker import consume_publish_outbox
     from workers.study_reminder import run as study_reminder_run
     from workers.study_weakness import run as study_weakness_run
     from workers.study_question_embed_worker import (
@@ -84,6 +88,13 @@ async def lifespan(app: FastAPI):
     # 시작: DB 연결 확인
     await init_db()
 
+    # 2026-06-20: JWT_SECRET 빈값 fail-loud — credentials.env 미로드/누락 시 빈 키로 전 토큰
+    # 서명하며 부팅하던 침묵 인증붕괴 차단 (totp_secret 은 per-user 라 미가드).
+    if not settings.jwt_secret:
+        raise RuntimeError(
+            "JWT_SECRET 미설정 — 빈 키 서명 방지. credentials.env / 환경변수 확인."
+        )
+
     # NAS 마운트 확인 (NFS 미마운트 시 로컬 빈 디렉토리에 쓰는 것 방지)
     from pathlib import Path
     nas_check = Path(settings.nas_mount_path) / "PKM"
@@ -94,7 +105,12 @@ async def lifespan(app: FastAPI):
         )
 
     # APScheduler: 백그라운드 작업
-    scheduler = AsyncIOScheduler(timezone="Asia/Seoul")
+    scheduler = AsyncIOScheduler(
+        timezone="Asia/Seoul",
+        # 2026-06-20 H4: 기본 misfire_grace_time=1s 는 단일 asyncio 루프가 1초만 혼잡해도
+        # 1분 컨슈머 틱을 run time missed 로 침묵 스킵(에러·failed row 0). 45s 완화 + coalesce.
+        job_defaults={"misfire_grace_time": 45, "coalesce": True, "max_instances": 1},
+    )
     # 상시 실행
     scheduler.add_job(consume_queue, "interval", minutes=1, id="queue_consumer")
     # PR-DocSrv-Markdown-Consumer-Split-1: markdown(marker) 전용 consumer.
@@ -128,6 +144,9 @@ async def lifespan(app: FastAPI):
     # 별 테이블/별 consumer 로 기존 study queue 와 격리. settings.study_card_extract_enabled 게이트.
     scheduler.add_job(consume_study_memo_card_queue, "interval", minutes=1, id="study_memo_card_consumer")
     scheduler.add_job(study_card_enqueue_run, "interval", minutes=1, id="study_card_enqueue")
+    # 발행 레이어(docsrv-viewer-publish): publish_outbox drain → published rev 부여.
+    # study_publish_enabled=false(기본) 면 worker 내부 no-op. 단일 라이터(pg_advisory_xact_lock) max_instances=1.
+    scheduler.add_job(consume_publish_outbox, "interval", minutes=1, id="publish_outbox_consumer", max_instances=1)
     # PR-B 레거시 tier 백필 — 30분 주기로 호출되지만 KST 00:00~06:00 시간대만 실제 enqueue.
     # safety > law > manual 우선순위로 25건씩. 6720 레거시 → 야간당 ~150건 → 약 45일 소화.
     scheduler.add_job(tier_backfill_run, "interval", minutes=30, id="tier_backfill")
@@ -144,7 +163,7 @@ async def lifespan(app: FastAPI):
     scheduler.add_job(study_reminder_run, CronTrigger(hour="9,13,19", timezone=KST), id="study_reminder")
     # 이드 W3-2: 공부중 토픽 약점 derived 스냅샷 (nightly 04:30 KST, LLM 0). study_diagnosis 표면 source.
     scheduler.add_job(study_weakness_run, CronTrigger(hour=4, minute=30, timezone=KST), id="study_weakness")
-    scheduler.add_job(news_collector_run, "interval", hours=6, id="news_collector")
+    scheduler.add_job(news_collector_run, CronTrigger(hour="0,6,12,18", timezone=KST), id="news_collector")
     # crawl-24x7 A-2 안전망: fulltext 영구 실패(3회 소진) 문서를 RSS 요약 기준으로
     # 후속 enqueue (silent skip 누적 방지). 03:40 = dedup_reconcile(03:30) 직후 비충돌 슬롯.
     scheduler.add_job(fulltext_reconcile_run, CronTrigger(hour=3, minute=40, timezone=KST), id="fulltext_reconcile")
@@ -220,6 +239,8 @@ app.include_router(briefing_router, prefix="/api/briefing", tags=["briefing"])
 app.include_router(audio_router, prefix="/api/audio", tags=["audio"])
 app.include_router(internal_study_router, prefix="/internal/study", tags=["internal-study"])
 app.include_router(internal_worker_router, prefix="/internal/worker", tags=["internal-worker"])
+app.include_router(published_router, prefix="/published", tags=["published"])
+app.include_router(ingest_study_router, prefix="/ingest/study", tags=["ingest-study"])
 app.include_router(video_router, prefix="/api/video", tags=["video"])
 app.include_router(study_sessions_router, prefix="/api/study-sessions", tags=["study-sessions"])
 app.include_router(study_topics_router, prefix="/api/study-topics", tags=["study-topics"])
@@ -229,6 +250,8 @@ app.include_router(study_reminders_router, prefix="/api/study-reminders", tags=[
 app.include_router(study_cards_router, prefix="/api/study-cards", tags=["study-cards"])
 # Phase 1: 학습 진행 상태 (review-complete + review-queue). prefix=/api/study-topics 안에 정의됨.
 app.include_router(study_question_progress_router, prefix="/api", tags=["study-progress"])
+# 이론공부 홈: 오늘의 개념·진도·회독 SR (개념문서 소비 표면, 문제풀이 무접촉).
+app.include_router(study_concepts_router, prefix="/api/study", tags=["study-theory"])
 
 # TODO: Phase 5에서 추가
 # app.include_router(tasks.router, prefix="/api/tasks", tags=["tasks"])

app/models/document.py

@@ -41,6 +41,14 @@ class Document(Base):
         Integer, nullable=False, default=0, server_default="0"
     )
 
+    # G2 pre-segmentation (migration 362): 번들 PDF → N 자식 분할.
+    # presegment_role: NULL=일반 단일문서 / 'parent'=번들원본(자체 extract/embed 안 함) /
+    #   'child'=논리 하위문서(부모 file_path 공유 + bundle_page_start/end 1-based inclusive 범위).
+    # 부모-자식 관계 자체는 document_lineage(relation_type='segmented_from').
+    bundle_page_start: Mapped[int | None] = mapped_column(Integer)
+    bundle_page_end: Mapped[int | None] = mapped_column(Integer)
+    presegment_role: Mapped[str | None] = mapped_column(Text)
+
     # 2계층: 텍스트 추출
     extracted_text: Mapped[str | None] = mapped_column(Text)
     extracted_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))

app/models/document_lineage.py

@@ -0,0 +1,31 @@
+"""document_lineage 테이블 ORM — 문서 파생 관계 이력 (migration 217).
+
+G2 pre-segmentation 이 relation_type='segmented_from'(번들 → 자식) 으로 사용 (migration 363).
+이력 테이블 FK = ON DELETE RESTRICT (부모 hard delete 차단, soft delete 만 허용).
+"""
+from datetime import datetime
+
+from sqlalchemy import BigInteger, ForeignKey, Text, func
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.types import TIMESTAMP
+
+from core.database import Base
+
+
+class DocumentLineage(Base):
+    __tablename__ = "document_lineage"
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
+    source_document_id: Mapped[int] = mapped_column(
+        BigInteger, ForeignKey("documents.id", ondelete="RESTRICT"), nullable=False
+    )
+    derived_document_id: Mapped[int] = mapped_column(
+        BigInteger, ForeignKey("documents.id", ondelete="RESTRICT"), nullable=False
+    )
+    relation_type: Mapped[str] = mapped_column(Text, nullable=False)
+    # 'metadata' 는 SQLAlchemy 예약속성 → Python 속성명은 meta, DB 컬럼명은 metadata.
+    meta: Mapped[dict] = mapped_column(
+        "metadata", JSONB, nullable=False, default=dict, server_default="{}"
+    )
+    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

app/models/published.py

@@ -0,0 +1,64 @@
+"""발행 레이어 ORM (docsrv-viewer-publish) — published projection + publish_outbox.
+
+관계(relationship) 없음 = 독립 테이블, configure_mappers 무영향. 마이그 367~372.
+  published      = 뷰어가 read API(P0-2)로 당기는 render-ready projection(kind-discriminated).
+  publish_outbox = 저작/4-A 트랜잭션이 같은 tx에서 INSERT, 발행 워커가 drain 하며 rev 부여.
+
+불변식(plan study-to-viewer-slice1):
+  pub_id opaque+stable = dedup키 = progress키 / rev = 워커 커밋순 gapless(pg_advisory_lock 단일 라이터)
+  / (payload_hash, deleted) 디둡 / 삭제 = tombstone(deleted=true) / schema_version = 엔벨로프 버전.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import BigInteger, Boolean, DateTime, SmallInteger, String, Text
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from core.database import Base
+
+
+class Published(Base):
+    __tablename__ = "published"
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
+    kind: Mapped[str] = mapped_column(String(40), nullable=False)
+    source_id: Mapped[int] = mapped_column(BigInteger, nullable=False)
+    pub_id: Mapped[str] = mapped_column(Text, nullable=False)
+    payload: Mapped[dict] = mapped_column(JSONB, nullable=False)
+    payload_hash: Mapped[str] = mapped_column(Text, nullable=False)
+    schema_version: Mapped[int] = mapped_column(SmallInteger, nullable=False, default=1)
+    rev: Mapped[int] = mapped_column(BigInteger, nullable=False)
+    deleted: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.now, nullable=False
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.now, nullable=False
+    )
+
+    # UNIQUE(kind, pub_id)=mig368, UNIQUE(kind, source_id)=mig369, idx(rev)=mig370.
+
+
+class PublishOutbox(Base):
+    __tablename__ = "publish_outbox"
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
+    kind: Mapped[str] = mapped_column(String(40), nullable=False)
+    source_id: Mapped[int] = mapped_column(BigInteger, nullable=False)
+    payload: Mapped[dict] = mapped_column(JSONB, nullable=False)
+    payload_hash: Mapped[str] = mapped_column(Text, nullable=False)
+    schema_version: Mapped[int] = mapped_column(SmallInteger, nullable=False, default=1)
+    deleted: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.now, nullable=False
+    )
+    processed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+    # mig378: 행별 격리 재시도/terminal. attempts=savepoint 실패 누적, failed_at=MAX 초과 terminal
+    # (set 시 워커 select 에서 제외 → head-of-line block 방지).
+    attempts: Mapped[int] = mapped_column(SmallInteger, nullable=False, default=0)
+    failed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+
+    # 미처리 부분 인덱스 idx(id) WHERE processed_at IS NULL = mig372.

app/models/queue.py

@@ -46,9 +46,10 @@ class ProcessingQueue(Base):
         # 'stt' (audio): migration 150 / 'thumbnail' (video): queue_consumer 가 enqueue.
         # 'deep_summary' (PR-B B-1): classify_worker 가 에스컬레이션 시 enqueue.
         # 'fulltext' (crawl-24x7 A-2): migration 321 — 기사 페이지 fetch 후 본문 승격.
+        # 'presegment' (G2): migration 364 — extract 前 번들 PDF → N 자식 분할.
         # DB enum 변경은 마이그레이션이 처리하므로 create_type=False.
         Enum(
-            "extract", "classify", "summarize", "embed", "chunk", "preview",
+            "presegment", "extract", "classify", "summarize", "embed", "chunk", "preview",
             "stt", "thumbnail", "deep_summary", "markdown", "fulltext",
             name="process_stage",
             create_type=False,

app/models/study_concept_progress.py

@@ -0,0 +1,46 @@
+"""study_concept_progress — 사용자 × 개념문서 단위 간격반복(SR) 진행 (이론공부 홈).
+
+문제 SR(study_question_progress)의 개념(이론)판. '개념문서' = documents 한 건(가스기사 태그).
+회독(첫 read) → 복습 큐 진입, 이후 회독마다 sr_schedule 산술(1·3·7·14·졸업) 공용 전진.
+concept_doc_id 는 documents.id 를 가리키나 FK 미설정 — hot 테이블(documents) 락 회피(clause_study 선례).
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import BigInteger, DateTime, ForeignKey, SmallInteger, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column
+
+from core.database import Base
+
+
+class StudyConceptProgress(Base):
+    __tablename__ = "study_concept_progress"
+    __table_args__ = (
+        UniqueConstraint(
+            "user_id", "concept_doc_id", name="uq_concept_progress_user_doc"
+        ),
+    )
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
+    user_id: Mapped[int] = mapped_column(
+        BigInteger, ForeignKey("users.id", ondelete="CASCADE"), nullable=False
+    )
+    study_topic_id: Mapped[int] = mapped_column(
+        BigInteger, ForeignKey("study_topics.id", ondelete="CASCADE"), nullable=False
+    )
+    # documents.id 참조 — FK 없음(락 회피). 개념문서 삭제 시 고아 행은 read 집계에서 자연 제외.
+    concept_doc_id: Mapped[int] = mapped_column(BigInteger, nullable=False)
+
+    # 복습 큐 (sr_schedule 공용): stage 0~3 = 1·3·7·14일, 4 = 졸업(due_at NULL)
+    review_stage: Mapped[int | None] = mapped_column(SmallInteger)
+    due_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+    last_read_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.now, nullable=False
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.now, onupdate=datetime.now, nullable=False
+    )

app/models/study_memo_card.py

@@ -25,6 +25,7 @@ from sqlalchemy import (
     String,
     Text,
     func,
+    select,
     text,
     update,
 )
@@ -99,13 +100,25 @@ async def supersede_old_cards(
     *,
     source_question_id: int,
     keep_generated_at: datetime | None,
-) -> int:
+) -> list[int]:
     """같은 문제의 '다른 버전' 카드를 deleted_at 마킹(retire).
 
     새 source_generated_at 카드 적재 '전에' 호출 — 살아있는 구버전 카드가 dedup PARTIAL
     UNIQUE 로 새 추출을 막는 것을 방지(정정-후 stale 잔류 0). 같은 버전은 보존.
-    Returns: retire 된 행 수.
+    Returns: retire 되며 '발행 중이던'(needs_review=False) 카드 id 목록 — 발행 tombstone
+      대상(호출측이 enqueue). 검수 안 됐던(미발행) retire 카드는 tombstone 불요라 제외.
     """
+    # 발행 중이던 retire 대상 선캡처(update 전) — 미발행 카드 스푸리어스 tombstone 회피.
+    published_retired = (
+        await session.execute(
+            select(StudyMemoCard.id).where(
+                StudyMemoCard.source_question_id == source_question_id,
+                StudyMemoCard.deleted_at.is_(None),
+                StudyMemoCard.source_generated_at.is_distinct_from(keep_generated_at),
+                StudyMemoCard.needs_review.is_(False),
+            )
+        )
+    ).scalars().all()
     stmt = (
         update(StudyMemoCard)
         .where(
@@ -115,8 +128,8 @@ async def supersede_old_cards(
         )
         .values(deleted_at=func.now())
     )
-    result = await session.execute(stmt)
-    return result.rowcount or 0
+    await session.execute(stmt)
+    return list(published_retired)
 
 
 async def append_card(
@@ -216,13 +229,24 @@ async def flag_cards_for_source(
     *,
     source_question_id: int,
     reason: str,
-) -> int:
+) -> list[int]:
     """소스 문제 정정/삭제 시 파생 카드를 needs_review=auto 마킹(임시 플래그).
 
     최종 stale 정리는 워커 supersede 가 책임 — 이건 사용자 가시화용 즉시 플래그.
     reason: 'source_changed' | 'source_deleted'.
-    Returns: 마킹된 행 수.
+    Returns: 플래그로 '발행 자격을 잃은'(직전 needs_review=False) 카드 id 목록 — 발행
+      tombstone 대상(호출측 enqueue). 이미 검수대기였던(미발행) 카드는 제외.
     """
+    # 발행 중이던 카드 선캡처(update 전) — 플래그로 needs_review=True 가 되면 발행 자격 상실.
+    published_ids = (
+        await session.execute(
+            select(StudyMemoCard.id).where(
+                StudyMemoCard.source_question_id == source_question_id,
+                StudyMemoCard.deleted_at.is_(None),
+                StudyMemoCard.needs_review.is_(False),
+            )
+        )
+    ).scalars().all()
     stmt = (
         update(StudyMemoCard)
         .where(
@@ -231,5 +255,5 @@ async def flag_cards_for_source(
         )
         .values(needs_review=True, flagged_by=reason, flagged_at=func.now())
     )
-    result = await session.execute(stmt)
-    return result.rowcount or 0
+    await session.execute(stmt)
+    return list(published_ids)

app/models/study_quiz_session.py

@@ -50,6 +50,10 @@ class StudyQuizSession(Base):
     chronic_remaining_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
 
     finished_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+    # study-to-viewer P2: 뷰어 ingest 멱등/출처. 라이브 세션=finalized_at·client_session_uuid NULL, source='live'.
+    finalized_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))  # 멱등 마커(mig 373)
+    client_session_uuid: Mapped[str | None] = mapped_column(String(64))  # 뷰어 세션 UUID(mig 374, uq mig376)
+    source: Mapped[str] = mapped_column(String(20), nullable=False, default="live")  # live|viewer(mig 375)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), default=datetime.now, nullable=False
     )

app/prompts/classifier.txt

@@ -1,33 +0,0 @@
-You are an answerability judge. Given a query and evidence chunks, determine if the evidence can answer the query. Respond ONLY in JSON.
-
-## CALIBRATION (CRITICAL)
-- verdict=full: evidence is SUFFICIENT to answer the CORE of the query. Missing minor details does NOT make it insufficient.
-- verdict=partial: evidence covers SOME major aspects but CLEARLY MISSES others the user explicitly asked about.
-- verdict=insufficient: evidence has NO relevant information for the query, or is completely off-topic.
-
-Example: Query="제6장 주요 내용", Evidence covers 제6장 definition+scope → verdict=full (core is covered).
-Example: Query="제6장 처벌 조항", Evidence covers 제6장 definition but NOT 처벌 → verdict=partial.
-Example: Query="감귤 출하량", Evidence about 산업안전보건법 → verdict=insufficient.
-
-## Rules
-1. Your "verdict" must be based ONLY on whether the CONTENT semantically answers the query. Ignore retrieval scores for this field.
-2. "covered_aspects": query aspects that evidence covers. Korean labels for Korean queries.
-3. "missing_aspects": query aspects that evidence does NOT cover. Korean labels.
-4. Keep aspects concise (2-5 words each), non-overlapping.
-
-## Output Schema
-{
-  "verdict": "full" | "partial" | "insufficient",
-  "covered_aspects": ["aspect1"],
-  "missing_aspects": ["aspect2"],
-  "confidence": "high" | "medium" | "low"
-}
-
-## Query
-{query}
-
-## Evidence chunks:
-{chunks}
-
-## Retrieval scores (for reference only, NOT for verdict):
-[{scores}]

app/prompts/policy/p3a_short_summary.txt

@@ -1,5 +1,5 @@
 [System]
-너는 한국어 문서 태거 + 짧은 요약기다. 입력 본문을 읽고 TL;DR + 핵심 bullets + tags 만 생성한다. **상세 문단·entities 는 생성하지 않는다** (깊은 요약은 26B, entity 는 P3b 담당).
+너는 한국어 문서 태거 + 요약기다. 입력 본문을 읽고 짧은 요약(ai_summary 2~3문장) + TL;DR + 핵심 bullets + tags 를 생성한다. **여러 문단의 상세 심층요약·entities 는 생성하지 않는다** (깊은 요약은 26B, entity 는 P3b 담당).
 
 subject_description: {subject_description}
 
@@ -13,6 +13,7 @@ subject_description: {subject_description}
 - pii 감지 시 "pii" 추가 + confidence 감점.
 
 요약 규칙:
+- **ai_summary**: 2~3문장 문단. 문서의 핵심 내용·목적을 서술 (검색·표시용 요약).
 - **TL;DR**: 1문장, 최대 60자.
 - **Bullets**: 정확히 5개, 각 30~60자.
 - 본문에 없는 정보 추가 금지 (hallucination 금지).
@@ -20,6 +21,7 @@ subject_description: {subject_description}
 
 출력 (JSON only):
 {{
+  "ai_summary": "2~3문장 문단 요약",
   "tldr": "1문장 최대 60자",
   "bullets": ["...", "...", "...", "...", "..."],
   "tags": ["..."],

app/prompts/presegment_boundaries.txt

@@ -0,0 +1,41 @@
+You are a document-boundary detector. Output ONLY JSON {is_bundle, segments:[{start_page,end_page,title}]}.
+
+You are given a single PDF that may be a "bundle" — several independent logical documents
+concatenated into one file (for example: multiple laws, multiple reports, or multiple papers
+scanned together). Your job is to decide whether it is a bundle and, if so, where each logical
+document starts and ends.
+
+You receive only a compact sample per page: the page number and the first line / heading of that
+page (text may be truncated). Use these heading/first-line signals to detect where a new logical
+document begins (a new title page, a new cover, a clearly new document title, a restart of
+numbering, etc.). You do NOT receive the full text.
+
+Output rules:
+- Respond with STRICT JSON only. No prose, no markdown, no code fence.
+- Schema:
+  {
+    "is_bundle": true | false,
+    "segments": [
+      {"start_page": <int>, "end_page": <int>, "title": "<string or null>"}
+    ]
+  }
+- Page numbers are 1-based and INCLUSIVE. start_page=1 is the first page; end_page equals the last
+  page of that segment.
+- Segments MUST fully cover every page with NO gaps and NO overlaps:
+  - the first segment MUST start at page 1,
+  - each next segment MUST start exactly one page after the previous segment's end_page,
+  - the last segment MUST end at the final page (page_count).
+- Order segments by start_page ascending.
+- title = a short title for that logical document if you can infer one from its first page,
+  otherwise null.
+
+If the file is NOT a bundle (it is a single logical document), respond:
+  {"is_bundle": false, "segments": []}
+
+Be conservative: only report is_bundle=true when the heading signals clearly indicate separate
+logical documents. When unsure, return is_bundle=false.
+
+page_count: {page_count}
+
+Per-page samples (one per line, "p{n}: {first line}"):
+{page_samples}

app/prompts/verifier.txt

@@ -1,42 +0,0 @@
-You are a grounding verifier. Given an answer and its evidence sources, check if the answer contradicts or fabricates information. Respond ONLY in JSON.
-
-## Contradiction Types (IMPORTANT — severity depends on type)
-- **direct_negation** (CRITICAL): Answer directly contradicts evidence. Examples: evidence "의무" but answer "권고"; evidence "금지" but answer "허용"; negation reversal ("~해야 한다" vs "~할 필요 없다").
-- **numeric_conflict**: Answer states a number different from evidence. "50명" in evidence but "100명" in answer. Only flag if the same concept is referenced. severity=critical when the number is the CORE answered quantity (amount/count/rate/date/duration that the query asked for); severity=minor when the number is peripheral (e.g., example/footnote).
-- **intent_core_mismatch**: Answer addresses a fundamentally different topic than the query asked about.
-- **nuance**: Answer overgeneralizes or adds qualifiers not in evidence (e.g., "모든" when evidence says "일부").
-- **unsupported_claim**: Answer makes a factual claim with no basis in any evidence.
-
-## Rules
-1. Compare each claim in the answer against the cited evidence. A claim with [n] citation should be checked against evidence [n].
-2. NOT a contradiction: Paraphrasing, summarizing, or restating the same fact in different words. Korean formal/informal style (합니다/한다) differences.
-3. Numbers must match exactly after normalization (1,000 = 1000). Range values (e.g., "100~200명") satisfy any answer within range.
-4. Legal/regulatory terms must preserve original meaning (의무 ≠ 권고, 금지 ≠ 제한, 허용 ≠ 금지).
-5. Maximum 5 contradictions (most severe first: direct_negation > numeric_conflict > intent_core_mismatch > nuance > unsupported_claim).
-
-## Output Schema
-{
-  "contradictions": [
-    {
-      "type": "direct_negation" | "numeric_conflict" | "intent_core_mismatch" | "nuance" | "unsupported_claim",
-      "severity": "critical" | "minor",
-      "claim": "answer 내 해당 구절 (50자 이내)",
-      "evidence_ref": "대응 근거 내용 (50자 이내, [n] 포함)",
-      "explanation": "모순 이유 (한국어, 30자 이내)"
-    }
-  ],
-  "verdict": "clean" | "minor_issues" | "major_issues"
-}
-
-severity mapping:
-- direct_negation → "critical"
-- numeric_conflict → "critical" if the number is the CORE answered quantity, else "minor"
-- All other types → "minor"
-
-If no contradictions: {"contradictions": [], "verdict": "clean"}
-
-## Answer
-{answer}
-
-## Evidence
-{numbered_evidence}

app/services/briefing/loader.py

@@ -42,6 +42,7 @@ _NEWS_WINDOW_SQL = text(f"""
       AND d.created_at < :window_end
       AND d.embedding IS NOT NULL
       AND d.ai_summary IS NOT NULL
+      AND length(d.ai_summary) > 0
       -- 안전 자료실 B-4: licensed_restricted 발행 차단 (digest 와 동일 공유 술어, 경로 일관성)
       AND {restricted_exclude_sql("d")}
 """)
@@ -66,6 +67,7 @@ _HISTORICAL_CANDIDATES_SQL = text(f"""
       AND d.created_at < :hist_end
       AND d.embedding IS NOT NULL
       AND d.ai_summary IS NOT NULL
+      AND length(d.ai_summary) > 0
       -- 안전 자료실 B-4: licensed_restricted 발행 차단 (공유 술어)
       AND {restricted_exclude_sql("d")}
 """)

app/services/digest/loader.py

@@ -42,6 +42,7 @@ _NEWS_WINDOW_SQL = text(f"""
       AND d.created_at < :window_end
       AND d.embedding IS NOT NULL
       AND d.ai_summary IS NOT NULL
+      AND length(d.ai_summary) > 0
       -- 안전 자료실 B-4: licensed_restricted 발행 차단 (모든 경로 공유 술어 = license_filter).
       -- news 채널엔 현재 restricted 부재 = 방어적 게이트(미래 유료 news 소스 대비, 경로 누락 방지).
       AND {restricted_exclude_sql("d")}

app/services/hier_decomp/builder.py

@@ -42,6 +42,21 @@ _ENG = re.compile(
 _FENCE = re.compile(r'^\s{0,3}(```|~~~)')
 
 
+# ASME 절 식별자 (A-1): UG-79 · PG-27.4.1 · UW-11 · UCS-56 · A-69 · PFT-14
+# (대문자 1~4 + 하이픈 + 숫자[.숫자]*). _detect_heading 의 ATX 분기에서 node_type='clause' 판정에 사용.
+# 한국 법령(제N조)은 _KO_JO 가 별도 처리 — 본 패턴/정제와 무관(무회귀).
+_ASME_CLAUSE = re.compile(r'^[A-Z]{1,4}-\d+(?:\.\d+)*\b')
+
+
+def _clean_label(title: str) -> str:
+    r"""C-4: marker 가 박는 LaTeX/markdown/페이지번호 아티팩트 제거 — 절번호 패턴 매칭의 전처리 겸 표시 라벨 정제.
+    실데이터 예: '$\textbf{PG-20.1 …} \hspace{0.2cm} \textbf{(25)}$' → 'PG-20.1 …' / '(25) **A-69**' → 'A-69'.
+    노이즈 없는 제목(한국 법령·일반 ATX 등)엔 inert(무회귀)."""
+    t = re.sub(r'\\textbf|\\textit|\\mathbf|\\hspace\{[^}]*\}|[${}]|\*\*', '', title)
+    t = re.sub(r'^\s*\(\d+\)\s*', '', t)        # 선두 페이지번호 '(25) '
+    return re.sub(r'\s{2,}', ' ', t).strip()
+
+
 def _utf16_units(s: str) -> int:
     """JS 문자열 .length(= UTF-16 code unit 수) 와 동일. astral(BMP 밖)=surrogate pair=2 units.
     FE 의 `raw.length` / `out.slice(off)` 가 UTF-16 code unit 단위라 char_start 도 같은 단위여야 함.
@@ -72,7 +87,9 @@ def _detect_heading(line: str) -> tuple[int, str, str] | None:
     """(level, title, node_type) 또는 None. level 은 상대 깊이."""
     m = _ATX.match(line)
     if m:
-        return (len(m.group(1)), m.group("title").strip(), None)  # node_type 은 후처리에서
+        title = _clean_label(m.group("title").strip())  # C-4: LaTeX/md/페이지번호 정제(전처리)
+        nt = "clause" if _ASME_CLAUSE.match(title) else None  # A-1: ASME 절 식별자(UG-79 등) → clause
+        return (len(m.group(1)), title, nt)
     for pat, lvl, nt in ((_KO_JANG, 1, "chapter"), (_KO_JEOL, 2, "section"),
                           (_KO_JO, 3, "clause"), (_ENG, 1, "chapter")):
         m = pat.match(line)

app/services/search/classifier_service.py

@@ -1,156 +0,0 @@
-"""Answerability classifier (Phase 3.5a).
-
-Mac mini 26B MLX 기반 (config.yaml ai.models.classifier — PR #20 이후 triage/primary/classifier 동일 endpoint). MLX gate 밖 — evidence extraction 과 병렬 실행 (concurrent 안전성 별 검토).
-
-P1 실측 결과: ternary (full/partial/insufficient) 불안정 → **binary (sufficient/insufficient)**.
-"full" vs "partial" 구분은 grounding_check 의 intent alignment 이 담당.
-
-Classifier verdict 는 "relevant evidence 가 있나" 의 binary 판단.
-covered_aspects / missing_aspects 는 로깅용으로 유지 (refusal gate 에서 사용 안 함).
-"""
-
-from __future__ import annotations
-
-import asyncio
-import time
-from dataclasses import dataclass
-from typing import Literal
-
-from ai.client import AIClient, _load_prompt, parse_json_response
-from core.config import settings
-from core.utils import setup_logger
-
-from .llm_gate import Priority, acquire_mlx_gate
-
-logger = setup_logger("classifier")
-
-LLM_TIMEOUT_MS = 30000
-CIRCUIT_THRESHOLD = 5
-CIRCUIT_RECOVERY_SEC = 60
-
-_failure_count = 0
-_circuit_open_until: float | None = None
-
-
-@dataclass(slots=True)
-class ClassifierResult:
-    status: Literal["ok", "timeout", "error", "circuit_open", "skipped"]
-    verdict: Literal["sufficient", "insufficient"] | None
-    covered_aspects: list[str]
-    missing_aspects: list[str]
-    elapsed_ms: float
-
-
-try:
-    CLASSIFIER_PROMPT = _load_prompt("classifier.txt")
-except FileNotFoundError:
-    CLASSIFIER_PROMPT = ""
-    logger.warning("classifier.txt not found — classifier will always skip")
-
-
-def _build_input(
-    query: str,
-    top_chunks: list[dict],
-    rerank_scores: list[float],
-) -> str:
-    """Y+ input (content + scores with role separation)."""
-    chunk_block = "\n".join(
-        f"[{i+1}] title: {c.get('title','')}\n"
-        f"    section: {c.get('section','')}\n"
-        f"    snippet: {c.get('snippet','')}"
-        for i, c in enumerate(top_chunks[:3])
-    )
-    scores_str = ", ".join(f"{s:.2f}" for s in rerank_scores[:3])
-    return (
-        CLASSIFIER_PROMPT
-        .replace("{query}", query)
-        .replace("{chunks}", chunk_block)
-        .replace("{scores}", scores_str)
-    )
-
-
-async def classify(
-    query: str,
-    top_chunks: list[dict],
-    rerank_scores: list[float],
-) -> ClassifierResult:
-    """Always-on binary classifier. Parallel with evidence extraction.
-
-    Returns:
-        ClassifierResult with verdict=sufficient|insufficient.
-        Status "ok" 이 아니면 verdict=None (caller 가 fallback 처리).
-    """
-    global _failure_count, _circuit_open_until
-    t_start = time.perf_counter()
-
-    # Circuit breaker
-    if _circuit_open_until and time.time() < _circuit_open_until:
-        return ClassifierResult("circuit_open", None, [], [], 0.0)
-
-    if not CLASSIFIER_PROMPT:
-        return ClassifierResult("skipped", None, [], [], 0.0)
-
-    if not hasattr(settings.ai, "classifier") or settings.ai.classifier is None:
-        return ClassifierResult("skipped", None, [], [], 0.0)
-
-    prompt = _build_input(query, top_chunks, rerank_scores)
-    client = AIClient()
-    try:
-        # 2026-05-17: PR #20 이후 endpoint 가 Mac mini 26B → llm_gate Semaphore(1) 필수.
-        # Gate 미사용 시 classifier + evidence + synthesis 가 동시에 single-inference
-        # MLX 에 race → 거의 모두 timeout (실측: 8/10 fixture query). docstring 영구 룰:
-        # "MLX primary 호출 경로는 예외 없이 gate 획득 필수".
-        async with acquire_mlx_gate(Priority.FOREGROUND):
-            async with asyncio.timeout(LLM_TIMEOUT_MS / 1000):
-                raw = await client._request(settings.ai.classifier, prompt)
-        _failure_count = 0
-    except asyncio.TimeoutError:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"classifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("classifier timeout")
-        return ClassifierResult(
-            "timeout", None, [], [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    except Exception as e:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"classifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("classifier error: type=%s repr=%r", type(e).__name__, e)
-        return ClassifierResult(
-            "error", None, [], [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    finally:
-        await client.close()
-
-    elapsed_ms = (time.perf_counter() - t_start) * 1000
-    parsed = parse_json_response(raw)
-    if not isinstance(parsed, dict):
-        logger.warning("classifier parse failed raw=%r", (raw or "")[:200])
-        return ClassifierResult("error", None, [], [], elapsed_ms)
-
-    # ternary → binary 매핑
-    raw_verdict = parsed.get("verdict", "")
-    if raw_verdict == "insufficient":
-        verdict: Literal["sufficient", "insufficient"] | None = "insufficient"
-    elif raw_verdict in ("full", "partial", "sufficient"):
-        verdict = "sufficient"
-    else:
-        verdict = None
-
-    covered = parsed.get("covered_aspects") or []
-    missing = parsed.get("missing_aspects") or []
-    if not isinstance(covered, list):
-        covered = []
-    if not isinstance(missing, list):
-        missing = []
-
-    logger.info(
-        "classifier ok query=%r verdict=%s (raw=%s) covered=%d missing=%d elapsed_ms=%.0f",
-        query[:60], verdict, raw_verdict, len(covered), len(missing), elapsed_ms,
-    )
-    return ClassifierResult("ok", verdict, covered, missing, elapsed_ms)

app/services/search/freshness_decay.py

@@ -1,6 +1,6 @@
 """Time-aware retrieval freshness decay (PR-RAG-Time-1).
 
-뉴스(source_channel='news') / 법령 알림(source_channel='law_monitor') 도메인은
+뉴스(source_channel='news') / 재해사례(material_type='incident', KOSHA) 도메인은
 시간이 중요한 문서. 단순 relevance score 만으로는 오래된 문서가 상위에 머물러
 검색 품질이 떨어짐. 본 모듈은 reranker 이후 final score 합성 단계에서
 soft multiplier 로 시간 가중치 적용. 삭제는 없음 — ranking 만 demote.
@@ -9,9 +9,10 @@ soft multiplier 로 시간 가중치 적용. 삭제는 없음 — ranking 만 de
 - reranker = 의미 관련도, freshness decay = 운영 정책. 두 단계 분리 유지.
 - floor 0.7 (multiplier 가 0.7 미만으로 안 떨어짐) — 오래되어도 죽지 않음.
 - 일반 업로드 / 학습 자료 / KGS Code 원문 / ai_drafted 는 비적용 (no-op).
+- ★법령(law)은 C-1 후속에서 freshness 제외 — 현행성은 version_status(B-1 버전체인)가 처리.
 
 published_date 컬럼이 documents 에 없음 → created_at(수집 시점) 을 임시 proxy.
-news/law_monitor 워커가 수집 즉시 indexing 하므로 created_at ≈ published_date.
+news/KOSHA 워커가 수집 즉시 indexing 하므로 created_at ≈ published_date.
 정확도 향상은 후속 PR (worker 가 published_date 메타 채우기) 로 분리.
 """
 
@@ -32,10 +33,10 @@ if TYPE_CHECKING:
 # ─── Policy ────────────────────────────────────────────────────────
 
 # half-life (일). 90 일: 한 달 ~0.79 / 6개월 ~0.25.
-# 365 일: 1년 ~0.5 / 3년 ~0.13.
+# C-1 후속(2026-06-13): law_365d 폐기 — 법령 현행성은 version_status(B-1 버전체인)가 처리,
+#   age-decay 는 current 법령을 부당 강등(의도 변경 기록). 재해사례(incident)는 news_90d 흡수.
 HALF_LIFE_DAYS: dict[str, int] = {
     "news_90d": 90,
-    "law_365d": 365,
 }
 
 # soft multiplier — final = base * (FLOOR + (1-FLOOR) * decay).
@@ -52,32 +53,35 @@ class _DocMeta:
     source_channel: str | None
     content_origin: str | None
     created_at: datetime | None
+    material_type: str | None = None
 
 
 def freshness_policy(meta: _DocMeta | None) -> str | None:
     """문서 메타 → freshness 정책 이름 또는 None (no-op).
 
     적용:
-      - source_channel='news'        → news_90d
-      - source_channel='law_monitor' → law_365d
+      - material_type='incident' (KOSHA 재해사례/사망사고) → news_90d (C-1 후속 흡수, 시간 민감)
+      - source_channel='news'                            → news_90d
 
     비적용 (None 반환):
       - meta 자체가 None
       - content_origin='ai_drafted' (생성 시점 = 가치 시점, 시간 demote 부적합)
-      - 그 외 모든 source_channel (manual, drive_sync, inbox_route, memo,
-        Study/Manual/Reference/Academic/Checklist 류 — 자연 비적용)
+      - ★법령(source_channel='law_monitor'/material_type='law'): C-1 후속에서 law_365d 폐기.
+        법령 현행성은 version_status(B-1 버전체인 current/superseded)가 처리 — age-decay 는
+        current 법령을 부당 강등(의도 변경 기록). law 검색 ranking = version_status decorate.
+      - 그 외 모든 source_channel (manual, drive_sync, inbox_route, memo 등 — 자연 비적용)
     """
     if meta is None:
         return None
     # 가드 2: content_origin='ai_drafted' 비적용
     if meta.content_origin == "ai_drafted":
         return None
-    sc = meta.source_channel
-    if sc == "news":
+    # 재해사례/사망사고 = 시간 민감 → news 와 동일 90d (source 무관, 업로드 incident 도 포함)
+    if meta.material_type == "incident":
         return "news_90d"
-    if sc == "law_monitor":
-        return "law_365d"
-    # 가드 6: unknown source_channel → no decay
+    if meta.source_channel == "news":
+        return "news_90d"
+    # 법령 law_365d 폐기 + unknown source_channel → no decay
     return None
 
 
@@ -129,7 +133,7 @@ async def _fetch_meta(
         text(
             """
             SELECT id, source_channel::text AS source_channel,
-                   content_origin, created_at
+                   content_origin, material_type, created_at
             FROM documents
             WHERE id = ANY(:ids)
             """
@@ -141,6 +145,7 @@ async def _fetch_meta(
             source_channel=row.source_channel,
             content_origin=row.content_origin,
             created_at=row.created_at,
+            material_type=getattr(row, "material_type", None),
         )
         for row in rows
     }

app/services/search/grounding_check.py

@@ -1,505 +0,0 @@
-"""Grounding check — post-synthesis 검증 (Phase 3.5a).
-
-Strong/weak flag 분리:
-- **Strong** (→ partial 강등 or refuse): fabricated_number, intent_misalignment(important)
-- **Weak** (→ confidence lower only): uncited_claim, low_overlap, intent_misalignment(generic)
-
-Re-gate 로직 (Phase 3.5a 9라운드 토론 결과):
-- strong 1개 → partial 강등
-- strong 2개 이상 → refuse
-- weak → confidence "low" 만
-
-Intent alignment (rule-based):
-- query 의 핵심 명사가 answer 에 등장하는지 확인
-- "처벌" 같은 중요 키워드 누락은 strong
-- "주요", "관련" 같은 generic 은 무시
-"""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from core.utils import setup_logger
-
-if TYPE_CHECKING:
-    from .evidence_service import EvidenceItem
-
-logger = setup_logger("grounding")
-
-# "주요", "관련" 등 intent alignment 에서 제외할 generic 단어
-GENERIC_TERMS = frozenset({
-    "주요", "관련", "내용", "정의", "기준", "방법", "설명", "개요",
-    "대한", "위한", "대해", "무엇", "어떤", "어떻게", "있는",
-    "하는", "되는", "이런", "그런", "이것", "그것",
-})
-
-
-@dataclass(slots=True)
-class GroundingResult:
-    strong_flags: list[str]
-    weak_flags: list[str]
-
-
-_UNIT_CHARS = r'명인개%년월일조항호세건원회'
-
-# "이상/이하/초과/미만" — threshold 표현 (numeric conflict 에서 skip 대상)
-_THRESHOLD_SUFFIXES = re.compile(r'이상|이하|초과|미만')
-
-# 약칭/근사치 prefix — 매칭 전 제거 (Phase 3.5 B1).
-# ⚠ 최대/최소 는 의도적으로 제외 — 이들은 bound operator 라 의미가 다름 (Phase 3.5 B1 fix3).
-# 약/대략/거의/얼추 만 노이즈 prefix 로 strip.
-_APPROX_PREFIX_RE = re.compile(r'(약|대략|거의|얼추)\s*')
-
-# 단위 동의어 dict — 추출 직후 정규화 (Phase 3.5 B1)
-# 의미가 동일한 단위는 같은 표기로 통일해서 set 비교/range overlap 안정화.
-_UNIT_SYNONYMS: dict[str, str] = {
-    "인": "명",
-    "사람": "명",
-    "퍼센트": "%",
-    "프로": "%",
-    "KRW": "원",
-    "krw": "원",
-}
-
-# tolerance(±1%) 허용 단위 — 양적 측정값 (Phase 3.5 B1)
-_TOLERANCE_UNITS: frozenset[str] = frozenset({"명", "원", "%", "건", "개"})
-
-# tolerance 미적용 단위 — 식별자성 숫자 (연도/조문/횟수)
-_EXACT_ONLY_UNITS: frozenset[str] = frozenset({"년", "월", "일", "조", "항", "호", "회"})
-
-# 최대/최소 prefix 패턴 — bound operator (Phase 3.5 B1 fix3).
-# 매칭된 숫자는 exact pool 에서 제외하고 one-sided range 로 변환.
-# 경계값 자체는 clear 대상 아님 (Codex 권장: "최대 100명" + answer "100명" → flag 유지).
-_BOUND_PATTERN_RE = re.compile(
-    rf'(최대|최소)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)'
-)
-_RANGE_INF = 10**18  # one-sided range 상한 sentinel
-
-
-def _normalize_unit(unit: str) -> str:
-    """단위 동의어 → 대표 표기."""
-    return _UNIT_SYNONYMS.get(unit, unit)
-
-
-def _extract_unit(literal: str) -> str | None:
-    """리터럴에서 숫자 뒤 단위(한 글자 또는 동의어) 추출 + 정규화."""
-    # 천단위 콤마 + 옵션 소수 + 한글 단위 한 글자 또는 동의어
-    m = re.match(rf'[\d,.]+\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)', literal)
-    if not m:
-        return None
-    return _normalize_unit(m.group(1))
-
-
-def _extract_numeric_corpus(text: str) -> dict:
-    """단위별 숫자 + 범위 + bound 통합 추출 (Phase 3.5 B1 fix1+fix3).
-
-    Returns:
-      {
-        "exact_by_unit": {unit_or_None: set(digits)},   # 평범한 숫자 (bound 제외)
-        "ranges_by_unit": {unit: [(lo, hi), ...]},       # 양방향(A~B) + 단방향(최대/최소)
-      }
-
-    None 키는 단위 없는 bare 숫자.
-    `최대 N <unit>` → ranges[(0, N-1)] (경계값 자체는 cleared 대상 아님)
-    `최소 N <unit>` → ranges[(N+1, INF)]
-    """
-    cleaned = _APPROX_PREFIX_RE.sub('', text)
-
-    exact_by_unit: dict[str | None, set[str]] = {None: set()}
-    ranges_by_unit: dict[str, list[tuple[int, int]]] = {}
-
-    # 1) 최대/최소 — bound. exact pool 에서 제외, one-sided range 로 변환.
-    bound_spans: list[tuple[int, int]] = []  # 매칭 substring 위치 — 이후 단계에서 skip
-    for m in _BOUND_PATTERN_RE.finditer(cleaned):
-        bound_kind = m.group(1)
-        try:
-            n = int(m.group(2).replace(',', '').split('.')[0])
-        except ValueError:
-            continue
-        unit = _normalize_unit(m.group(3))
-        if bound_kind == "최대":
-            ranges_by_unit.setdefault(unit, []).append((0, max(0, n - 1)))
-        else:  # 최소
-            ranges_by_unit.setdefault(unit, []).append((n + 1, _RANGE_INF))
-        bound_spans.append((m.start(), m.end()))
-
-    def _in_bound_span(pos: int) -> bool:
-        return any(s <= pos < e for s, e in bound_spans)
-
-    # 2) 천단위 콤마 bare number
-    for m in re.finditer(r'\d{1,3}(?:,\d{3})+(?:\.\d+)?', cleaned):
-        if _in_bound_span(m.start()):
-            continue
-        exact_by_unit[None].add(m.group().replace(',', ''))
-
-    # 3) 단위 있는 숫자 (단위 동의어 포함)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)',
-        cleaned,
-    ):
-        if _in_bound_span(m.start()):
-            continue
-        digits = m.group(1).replace(',', '').split('.')[0]
-        if not digits:
-            continue
-        unit = _normalize_unit(m.group(2))
-        exact_by_unit.setdefault(unit, set()).add(digits)
-
-    # 4) 양방향 범위 표현 (A~B / A 부터 B)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로)',
-        cleaned,
-    ):
-        if _in_bound_span(m.start()):
-            continue
-        try:
-            lo = int(m.group(1).replace(',', '').split('.')[0])
-            hi = int(m.group(2).replace(',', '').split('.')[0])
-        except ValueError:
-            continue
-        unit = _normalize_unit(m.group(3))
-        ranges_by_unit.setdefault(unit, []).append((min(lo, hi), max(lo, hi)))
-
-    # 5) bare 2자리+ 단독 숫자
-    for m in re.finditer(r'\b(\d{2,})\b', cleaned):
-        if _in_bound_span(m.start()):
-            continue
-        exact_by_unit[None].add(m.group())
-
-    return {
-        "exact_by_unit": exact_by_unit,
-        "ranges_by_unit": ranges_by_unit,
-    }
-
-
-def _within_unit_range(
-    n: int, unit: str | None, ranges_by_unit: dict[str, list[tuple[int, int]]]
-) -> bool:
-    """unit-matching range 검증.
-
-    answer unit 이 None (bare 숫자) 면 보수적으로 False — bare 답변은 range clear 대상 아님.
-    """
-    if unit is None:
-        return False
-    return any(lo <= n <= hi for lo, hi in ranges_by_unit.get(unit, []))
-
-
-def _close_to_unit_pool(
-    n: int, unit: str | None, exact_by_unit: dict[str | None, set[str]], tol: float
-) -> bool:
-    """unit-matching tolerance 검증.
-
-    answer unit 이 None 이면 False — bare 답변은 tolerance 대상 아님.
-    같은 unit bucket 안의 후보만 비교.
-    """
-    if unit is None:
-        return False
-    candidates = exact_by_unit.get(unit, set())
-    for c in candidates:
-        try:
-            cn = int(c)
-        except ValueError:
-            continue
-        if cn == 0:
-            continue
-        if abs(n - cn) / cn <= tol:
-            return True
-    return False
-
-
-def _extract_number_literals(text: str) -> set[str]:
-    """숫자 + 단위 추출 + normalize (Phase 3.5 B1: 6단계 확장).
-
-    1) 약칭 prefix 제거 ("약 100명" → "100명")
-    2) 천단위 콤마 bare number 우선 ("1,000" → "1000" set 등록)
-    3) 한국어 단위 접미사 매칭 (기존)
-    4) 범위 표현 양쪽 숫자 추출 (separator: ~, -, –, 부터)
-    5) 단위 동의어 정규화 (인→명, 퍼센트→%, KRW→원)
-    6) bare 2자리+ 추출 (기존)
-    """
-    # 1. 약칭 prefix 제거 (전체 텍스트에서)
-    cleaned = _APPROX_PREFIX_RE.sub('', text)
-
-    # 2. 천단위 콤마 bare number — normalize 된 값을 set 에 선등록
-    normalized: set[str] = set()
-    for m in re.finditer(r'\d{1,3}(?:,\d{3})+(?:\.\d+)?', cleaned):
-        normalized.add(m.group().replace(',', ''))
-
-    # 3. 숫자 + 한국어 단위 접미사 (동의어 포함)
-    raw: set[str] = set(re.findall(
-        rf'\d[\d,.]*\s*(?:[{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)\w{{0,2}}',
-        cleaned,
-    ))
-
-    # 4. 범위 표현 — separator 에 "부터" 추가
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로)',
-        cleaned,
-    ):
-        unit_norm = _normalize_unit(m.group(3))
-        raw.add(m.group(1) + unit_norm)
-        raw.add(m.group(2) + unit_norm)
-
-    # 5. normalize: 단위 동의어 통일 + 콤마 제거
-    for r in raw:
-        # 단위 부분 정규화
-        m = re.match(r'([\d,.]+)\s*([^\d\s]+)', r)
-        if m:
-            digits_part = m.group(1)
-            unit_part = _normalize_unit(m.group(2))
-            normalized.add(digits_part + unit_part)
-            normalized.add(digits_part.replace(',', '') + unit_part)
-        normalized.add(r.strip())
-        num_only = re.match(r'[\d,.]+', r)
-        if num_only:
-            normalized.add(num_only.group().replace(',', ''))
-
-    # 6. 단독 숫자 (2자리+ 만)
-    for d in re.findall(r'\b(\d{2,})\b', cleaned):
-        normalized.add(d)
-    return normalized
-
-
-def _within_evidence_range(digits: str, raw: str, evidence_text: str) -> bool:
-    """evidence 에 'A~B 단위' 가 있고 answer 의 숫자가 그 범위 안이면 True.
-
-    범위 단위는 무시 (단위 비교는 호출 전 단계). digits = 정수 문자열.
-    """
-    try:
-        n = int(digits)
-    except ValueError:
-        return False
-    cleaned_ev = _APPROX_PREFIX_RE.sub('', evidence_text)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*[{_UNIT_CHARS}]',
-        cleaned_ev,
-    ):
-        try:
-            lo = int(m.group(1).replace(',', '').split('.')[0])
-            hi = int(m.group(2).replace(',', '').split('.')[0])
-            if min(lo, hi) <= n <= max(lo, hi):
-                return True
-        except ValueError:
-            continue
-    return False
-
-
-def _close_to_any(n: int, candidates: set[str], tol: float) -> bool:
-    """candidates 중 하나라도 (1±tol) 배율 안에 들어오면 True.
-
-    n 은 정수, candidates 는 digits-only 문자열 집합.
-    """
-    for c in candidates:
-        try:
-            cn = int(c)
-        except ValueError:
-            continue
-        if cn == 0:
-            continue
-        if abs(n - cn) / cn <= tol:
-            return True
-    return False
-
-
-def _extract_content_tokens(text: str) -> set[str]:
-    """한국어 2자 이상 명사 + 영어 3자 이상 단어."""
-    return set(re.findall(r'[가-힣]{2,}|[a-zA-Z]{3,}', text))
-
-
-def _parse_number_with_unit(literal: str) -> tuple[str, str] | None:
-    """숫자 리터럴에서 (digits_only, unit) 분리. 단위 없으면 None."""
-    m = re.match(rf'([\d,.]+)\s*([{_UNIT_CHARS}])', literal)
-    if not m:
-        return None
-    digits = m.group(1).replace(',', '')
-    unit = m.group(2)
-    return (digits, unit)
-
-
-def _check_evidence_numeric_conflicts(evidence: list["EvidenceItem"]) -> list[str]:
-    """evidence 간 숫자 충돌 감지 (Phase 3.5b). evidence >= 2 일 때만 활성.
-
-    동일 단위, 다른 숫자 → weak flag. "이상/이하/초과/미만" 포함 시 skip.
-    bare number 는 비교 안 함 (조항 번호 등 false positive 방지).
-    """
-    if len(evidence) < 2:
-        return []
-
-    # 각 evidence 에서 단위 있는 숫자 + threshold 여부 추출
-    # {evidence_idx: [(digits, unit, has_threshold), ...]}
-    per_evidence: dict[int, list[tuple[str, str, bool]]] = {}
-    for idx, ev in enumerate(evidence):
-        nums = re.findall(
-            rf'\d[\d,.]*\s*[{_UNIT_CHARS}]\w{{0,4}}',
-            ev.span_text,
-        )
-        entries = []
-        for raw in nums:
-            parsed = _parse_number_with_unit(raw)
-            if not parsed:
-                continue
-            has_thr = bool(_THRESHOLD_SUFFIXES.search(raw))
-            entries.append((parsed[0], parsed[1], has_thr))
-        if entries:
-            per_evidence[idx] = entries
-
-    if len(per_evidence) < 2:
-        return []
-
-    # 단위별로 evidence 간 숫자 비교
-    # {unit: {digits: [evidence_idx, ...]}}
-    unit_map: dict[str, dict[str, list[int]]] = {}
-    for idx, entries in per_evidence.items():
-        for digits, unit, has_thr in entries:
-            if has_thr:
-                continue  # threshold 표현은 skip
-            if unit not in unit_map:
-                unit_map[unit] = {}
-            if digits not in unit_map[unit]:
-                unit_map[unit][digits] = []
-            if idx not in unit_map[unit][digits]:
-                unit_map[unit][digits].append(idx)
-
-    flags: list[str] = []
-    for unit, digits_map in unit_map.items():
-        distinct_values = list(digits_map.keys())
-        if len(distinct_values) >= 2:
-            # 가장 많이 등장하는 2개 비교
-            top2 = sorted(distinct_values, key=lambda d: len(digits_map[d]), reverse=True)[:2]
-            flags.append(
-                f"evidence_numeric_conflict:{top2[0]}{unit}_vs_{top2[1]}{unit}"
-            )
-
-    return flags
-
-
-def check(
-    query: str,
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> GroundingResult:
-    """답변 vs evidence grounding 검증 + query intent alignment."""
-    strong: list[str] = []
-    weak: list[str] = []
-
-    if not answer or not evidence:
-        return GroundingResult([], [])
-
-    # ⚠ citation marker [n] 양측 제거 (대칭성 — Phase 3.5 B1)
-    evidence_text = re.sub(r'\[\d+\]', '', " ".join(e.span_text for e in evidence))
-
-    # ── Strong 1: fabricated number (unit-aware 3단계 — Phase 3.5 B1 fix1+fix3) ──
-    # Codex 지적 반영:
-    #   - fix1: range/tolerance/exact 모두 단위 일치 시에만 clear
-    #     (예: "150원" vs "100~200명" → flag 유지)
-    #   - fix3: 최대/최소 prefix 는 bound 의미 보존
-    #     (예: "최대 100명" + answer "100명" → flag 유지, "최대 100명" + answer "50명" → cleared)
-    answer_clean = re.sub(r'\[\d+\]', '', answer)
-    answer_corpus = _extract_numeric_corpus(answer_clean)
-    evidence_corpus = _extract_numeric_corpus(evidence_text)
-    ev_exact_by_unit = evidence_corpus["exact_by_unit"]
-    ev_ranges_by_unit = evidence_corpus["ranges_by_unit"]
-
-    # cleared 는 (unit, digits) 쌍 단위로 추적 — 단위 충돌 케이스 방어
-    cleared_pairs: set[tuple[str | None, str]] = set()
-
-    # Pass 1: 각 (unit, digits) 가 evidence 에서 정당화되는지 판정
-    for unit, digits_set in answer_corpus["exact_by_unit"].items():
-        for d in digits_set:
-            # 1) exact match — 같은 unit bucket 내에서만
-            if d in ev_exact_by_unit.get(unit, set()):
-                cleared_pairs.add((unit, d))
-                continue
-            # bare answer (unit=None) 는 evidence bare bucket 도 보조 매칭
-            if unit is None and d in ev_exact_by_unit.get(None, set()):
-                cleared_pairs.add((unit, d))
-                continue
-            try:
-                n = int(d)
-            except ValueError:
-                continue
-            # 2) range — same-unit 만 (bare answer 는 range clear 대상 아님)
-            if _within_unit_range(n, unit, ev_ranges_by_unit):
-                cleared_pairs.add((unit, d))
-                continue
-            # 3) ±1% tolerance — 단위가 양적(_TOLERANCE_UNITS) + 4자리+ + same-unit
-            if (
-                unit in _TOLERANCE_UNITS
-                and len(d) >= 4
-                and _close_to_unit_pool(n, unit, ev_exact_by_unit, tol=0.01)
-            ):
-                cleared_pairs.add((unit, d))
-                continue
-            # 식별자성 단위(_EXACT_ONLY_UNITS) 는 tolerance 패스 X.
-
-    # Pass 2: cleared 되지 않은 (unit, digits) 를 strong flag.
-    # 1자리 무시는 unit 이 식별자성(_EXACT_ONLY_UNITS: 년/월/일/조/항/호/회) 이 아닐 때만 적용.
-    # bare(None) 답변 숫자는 같은 digit 이 다른 unit 에서 cleared 됐으면 skip — 추출 부산물 방어.
-    # ⚠ 단위 cross-clear (예: "원" cleared → "명" 도 skip) 은 금지: Codex unit-mismatch 케이스가 깨짐.
-    unit_anchored_cleared: set[str] = {d for (u, d) in cleared_pairs if u is not None}
-    flagged_keys: set[tuple[str | None, str]] = set()
-    for unit, digits_set in answer_corpus["exact_by_unit"].items():
-        for d in digits_set:
-            if (unit, d) in cleared_pairs or (unit, d) in flagged_keys:
-                continue
-            # bare(None) 답변 숫자가 임의의 단위 bucket 에서 cleared 됐으면 duplicate 로 처리.
-            # 사례: "1,000명" → unit bucket "명" 에 1000 + bare bucket None 에 1000 (comma normalize 부산물).
-            # 이미 ("명", "1000") 가 cleared 라면 (None, "1000") 도 같은 사실을 가리키므로 skip.
-            if unit is None and d in unit_anchored_cleared:
-                continue
-            if len(d) < 2 and unit not in _EXACT_ONLY_UNITS:
-                continue
-            flagged_keys.add((unit, d))
-            # 사람이 읽기 좋게 "{digits}{unit}" 또는 bare 형태로 표기
-            label = f"{d}{unit}" if unit else d
-            strong.append(f"fabricated_number:{label}")
-
-    # ── Strong/Weak 2: query-answer intent alignment ──
-    query_content = _extract_content_tokens(query)
-    answer_content = _extract_content_tokens(answer)
-    if query_content:
-        missing_terms = query_content - answer_content
-        important_missing = [
-            t for t in missing_terms
-            if t not in GENERIC_TERMS and len(t) >= 2
-        ]
-        if important_missing:
-            strong.append(
-                f"intent_misalignment:{','.join(important_missing[:3])}"
-            )
-        elif len(missing_terms) > len(query_content) * 0.5:
-            weak.append(
-                f"intent_misalignment_generic:"
-                f"missing({','.join(list(missing_terms)[:5])})"
-            )
-
-    # ── Weak 1: uncited claim ──
-    sentences = re.split(r'(?<=[.!?。])\s+', answer)
-    for s in sentences:
-        if len(s.strip()) > 20 and not re.search(r'\[\d+\]', s):
-            weak.append(f"uncited_claim:{s[:40]}")
-
-    # ── Weak: evidence 간 숫자 충돌 (Phase 3.5b) ──
-    conflicts = _check_evidence_numeric_conflicts(evidence)
-    weak.extend(conflicts)
-
-    # ── Weak 2: token overlap ──
-    answer_tokens = _extract_content_tokens(answer)
-    evidence_tokens = _extract_content_tokens(evidence_text)
-    if answer_tokens:
-        overlap = len(answer_tokens & evidence_tokens) / len(answer_tokens)
-        if overlap < 0.4:
-            weak.append(f"low_overlap:{overlap:.2f}")
-
-    if strong or weak:
-        logger.info(
-            "grounding query=%r strong=%d weak=%d flags=%s",
-            query[:60],
-            len(strong),
-            len(weak),
-            ",".join(strong[:3] + weak[:3]),
-        )
-
-    return GroundingResult(strong, weak)

app/services/search/refusal_gate.py

@@ -1,105 +0,0 @@
-"""Refusal gate — multi-signal fusion (Phase 3.5a).
-
-Score gate (deterministic) + classifier verdict (semantic, binary) 를 독립 평가 후 합성.
-Classifier 부재 시 3-tier conservative fallback.
-
-P1 실측 결과: exaone ternary 불안정 → binary (sufficient/insufficient) 로 축소.
-"full" vs "partial" 구분은 grounding check (intent alignment) 가 담당.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal
-
-from core.utils import setup_logger
-
-if TYPE_CHECKING:
-    from .classifier_service import ClassifierResult
-
-logger = setup_logger("refusal_gate")
-
-# Placeholder thresholds — Phase 3.5b 에서 실측 기반 tuning
-# AND 조건이라 false refusal 방어됨 (둘 다 만족해야 refuse)
-SCORE_MAX_REFUSE = 0.25
-SCORE_AGG_REFUSE = 0.70
-
-# Conservative fallback tiers (classifier 부재 시)
-CONSERVATIVE_WEAK = 0.35
-CONSERVATIVE_MID = 0.55
-
-
-@dataclass(slots=True)
-class RefusalDecision:
-    refused: bool
-    confidence_cap: Literal["high", "medium", "low"] | None  # None = no cap
-    rule_triggered: str | None  # 디버깅: 어느 signal 이 결정에 기여?
-
-
-def decide(
-    rerank_scores: list[float],
-    classifier: ClassifierResult | None,
-) -> RefusalDecision:
-    """Multi-signal fusion. Binary classifier verdict 기반.
-
-    Returns:
-        RefusalDecision. refused=True 이면 synthesis skip.
-        confidence_cap 은 synthesis 결과의 confidence 에 upper bound 적용.
-    """
-    max_score = max(rerank_scores) if rerank_scores else 0.0
-    agg_top3 = sum(sorted(rerank_scores, reverse=True)[:3])
-
-    score_gate_fails = (
-        max_score < SCORE_MAX_REFUSE and agg_top3 < SCORE_AGG_REFUSE
-    )
-
-    # ── Classifier 사용 가능 (정상 경로) ──
-    if classifier and classifier.verdict is not None:
-        if classifier.verdict == "insufficient":
-            # Evidence quality override: classifier 가 insufficient 라 해도
-            # evidence 가 충분히 좋으면 override (토론 8라운드 합의)
-            # (evidence quality 는 이 함수 밖에서 별도 체크 — caller 에서 처리)
-            logger.info(
-                "refusal gate: classifier=insufficient max=%.2f agg=%.2f",
-                max_score, agg_top3,
-            )
-            return RefusalDecision(
-                refused=True,
-                confidence_cap=None,
-                rule_triggered="classifier_insufficient",
-            )
-        if score_gate_fails:
-            logger.info(
-                "refusal gate: score_low max=%.2f agg=%.2f classifier=%s",
-                max_score, agg_top3, classifier.verdict,
-            )
-            return RefusalDecision(
-                refused=True,
-                confidence_cap=None,
-                rule_triggered="score_low",
-            )
-        # Classifier says sufficient → proceed
-        return RefusalDecision(
-            refused=False,
-            confidence_cap=None,
-            rule_triggered=None,
-        )
-
-    # ── Classifier 부재 → 3-tier conservative ──
-    if max_score < CONSERVATIVE_WEAK:
-        return RefusalDecision(
-            refused=True,
-            confidence_cap=None,
-            rule_triggered="conservative_refuse(no_classifier)",
-        )
-    if max_score < CONSERVATIVE_MID:
-        return RefusalDecision(
-            refused=False,
-            confidence_cap="low",
-            rule_triggered="conservative_low(no_classifier)",
-        )
-    return RefusalDecision(
-        refused=False,
-        confidence_cap="medium",
-        rule_triggered="conservative_medium(no_classifier)",
-    )

app/services/search/retrieval_service.py

@@ -76,10 +76,15 @@ class AxisFilter:
     jurisdiction: str | None = None
     year_from: int | None = None
     year_to: int | None = None
+    domain_buckets: list[str] | None = None    # 377: domain_bucket = ANY (도메인 스코프)
+    exclude_buckets: list[str] | None = None    # 377: domain_bucket <> ALL (예: News 제외)
+    cloud_egress: bool = False    # 갭2: 클라우드 소비자 cloud-eligibility allowlist 강제(토큰 claim 유래)
 
     def active(self) -> bool:
         return bool(self.material_types or self.jurisdiction
-                    or self.year_from is not None or self.year_to is not None)
+                    or self.year_from is not None or self.year_to is not None
+                    or self.domain_buckets or self.exclude_buckets
+                    or self.cloud_egress)
 
 
 def _axis_sql(alias: str, af: "AxisFilter | None", params: dict) -> str:
@@ -104,6 +109,22 @@ def _axis_sql(alias: str, af: "AxisFilter | None", params: dict) -> str:
     if af.year_to is not None:
         cl.append(f"COALESCE({p}published_date, {p}created_at::date) <= make_date(:af_yt, 12, 31)")
         params["af_yt"] = af.year_to
+    if af.domain_buckets:
+        cl.append(f"{p}domain_bucket = ANY(:af_db)")
+        params["af_db"] = af.domain_buckets
+    if af.exclude_buckets:
+        cl.append(f"{p}domain_bucket <> ALL(:af_xdb)")
+        params["af_xdb"] = af.exclude_buckets
+    if af.cloud_egress:
+        # 갭2 클라우드 egress allowlist(default-deny). restricted 는 _license_sql 별도 차단.
+        cl.append(
+            f"({p}data_origin = 'external' OR ("
+            f"{p}data_origin = 'work' "
+            f"AND {p}domain_bucket IN ('Engineering','Safety','Law') "
+            f"AND ({p}source_channel IS NULL OR {p}source_channel::text NOT IN ('voice','chat','memo')) "
+            f"AND {p}category::text IS DISTINCT FROM 'memo' "
+            f"AND ({p}user_note IS NULL OR {p}user_note = '')))"
+        )
     return " AND " + " AND ".join(cl)
 
 
@@ -121,7 +142,21 @@ def _license_sql(alias: str) -> str:
     술어 정의 = license_filter.restricted_exclude_sql 공유(digest/briefing/study 풀이와 단일 source).
     """
     from services.search.license_filter import restricted_exclude_sql
-    return " AND " + restricted_exclude_sql(alias)
+    _p = (alias + ".") if alias else ""
+    # ASME clause-KB(379): clause docs (doc_kind='clause') = read/nav/backlink only, excluded from retrieval/digest legs.
+    return " AND " + restricted_exclude_sql(alias) + f" AND {_p}doc_kind = 'standard'"
+
+
+def cloud_eligible_doc_sql(alias: str = "") -> str:
+    """단일 문서가 cloud 소비자(예: Claude/MCP)에게 노출 가능한가 = search retrieval 과
+    동일한 egress allowlist(갭2) + license 제한(B-4) 결합 술어. fetch_document(cloud) 가
+    search 와 byte-동일 게이트를 공유하도록 단일 source([[feedback_structural_integrity_over_path_discipline]]).
+
+    cloud_egress·license leg 모두 bind 파라미터 없는 리터럴 술어라 호출측 추가 params 불요.
+    주의: _license_sql 은 소유자 단건 다운로드엔 미적용(a안)이지만, cloud 노출은 구매 전자책
+    verbatim 누출을 막아야 하므로 여기선 항상 적용 = search 와 동일(local 토큰은 이 게이트 미발동).
+    반환 ' AND (egress allowlist) AND (license)' (alias='' = 컬럼 직접 참조). default-deny."""
+    return _axis_sql(alias, AxisFilter(cloud_egress=True), {}) + _license_sql(alias)
 
 
 # 2단계 gate (R2-B1) — SQL string interpolation 직전 final allowlist.

app/services/search/verifier_service.py

@@ -1,194 +0,0 @@
-"""Exaone semantic verifier (Phase 3.5b).
-
-답변-근거 간 의미적 모순(contradiction) 감지. rule-based grounding_check 가 못 잡는
-미묘한 모순 포착. classifier 와 동일 패턴: circuit breaker + timeout + fail open.
-
-## Severity 3단계
-- strong: direct_negation (완전 모순) → re-gate 교차 자격
-- medium: numeric_conflict, intent_core_mismatch → confidence 하향 (누적 시 강제 low)
-- weak: nuance, unsupported_claim → 로깅 + mild confidence 하향
-
-## 핵심 원칙
-- **Verifier strong 단독 refuse 금지** — grounding strong 과 교차해야 refuse
-- **Timeout 3s** — 느리면 없는 게 낫다 (fail open)
-- MLX gate 미사용 (PR #20 이후 Mac mini 26B endpoint — concurrent 안전성 별 검토)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import os
-import time
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal
-
-from ai.client import AIClient, _load_prompt, parse_json_response
-from core.config import settings
-from core.utils import setup_logger
-
-if TYPE_CHECKING:
-    from .evidence_service import EvidenceItem
-
-logger = setup_logger("verifier")
-
-LLM_TIMEOUT_MS = 10000  # 2026-05-17 B-3: 3s 시 동시 부하 시 verifier 빈발 skip → grounding 약화. Mac mini 26B 가 verifier-style 짧은 LLM call 도 concurrent 호출 시 3s 초과 빈번 — 10s 로 raise
-CIRCUIT_THRESHOLD = 5
-CIRCUIT_RECOVERY_SEC = 60
-
-_failure_count = 0
-_circuit_open_until: float | None = None
-
-# Phase 3.5 B2: numeric_conflict severity promote 실험.
-# import time 평가 — env 변경 후 process restart 필수 (docker compose restart fastapi).
-# default=0 (off). production 적용은 B3 FP 검증 통과 후만.
-_NUMERIC_PROMOTE = os.getenv("VERIFIER_NUMERIC_PROMOTE", "0") == "1"
-
-# severity 매핑 (프롬프트 "critical"/"minor" → 코드 strong/medium/weak)
-# Tier 4 (B2): _NUMERIC_PROMOTE=1 일 때 numeric_conflict critical → strong 으로 격상.
-# minor 는 medium 유지 (FP 위험 분리).
-_SEVERITY_MAP: dict[str, dict[str, Literal["strong", "medium", "weak"]]] = {
-    "direct_negation": {"critical": "strong", "minor": "strong"},
-    "numeric_conflict": (
-        {"critical": "strong", "minor": "medium"} if _NUMERIC_PROMOTE
-        else {"critical": "medium", "minor": "medium"}
-    ),
-    "intent_core_mismatch": {"critical": "medium", "minor": "medium"},
-    "nuance": {"critical": "weak", "minor": "weak"},
-    "unsupported_claim": {"critical": "weak", "minor": "weak"},
-}
-
-
-@dataclass(slots=True)
-class Contradiction:
-    """개별 모순 발견."""
-    type: str  # direct_negation / numeric_conflict / intent_core_mismatch / nuance / unsupported_claim
-    severity: Literal["strong", "medium", "weak"]
-    claim: str
-    evidence_ref: str
-    explanation: str
-
-
-@dataclass(slots=True)
-class VerifierResult:
-    status: Literal["ok", "timeout", "error", "circuit_open", "skipped"]
-    contradictions: list[Contradiction]
-    elapsed_ms: float
-
-
-try:
-    VERIFIER_PROMPT = _load_prompt("verifier.txt")
-except FileNotFoundError:
-    VERIFIER_PROMPT = ""
-    logger.warning("verifier.txt not found — verifier will always skip")
-
-
-def _build_input(
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> str:
-    """답변 + evidence spans → 프롬프트."""
-    spans = "\n\n".join(
-        f"[{e.n}] {(e.title or '').strip()}\n{e.span_text}"
-        for e in evidence
-    )
-    return (
-        VERIFIER_PROMPT
-        .replace("{answer}", answer)
-        .replace("{numbered_evidence}", spans)
-    )
-
-
-def _map_severity(ctype: str, raw_severity: str) -> Literal["strong", "medium", "weak"]:
-    """type + raw severity → 코드 severity 3단계."""
-    type_map = _SEVERITY_MAP.get(ctype, {"critical": "weak", "minor": "weak"})
-    return type_map.get(raw_severity, "weak")
-
-
-async def verify(
-    query: str,
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> VerifierResult:
-    """답변-근거 semantic 검증. Parallel with grounding_check.
-
-    Returns:
-        VerifierResult. status "ok" 이 아니면 contradictions 빈 리스트 (fail open).
-    """
-    global _failure_count, _circuit_open_until
-    t_start = time.perf_counter()
-
-    if _circuit_open_until and time.time() < _circuit_open_until:
-        return VerifierResult("circuit_open", [], 0.0)
-
-    if not VERIFIER_PROMPT:
-        return VerifierResult("skipped", [], 0.0)
-
-    if not hasattr(settings.ai, "verifier") or settings.ai.verifier is None:
-        return VerifierResult("skipped", [], 0.0)
-
-    if not answer or not evidence:
-        return VerifierResult("skipped", [], 0.0)
-
-    prompt = _build_input(answer, evidence)
-    client = AIClient()
-    try:
-        async with asyncio.timeout(LLM_TIMEOUT_MS / 1000):
-            raw = await client._request(settings.ai.verifier, prompt)
-        _failure_count = 0
-    except asyncio.TimeoutError:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"verifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("verifier timeout")
-        return VerifierResult(
-            "timeout", [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    except Exception as e:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"verifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning(f"verifier error: {e}")
-        return VerifierResult(
-            "error", [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    finally:
-        await client.close()
-
-    elapsed_ms = (time.perf_counter() - t_start) * 1000
-    parsed = parse_json_response(raw)
-    if not isinstance(parsed, dict):
-        logger.warning("verifier parse failed raw=%r", (raw or "")[:200])
-        return VerifierResult("error", [], elapsed_ms)
-
-    # contradiction 파싱
-    raw_items = parsed.get("contradictions") or []
-    if not isinstance(raw_items, list):
-        raw_items = []
-
-    results: list[Contradiction] = []
-    for item in raw_items[:5]:
-        if not isinstance(item, dict):
-            continue
-        ctype = item.get("type", "")
-        if ctype not in _SEVERITY_MAP:
-            ctype = "unsupported_claim"
-        raw_sev = item.get("severity", "minor")
-        severity = _map_severity(ctype, raw_sev)
-        claim = str(item.get("claim", ""))[:50]
-        ev_ref = str(item.get("evidence_ref", ""))[:50]
-        explanation = str(item.get("explanation", ""))[:30]
-        results.append(Contradiction(ctype, severity, claim, ev_ref, explanation))
-
-    logger.info(
-        "verifier ok query=%r contradictions=%d strong=%d medium=%d elapsed_ms=%.0f",
-        query[:60],
-        len(results),
-        sum(1 for c in results if c.severity == "strong"),
-        sum(1 for c in results if c.severity == "medium"),
-        elapsed_ms,
-    )
-    return VerifierResult("ok", results, elapsed_ms)

app/services/study/concept_curriculum.py

@@ -0,0 +1,284 @@
+"""concept_curriculum — 이론공부 홈 재료 (오늘의 개념 · 진도 · 회독 SR).
+
+개념문서 = documents (user_tags = @library/{topic}/{과목}/... , 가스기사). is_read = 회독,
+md_content 의 ★ 개수 = 빈출 tier(★★★=3 / ★★=2 / else 1). 회독 SR = study_concept_progress
++ sr_schedule(문제 SR 공용 산술). 읽기 전용 집계 + mark_read(회독+SR 입고)만 write. LLM 0.
+
+문제풀이 표면 무접촉 — 여기서 읽는 study_question_progress 는 '문항 due 카운트'만(홈 표시용).
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from sqlalchemy import func, or_, select, text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from models.document_read import DocumentRead
+from models.study_concept_progress import StudyConceptProgress
+from models.study_question_progress import StudyQuestionProgress
+from models.study_topic import StudyTopic
+from services.study.concept_parser import parse_concept, resolve_related
+from services.study.sr_schedule import advance, first_due
+
+# 개념 행 조회 — 태그로 개념문서 필터 + 회독 진행 LEFT JOIN. md_content 는 전송 안 하고
+# ★ 유무만 서버측 boolean 으로(홈이 자주 호출돼도 페이로드 최소).
+# is_read = document_reads(회독 정본, is_read 컬럼 아님) EXISTS. library unread 와 동일 기준.
+_CONCEPT_ROWS_SQL = text(
+    """
+    SELECT d.id AS doc_id,
+           d.title AS title,
+           EXISTS (
+             SELECT 1 FROM document_reads r
+             WHERE r.document_id = d.id AND r.user_id = :uid
+           ) AS is_read,
+           (d.md_content LIKE '%★★★%') AS f3,
+           (d.md_content LIKE '%★★%')  AS f2,
+           split_part(replace(d.user_tags::text, '"', ''), '/', 3) AS subject,
+           p.review_stage AS review_stage,
+           p.due_at AS due_at,
+           p.last_read_at AS last_read_at
+    FROM documents d
+    LEFT JOIN study_concept_progress p
+      ON p.concept_doc_id = d.id AND p.user_id = :uid
+    WHERE d.user_tags::text LIKE :like
+      AND d.deleted_at IS NULL
+    """
+)
+
+
+async def _topic_name(session: AsyncSession, topic_id: int) -> str | None:
+    return (
+        await session.execute(select(StudyTopic.name).where(StudyTopic.id == topic_id))
+    ).scalar_one_or_none()
+
+
+async def _concept_rows(session: AsyncSession, user_id: int, topic_name: str):
+    like = f"%@library/{topic_name}/%"
+    return (
+        await session.execute(_CONCEPT_ROWS_SQL, {"uid": user_id, "like": like})
+    ).mappings().all()
+
+
+def _freq(row) -> int:
+    if row["f3"]:
+        return 3
+    if row["f2"]:
+        return 2
+    return 1
+
+
+def _is_due(row, now: datetime) -> bool:
+    return (
+        row["due_at"] is not None
+        and row["due_at"] <= now
+        and (row["review_stage"] or 0) < 4
+    )
+
+
+def _item(row) -> dict:
+    return {
+        "doc_id": row["doc_id"],
+        "title": row["title"],
+        "subject": row["subject"],
+        "freq": _freq(row),
+        "review_stage": row["review_stage"],
+        "due_at": row["due_at"],
+    }
+
+
+async def _question_due_count(session: AsyncSession, user_id: int, topic_id: int, now: datetime) -> int:
+    """문항 복습 due (기존 study_question_progress 엔진 재사용, 홈 표시용)."""
+    return (
+        await session.execute(
+            select(func.count())
+            .select_from(StudyQuestionProgress)
+            .where(
+                StudyQuestionProgress.user_id == user_id,
+                StudyQuestionProgress.study_topic_id == topic_id,
+                StudyQuestionProgress.due_at.is_not(None),
+                StudyQuestionProgress.due_at <= now,
+                or_(
+                    StudyQuestionProgress.review_stage.is_(None),
+                    StudyQuestionProgress.review_stage < 4,
+                ),
+            )
+        )
+    ).scalar_one()
+
+
+async def curriculum(session: AsyncSession, user_id: int, topic_id: int) -> dict:
+    """과목별 회독 진도 + 개념/문항 복습 due 요약 (진도 대시보드)."""
+    name = await _topic_name(session, topic_id)
+    rows = await _concept_rows(session, user_id, name) if name else []
+    now = datetime.now(timezone.utc)
+
+    subj: dict[str, dict] = {}
+    for r in rows:
+        s = subj.setdefault(r["subject"], {"subject": r["subject"], "total": 0, "read": 0})
+        s["total"] += 1
+        if r["is_read"]:
+            s["read"] += 1
+
+    total = len(rows)
+    read = sum(1 for r in rows if r["is_read"])
+    concept_due = sum(1 for r in rows if _is_due(r, now))
+    question_due = await _question_due_count(session, user_id, topic_id, now)
+
+    return {
+        "topic_id": topic_id,
+        "topic_name": name,
+        "subjects": sorted(subj.values(), key=lambda x: x["subject"]),
+        "total": total,
+        "read": read,
+        "concept_due": concept_due,
+        "question_due": question_due,
+    }
+
+
+async def today_concepts(
+    session: AsyncSession, user_id: int, topic_id: int, limit: int = 6
+) -> dict:
+    """오늘 공부할 개념 = 재복습(SR due) 먼저 → 미독(빈출 우선). 졸업/재복습대기 제외."""
+    name = await _topic_name(session, topic_id)
+    rows = await _concept_rows(session, user_id, name) if name else []
+    now = datetime.now(timezone.utc)
+
+    due = [r for r in rows if _is_due(r, now)]
+    due.sort(key=lambda r: r["due_at"])
+
+    # 미독 & 아직 SR 큐 진입 전(due_at NULL) → 빈출 높은 순
+    unread = [r for r in rows if not r["is_read"] and r["due_at"] is None]
+    unread.sort(key=lambda r: (-_freq(r), r["subject"], r["title"]))
+
+    picked = [{**_item(r), "reason": "재복습"} for r in due]
+    picked += [{**_item(r), "reason": "신규"} for r in unread]
+
+    return {
+        "concepts": picked[:limit],
+        "due_total": len(due),
+        "unread_total": len(unread),
+    }
+
+
+async def mark_read(
+    session: AsyncSession, user_id: int, topic_id: int, doc_id: int, now: datetime | None = None
+) -> dict:
+    """개념 회독 처리 = document_reads(+1) + 회독 SR 입고/전진.
+
+    회독 정본 = document_reads(append-only), documents.is_read 컬럼 아님(library unread 와 정합).
+    첫 회독 → first_due(stage 0, 내일). 이후 회독은 'due 도래(due_at<=now)' 때만 correct 로 전진
+    (이른 재열람/다중클릭 과전진 방지). stage 4 졸업 후엔 due_at NULL 이라 전진 없음.
+    """
+    now = now or datetime.now(timezone.utc)
+
+    # 회독 로그 append (+1) — 사용자 명시 회독. 자동 아님(엔드포인트 = 명시 POST).
+    session.add(DocumentRead(user_id=user_id, document_id=doc_id, read_at=now))
+
+    prog = (
+        await session.execute(
+            select(StudyConceptProgress).where(
+                StudyConceptProgress.user_id == user_id,
+                StudyConceptProgress.concept_doc_id == doc_id,
+            )
+        )
+    ).scalar_one_or_none()
+
+    if prog is None:
+        stage, due = first_due(now)
+        prog = StudyConceptProgress(
+            user_id=user_id,
+            study_topic_id=topic_id,
+            concept_doc_id=doc_id,
+            review_stage=stage,
+            due_at=due,
+            last_read_at=now,
+        )
+        session.add(prog)
+    else:
+        # due 도래 시에만 전진 — 미래 due(재열람 이른 클릭)는 stage 불변, last_read_at 만 갱신.
+        if prog.due_at is not None and prog.due_at <= now:
+            res = advance(prog.review_stage, "correct", now)
+            if res is not None:
+                prog.review_stage, prog.due_at = res
+        prog.last_read_at = now
+
+    await session.commit()
+    await session.refresh(prog)
+    return {"ok": True, "review_stage": prog.review_stage, "due_at": prog.due_at}
+
+
+_CONCEPT_ONE_SQL = text(
+    """
+    SELECT d.id AS doc_id, d.title AS title, d.md_content AS md_content,
+           split_part(replace(d.user_tags::text, '"', ''), '/', 3) AS subject,
+           (d.md_content LIKE '%★★★%') AS f3,
+           (d.md_content LIKE '%★★%')  AS f2,
+           EXISTS (
+             SELECT 1 FROM document_reads r
+             WHERE r.document_id = d.id AND r.user_id = :uid
+           ) AS is_read,
+           p.review_stage AS review_stage,
+           p.due_at AS due_at
+    FROM documents d
+    LEFT JOIN study_concept_progress p ON p.concept_doc_id = d.id AND p.user_id = :uid
+    WHERE d.id = :doc_id AND d.deleted_at IS NULL AND d.user_tags::text LIKE :like
+    """
+)
+
+
+async def concept_detail(
+    session: AsyncSession, user_id: int, topic_id: int, doc_id: int
+) -> dict | None:
+    """개념 리더 재료 — md 구조 파싱 + 관련개념 백링크 해소 + 회독/SR 상태 + 같은 과목 이전/다음."""
+    name = await _topic_name(session, topic_id)
+    if not name:
+        return None
+    like = f"%@library/{name}/%"
+    row = (
+        await session.execute(
+            _CONCEPT_ONE_SQL, {"uid": user_id, "doc_id": doc_id, "like": like}
+        )
+    ).mappings().first()
+    if row is None:
+        return None
+
+    parsed = parse_concept(row["md_content"] or "")
+
+    # 백링크 해소 + 이전/다음 = 같은 토픽 개념 title 인덱스(회독 rows 재사용)
+    idx = await _concept_rows(session, user_id, name)
+    title_index = [(r["doc_id"], r["title"], r["subject"]) for r in idx]
+    resolved = resolve_related(parsed["related"], title_index)
+
+    # 이전/다음 = 같은 과목, title 순
+    same = sorted(
+        [(r["doc_id"], r["title"]) for r in idx if r["subject"] == row["subject"]],
+        key=lambda x: (x[1] or "", x[0]),
+    )
+    ids = [d for d, _ in same]
+    prev_id = next_id = None
+    if doc_id in ids:
+        pos = ids.index(doc_id)
+        if pos > 0:
+            prev_id = ids[pos - 1]
+        if pos < len(ids) - 1:
+            next_id = ids[pos + 1]
+
+    freq = 3 if row["f3"] else (2 if row["f2"] else 1)
+
+    return {
+        "doc_id": row["doc_id"],
+        "db_title": row["title"],
+        "title": parsed["title"] or row["title"],
+        "subject": row["subject"],
+        "freq": freq,
+        "summary": parsed["summary"],
+        "body": parsed["body"],
+        "bincheol": parsed["bincheol"],
+        "related": resolved,
+        "is_read": row["is_read"],
+        "review_stage": row["review_stage"],
+        "due_at": row["due_at"],
+        "prev_id": prev_id,
+        "next_id": next_id,
+    }

app/services/study/concept_links.py

@@ -0,0 +1,139 @@
+"""concept_links — 이론↔문제 브리지 롤업 (Stage B).
+
+study_concept_links(개념 doc ↔ 기출문항, 임베딩 코사인) + study_question_progress(내 풀이상태)를
+조인해 (a) 개념별 관련 기출 + 내 정답률(related_questions), (b) 개념 약점 지도(weakness_map) 산출.
+읽기 전용 집계 · LLM 0. 링크 적재는 scripts/concept_links_backfill.sql(임베딩) 배치.
+정답률 = 링크된 문항 중 progress.last_outcome 기준(attempted=풀이이력 보유, correct=최근정답).
+"""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+_ACCURACY_WEAK_PCT = 60  # 정답률 < 60% = 약점(attempted>0 일 때만)
+
+_AGG_SQL = text(
+    """
+    SELECT count(*) AS linked,
+           count(pr.study_question_id) FILTER (WHERE pr.last_outcome IS NOT NULL) AS attempted,
+           count(*) FILTER (WHERE pr.last_outcome = 'correct') AS correct
+    FROM study_concept_links l
+    LEFT JOIN study_question_progress pr
+      ON pr.study_question_id = l.question_id AND pr.user_id = :uid
+    WHERE l.concept_doc_id = :doc_id AND l.link_source = 'embedding'
+    """
+)
+
+_QROWS_SQL = text(
+    """
+    SELECT q.id AS id, q.subject AS subject, q.exam_round AS exam_round,
+           q.exam_question_number AS qnum, l.score AS score,
+           pr.last_outcome AS last_outcome, pr.review_stage AS review_stage
+    FROM study_concept_links l
+    JOIN study_questions q ON q.id = l.question_id AND q.deleted_at IS NULL AND q.is_active
+    LEFT JOIN study_question_progress pr
+      ON pr.study_question_id = q.id AND pr.user_id = :uid
+    WHERE l.concept_doc_id = :doc_id AND l.link_source = 'embedding'
+    ORDER BY l.score DESC
+    LIMIT :limit
+    """
+)
+
+_WEAKNESS_SQL = text(
+    """
+    SELECT d.id AS doc_id, d.title AS title,
+           split_part(replace(d.user_tags::text, '"', ''), '/', 3) AS subject,
+           count(l.id) AS linked,
+           count(pr.study_question_id) FILTER (WHERE pr.last_outcome IS NOT NULL) AS attempted,
+           count(*) FILTER (WHERE pr.last_outcome = 'correct') AS correct
+    FROM documents d
+    JOIN study_concept_links l ON l.concept_doc_id = d.id AND l.link_source = 'embedding'
+    LEFT JOIN study_question_progress pr
+      ON pr.study_question_id = l.question_id AND pr.user_id = :uid
+    WHERE d.user_tags::text LIKE :like AND d.deleted_at IS NULL
+    GROUP BY d.id, d.title, subject
+    """
+)
+
+
+async def related_questions(
+    session: AsyncSession, user_id: int, doc_id: int, limit: int = 20
+) -> dict:
+    """개념 doc 의 관련 기출 + 내 정답률(전체 링크 기준 집계 + 상위 N 표시용)."""
+    agg = (
+        await session.execute(_AGG_SQL, {"uid": user_id, "doc_id": doc_id})
+    ).mappings().first()
+    rows = (
+        await session.execute(
+            _QROWS_SQL, {"uid": user_id, "doc_id": doc_id, "limit": limit}
+        )
+    ).mappings().all()
+
+    linked = (agg["linked"] if agg else 0) or 0
+    attempted = (agg["attempted"] if agg else 0) or 0
+    correct = (agg["correct"] if agg else 0) or 0
+    accuracy = round(100 * correct / attempted) if attempted else None
+
+    return {
+        "linked": linked,
+        "attempted": attempted,
+        "correct": correct,
+        "accuracy": accuracy,
+        "questions": [
+            {
+                "id": r["id"],
+                "subject": r["subject"],
+                "exam_round": r["exam_round"],
+                "qnum": r["qnum"],
+                "score": round(r["score"], 3) if r["score"] is not None else None,
+                "last_outcome": r["last_outcome"],
+                "review_stage": r["review_stage"],
+            }
+            for r in rows
+        ],
+    }
+
+
+async def weakness_map(
+    session: AsyncSession, user_id: int, topic_name: str, limit: int = 12
+) -> dict:
+    """개념 약점 지도 — 링크된 기출 정답률로 개념 채색. 약점(attempted>0·정답률<60%) 우선 정렬."""
+    like = f"%@library/{topic_name}/%"
+    rows = (
+        await session.execute(_WEAKNESS_SQL, {"uid": user_id, "like": like})
+    ).mappings().all()
+
+    concepts = []
+    for r in rows:
+        attempted = r["attempted"] or 0
+        correct = r["correct"] or 0
+        accuracy = round(100 * correct / attempted) if attempted else None
+        if accuracy is None:
+            state = "unattempted"
+        elif accuracy < _ACCURACY_WEAK_PCT:
+            state = "weak"
+        else:
+            state = "ok"
+        concepts.append(
+            {
+                "doc_id": r["doc_id"],
+                "title": r["title"],
+                "subject": r["subject"],
+                "linked": r["linked"] or 0,
+                "attempted": attempted,
+                "accuracy": accuracy,
+                "state": state,
+            }
+        )
+
+    # 약점 우선(정답률 오름차순) → 미평가는 뒤로. 홈 위젯용 상위 N.
+    weak = sorted(
+        [c for c in concepts if c["state"] == "weak"],
+        key=lambda c: (c["accuracy"], -c["attempted"], c["doc_id"]),
+    )
+    return {
+        "weak": weak[:limit],
+        "weak_total": len(weak),
+        "evaluated_total": sum(1 for c in concepts if c["state"] != "unattempted"),
+    }

app/services/study/concept_parser.py

@@ -0,0 +1,175 @@
+"""concept_parser — 개념노트 markdown 구조 파서 + 관련개념 백링크 해소 (이론 리더용).
+
+정찰 실측 불변식(273/273): 개념노트는 고정 골격을 100% 따름 —
+    # {H1 제목}                     (첫 줄, DB title 과 다른 표시용 제목)
+    > **한 줄 요약**: {요약}          (blockquote, 라벨 고정)
+    ## {본문 라벨}  ...              (BODY, 자유 라벨 H2 0~N, 트레일 ★ 가능)
+    ## 빈출 포인트                    (항상, 관련개념 직전)
+    ## 관련 개념                      (항상, 문서 최종 섹션)
+
+코드펜스(``` ASCII 도식) 내부의 ##/- 는 무시. 헤딩 트레일 ★ 는 스트립(라벨 정규화).
+'빈출 포인트'/'관련 개념' 앵커만 이름으로 잡고 나머지 BODY 는 순서·위치로 처리(라벨 화이트리스트 금지).
+순수 함수 · LLM 0.
+"""
+
+from __future__ import annotations
+
+import re
+
+_FENCE = re.compile(r"^\s*```")
+_H1 = re.compile(r"^#\s+(.+?)\s*$")
+_H2 = re.compile(r"^##\s+(.+?)\s*$")  # ### 는 매칭 안 됨(## 뒤 \s 요구)
+_SUMMARY = re.compile(r"^>\s*\*\*한 줄 요약\*\*:\s*(.+)$")
+_STAR_SUFFIX = re.compile(r"\s*★+\s*$")
+_TRAIL_STARS = re.compile(r"★+\s*$")
+_BINCHEOL_ITEM = re.compile(r"^\s*-\s+(★*)\s*(.+)$")
+_RELATED_ITEM = re.compile(r"^\s*-\s+(.+)$")
+_PAREN = re.compile(r"\s*\(.*$")  # 괄호부터 끝(clarifier 힌트 절단)
+_NUM_PREFIX = re.compile(r"^\d+_")
+_STRIP_SYM = re.compile(r"[\s_·,./()\-]")
+
+_ANCHOR_BINCHEOL = "빈출 포인트"
+_ANCHOR_RELATED = "관련 개념"
+
+
+def parse_concept(md: str) -> dict:
+    """개념노트 md → {title, summary, body[{label,stars,md}], bincheol[{tier,text}], related[{raw,phrase,hint}]}."""
+    lines = (md or "").split("\n")
+    title: str | None = None
+    summary: str | None = None
+    body: list[dict] = []
+    bincheol_lines: list[str] = []
+    related_lines: list[str] = []
+
+    in_fence = False
+    zone = "pre"  # pre | body | bincheol | related
+    body_cur: dict | None = None
+
+    def emit(line: str) -> None:
+        if body_cur is not None:
+            body_cur["_lines"].append(line)
+        elif zone == "bincheol":
+            bincheol_lines.append(line)
+        elif zone == "related":
+            related_lines.append(line)
+        # pre-zone 내용(요약 앞 잡음)은 버림
+
+    for ln in lines:
+        if _FENCE.match(ln):
+            in_fence = not in_fence
+            emit(ln)
+            continue
+        if in_fence:
+            emit(ln)
+            continue
+
+        if title is None:
+            m = _H1.match(ln)
+            if m:
+                title = m.group(1).strip()
+                continue
+        if summary is None:
+            m = _SUMMARY.match(ln)
+            if m:
+                summary = m.group(1).strip()
+                continue
+
+        m2 = _H2.match(ln)
+        if m2:
+            raw_label = m2.group(1).strip()
+            star_m = _TRAIL_STARS.search(raw_label)
+            stars = len(star_m.group(0).strip()) if star_m else 0
+            label = _STAR_SUFFIX.sub("", raw_label).strip()
+            if label == _ANCHOR_BINCHEOL:
+                zone = "bincheol"
+                body_cur = None
+                continue
+            if label == _ANCHOR_RELATED:
+                zone = "related"
+                body_cur = None
+                continue
+            body_cur = {"label": label, "stars": stars, "_lines": []}
+            body.append(body_cur)
+            zone = "body"
+            continue
+
+        emit(ln)
+
+    body_out = []
+    for s in body:
+        text = "\n".join(s["_lines"]).strip()
+        if text or s["label"]:
+            body_out.append({"label": s["label"], "stars": s["stars"], "md": text})
+
+    bincheol = []
+    for ln in bincheol_lines:
+        m = _BINCHEOL_ITEM.match(ln)
+        if m:
+            bincheol.append({"tier": len(m.group(1)), "text": m.group(2).strip()})
+
+    related = []
+    for ln in related_lines:
+        m = _RELATED_ITEM.match(ln)
+        if m:
+            raw = m.group(1).strip()
+            phrase = _PAREN.sub("", raw).strip()
+            hint = raw[len(phrase):].strip() if len(raw) > len(phrase) else ""
+            if phrase:
+                related.append({"raw": raw, "phrase": phrase, "hint": hint})
+
+    return {
+        "title": title,
+        "summary": summary,
+        "body": body_out,
+        "bincheol": bincheol,
+        "related": related,
+    }
+
+
+def _normalize(s: str) -> str:
+    """해소용 정규화: NN_ 접두 제거 → 소문자 → 공백/기호 제거. 영문은 lowercase 유지."""
+    s = _NUM_PREFIX.sub("", s or "")
+    s = s.lower()
+    s = _STRIP_SYM.sub("", s)
+    return s
+
+
+def resolve_related(related: list[dict], title_index: list[tuple]) -> list[dict]:
+    """관련개념 구절 → 개념 doc 해소. title_index = [(doc_id, title, subject), ...].
+
+    다단 fallback(정찰 ~79%): 정규화 exact → 양방향 substring(≥2자 가드) → 미해소=dangling(doc_id None).
+    """
+    norm_exact: dict[str, int] = {}
+    norm_list: list[tuple[str, int, str]] = []
+    for did, ttl, _subj in title_index:
+        n = _normalize(ttl)
+        if n:
+            norm_exact.setdefault(n, did)
+            norm_list.append((n, did, ttl))
+
+    out = []
+    for it in related:
+        pn = _normalize(it["phrase"])
+        did: int | None = None
+        rtitle: str | None = None
+        if pn and len(pn) >= 2:
+            if pn in norm_exact:
+                did = norm_exact[pn]
+            else:
+                # substring 폴백: title-norm ⊆ phrase-norm 방향만(짧은 phrase 가 더 큰 title 을
+                # 삼키는 오결선 방지, 예: '염산'→'염산나트륨' X) + 길이차 최소(가장 구체적) +
+                # doc_id tiebreak(순서 무관 결정성). 후보 없으면 dangling(doc_id None).
+                cands = [
+                    (abs(len(n) - len(pn)), cand, ttl)
+                    for n, cand, ttl in norm_list
+                    if len(n) >= 2 and n in pn
+                ]
+                if cands:
+                    cands.sort(key=lambda c: (c[0], c[1]))
+                    _, did, rtitle = cands[0]
+        if did is not None and rtitle is None:
+            rtitle = next((t for d, t, _ in title_index if d == did), None)
+        out.append(
+            {"phrase": it["phrase"], "hint": it["hint"], "doc_id": did, "title": rtitle}
+        )
+    return out

app/services/study/outcome.py

@@ -0,0 +1,25 @@
+"""채점(outcome) 산출 단일 소스 (study-to-viewer P2).
+
+라이브 attempt 엔드포인트(submit_attempt)와 뷰어 ingest 가 **동일 함수**로 채점 →
+정오 어휘가 한 곳(서버)에서 결정(plan r2: ingest 는 raw 신호 selected+unsure 만 싣고
+DS 가 산출 = '무수정 재생'을 실제로 성립시키는 형태). correct_choice 는 항상 현재 DB 값.
+
+규칙(라이브 study_questions.py:1008-1020 동일):
+  is_unsure=True  → (None, False, 'unsure')   # unsure 가 정오 override, selected 폐기
+  selected None   → ValueError                # 선택 없고 unsure 도 아니면 무효(엔드포인트가 처리)
+  그 외          → selected==correct → (selected, is_correct, 'correct'|'wrong')
+"""
+
+from __future__ import annotations
+
+
+def derive_outcome(
+    selected_choice: int | None, is_unsure: bool, correct_choice: int
+) -> tuple[int | None, bool, str]:
+    """(selected, is_correct, outcome) 반환. skipped 는 여기서 안 나옴(선택 없으면 호출측이 거부/skip)."""
+    if is_unsure:
+        return None, False, "unsure"
+    if selected_choice is None:
+        raise ValueError("selected_choice (1~4) 또는 is_unsure=true 가 필요합니다")
+    is_correct = selected_choice == correct_choice
+    return selected_choice, is_correct, ("correct" if is_correct else "wrong")

app/services/study/publish_enqueue.py

@@ -0,0 +1,174 @@
+"""발행 outbox enqueue + 초기 백필 (docsrv-viewer-publish).
+
+enqueue_publish: 저작/4-A 트랜잭션이 같은 session(=같은 Postgres tx)에서 호출 → caller commit
+  (P0-1 규율: 콘텐츠 변경과 outbox INSERT 원자성, dual-write 회피). payload/hash 스냅샷.
+enqueue_question_publish: 문항 + (ready면)해설을 함께 적재. 저작 쓰기/4-A 완료/백필 공용.
+backfill_publish_questions: 기존 active 문항을 bounded 로 1회 outbox 적재(초기 백필, P2-1 bounded page).
+  멱등 = 발행 워커의 (payload_hash, deleted) 디둡이 no-op 재투영 흡수(중복 enqueue 무해).
+
+★주의: 저작 엔드포인트(study_questions create/update)·4-A 워커에서의 enqueue 결선은 P0-1b
+  (기존 hot 파일 수정이라 별 increment). 본 모듈은 호출 라이브러리 + 수동/백필 진입점.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from models.published import PublishOutbox
+from models.study_memo_card import StudyMemoCard
+from models.study_memo_card_progress import StudyMemoCardProgress
+from models.study_question import StudyQuestion
+from models.study_topic import StudyTopic
+from services.study.publish_projection import (
+    KIND_CARD,
+    KIND_CARD_PROGRESS,
+    KIND_EXPLANATION,
+    KIND_QUESTION,
+    KIND_TOPIC,
+    SCHEMA_VERSION,
+    payload_hash,
+    project_card,
+    project_card_progress,
+    project_explanation,
+    project_question,
+    project_topic,
+)
+
+
+async def enqueue_publish(
+    session: AsyncSession,
+    *,
+    kind: str,
+    source_id: int,
+    payload: dict[str, Any] | None,
+    deleted: bool = False,
+) -> None:
+    """outbox 1행 INSERT. caller 가 commit (저자 tx 동봉). deleted=True 면 tombstone(payload={})."""
+    body: dict[str, Any] = payload if payload is not None else {}
+    session.add(
+        PublishOutbox(
+            kind=kind,
+            source_id=source_id,
+            payload=body,
+            payload_hash=payload_hash(body),
+            schema_version=SCHEMA_VERSION,
+            deleted=deleted,
+        )
+    )
+
+
+async def enqueue_question_publish(session: AsyncSession, q: Any) -> None:
+    """문항 + (ready면)해설을 outbox 적재. caller commit."""
+    await enqueue_publish(session, kind=KIND_QUESTION, source_id=q.id, payload=project_question(q))
+    expl = project_explanation(q)
+    if expl is not None:
+        await enqueue_publish(session, kind=KIND_EXPLANATION, source_id=q.id, payload=expl)
+
+
+async def backfill_publish_questions(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
+    """active(미삭제) 문항을 id>after_id 부터 bounded 로 outbox 적재.
+
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. caller commit.
+    """
+    rows = (
+        await session.execute(
+            select(StudyQuestion)
+            .where(StudyQuestion.deleted_at.is_(None), StudyQuestion.id > after_id)
+            .order_by(StudyQuestion.id.asc())
+            .limit(limit)
+        )
+    ).scalars().all()
+    for q in rows:
+        await enqueue_question_publish(session, q)
+    return len(rows), (rows[-1].id if rows else after_id)
+
+
+async def enqueue_topic_publish(session: AsyncSession, topic: Any) -> None:
+    """주제 메타를 outbox 적재(S-1). caller commit. 저작 create/update 결선 + 백필 공용."""
+    await enqueue_publish(session, kind=KIND_TOPIC, source_id=topic.id, payload=project_topic(topic))
+
+
+async def backfill_publish_topics(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
+    """active(미삭제) 주제를 id>after_id 부터 bounded 로 outbox 적재(S-1 초기 백필).
+
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. caller commit.
+    멱등 = 발행 워커의 (payload_hash, deleted) 디둡이 no-op 재투영 흡수(중복 enqueue 무해).
+    """
+    rows = (
+        await session.execute(
+            select(StudyTopic)
+            .where(StudyTopic.deleted_at.is_(None), StudyTopic.id > after_id)
+            .order_by(StudyTopic.id.asc())
+            .limit(limit)
+        )
+    ).scalars().all()
+    for t in rows:
+        await enqueue_topic_publish(session, t)
+    return len(rows), (rows[-1].id if rows else after_id)
+
+
+async def enqueue_card_publish(session: AsyncSession, card: Any) -> None:
+    """카드 상태 기반 발행/tombstone (S-2). caller commit.
+
+    검수완료(needs_review=False) & 미삭제 만 발행 — 그 외(검수대기 복귀·삭제·retire)는
+    tombstone(feed 1급 삭제 이벤트). 발행 자격이 카드 상태에 매여 있어 호출측은 '카드를
+    건드렸다'만 알면 되고 publish/tombstone 분기는 여기 단일화(경로별 가드 기억 회피).
+    """
+    if card.deleted_at is not None or card.needs_review:
+        await enqueue_publish(session, kind=KIND_CARD, source_id=card.id, payload=None, deleted=True)
+    else:
+        await enqueue_publish(session, kind=KIND_CARD, source_id=card.id, payload=project_card(card))
+
+
+async def backfill_publish_cards(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
+    """검수완료(needs_review=False)·미삭제 카드를 id>after_id 부터 bounded 로 outbox 적재(S-2 초기 백필).
+
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. 멱등 = 워커 디둡. caller commit.
+    """
+    rows = (
+        await session.execute(
+            select(StudyMemoCard)
+            .where(
+                StudyMemoCard.deleted_at.is_(None),
+                StudyMemoCard.needs_review.is_(False),
+                StudyMemoCard.id > after_id,
+            )
+            .order_by(StudyMemoCard.id.asc())
+            .limit(limit)
+        )
+    ).scalars().all()
+    for c in rows:
+        await enqueue_card_publish(session, c)
+    return len(rows), (rows[-1].id if rows else after_id)
+
+
+async def enqueue_card_progress_publish(session: AsyncSession, progress: Any) -> None:
+    """카드 SR progress row 발행(S-4). caller commit. rate_card 결과(ALL row, sentinel/terminal 포함)."""
+    await enqueue_publish(
+        session,
+        kind=KIND_CARD_PROGRESS,
+        source_id=progress.id,
+        payload=project_card_progress(progress),
+    )
+
+
+async def backfill_publish_card_progress(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
+    """모든 card progress row 를 id>after_id 부터 bounded 로 outbox 적재(S-4 초기 백필).
+
+    ★필터 없음 = ALL row(due_at NULL sentinel·terminal 포함) — due-only 백필은 sentinel 누락.
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. 멱등 = 워커 디둡. caller commit.
+    """
+    rows = (
+        await session.execute(
+            select(StudyMemoCardProgress)
+            .where(StudyMemoCardProgress.id > after_id)
+            .order_by(StudyMemoCardProgress.id.asc())
+            .limit(limit)
+        )
+    ).scalars().all()
+    for p in rows:
+        await enqueue_card_progress_publish(session, p)
+    return len(rows), (rows[-1].id if rows else after_id)

app/services/study/publish_projection.py

@@ -0,0 +1,112 @@
+"""발행 projection — 소스 행을 render-ready payload + 안정 해시로 변환 (순수 함수).
+
+뷰어가 보는 '단일 진실'은 이 payload 까지 (DS 내부 실험 스키마는 계약 뒤 격리).
+kind 별 projector. payload_hash = 정렬된 JSON 의 sha256 = (payload_hash, deleted) 디둡 키.
+
+★주의(plan study-to-viewer-slice1 r2): 과목/시험메타를 per-question payload 에 인라인 —
+  bulk subject rename 시 N행 churn. 정규화(과목=별 kind subject ref)는 churn 최적화 후속(P0-1b),
+  읽기 정합엔 무영향. 지금은 인라인(상관관계 단순)으로 두고 후속 PR 에서 분리.
+SCHEMA_VERSION = 엔벨로프 버전. payload 모양 진화 시 bump + 뷰어 range 수용(P0-2).
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import Any
+
+SCHEMA_VERSION = 1
+
+KIND_QUESTION = "study_question"
+KIND_EXPLANATION = "study_explanation"
+KIND_TOPIC = "study_topic"
+KIND_CARD = "study_card"  # ★뷰어 pubstudy.ts 의 KIND_CARD 와 일치 필수(S-3 forward-contract).
+KIND_CARD_PROGRESS = "study_card_progress"  # 카드 SR 상태 read model (S-4, viewer C-4 소비).
+
+
+def payload_hash(payload: dict[str, Any]) -> str:
+    """정렬 JSON 의 sha256 — (payload_hash, deleted) 디둡 키. 키 순서/공백 비의존."""
+    canonical = json.dumps(payload, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
+    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()
+
+
+def project_question(q: Any) -> dict[str, Any]:
+    """study_question → 발행 payload. 정답 포함(개인 학습툴, plan Q2). 이미지는 ref 만(P0-4, 후속)."""
+    return {
+        "topic_id": q.study_topic_id,
+        "question_text": q.question_text,
+        "choices": [q.choice_1, q.choice_2, q.choice_3, q.choice_4],
+        "correct_choice": q.correct_choice,
+        "subject": q.subject,
+        "scope": q.scope,
+        "exam_name": q.exam_name,
+        "exam_round": q.exam_round,
+        "exam_question_number": q.exam_question_number,
+        "explanation": q.explanation,  # 수동 해설(있으면). AI 해설은 별 kind.
+    }
+
+
+def project_explanation(q: Any) -> dict[str, Any] | None:
+    """study_question 의 AI 해설 → 별 발행 kind. ready 일 때만(없으면 None=발행 안 함).
+
+    재조우 표시용 선발행. 신규 오답은 4-A 워커가 ~90s 후 ready→재발행(P2-3 결선, P0-1b).
+    """
+    if getattr(q, "ai_explanation_status", None) != "ready" or not getattr(q, "ai_explanation", None):
+        return None
+    gen = getattr(q, "ai_explanation_generated_at", None)
+    return {
+        "question_source_id": q.id,
+        "explanation_md": q.ai_explanation,
+        "model": getattr(q, "ai_explanation_model", None),
+        "generated_at": gen.isoformat() if gen else None,
+    }
+
+
+def project_card(c: Any) -> dict[str, Any]:
+    """study_memo_card → 발행 payload (S-2). 순수 변환 — 발행 자격(needs_review=false &
+    미삭제) 판단은 호출측(enqueue_card_publish)이 카드 상태로. payload 계약 = 뷰어
+    pubstudy.ts getCards 와 동형(format·cue·fact·cloze_text·source_question_id·source_generated_at).
+    """
+    gen = getattr(c, "source_generated_at", None)
+    return {
+        "format": c.format,
+        "cue": c.cue,
+        "fact": c.fact,
+        "cloze_text": c.cloze_text,
+        "source_question_id": c.source_question_id,
+        "source_generated_at": gen.isoformat() if gen else None,
+    }
+
+
+def project_card_progress(p: Any) -> dict[str, Any]:
+    """study_memo_card_progress → 발행 payload (S-4) = 카드 SR 상태 read model.
+
+    ★ALL row 발행(due_at NULL sentinel=암-on-new · terminal=졸업 포함). due-only 발행하면
+    sentinel 누락 → viewer 가 '미확인' 오분류. SR 계산은 DS(sr_schedule), 여긴 결과만.
+    card_id = pub_card 의 source_id(=DS card.id) → viewer C-4 가 pub_card LEFT JOIN 하는 키.
+    """
+    due = getattr(p, "due_at", None)
+    rev = getattr(p, "last_reviewed_at", None)
+    return {
+        "card_id": p.card_id,
+        "topic_id": p.study_topic_id,
+        "last_outcome": p.last_outcome,
+        "last_reviewed_at": rev.isoformat() if rev else None,
+        "due_at": due.isoformat() if due else None,
+        "review_stage": p.review_stage,
+    }
+
+
+def project_topic(t: Any) -> dict[str, Any]:
+    """study_topic → 발행 payload (S-1, plan study-viewer-port).
+
+    topic 메타만 신규 발행 — viewer 가 주제 단위 퀴즈를 만들 최소 정보.
+    회차 목록은 발행 안 함 = viewer 가 pub_content(study_question) 의 exam_name/exam_round 로
+    파생(추가 발행 불요, plan S-1 결정). topic_id 는 project_question 의 topic_id(=study_topic_id)
+    와 동일 DS 식별자라 viewer 가 문항→주제 상관에 사용(pub_id 는 opaque 라 상관 키 아님).
+    """
+    return {
+        "topic_id": t.id,
+        "name": t.name,
+        "exam_round_size": t.exam_round_size,
+    }

app/services/study/subject_note_rag.py

@@ -20,6 +20,7 @@ from models.chunk import DocumentChunk
 from models.document import Document
 from models.study_question import StudyQuestion
 from models.study_topic import StudyTopicDocument
+from services.search.license_filter import restricted_exclude_orm
 
 logger = logging.getLogger(__name__)
 
@@ -113,6 +114,9 @@ async def _gather_document_evidence(
             select(Document.id, Document.title, Document.ai_summary).where(
                 Document.id.in_(doc_ids),
                 Document.deleted_at.is_(None),
+                # B-4: licensed_restricted 제외 — explanation_rag 와 동일 술어(a안 U-2①). 누락 시
+                # 구매 자료 verbatim 이 분야노트 RAG 로 새던 보안 drift(복제 과정 누락).
+                restricted_exclude_orm(),
             )
         )
     ).all()

app/workers/classify_worker.py

@@ -40,6 +40,7 @@ from ai.client import (
 )
 from ai.envelope import EscalationEnvelope
 from core.config import settings
+from services.search.llm_gate import Priority, acquire_mlx_gate
 from core.utils import setup_logger
 from models.document import Document
 from models.queue import StageDeferred, enqueue_stage
@@ -89,6 +90,7 @@ HARD_ESCALATE_REASONS = {
 class TriageOutput(BaseModel):
     """p3a_short_summary (4B) 응답 스키마. 파싱 실패 시 기본값 + escalate=True fallback."""
 
+    ai_summary: str = ""  # B-1 3→2: triage 가 ai_summary 도 생산 (별 summarize 콜 대체)
     tldr: str = ""
     bullets: list[str] = Field(default_factory=list)
     tags: list[str] = Field(default_factory=list)
@@ -578,16 +580,7 @@ async def process(
                 "reason": "classify pipeline",
             }
 
-        # ─── 2. Legacy 요약 (primary 또는 deep) ───
-        try:
-            summary = await client.summarize(doc.extracted_text[:50000], cfg=legacy_cfg)
-        except Exception as exc:
-            if legacy_cfg is not None and is_deferrable_error(exc):
-                raise StageDeferred(f"macbook_unavailable:{type(exc).__name__}") from exc
-            raise
-        doc.ai_summary = strip_thinking(summary)
-
-        # ─── 메타데이터 (legacy 완료) — 실제 처리 머신 귀속 (drain=qwen-macbook) ───
+        # ─── 메타데이터 (classify 완료) — 실제 처리 머신 귀속 (drain=qwen-macbook) ───
         doc.ai_model_version = (legacy_cfg or settings.ai.primary).model
         doc.ai_processed_at = datetime.now(timezone.utc)
 
@@ -597,13 +590,27 @@ async def process(
             f"confidence={doc.ai_confidence:.2f}, tags={doc.ai_tags}"
         )
 
-        # ─── 3. PR-B B-1 — tier triage (4B, 실패는 legacy 결과 보존) ───
+        # ─── 2+3 통합 (B-1 3→2): tier triage 가 tldr/bullets/tier + ai_summary 생산.
+        # 기존 별도 summarize 콜 제거 → 본문 prefill 1회 절감 (Mac mini 부하). 실패는 fallback.
         try:
             await _run_tier_triage(client, doc, session, use_deep=use_deep)
         except StageDeferred:
             raise  # 보류는 실패가 아님 — drain/consumer 가 attempts 미소모 처리
         except Exception as exc:
-            logger.exception(f"[triage] id={document_id} 전체 실패 — legacy 유지: {exc}")
+            logger.exception(f"[triage] id={document_id} 전체 실패: {exc}")
+
+        # ─── ai_summary fallback: triage 가 못 채운 경우만 summarize ───
+        # (>120K long_context 는 triage 가 LLM skip, 또는 triage 파싱실패). 정상 경로는 미발동.
+        if not doc.ai_summary:
+            try:
+                summary = await client.summarize(doc.extracted_text[:50000], cfg=legacy_cfg)
+                doc.ai_summary = strip_thinking(summary)
+            except Exception as exc:
+                if legacy_cfg is not None and is_deferrable_error(exc):
+                    raise StageDeferred(f"macbook_unavailable:{type(exc).__name__}") from exc
+                # ai_summary=NULL 로 완료되면 digest/briefing 이 조용히 제외 → ERROR 로 가시화
+                # (best-effort 강등 자체는 유지, 운영 추적성만 보강).
+                logger.error(f"[summary-fallback] id={document_id} ai_summary 미생성: {exc}")
 
     finally:
         await client.close()
@@ -673,7 +680,10 @@ async def _run_tier_triage(
             # 는 아래 generic except 에 먹히지 않게 먼저 전파.
             raw_triage = await call_deep_or_defer(client, prompt, cfg=deep_triage_cfg)
         else:
-            raw_triage = await client.call_triage(prompt)
+            # consumer 경로 call_triage 는 PR #20 이후 primary 와 동일 Mac mini endpoint —
+            # evidence/classifier 처럼 gate 안에서 호출(영구 룰: 같은 endpoint 예외 없이 gate).
+            async with acquire_mlx_gate(Priority.BACKGROUND):
+                raw_triage = await client.call_triage(prompt)
     except StageDeferred:
         raise  # drain 이 attempts 미소모 + 백오프로 처리 (sleep-안전)
     except Exception as exc:
@@ -770,6 +780,9 @@ async def _apply_triage_result(
     if not parse_error:
         doc.ai_tldr = (triage_out.tldr or "").strip() or None
         doc.ai_bullets = triage_out.bullets or []
+        # B-1 3→2: triage 가 ai_summary 도 생산(summarize 콜 대체). 비면 process() 가 fallback.
+        if triage_out.ai_summary.strip():
+            doc.ai_summary = triage_out.ai_summary.strip()
         # Memo Intake Upgrade PR-2B — event kind hint (4B 가 출력했을 때만)
         # 허용 enum 외 값이면 무시 (DB enum 제약). AI worker 는 events row 직접 생성 X.
         valid_kinds = {"note", "task", "calendar_event", "activity_log", "reference"}

app/workers/csb_collector.py

@@ -140,7 +140,8 @@ async def _download_pdf(url: str, dest: Path) -> int:
     if len(resp.content) > _MAX_PDF_BYTES:
         raise FeedError(f"PDF 크기 초과 ({len(resp.content)} bytes): {url}")
     dest.parent.mkdir(parents=True, exist_ok=True)
-    dest.write_bytes(resp.content)
+    # 최대 50MB PDF write 는 동기 blocking — 이벤트루프 점유 회피 to_thread (R5 동형).
+    await asyncio.to_thread(dest.write_bytes, resp.content)
     return len(resp.content)
 
 
@@ -190,9 +191,11 @@ async def _ingest_pdf(session, page_slug: str, pdf_url: str) -> bool:
 
     dest = Path(settings.nas_mount_path) / rel_path
     size = await _download_pdf(pdf_url, dest)
+    # 50MB PDF read + sha256 는 동기 blocking(I/O+CPU) — 이벤트루프 점유 회피 to_thread (R5 동형).
+    file_hash = await asyncio.to_thread(lambda: hashlib.sha256(dest.read_bytes()).hexdigest())
     doc = Document(
         file_path=rel_path,
-        file_hash=hashlib.sha256(dest.read_bytes()).hexdigest(),
+        file_hash=file_hash,
         file_format="pdf",
         file_size=size,
         file_type="immutable",
@@ -374,11 +377,17 @@ async def run(bulk: bool = False, limit: int = 0) -> None:
 
     totals = {"page": 0, "pdf": 0, "skip": 0}
     for i, (url, lastmod) in enumerate(todo, 1):
-        async with async_session() as session:
-            src = await session.get(NewsSource, source_id)
-            counts = await _ingest_url(session, src, url, lastmod)
-            _set_watermark(src, lastmod)
-            await session.commit()
+        # 2026-06-20 C2: URL 1건 실패가 주간 run 전체를 중단(이후 URL 스킵·watermark 정지)하던 것 차단.
+        # 각 iteration 은 자체 session(async with) 이라 실패 격리 — 건너뛰고 계속.
+        try:
+            async with async_session() as session:
+                src = await session.get(NewsSource, source_id)
+                counts = await _ingest_url(session, src, url, lastmod)
+                _set_watermark(src, lastmod)
+                await session.commit()
+        except Exception as e:
+            logger.error(f"[csb] URL 처리 실패 (건너뜀): {url} — {str(e) or repr(e)}")
+            continue
         for k in totals:
             totals[k] += counts[k]
         if i % 10 == 0:

app/workers/extract_worker.py

@@ -67,21 +67,45 @@ def _postprocess_ocr(text: str) -> str:
     return text.strip()
 
 
-def _extract_pdf_pymupdf(file_path: Path) -> str:
-    """PyMuPDF fallback — 페이지 단위 스트리밍으로 대형 PDF도 저메모리 처리"""
+def _extract_pdf_pymupdf(
+    file_path: Path, start_page: int | None = None, end_page: int | None = None
+) -> str:
+    """PyMuPDF fallback — 페이지 단위 스트리밍으로 대형 PDF도 저메모리 처리.
+
+    G2 (PR-G2-2): start_page/end_page(1-based inclusive) 가 주어지면 그 범위만 추출
+    (번들 자식 doc = 부모 파일 공유 + 자기 page 범위). 둘 다 None = 전체(기존 동작 동일).
+    """
     import fitz
     text_parts = []
     with fitz.open(str(file_path)) as doc:
-        for page in doc:
-            text_parts.append(page.get_text())
+        if start_page is None and end_page is None:
+            for page in doc:
+                text_parts.append(page.get_text())
+        else:
+            # 1-based inclusive → 0-based range. 범위는 [0, page_count] 로 클램프(방어).
+            total = doc.page_count
+            lo = max(1, start_page or 1) - 1
+            hi = min(total, end_page or total)        # inclusive 끝 (0-based 마지막 인덱스 = hi-1)
+            for i in range(lo, hi):
+                text_parts.append(doc.load_page(i).get_text())
     return "\n".join(text_parts)
 
 
-def _get_pdf_page_count(file_path: Path) -> int:
-    """PDF 페이지 수 확인"""
+def _get_pdf_page_count(
+    file_path: Path, start_page: int | None = None, end_page: int | None = None
+) -> int:
+    """PDF 페이지 수 확인. G2: 범위가 주어지면 그 범위의 페이지 수(자식 doc 밀도 계산용).
+
+    둘 다 None = 전체 페이지 수(기존 동작 동일).
+    """
     import fitz
     with fitz.open(str(file_path)) as doc:
-        return len(doc)
+        total = len(doc)
+        if start_page is None and end_page is None:
+            return total
+        lo = max(1, start_page or 1)
+        hi = min(total, end_page or total)
+        return max(0, hi - lo + 1)
 
 
 async def _call_ocr(file_path: Path, is_image: bool, max_pages: int = 200) -> str | None:
@@ -310,6 +334,49 @@ async def process(document_id: int, session: AsyncSession) -> None:
         doc.extracted_at = datetime.now(timezone.utc)
         return
 
+    # ─── G2 (PR-G2-2): 번들 자식 PDF — 부모 파일 공유 + 자기 page 범위만 추출 ───
+    # kordoc 서비스는 page-range 파라미터가 없어 전체 파일을 파싱한다(자식엔 부적합) → kordoc
+    # 우회, PyMuPDF 로 [bundle_page_start, bundle_page_end] 범위만 추출. range OCR 은 본 PR 범위
+    # 밖(자식은 ToC 존재 = digital text layer 전제 → 대개 OCR 불필요). PyMuPDF 텍스트가 빈약해도
+    # 그대로 보존하고 사유를 남긴다.
+    if fmt == "pdf" and doc.bundle_page_start is not None and doc.bundle_page_end is not None:
+        # 후보 A: 자식 file_path 는 합성값(`{부모}#p{s}-{e}`) → 실파일 = bundle_source_path 로 부모경로
+        # 복원 + NFC/NFD resolve. (자식 file_path 는 디스크에 없음.)
+        from workers.presegment_worker import _resolve_path as _resolve_bundle_path
+        from workers.presegment_worker import bundle_source_path
+        real_rel = bundle_source_path(doc.file_path)
+        src = _resolve_bundle_path(str(Path(settings.nas_mount_path) / real_rel))
+        if src is None:
+            raise FileNotFoundError(f"번들 원본 파일 없음: {real_rel}")
+        start, end = doc.bundle_page_start, doc.bundle_page_end
+        try:
+            pymupdf_text = _extract_pdf_pymupdf(src, start, end)
+            page_count = _get_pdf_page_count(src, start, end)
+        except Exception as e:
+            logger.error(f"[pymupdf:child] {doc.file_path} pages={start}-{end} 실패: {e}")
+            raise
+
+        meta = doc.extract_meta or {}
+        meta["presegment_child_range"] = {"start_page": start, "end_page": end}
+        meta["pymupdf_chars"] = len(pymupdf_text.strip())
+        should, reason = _should_ocr(pymupdf_text, page_count)
+        if should:
+            # range OCR 미지원(후속 PR) — PyMuPDF 결과 유지 + 사유 기록(silent skip 아님).
+            meta["ocr_skip_reason"] = "presegment_child_range_ocr_unsupported"
+            meta["ocr_reason"] = reason
+            logger.warning(
+                f"[pymupdf:child] {doc.file_path} pages={start}-{end} "
+                f"OCR 필요({reason})하나 range OCR 미지원 → PyMuPDF 결과 유지"
+            )
+        doc.extracted_text = pymupdf_text.replace("\x00", "")
+        doc.extracted_at = datetime.now(timezone.utc)
+        doc.extractor_version = PYMUPDF_VERSION if pymupdf_text.strip() else None
+        doc.extract_meta = meta
+        logger.info(
+            f"[pymupdf:child] {doc.file_path} pages={start}-{end} ({len(pymupdf_text)}자)"
+        )
+        return
+
     # ─── kordoc 파싱 (HWP/HWPX/PDF) + PyMuPDF fallback + OCR ───
     if fmt in KORDOC_FORMATS:
         container_path = f"/documents/{doc.file_path}"

app/workers/file_watcher.py

@@ -118,16 +118,18 @@ def _route_media(path: Path, expected_category: str | None) -> tuple[str | None,
     if expected_category == "library":
         # 외부 작성 학습 자료 (KGS Code, 시행규칙 등). 문서 확장자만 수락.
         # frontmatter 해석은 classify_worker (옵션 C) 가 담당. file_watcher 는 라우팅만.
+        # G2: 첫 stage=presegment (후보 A 검증완료). 非PDF/단일 통과, 번들 PDF 만 분할.
         if ext in LIBRARY_DOC_EXTS:
-            return ("library", False, "extract")
+            return ("library", False, "presegment")
         if ext in AUDIO_EXTS or ext in VIDEO_DIRECT_EXTS or ext in VIDEO_QUARANTINE_EXTS:
             return (None, False, None)  # audio/video 잘못 들어오면 skip
         return (None, False, None)  # 기타 알 수 없는 확장자 skip
 
     # Inbox: 문서 파이프 (기존). audio/video 확장자가 실수로 여기 들어오면 skip.
+    # G2: 첫 stage=presegment (후보 A 검증완료). 非PDF/단일 통과, 번들 PDF 만 분할.
     if ext in AUDIO_EXTS or ext in VIDEO_DIRECT_EXTS or ext in VIDEO_QUARANTINE_EXTS:
         return (None, False, None)
-    return (None, False, "extract")
+    return (None, False, "presegment")
 
 
 # ─── Web/Blog ingest (devonagent 트랙) 헬퍼 ──────────────────────────────────
@@ -226,7 +228,8 @@ async def _ingest_web_file(session, file_path: Path, rel_path: str) -> tuple[int
     )
     session.add(doc)
     await session.flush()
-    await enqueue_stage(session, doc.id, "extract")
+    # G2: 첫 stage=presegment (후보 A 검증완료). HTML(非PDF)은 presegment 가 무변 통과 → extract.
+    await enqueue_stage(session, doc.id, "presegment")
     return (1, 0)
 
 
@@ -248,104 +251,118 @@ async def watch_inbox():
     for extra_path in settings.additional_watch_targets:
         targets.append((extra_path, "library"))
 
-    async with async_session() as session:
-        # ─── Web/ 트랙 (devonagent) — DEVONthink Smart Rule 이 떨군 .html 만 진입 ───
-        if web_root.exists():
-            # rglob NFS 디렉토리 walk(blocking stat 다발)를 off-thread 로 수집 (R5).
-            for file_path in await asyncio.to_thread(lambda: list(web_root.rglob("*.html"))):
-                if not file_path.is_file() or should_skip(file_path):
-                    continue
-                rel_path = str(file_path.relative_to(nas_root))
-                added, _ = await _ingest_web_file(session, file_path, rel_path)
+    # 파일별 독립 세션+commit 으로 격리 — 한 파일 실패(예: rglob↔stat 사이 삭제로 FileNotFoundError,
+    # flush 오류)가 watch_inbox 전체를 raise·롤백해 그 사이클 등록분을 모두 잃거나, 결정적 poison
+    # 파일이 매 사이클 같은 지점에서 중단시키는 것을 차단 (news_collector/csb_collector 와 동형).
+    # ─── Web/ 트랙 (devonagent) — DEVONthink Smart Rule 이 떨군 .html 만 진입 ───
+    if web_root.exists():
+        # rglob NFS 디렉토리 walk(blocking stat 다발)를 off-thread 로 수집 (R5).
+        for file_path in await asyncio.to_thread(lambda: list(web_root.rglob("*.html"))):
+            if not file_path.is_file() or should_skip(file_path):
+                continue
+            rel_path = str(file_path.relative_to(nas_root))
+            try:
+                async with async_session() as session:
+                    added, _ = await _ingest_web_file(session, file_path, rel_path)
+                    await session.commit()
                 new_count += added
-
-        # ─── PKM 트랙 (기존 drive_sync) ─────────────────────────────────────────
-        for sub, expected_category in targets:
-            scan_root = pkm_root / sub
-            if not scan_root.exists():
+            except Exception as e:
+                logger.warning("[Web] 파일 처리 실패 skip path=%s: %s", rel_path, e)
                 continue
 
-            # 안전 자료실 A-2/B-4 — 타깃 폴더 기반 (material, jurisdiction, license)
-            target_mt, target_jur, target_license = _TARGET_AXIS.get(
-                Path(sub).name, (None, None, None)
+    # ─── PKM 트랙 (기존 drive_sync) ─────────────────────────────────────────
+    for sub, expected_category in targets:
+        scan_root = pkm_root / sub
+        if not scan_root.exists():
+            continue
+
+        # 안전 자료실 A-2/B-4 — 타깃 폴더 기반 (material, jurisdiction, license)
+        target_mt, target_jur, target_license = _TARGET_AXIS.get(
+            Path(sub).name, (None, None, None)
+        )
+
+        # NFS 디렉토리 walk(blocking) off-thread 수집 (R5).
+        for file_path in await asyncio.to_thread(lambda: list(scan_root.rglob("*"))):
+            if not file_path.is_file() or should_skip(file_path):
+                continue
+
+            category, needs_conversion, next_stage = _route_media(
+                file_path, expected_category
             )
 
-            # NFS 디렉토리 walk(blocking) off-thread 수집 (R5).
-            for file_path in await asyncio.to_thread(lambda: list(scan_root.rglob("*"))):
-                if not file_path.is_file() or should_skip(file_path):
-                    continue
+            # audio/video 폴더에 엉뚱한 확장자가 들어왔거나 Inbox 에
+            # audio/video 가 잘못 떨어진 경우 — 이 라운드에서 아예 skip
+            if category is None and next_stage is None:
+                continue
 
-                category, needs_conversion, next_stage = _route_media(
-                    file_path, expected_category
-                )
-
-                # audio/video 폴더에 엉뚱한 확장자가 들어왔거나 Inbox 에
-                # audio/video 가 잘못 떨어진 경우 — 이 라운드에서 아예 skip
-                if category is None and next_stage is None:
-                    continue
-
-                rel_path = str(file_path.relative_to(nas_root))
+            rel_path = str(file_path.relative_to(nas_root))
+            try:
                 # GB 파일 SHA-256 은 이벤트 루프를 점유 → 같은 루프의 모든 1분 주기 consumer
                 # + FastAPI 요청이 수십초~분 동시 정지. to_thread 오프로드. 스캔 루프가 이미
                 # 순차라 file_hash 는 한 번에 하나만 실행(직렬화) — 병렬 해싱 X = NFS 2.5GbE
-                # 대역폭·버퍼 메모리 blowup 방지 (R5).
+                # 대역폭·버퍼 메모리 blowup 방지 (R5). 세션 밖에서 계산(커넥션 미점유).
                 fhash = await asyncio.to_thread(file_hash, file_path)
 
-                result = await session.execute(
-                    select(Document).where(Document.file_path == rel_path)
-                )
-                existing = result.scalar_one_or_none()
-
-                if existing is None:
-                    ext = file_path.suffix.lstrip(".").lower() or "unknown"
-                    doc = Document(
-                        file_path=rel_path,
-                        file_hash=fhash,
-                        file_format=ext,
-                        file_size=file_path.stat().st_size,
-                        file_type="immutable",
-                        title=file_path.stem,
-                        source_channel="drive_sync",
-                        category=category,
-                        needs_conversion=needs_conversion,
-                        # 안전 자료실 A-2/B-4 — watch 타깃 매핑 (KGS=law/KR 등, 비대상=NULL)
-                        material_type=target_mt,
-                        jurisdiction=target_jur,
+                async with async_session() as session:
+                    result = await session.execute(
+                        select(Document).where(Document.file_path == rel_path)
                     )
-                    # B-4 — 타깃 폴더 license 주입(restricted 포함, 비대상=미주입). classify 는
-                    # material_type IS NULL 일 때만 제안 + extract_meta 미기록이라 주입 보존.
-                    if target_license:
-                        doc.extract_meta = {"license": dict(target_license)}
-                    session.add(doc)
-                    await session.flush()
+                    existing = result.scalar_one_or_none()
 
-                    if next_stage:
-                        await enqueue_stage(session, doc.id, next_stage)
-                    new_count += 1
+                    if existing is None:
+                        ext = file_path.suffix.lstrip(".").lower() or "unknown"
+                        doc = Document(
+                            file_path=rel_path,
+                            file_hash=fhash,
+                            file_format=ext,
+                            file_size=file_path.stat().st_size,
+                            file_type="immutable",
+                            title=file_path.stem,
+                            source_channel="drive_sync",
+                            category=category,
+                            needs_conversion=needs_conversion,
+                            # 안전 자료실 A-2/B-4 — watch 타깃 매핑 (KGS=law/KR 등, 비대상=NULL)
+                            material_type=target_mt,
+                            jurisdiction=target_jur,
+                        )
+                        # B-4 — 타깃 폴더 license 주입(restricted 포함, 비대상=미주입). classify 는
+                        # material_type IS NULL 일 때만 제안 + extract_meta 미기록이라 주입 보존.
+                        if target_license:
+                            doc.extract_meta = {"license": dict(target_license)}
+                        session.add(doc)
+                        await session.flush()
 
-                elif existing.file_hash != fhash:
-                    existing.file_hash = fhash
-                    existing.file_size = file_path.stat().st_size
-                    # 기존 문서에 category/quarantine flag 가 비어있으면 보정
-                    if existing.category is None and category is not None:
-                        existing.category = category
-                    if needs_conversion and not getattr(existing, "needs_conversion", False):
-                        existing.needs_conversion = True
-                    # B-4 — 축/license 보정(B-4 이전 적재분이 재변경 시): material 미설정 시 주입,
-                    # license 부재 시에만 merge 주입(clobber 회피 — 기존 extract_meta 키 보존).
-                    if existing.material_type is None and target_mt is not None:
-                        existing.material_type = target_mt
-                        existing.jurisdiction = target_jur
-                    if target_license and not (existing.extract_meta or {}).get("license"):
-                        meta = dict(existing.extract_meta or {})
-                        meta["license"] = dict(target_license)
-                        existing.extract_meta = meta
+                        if next_stage:
+                            await enqueue_stage(session, doc.id, next_stage)
+                        await session.commit()
+                        new_count += 1
 
-                    if next_stage:
-                        await enqueue_stage(session, existing.id, next_stage)
-                    changed_count += 1
+                    elif existing.file_hash != fhash:
+                        existing.file_hash = fhash
+                        existing.file_size = file_path.stat().st_size
+                        # 기존 문서에 category/quarantine flag 가 비어있으면 보정
+                        if existing.category is None and category is not None:
+                            existing.category = category
+                        if needs_conversion and not getattr(existing, "needs_conversion", False):
+                            existing.needs_conversion = True
+                        # B-4 — 축/license 보정(B-4 이전 적재분이 재변경 시): material 미설정 시 주입,
+                        # license 부재 시에만 merge 주입(clobber 회피 — 기존 extract_meta 키 보존).
+                        if existing.material_type is None and target_mt is not None:
+                            existing.material_type = target_mt
+                            existing.jurisdiction = target_jur
+                        if target_license and not (existing.extract_meta or {}).get("license"):
+                            meta = dict(existing.extract_meta or {})
+                            meta["license"] = dict(target_license)
+                            existing.extract_meta = meta
 
-        await session.commit()
+                        if next_stage:
+                            await enqueue_stage(session, existing.id, next_stage)
+                        await session.commit()
+                        changed_count += 1
+                    # else: 무변경 → 쓰기 없음 (세션 자동 닫힘, commit 불요)
+            except Exception as e:
+                logger.warning("[PKM] 파일 처리 실패 skip path=%s: %s", rel_path, e)
+                continue
 
     if new_count or changed_count:
         logger.info(f"[Inbox+§3] 새 파일 {new_count}건, 변경 파일 {changed_count}건 등록")

app/workers/law_monitor.py

@@ -1,367 +0,0 @@
-"""법령 모니터 워커 — 국가법령정보센터 API 연동
-
-26개 법령 모니터링, 편/장 단위 분할 저장, 변경 이력 추적.
-매일 07:00 실행 (APScheduler).
-"""
-
-import os
-import re
-from datetime import date, datetime, timezone
-from pathlib import Path
-from xml.etree import ElementTree as ET
-
-import httpx
-from sqlalchemy import select
-
-from core.config import settings
-from core.database import async_session
-from core.utils import create_caldav_todo, file_hash, setup_logger
-from models.automation import AutomationState
-from models.document import Document
-from models.queue import enqueue_stage
-
-logger = setup_logger("law_monitor")
-
-LAW_SEARCH_URL = "https://www.law.go.kr/DRF/lawSearch.do"
-LAW_SERVICE_URL = "https://www.law.go.kr/DRF/lawService.do"
-
-# 모니터링 대상 법령 (26개)
-MONITORED_LAWS = [
-    # 산업안전보건 핵심
-    "산업안전보건법",
-    "산업안전보건법 시행령",
-    "산업안전보건법 시행규칙",
-    "산업안전보건기준에 관한 규칙",
-    "유해위험작업의 취업 제한에 관한 규칙",
-    "중대재해 처벌 등에 관한 법률",
-    "중대재해 처벌 등에 관한 법률 시행령",
-    # 건설안전
-    "건설기술 진흥법",
-    "건설기술 진흥법 시행령",
-    "건설기술 진흥법 시행규칙",
-    "시설물의 안전 및 유지관리에 관한 특별법",
-    # 위험물/화학
-    "위험물안전관리법",
-    "위험물안전관리법 시행령",
-    "위험물안전관리법 시행규칙",
-    "화학물질관리법",
-    "화학물질관리법 시행령",
-    "화학물질의 등록 및 평가 등에 관한 법률",
-    # 소방/전기/가스
-    "소방시설 설치 및 관리에 관한 법률",
-    "소방시설 설치 및 관리에 관한 법률 시행령",
-    "전기사업법",
-    "전기안전관리법",
-    "고압가스 안전관리법",
-    "고압가스 안전관리법 시행령",
-    "액화석유가스의 안전관리 및 사업법",
-    # 근로/환경
-    "근로기준법",
-    "환경영향평가법",
-]
-
-
-async def run():
-    """법령 변경 모니터링 실행"""
-    law_oc = os.getenv("LAW_OC", "")
-    if not law_oc:
-        logger.warning("LAW_OC 미설정 — 법령 API 승인 대기 중")
-        return
-
-    async with async_session() as session:
-        state = await session.execute(
-            select(AutomationState).where(AutomationState.job_name == "law_monitor")
-        )
-        state_row = state.scalar_one_or_none()
-        last_check = state_row.last_check_value if state_row else None
-
-        today = datetime.now(timezone.utc).strftime("%Y%m%d")
-        if last_check == today:
-            logger.info("오늘 이미 체크 완료")
-            return
-
-        new_count = 0
-        async with httpx.AsyncClient(timeout=30) as client:
-            for law_name in MONITORED_LAWS:
-                try:
-                    count = await _check_law(client, law_oc, law_name, session)
-                    new_count += count
-                except Exception as e:
-                    logger.error(f"[{law_name}] 체크 실패: {e}")
-
-        # 상태 업데이트
-        if state_row:
-            state_row.last_check_value = today
-            state_row.last_run_at = datetime.now(timezone.utc)
-        else:
-            session.add(AutomationState(
-                job_name="law_monitor",
-                last_check_value=today,
-                last_run_at=datetime.now(timezone.utc),
-            ))
-
-        await session.commit()
-        logger.info(f"법령 모니터 완료: {new_count}건 신규/변경 감지")
-
-
-async def _check_law(
-    client: httpx.AsyncClient,
-    law_oc: str,
-    law_name: str,
-    session,
-) -> int:
-    """단일 법령 검색 → 변경 감지 → 분할 저장"""
-    # 법령 검색 (lawSearch.do)
-    resp = await client.get(
-        LAW_SEARCH_URL,
-        params={"OC": law_oc, "target": "law", "type": "XML", "query": law_name},
-    )
-    resp.raise_for_status()
-
-    root = ET.fromstring(resp.text)
-    total = root.findtext(".//totalCnt", "0")
-    if total == "0":
-        logger.debug(f"[{law_name}] 검색 결과 없음")
-        return 0
-
-    # 정확히 일치하는 법령 찾기
-    for law_elem in root.findall(".//law"):
-        found_name = law_elem.findtext("법령명한글", "").strip()
-        if found_name != law_name:
-            continue
-
-        mst = law_elem.findtext("법령일련번호", "")
-        proclamation_date = law_elem.findtext("공포일자", "")
-        revision_type = law_elem.findtext("제개정구분명", "")
-
-        if not mst:
-            continue
-
-        # 이미 등록된 법령인지 확인 (같은 법령명 + 공포일자)
-        existing = await session.execute(
-            select(Document).where(
-                Document.title.like(f"{law_name}%"),
-                Document.source_channel == "law_monitor",
-            )
-        )
-        existing_docs = existing.scalars().all()
-
-        # 같은 공포일자 이미 있으면 skip
-        for doc in existing_docs:
-            if proclamation_date in (doc.title or ""):
-                return 0
-
-        # 이전 공포일 찾기 (변경 이력용)
-        prev_date = ""
-        if existing_docs:
-            prev_date = max(
-                (re.search(r'\d{8}', doc.title or "").group() for doc in existing_docs
-                 if re.search(r'\d{8}', doc.title or "")),
-                default=""
-            )
-
-        # 본문 조회 (lawService.do)
-        text_resp = await client.get(
-            LAW_SERVICE_URL,
-            params={"OC": law_oc, "target": "law", "MST": mst, "type": "XML"},
-        )
-        text_resp.raise_for_status()
-
-        # 분할 저장
-        count = await _save_law_split(
-            session, text_resp.text, law_name, proclamation_date,
-            revision_type, prev_date,
-        )
-
-        # DB 먼저 커밋 (알림 실패가 저장을 막지 않도록)
-        await session.commit()
-
-        # CalDAV + SMTP 알림 (실패해도 무시)
-        try:
-            _send_notifications(law_name, proclamation_date, revision_type)
-        except Exception as e:
-            logger.warning(f"[{law_name}] 알림 발송 실패 (무시): {e}")
-
-        return count
-
-    return 0
-
-
-async def _save_law_split(
-    session, xml_text: str, law_name: str, proclamation_date: str,
-    revision_type: str, prev_date: str,
-) -> int:
-    """법령 XML → 장(章) 단위 Markdown 분할 저장"""
-    root = ET.fromstring(xml_text)
-
-    # 조문단위에서 장 구분자 찾기 (조문키가 000으로 끝나는 조문)
-    units = root.findall(".//조문단위")
-    chapters = []  # [(장제목, [조문들])]
-    current_chapter = None
-    current_articles = []
-
-    for unit in units:
-        key = unit.attrib.get("조문키", "")
-        content = (unit.findtext("조문내용", "") or "").strip()
-
-        # 장 구분자: 키가 000으로 끝나고 내용에 "제X장" 포함
-        if key.endswith("000") and re.search(r"제\d+장", content):
-            # 이전 장/서문 저장
-            if current_articles:
-                chapter_name = current_chapter or "서문"
-                chapters.append((chapter_name, current_articles))
-            chapter_match = re.search(r"(제\d+장\s*.+)", content)
-            current_chapter = chapter_match.group(1).strip() if chapter_match else content.strip()
-            current_articles = []
-        else:
-            current_articles.append(unit)
-
-    # 마지막 장 저장
-    if current_articles:
-        chapter_name = current_chapter or "서문"
-        chapters.append((chapter_name, current_articles))
-
-    # 장 분할 성공
-    sections = []
-    if chapters:
-        for chapter_title, articles in chapters:
-            md_lines = [f"# {law_name}\n", f"## {chapter_title}\n"]
-            for article in articles:
-                title = article.findtext("조문제목", "")
-                content = article.findtext("조문내용", "")
-                if title:
-                    md_lines.append(f"\n### {title}\n")
-                if content:
-                    md_lines.append(content.strip())
-            section_name = _safe_name(chapter_title)
-            sections.append((section_name, "\n".join(md_lines)))
-    else:
-        # 장 분할 실패 → 전체 1파일
-        full_md = _law_xml_to_markdown(xml_text, law_name)
-        sections.append(("전문", full_md))
-
-    # 각 섹션 저장
-    inbox_dir = Path(settings.nas_mount_path) / "PKM" / "Inbox"
-    inbox_dir.mkdir(parents=True, exist_ok=True)
-    count = 0
-
-    for section_name, content in sections:
-        filename = f"{law_name}_{proclamation_date}_{section_name}.md"
-        file_path = inbox_dir / filename
-        file_path.write_text(content, encoding="utf-8")
-
-        rel_path = str(file_path.relative_to(Path(settings.nas_mount_path)))
-
-        # 변경 이력 메모
-        note = ""
-        if prev_date:
-            note = (
-                f"[자동] 법령 개정 감지\n"
-                f"이전 공포일: {prev_date}\n"
-                f"현재 공포일: {proclamation_date}\n"
-                f"개정구분: {revision_type}"
-            )
-
-        # 안전 자료실 A-2 — 공포일 파싱 (law published_date = COALESCE(시행일, 공포일) 계약,
-        # 본 레거시 워커는 공포일만 보유 — 시행일 기반 버전 체인은 B-1 statute_collector 소관)
-        _digits = re.sub(r"\D", "", str(proclamation_date or ""))
-        pub_date = None
-        if len(_digits) == 8:
-            try:
-                pub_date = date(int(_digits[:4]), int(_digits[4:6]), int(_digits[6:8]))
-            except ValueError:
-                pub_date = None
-
-        doc = Document(
-            file_path=rel_path,
-            file_hash=file_hash(file_path),
-            file_format="md",
-            file_size=len(content.encode()),
-            file_type="immutable",
-            title=f"{law_name} ({proclamation_date}) {section_name}",
-            source_channel="law_monitor",
-            data_origin="work",
-            category="law",
-            # 안전 자료실 A-2 — ingest 시점 deterministic. 법령 텍스트 = 저작권법 제7조
-            # 비보호 저작물 (public domain). 본 워커는 휴면(LAW_OC 미설정)이나 코드 경로 유지.
-            material_type="law",
-            jurisdiction="KR",
-            published_date=pub_date,
-            extract_meta={"license": {"scheme": "public_domain", "redistribute": True,
-                                      "attribution": "국가법령정보센터"}},
-            user_note=note or None,
-        )
-        session.add(doc)
-        await session.flush()
-
-        await enqueue_stage(session, doc.id, "extract")
-        count += 1
-
-    logger.info(f"[법령] {law_name} ({proclamation_date}) → {count}개 섹션 저장")
-    return count
-
-
-def _xml_section_to_markdown(elem) -> str:
-    """XML 섹션(편/장)을 Markdown으로 변환"""
-    lines = []
-    for article in elem.iter():
-        tag = article.tag
-        text = (article.text or "").strip()
-        if not text:
-            continue
-        if "조" in tag:
-            lines.append(f"\n### {text}\n")
-        elif "항" in tag:
-            lines.append(f"\n{text}\n")
-        elif "호" in tag:
-            lines.append(f"- {text}")
-        elif "목" in tag:
-            lines.append(f"  - {text}")
-        else:
-            lines.append(text)
-    return "\n".join(lines)
-
-
-def _law_xml_to_markdown(xml_text: str, law_name: str) -> str:
-    """법령 XML 전체를 Markdown으로 변환"""
-    root = ET.fromstring(xml_text)
-    lines = [f"# {law_name}\n"]
-
-    for elem in root.iter():
-        tag = elem.tag
-        text = (elem.text or "").strip()
-        if not text:
-            continue
-        if "편" in tag and "제목" not in tag:
-            lines.append(f"\n## {text}\n")
-        elif "장" in tag and "제목" not in tag:
-            lines.append(f"\n## {text}\n")
-        elif "조" in tag:
-            lines.append(f"\n### {text}\n")
-        elif "항" in tag:
-            lines.append(f"\n{text}\n")
-        elif "호" in tag:
-            lines.append(f"- {text}")
-        elif "목" in tag:
-            lines.append(f"  - {text}")
-
-    return "\n".join(lines)
-
-
-def _safe_name(name: str) -> str:
-    """파일명 안전 변환"""
-    return re.sub(r'[^\w가-힣-]', '_', name).strip("_")
-
-
-def _send_notifications(law_name: str, proclamation_date: str, revision_type: str):
-    """CalDAV 할일 알림 (SMTP 발송은 2026-06-10 폐기 — CalDAV 가 단일 알림 채널)"""
-    caldav_url = os.getenv("CALDAV_URL", "")
-    caldav_user = os.getenv("CALDAV_USER", "")
-    caldav_pass = os.getenv("CALDAV_PASS", "")
-    if caldav_url and caldav_user:
-        create_caldav_todo(
-            caldav_url, caldav_user, caldav_pass,
-            title=f"법령 검토: {law_name}",
-            description=f"공포일자: {proclamation_date}, 개정구분: {revision_type}",
-            due_days=7,
-        )

app/workers/marker_worker.py

@@ -39,7 +39,11 @@ from models.queue import ProcessingQueue
 
 logger = logging.getLogger(__name__)
 
-MARKER_ENDPOINT = "http://marker-service:3300/convert"
+# 마크다운 추출 엔드포인트. compose env `MARKER_ENDPOINT`(base URL)에서 읽는다 —
+# 기본=marker(무변), 컷오버=`http://mineru-service:3301` 로 env 플립만으로 전환.
+# marker/mineru 가 동일 /convert 계약(file_path·start/end·md+base64 images)이라 워커 무변.
+_MARKDOWN_BASE = os.getenv("MARKER_ENDPOINT", "http://marker-service:3300").rstrip("/")
+MARKER_ENDPOINT = _MARKDOWN_BASE if _MARKDOWN_BASE.endswith("/convert") else _MARKDOWN_BASE + "/convert"
 MARKER_TIMEOUT = 300                    # 큰 PDF 5 분 한도
 MAX_PAGES = 200                         # 소형 1-shot 경로 /convert max_pages 안전장치
 
@@ -181,7 +185,10 @@ async def process(document_id: int, session: AsyncSession) -> None:
         await _fail(session, document_id, "no file_path")
         return
 
-    container_path = _to_marker_path(doc.file_path)
+    # 후보 A: 자식(bundle cols)은 합성 file_path(`{부모}#p{s}-{e}`) → 실파일 = bundle_source_path
+    # 로 부모경로 복원. 일반 doc 은 그대로(접미사 없음). marker/mineru 는 실파일 + page 범위로 변환.
+    from workers.presegment_worker import bundle_source_path
+    container_path = _to_marker_path(bundle_source_path(doc.file_path))
     suffix = Path(container_path).suffix.lower()
 
     # ---- (3) office/hwp → md (C-2): PDF 외 지원 포맷은 office_md 하이브리드 변환 ----
@@ -203,7 +210,21 @@ async def process(document_id: int, session: AsyncSession) -> None:
         return
 
     # ---- (4) page_count gauge + 분기 (LargeDoc split) ----
-    page_count = _get_page_count(container_path)
+    # G2 (PR-G2-2): 번들 자식 doc 은 부모 파일 공유 + 자기 page 범위([bundle_page_start, end],
+    # 1-based inclusive)만 변환해야 한다. page_offset = 절대 시작페이지(부모 파일 기준), page_count =
+    # 자식 범위의 페이지 수. cols 가 NULL(일반 doc)이면 page_offset=1 + 전체 page_count = 기존 동작 동일.
+    file_page_count = _get_page_count(container_path)
+    is_child = doc.bundle_page_start is not None and doc.bundle_page_end is not None
+    if is_child:
+        page_offset = doc.bundle_page_start
+        if file_page_count is not None:
+            child_end = min(doc.bundle_page_end, file_page_count)
+            page_count = max(0, child_end - doc.bundle_page_start + 1)
+        else:
+            page_count = doc.bundle_page_end - doc.bundle_page_start + 1
+    else:
+        page_offset = 1
+        page_count = file_page_count
 
     # >MAX_SPLIT_PAGES = 변환 안전상태(manual_review). silently skip 아님.
     if page_count is not None and page_count > MAX_SPLIT_PAGES:
@@ -222,20 +243,35 @@ async def process(document_id: int, session: AsyncSession) -> None:
 
     # ---- (6) 변환 분기: 소형 1-shot / 대형(>SPLIT_THRESHOLD) page-range 분할 ----
     if page_count is not None and page_count > SPLIT_THRESHOLD_PAGES:
-        await _process_split(doc, document_id, container_path, page_count, session)
+        await _process_split(doc, document_id, container_path, page_count, session, page_offset)
     else:
-        await _process_single(doc, document_id, container_path, session)
+        await _process_single(doc, document_id, container_path, session, page_count, page_offset)
 
 
 async def _process_single(
-    doc: Document, document_id: int, container_path: str, session: AsyncSession
+    doc: Document, document_id: int, container_path: str, session: AsyncSession,
+    page_count: int | None = None, page_offset: int = 1,
 ) -> None:
-    """소형 PDF(≤ SPLIT_THRESHOLD_PAGES) 통째 1-shot 변환 (Phase 1B/1B.5 기존 경로)."""
+    """소형 PDF(≤ SPLIT_THRESHOLD_PAGES) 통째 1-shot 변환 (Phase 1B/1B.5 기존 경로).
+
+    G2 (PR-G2-2): 번들 자식(page_offset>1)은 [page_offset, page_offset+page_count-1] 범위만
+    변환하도록 marker 에 start_page/end_page 를 명시한다. 일반 doc(page_offset=1)은 기존과
+    동일하게 max_pages 만 보낸다(payload byte-identical).
+    """
+    # 일반 doc = 기존 payload 유지. 자식만 절대 page 범위를 명시(부모 파일 기준 1-based inclusive).
+    if page_offset > 1 and page_count is not None:
+        req_json = {
+            "file_path": container_path,
+            "start_page": page_offset,
+            "end_page": page_offset + page_count - 1,
+        }
+    else:
+        req_json = {"file_path": container_path, "max_pages": MAX_PAGES}
     try:
         async with httpx.AsyncClient(timeout=MARKER_TIMEOUT) as client:
             resp = await client.post(
                 MARKER_ENDPOINT,
-                json={"file_path": container_path, "max_pages": MAX_PAGES},
+                json=req_json,
             )
             resp.raise_for_status()
             data = resp.json()
@@ -264,6 +300,11 @@ async def _process_single(
             f"[marker] transient error id={document_id} kind={type(exc).__name__}: {exc}"
         )
         raise
+    except json.JSONDecodeError as exc:
+        # 200 응답의 truncated/malformed body — 연결 흔들림 등 transient. _fail(non-retryable)
+        # 로 박지 말고 raise → queue retry (max_attempts 까지). 진짜 손상이면 재시도 후 failed.
+        logger.warning(f"[marker] malformed json body (200) id={document_id}: {exc}")
+        raise
     except Exception as exc:
         logger.exception(f"[marker] unexpected error id={document_id}: {exc}")
         await _fail(session, document_id, str(exc)[:1000])
@@ -271,6 +312,10 @@ async def _process_single(
 
     # ---- (7) image persist + md_content rewrite (Phase 1B.5) ----
     md_content_raw = data["md_content"]
+    # 2026-06-20 H1: 빈 추출(스캔/이미지 PDF)을 md_status=success + 빈 md 로 박제 X
+    # (계약: md_status in {success,partial} => md 非공백). office arm 동형 raise → queue 재시도 후 failed.
+    if not md_content_raw.strip():
+        raise ValueError("empty md_content (blank extraction) — success 박제 차단")
     images_resp = data.get("images") if MARKDOWN_IMAGE_PERSIST else None
 
     saved_images: list[dict[str, Any]] = []
@@ -509,6 +554,7 @@ async def _process_split(
     container_path: str,
     page_count: int,
     session: AsyncSession,
+    page_offset: int = 1,
 ) -> None:
     """대형 PDF page-range 분할 변환.
 
@@ -519,6 +565,10 @@ async def _process_split(
 
     invariant: page numbering = 1-based inclusive (batch1: 1..BATCH_PAGES, ...).
     marker slug(`_page_0_*`) 는 batch 마다 재시작 → batch 별 rewrite 후 stitch (충돌 회피).
+
+    G2 (PR-G2-2): page_offset = 부모 파일 기준 절대 시작페이지(번들 자식). marker 에 보내는
+    page 는 절대값(page_offset 가산), manifest/기록은 자식 상대값(1-based) 유지 — 일반 doc
+    (page_offset=1)은 abs==rel 이라 기존 동작과 동일.
     """
     n_batches = (page_count + BATCH_PAGES - 1) // BATCH_PAGES
     succeeded: list[dict[str, Any]] = []   # {start_page, end_page, md}
@@ -530,15 +580,17 @@ async def _process_split(
 
     async with httpx.AsyncClient(timeout=MARKER_TIMEOUT) as client:
         for b in range(n_batches):
-            start_page = b * BATCH_PAGES + 1
+            start_page = b * BATCH_PAGES + 1          # 자식 상대 1-based (manifest/기록용)
             end_page = min((b + 1) * BATCH_PAGES, page_count)
+            abs_start = start_page + (page_offset - 1)  # 부모 파일 절대 page (marker 요청용)
+            abs_end = end_page + (page_offset - 1)
             try:
                 resp = await client.post(
                     MARKER_ENDPOINT,
                     json={
                         "file_path": container_path,
-                        "start_page": start_page,
-                        "end_page": end_page,
+                        "start_page": abs_start,
+                        "end_page": abs_end,
                     },
                 )
                 resp.raise_for_status()
@@ -610,6 +662,8 @@ async def _process_split(
 
     md_status = "success" if not failed else "partial"
     stitched = "\n\n".join(b["md"] for b in succeeded)
+    if not stitched.strip():
+        raise ValueError("empty stitched md_content (all batches blank) — success 박제 차단")
     md_content = _build_large_md_content(stitched[:LARGE_DOC_MD_CONTENT_HEAD_CHARS], manifest)
 
     quality = _compute_quality(stitched, doc.extracted_text or "", {"page_count": page_count})

app/workers/news_collector.py

@@ -213,17 +213,25 @@ async def _run_locked():
         result = await session.execute(
             select(NewsSource).where(NewsSource.enabled == True)
         )
-        sources = result.scalars().all()
+        source_ids = [s.id for s in result.scalars().all()]
 
-        if not sources:
-            logger.info("활성화된 뉴스 소스 없음")
-            return
+    if not source_ids:
+        logger.info("활성화된 뉴스 소스 없음")
+        return
 
-        total = 0
-        for source in sources:
-            health = await _get_or_create_health(session, source.id)
+    # 2026-06-20 H3: 소스마다 독립 세션 — 한 소스의 DB 오류가 종단 단일 commit 을 깨뜨려
+    # 전 소스 insert 를 잃던 것 차단. 실패 시 rollback 후 깨끗한 상태에서 failure 기록.
+    # (csb_collector 의 per-iteration 세션 패턴과 동형.)
+    total = 0
+    for sid in source_ids:
+        async with async_session() as session:
+            source = await session.get(NewsSource, sid)
+            if source is None:
+                continue
+            sname = source.name
+            health = await _get_or_create_health(session, sid)
             if not _should_attempt(health, now):
-                logger.info(f"[{source.name}] circuit {health.circuit_state} — 이번 사이클 skip")
+                logger.info(f"[{sname}] circuit {health.circuit_state} — 이번 사이클 skip")
                 continue
             try:
                 if source.feed_type == "api":
@@ -234,14 +242,18 @@ async def _run_locked():
                 source.last_fetched_at = datetime.now(timezone.utc)
                 _record_success(health, count, status == "not_modified", now)
                 total += count
+                await session.commit()
             except Exception as e:
                 # str 이 빈 예외(httpx.ConnectError('')) 대비 — health 기록과 동일 규칙
-                logger.error(f"[{source.name}] 수집 실패: {str(e) or repr(e)}")
-                source.last_fetched_at = datetime.now(timezone.utc)
+                await session.rollback()
+                logger.error(f"[{sname}] 수집 실패: {str(e) or repr(e)}")
+                health = await _get_or_create_health(session, sid)
+                src = await session.get(NewsSource, sid)
+                if src is not None:
+                    src.last_fetched_at = datetime.now(timezone.utc)
                 _record_failure(health, str(e) or repr(e), now)
-
-        await session.commit()
-        logger.info(f"뉴스 수집 완료: {total}건 신규")
+                await session.commit()
+    logger.info(f"뉴스 수집 완료: {total}건 신규")
 
 
 MAX_RESPONSE_SIZE = 5 * 1024 * 1024  # 5MB

app/workers/presegment_worker.py

@@ -0,0 +1,568 @@
+"""presegment_worker — extract 前 번들 PDF(여러 논리문서 한 파일) → N 자식 분할 (G2 / PR-G2-2).
+
+전 문서가 presegment stage 로 진입한다(worker-side gating):
+  - 非PDF(file_format != pdf · suffix != .pdf) = 즉시 fast-exit → enqueue_next_stage 가 extract 로 흘림.
+  - PDF = PyMuPDF ToC(level-1) deterministic 분석. '명확한 번들' 만 자식 분할, 나머지는 단일문서로 extract.
+
+deterministic 경로(PR-G2-2): 판정이 애매하면 보수적으로 분할하지 않고 단일문서로 둔다
+(bias to NOT splitting). 분할 = '확실한 번들' 만:
+  - page_count >= MIN_BUNDLE_PAGES AND level-1 ToC 항목 >= 2 AND 모든 자식 >= MIN_CHILD_PAGES
+    AND 단조 증가·비중첩 AND [1, page_count] 전 범위 커버 AND 2 <= N <= MAX_CHILDREN.
+
+LLM 경계 폴백(PR-G2-3, env PRESEGMENT_LLM_FALLBACK, 기본 OFF — scaffold-first): deterministic
+이 '명확한 번들' 을 못 만든 대형 PDF(ToC 없음/level-1 없음/게이트 미달)에 한해, OFF 면 오늘과
+동일(단일문서)이고 ON 이면 off-card Qwen(맥북, 라우터 :8890, model=qwen-macbook)에게 경계를
+제안받는다. compact per-page heading 샘플만 전송(본문 미전송). LLM 출력은 **동일 검증 게이트
+(_is_clear_bundle)** 통과 시에만 deterministic 과 같은 _create_children 경로로 분할 —
+is_bundle=false / 파싱·검증 실패 = 단일문서(오늘과 동일) + presegment_llm_rejected 로깅.
+맥북 불가(503/연결/절단)는 StageDeferred 로 큐 재시도(백오프, no silent fallback).
+
+분할 시 ★후보 A(물리분할 없음, uq_documents_file_path 해소): 자식 file_path = unique 합성값
+`{부모경로}#p{start}-{end}` (UNIQUE 제약 통과), 실파일은 `bundle_source_path()` 로 부모 경로 복원.
+자식은 bundle_page_start/end(1-based inclusive) 로 부모 파일의 자기 page 범위만 가리킨다.
+부모-자식 관계 정본 = document_lineage(relation_type='segmented_from'). 부모(presegment_role='parent')는
+파일 홀더라 자체 extract/embed 안 함 — enqueue_next_stage 의 presegment→extract 전이가 'parent' 면
+억제된다(queue_consumer 참조). 자식의 extract 는 이 워커가 직접 enqueue. extract_worker/marker_worker
+가 자식 처리 시 bundle_source_path() 로 실파일 접근.
+
+멱등: 재실행 시 같은 부모로 이미 자식이 있으면(document_lineage segmented_from) 재생성하지 않고
+수렴(각 자식이 extract 활성/완료 상태인지만 보장)한다.
+
+★해결 이력 (2026-06-18): 최초 Option A(자식이 부모 file_path 그대로 공유)는 uq_documents_file_path
+UNIQUE 위반(실번들 검증서 발견) → 합성 file_path(후보 A)로 해소. 인제스트 재활성 = 합성번들 재검증 PASS 후.
+
+plan: G2 pre-segmentation (PR-G2-2 deterministic ToC segmentation)
+"""
+
+import hashlib
+import os
+import re
+import unicodedata
+from pathlib import Path
+
+from pydantic import BaseModel, ValidationError
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ai.client import AIClient, call_deep_or_defer, parse_json_response
+from core.config import settings
+from core.utils import setup_logger
+from models.document import Document
+from models.document_lineage import DocumentLineage
+from models.queue import enqueue_stage
+
+logger = setup_logger("presegment_worker")
+
+# ─── 임계값 (모듈 상수, env-override 가능, 보수적 = 분할 안 하는 쪽으로 bias) ───
+# MIN_BUNDLE_PAGES: 이 미만이면 번들로 보지 않음(단일문서). 짧은 문서의 우연한 level-1 ToC 보호.
+MIN_BUNDLE_PAGES = int(os.getenv("PRESEGMENT_MIN_BUNDLE_PAGES", "60"))
+# MIN_CHILD_PAGES: 자식 하나라도 이 미만이면 분할 거부(표지/목차만 떼지는 over-split 방지).
+MIN_CHILD_PAGES = int(os.getenv("PRESEGMENT_MIN_CHILD_PAGES", "5"))
+# MAX_CHILDREN: 자식 수 상한. 초과 = ToC 가 챕터/소제목 수준이라 논리문서 경계가 아님 → 분할 거부.
+MAX_CHILDREN = int(os.getenv("PRESEGMENT_MAX_CHILDREN", "50"))
+
+# marker_worker._to_marker_path 와 동일 — NAS 상대경로 → 컨테이너 절대경로 prefix.
+CONTAINER_PATH_PREFIX = os.getenv("MARKER_CONTAINER_PATH_PREFIX", "/documents")
+
+# ─── PR-G2-3 LLM 경계 폴백 (scaffold-first, 기본 OFF) ───
+# PRESEGMENT_LLM_FALLBACK: 기본 "false". OFF 면 deterministic 경로만(=오늘과 동일 — 애매하면
+# 단일문서). ON 이면 deterministic 이 '명확한 번들' 을 못 만든 대형 PDF(page_count >=
+# MIN_BUNDLE_PAGES) 에 한해 off-card Qwen(맥북, 라우터 :8890 경유)에게 경계를 제안받아
+# **동일 검증 게이트(_is_clear_bundle)** 통과 시에만 deterministic 과 같은 자식 생성 경로로 분할.
+# 검증 실패/파싱 실패/is_bundle=false = 단일문서(오늘과 동일) + presegment_llm_rejected 로깅.
+PRESEGMENT_LLM_FALLBACK = os.getenv("PRESEGMENT_LLM_FALLBACK", "false").lower() in (
+    "1", "true", "yes", "on",
+)
+# LLM 에 보내는 per-page 샘플의 page 당 char 상한 (heading/첫줄만 — 본문 미전송).
+PRESEGMENT_LLM_PAGE_CHARS = int(os.getenv("PRESEGMENT_LLM_PAGE_CHARS", "80"))
+# 전체 page-sample 블록의 char 상한 (수 KB 가드 — 초과 시 잘라냄, 본문 누출/페이로드 폭발 방지).
+PRESEGMENT_LLM_SAMPLE_CHARS = int(os.getenv("PRESEGMENT_LLM_SAMPLE_CHARS", "12000"))
+
+# 경계 폴백 프롬프트 (app/prompts/presegment_boundaries.txt). system 지시 + 1-based inclusive·
+# 전범위 커버·무중첩 규칙. {page_count}/{page_samples} 를 str.replace 로 주입.
+_PRESEGMENT_PROMPT_PATH = Path(__file__).parent.parent / "prompts" / "presegment_boundaries.txt"
+
+
+class Segment(BaseModel):
+    """LLM 이 제안하는 1-based inclusive page 범위 한 조각."""
+
+    start_page: int
+    end_page: int
+    title: str | None = None
+
+
+class SegmentationOutput(BaseModel):
+    """presegment_boundaries 응답 스키마. parse_json_response → model_validate."""
+
+    is_bundle: bool = False
+    segments: list[Segment] = []
+    confidence: float | None = None
+
+
+def _resolve_path(file_path: str) -> Path | None:
+    """NFC(DB) vs NFD(NFS) 한글 경로 차이 흡수. thumbnail_worker._resolve_path 와 동일 패턴."""
+    candidates = [
+        file_path,
+        unicodedata.normalize("NFD", file_path),
+        unicodedata.normalize("NFC", file_path),
+    ]
+    for c in candidates:
+        p = Path(c)
+        if p.exists():
+            return p
+    parent = Path(file_path).parent
+    if parent.exists():
+        target = unicodedata.normalize("NFC", Path(file_path).name)
+        for child in parent.iterdir():
+            if unicodedata.normalize("NFC", child.name) == target:
+                return child
+    return None
+
+
+def _to_container_path(file_path: str) -> str:
+    """file_path 를 컨테이너 내부 절대경로로 변환 (marker_worker._to_marker_path 와 동일)."""
+    if file_path.startswith("/"):
+        return file_path
+    return f"{CONTAINER_PATH_PREFIX}/{file_path}"
+
+
+# 후보 A: 자식 합성 file_path 패턴 `{부모경로}#p{start}-{end}` (uq_documents_file_path 유일성).
+_BUNDLE_SUFFIX_RE = re.compile(r"#p\d+-\d+$")
+
+
+def bundle_source_path(file_path: str | None) -> str | None:
+    """자식 합성 file_path → 부모 실파일 경로 복원. 일반 doc(접미사 없음)은 그대로 반환.
+
+    extract_worker/marker_worker 가 자식 처리 시 실제 파일 접근에 사용 (자식 file_path 는
+    합성값이라 디스크에 없음). 결정적·세션 불필요. lineage 가 부모-자식 관계의 정본 기록.
+    """
+    if not file_path:
+        return file_path
+    return _BUNDLE_SUFFIX_RE.sub("", file_path)
+
+
+def _is_pdf(doc: Document) -> bool:
+    """PDF 판정 — file_format=pdf 또는 .pdf 확장자."""
+    fmt = (doc.file_format or "").lower()
+    if fmt == "pdf":
+        return True
+    if doc.file_path:
+        return Path(doc.file_path).suffix.lower() == ".pdf"
+    return False
+
+
+def _level1_segments(toc: list, page_count: int) -> list[dict]:
+    """get_toc(simple=True) 결과에서 level-1 항목만 골라 자식 후보 segment 리스트 생성.
+
+    toc 항목 = [level, title, page] (page 는 1-based). level==1 만 채택.
+    end_page = 다음 level-1 항목의 page - 1, 마지막 = page_count.
+    동일 page 에서 시작하는 level-1 이 여럿이면 정렬 후 인접 항목으로 경계 계산되며,
+    그 경우 0-페이지 segment 가 생겨 후속 검증(MIN_CHILD_PAGES·단조)에서 거부된다.
+    """
+    starts = []
+    for entry in toc:
+        # simple=True 는 [level, title, page]. 방어적으로 길이 체크.
+        if not entry or len(entry) < 3:
+            continue
+        level, title, page = entry[0], entry[1], entry[2]
+        if level != 1:
+            continue
+        # ToC page 가 범위 밖(0/음수/page_count 초과)이면 깨진 ToC → 후속 검증에서 거부됨.
+        starts.append((int(page), (title or "").strip()))
+
+    # ToC 가 정렬돼 있지 않을 수 있으므로 page 기준 정렬(원본 순서 보존 위해 안정 정렬).
+    starts.sort(key=lambda x: x[0])
+
+    segments: list[dict] = []
+    for i, (start_page, title) in enumerate(starts):
+        if i + 1 < len(starts):
+            end_page = starts[i + 1][0] - 1
+        else:
+            end_page = page_count
+        segments.append({"start_page": start_page, "end_page": end_page, "title": title})
+    return segments
+
+
+def _is_clear_bundle(segments: list[dict], page_count: int) -> tuple[bool, str]:
+    """deterministic '명확한 번들' 판정. (clear, reason) 반환.
+
+    clear=True 면 reason="" / clear=False 면 reason 은 거부 사유(로깅용).
+    모든 조건은 보수적 — 하나라도 어긋나면 단일문서로 처리(분할 안 함).
+    """
+    n = len(segments)
+    if n < 2:
+        return False, f"too_few_level1_entries(n={n})"
+    if n > MAX_CHILDREN:
+        return False, f"too_many_children(n={n}>{MAX_CHILDREN})"
+
+    # 첫 segment 가 1페이지에서 시작 + 마지막이 page_count 에서 끝 = 전 범위 커버.
+    if segments[0]["start_page"] != 1:
+        return False, f"first_start_not_1(start={segments[0]['start_page']})"
+    if segments[-1]["end_page"] != page_count:
+        return False, f"last_end_not_page_count(end={segments[-1]['end_page']},pc={page_count})"
+
+    prev_end = 0
+    for seg in segments:
+        start, end = seg["start_page"], seg["end_page"]
+        # 단조 증가 · 비중첩: 각 start 는 직전 end + 1 이어야 빈틈/겹침 없이 [1,pc] 정확 분할.
+        if start != prev_end + 1:
+            return False, f"non_contiguous(start={start},prev_end={prev_end})"
+        if end < start:
+            return False, f"non_monotonic(start={start},end={end})"
+        if (end - start + 1) < MIN_CHILD_PAGES:
+            return False, f"child_too_small(pages={end - start + 1}<{MIN_CHILD_PAGES})"
+        prev_end = end
+
+    if prev_end != page_count:
+        return False, f"coverage_gap(covered={prev_end},pc={page_count})"
+
+    return True, ""
+
+
+def _child_title(parent: Document, seg: dict) -> str:
+    """자식 제목 = 부모 제목 + ' — ' + (segment 제목 또는 page 범위)."""
+    base = (parent.title or "").strip() or (parent.original_filename or "") or "문서"
+    seg_title = (seg.get("title") or "").strip()
+    suffix = seg_title if seg_title else f"p.{seg['start_page']}-{seg['end_page']}"
+    return f"{base} — {suffix}"
+
+
+def _child_file_hash(parent_hash: str, start: int, end: int) -> str:
+    """자식 file_hash = sha256(f"{parent.file_hash}:{start}-{end}"). 결정적 → 재실행 멱등.
+
+    부모 file_hash 가 NULL 일 수는 없으나(NOT NULL) 방어적으로 빈 문자열 처리.
+    """
+    return hashlib.sha256(f"{parent_hash or ''}:{start}-{end}".encode("utf-8")).hexdigest()
+
+
+async def _ensure_child_extract(session: AsyncSession, child_id: int) -> None:
+    """자식이 아직 extract 안 됐으면 extract enqueue (멱등 수렴 경로).
+
+    이미 extracted_text 가 채워졌거나 활성 큐 행이 있으면 enqueue_stage 가 no-op/skip.
+    """
+    child = await session.get(Document, child_id)
+    if child is None:
+        return
+    # 이미 추출 완료면 재enqueue 불필요 (큐 중복은 enqueue_stage 가 막지만 의미상으로도 skip).
+    if child.extracted_at is not None and child.extracted_text is not None:
+        return
+    await enqueue_stage(session, child_id, "extract")
+
+
+async def _create_children(
+    doc: Document, segments: list[dict], session: AsyncSession
+) -> int:
+    """검증된 segments 로 자식 N개 생성 + lineage + extract enqueue + 부모 표식 (멱등).
+
+    deterministic '명확한 번들' 경로와 LLM 폴백 경로가 공유하는 단일 자식 생성 경로.
+    호출 전 segments 는 반드시 _is_clear_bundle 검증을 통과해야 한다(여기선 재검증 X).
+    commit 까지 수행. 반환값 = 실제 생성한 자식 수(이미 존재해 수렴만 한 경우 0).
+    """
+    # ─── 멱등 체크: 이미 자식이 있으면 수렴만 (재생성 금지) ───
+    existing_children = (
+        await session.execute(
+            select(DocumentLineage.derived_document_id).where(
+                DocumentLineage.source_document_id == doc.id,
+                DocumentLineage.relation_type == "segmented_from",
+            )
+        )
+    ).scalars().all()
+
+    if existing_children:
+        # 부모 표식이 누락된 경우 보정(이전 부분실패 복구).
+        if doc.presegment_role != "parent":
+            doc.presegment_role = "parent"
+        for child_id in existing_children:
+            await _ensure_child_extract(session, child_id)
+        await session.commit()
+        logger.info(
+            f"[presegment] id={doc.id} children already exist "
+            f"(n={len(existing_children)}) → converge(ensure extract), no re-create"
+        )
+        return 0
+
+    # ─── 자식 N개 생성 + lineage + extract enqueue ───
+    created_ids: list[int] = []
+    for seg in segments:
+        start, end = seg["start_page"], seg["end_page"]
+        child = Document(
+            # 후보 A: 자식 file_path = unique 합성값 `{부모경로}#p{s}-{e}` (uq_documents_file_path
+            # 충돌 회피). 실파일은 bundle_source_path() 로 복원(부모 경로). 물리 분할 없음 —
+            # 자식은 bundle_page_start/end 로 부모 파일을 슬라이스.
+            file_path=f"{doc.file_path}#p{start}-{end}",
+            file_hash=_child_file_hash(doc.file_hash, start, end),
+            file_format=doc.file_format,
+            file_size=doc.file_size,
+            file_type=doc.file_type,
+            import_source=doc.import_source,
+            original_filename=doc.original_filename,
+            source_channel=doc.source_channel,
+            category=doc.category,
+            data_origin=doc.data_origin,
+            doc_purpose=doc.doc_purpose,
+            # 안전 자료실 축은 부모에서 상속(분할이 자료유형/관할을 바꾸지 않음).
+            material_type=doc.material_type,
+            jurisdiction=doc.jurisdiction,
+            title=_child_title(doc, seg),
+            bundle_page_start=start,
+            bundle_page_end=end,
+            presegment_role="child",
+        )
+        session.add(child)
+        await session.flush()  # child.id 확보
+        created_ids.append(child.id)
+
+        session.add(
+            DocumentLineage(
+                source_document_id=doc.id,
+                derived_document_id=child.id,
+                relation_type="segmented_from",
+                meta={"start_page": start, "end_page": end},
+            )
+        )
+        # 자식 extract 는 워커가 직접 enqueue (부모는 'parent' 라 extract 로 흐르지 않음).
+        await enqueue_stage(session, child.id, "extract")
+
+    # 부모 = 파일 홀더. presegment→extract 전이는 enqueue_next_stage 가 'parent' 면 억제.
+    doc.presegment_role = "parent"
+    await session.commit()
+
+    logger.info(
+        f"[presegment] id={doc.id} SPLIT into {len(created_ids)} children "
+        f"child_ids={created_ids}"
+    )
+    return len(created_ids)
+
+
+def _segments_from_output(out: "SegmentationOutput") -> list[dict]:
+    """SegmentationOutput.segments(Pydantic) → _is_clear_bundle / _create_children 가 쓰는 dict 형태."""
+    return [
+        {"start_page": s.start_page, "end_page": s.end_page, "title": (s.title or "")}
+        for s in out.segments
+    ]
+
+
+def _page_samples(pdf, page_count: int) -> str:
+    """LLM 입력용 compact per-page 샘플 — page 당 heading/첫줄만(`p{n}: {firstline}`).
+
+    PyMuPDF page.get_text() 로 page 별 텍스트를 스트리밍하되 page 당 첫 비공백 줄만,
+    PRESEGMENT_LLM_PAGE_CHARS 로 잘라 본문 누출 차단. 전체 블록은 PRESEGMENT_LLM_SAMPLE_CHARS
+    가드로 상한(수 KB) — 초과 시 그 지점에서 중단(앞쪽 페이지 우선 보존).
+    """
+    lines: list[str] = []
+    total = 0
+    for i in range(page_count):
+        try:
+            text = pdf[i].get_text() or ""
+        except Exception:
+            text = ""
+        first = ""
+        for ln in text.splitlines():
+            ln = ln.strip()
+            if ln:
+                first = ln
+                break
+        first = first[:PRESEGMENT_LLM_PAGE_CHARS]
+        entry = f"p{i + 1}: {first}"
+        if total + len(entry) + 1 > PRESEGMENT_LLM_SAMPLE_CHARS:
+            break
+        lines.append(entry)
+        total += len(entry) + 1
+    return "\n".join(lines)
+
+
+async def _llm_boundary_fallback(
+    doc: Document, source: Path, page_count: int, session: AsyncSession
+) -> bool:
+    """애매 + 대형(ToC-less 등) PDF 에 대해 off-card Qwen 으로 경계 제안 → 검증 → 분할.
+
+    반환 True = LLM 경로가 분할을 수행(또는 멱등 수렴)했으므로 호출자는 추가 처리 없이 return.
+    반환 False = is_bundle=false / 파싱 실패 / 검증 실패 → 호출자는 단일문서(오늘과 동일) 처리.
+    맥북 불가(503/연결/절단)는 call_deep_or_defer 가 StageDeferred 로 raise → 큐 재시도(백오프).
+    silent fallback 금지 — deep 슬롯 외 다른 backend 자동 호출 안 함.
+    """
+    import fitz  # PyMuPDF — deterministic 경로와 동일 의존
+
+    # per-page 샘플은 파일을 다시 열어 스트리밍(deterministic with 블록과 분리해 그 경로 무회귀).
+    try:
+        with fitz.open(str(source)) as pdf:
+            samples = _page_samples(pdf, page_count)
+    except Exception as exc:
+        logger.warning(
+            f"[presegment] id={doc.id} llm fallback sample 실패 "
+            f"({type(exc).__name__}: {exc}) → single doc(extract)"
+        )
+        return False
+
+    try:
+        template = _PRESEGMENT_PROMPT_PATH.read_text(encoding="utf-8")
+    except Exception as exc:
+        logger.warning(
+            f"[presegment] id={doc.id} prompt 로드 실패 ({type(exc).__name__}: {exc}) "
+            f"→ single doc(extract)"
+        )
+        return False
+
+    prompt = template.replace("{page_count}", str(page_count)).replace(
+        "{page_samples}", samples
+    )
+
+    # off-card 호출 — call_deep_or_defer 가 deep 슬롯(맥북, 라우터 :8890, model=qwen-macbook)
+    # 으로 라우팅. 맥북 불가는 StageDeferred 로 전파(여기서 잡지 않음 → 큐가 보류/백오프).
+    # classify_worker 와 동일하게 AIClient() 인스턴스화.
+    client = AIClient()
+    try:
+        raw = await call_deep_or_defer(client, prompt)
+    finally:
+        await client.close()
+
+    parsed = parse_json_response(raw)
+    if not parsed:
+        logger.info(
+            f"[presegment] presegment_llm_rejected id={doc.id} "
+            f"reason=parse_failed raw={raw[:160]!r} → single doc(extract)"
+        )
+        return False
+
+    try:
+        out = SegmentationOutput.model_validate(parsed)
+    except (ValidationError, ValueError, TypeError) as exc:
+        logger.info(
+            f"[presegment] presegment_llm_rejected id={doc.id} "
+            f"reason=schema_invalid({type(exc).__name__}) → single doc(extract)"
+        )
+        return False
+
+    if not out.is_bundle:
+        logger.info(
+            f"[presegment] presegment_llm_rejected id={doc.id} "
+            f"reason=is_bundle_false → single doc(extract)"
+        )
+        return False
+
+    segments = _segments_from_output(out)
+    clear, reason = _is_clear_bundle(segments, page_count)
+    if not clear:
+        # LLM 출력을 그대로 믿지 않음 — deterministic 과 동일 게이트 미달이면 단일문서.
+        logger.info(
+            f"[presegment] presegment_llm_rejected id={doc.id} "
+            f"reason={reason} n={len(segments)} pages={page_count} → single doc(extract)"
+        )
+        return False
+
+    n = await _create_children(doc, segments, session)
+    logger.info(
+        f"[presegment] id={doc.id} LLM-SPLIT accepted "
+        f"(pages={page_count} n={len(segments)} created={n} "
+        f"confidence={out.confidence})"
+    )
+    return True
+
+
+async def process(document_id: int, session: AsyncSession) -> None:
+    """presegment stage 워커 진입점. queue_consumer 가 호출.
+
+    전 문서가 진입하며, 非PDF·단일문서는 변경 없이 통과(presegment_role 그대로 NULL) → extract 로 흐른다.
+    '명확한 번들' PDF 만 자식 분할 + 부모를 'parent' 로 표식(이 경우 부모는 extract 로 흐르지 않음).
+    """
+    doc = await session.get(Document, document_id)
+    if doc is None:
+        logger.warning(f"[presegment] document {document_id} not found")
+        return
+
+    # ─── (0) 非PDF — fast-exit. presegment_role 그대로 NULL → enqueue_next_stage 가 extract 로 흘림 ───
+    if not _is_pdf(doc):
+        logger.info(f"[presegment] id={document_id} non-pdf (fmt={doc.file_format}) → extract")
+        return
+
+    # ─── (0.5) file_path 없음(예: note) — 분할 불가, 단일문서로 통과 ───
+    if not doc.file_path:
+        logger.info(f"[presegment] id={document_id} no file_path → extract")
+        return
+
+    # ─── (1) 이미 분할된 자식 자신이 presegment 로 다시 들어온 경우 — 재분할 금지 ───
+    # (정상 흐름에선 자식은 곧장 extract 로 enqueue 되지만, 재처리 스크립트 등으로 들어올 수 있음.)
+    if doc.presegment_role in ("child", "parent"):
+        logger.info(
+            f"[presegment] id={document_id} already presegment_role={doc.presegment_role} → skip"
+        )
+        return
+
+    # ─── (2) 파일 열기 + page_count ───
+    raw = str(Path(settings.nas_mount_path) / doc.file_path)
+    source = _resolve_path(raw)
+    if source is None:
+        # 파일 부재 = extract 가 동일 상황에서 FileNotFoundError 로 처리할 사안.
+        # presegment 는 분할 불가일 뿐이므로 단일문서로 통과시켜 extract 가 일관되게 처리하게 둔다.
+        logger.warning(f"[presegment] id={document_id} file not found ({raw}) → extract")
+        return
+
+    import asyncio
+
+    import fitz  # PyMuPDF — extract_worker/marker_worker 와 동일 의존
+
+    def _read_toc(path: str):
+        # fitz open/get_toc 는 동기 blocking — live 스테이지라 이벤트루프(같은 루프의 1분 consumer +
+        # FastAPI 요청) 점유 회피 위해 to_thread 오프로드(거대/손상 PDF 파싱 수백 ms~초).
+        with fitz.open(path) as pdf:
+            return pdf.page_count, (pdf.get_toc(simple=True) or [])
+
+    try:
+        page_count, toc = await asyncio.to_thread(_read_toc, str(source))
+    except Exception as exc:
+        # PDF 손상 등 — 분할 불가. 단일문서로 통과(extract 가 PyMuPDF/OCR 로 재시도하며 가시화).
+        logger.warning(
+            f"[presegment] id={document_id} fitz open/toc failed "
+            f"({type(exc).__name__}: {exc}) → extract"
+        )
+        return
+
+    # ─── (3) page_count 가 임계 미만 = 단일문서 (대다수 경로) ───
+    if page_count < MIN_BUNDLE_PAGES:
+        logger.info(
+            f"[presegment] id={document_id} single doc "
+            f"(pages={page_count}<{MIN_BUNDLE_PAGES}) → extract"
+        )
+        return
+
+    # ─── (4) level-1 ToC → 자식 후보 segment ───
+    segments = _level1_segments(toc, page_count)
+
+    if not segments:
+        # 큰 PDF 인데 ToC 없음/level-1 없음 = 애매. flag ON 이면 LLM 경계 폴백(PR-G2-3),
+        # OFF(기본) 이면 오늘과 동일 — 단일문서로 처리하고 사유를 남긴다.
+        if PRESEGMENT_LLM_FALLBACK:
+            logger.info(
+                f"[presegment] presegment_ambiguous id={document_id} "
+                f"reason=no_level1_toc pages={page_count} → LLM fallback"
+            )
+            if await _llm_boundary_fallback(doc, source, page_count, session):
+                return
+            # LLM 이 분할하지 않음(is_bundle=false / 검증·파싱 실패) — 단일문서.
+            return
+        logger.info(
+            f"[presegment] presegment_ambiguous id={document_id} "
+            f"reason=no_level1_toc pages={page_count} → single doc(extract)"
+        )
+        return
+
+    clear, reason = _is_clear_bundle(segments, page_count)
+    if not clear:
+        # 큰 PDF + ToC 는 있으나 '명확한 번들' 기준 미달 = 애매. flag ON 이면 LLM 경계 폴백,
+        # OFF(기본) 이면 오늘과 동일 — 단일문서(분할 안 함).
+        if PRESEGMENT_LLM_FALLBACK:
+            logger.info(
+                f"[presegment] presegment_ambiguous id={document_id} "
+                f"reason={reason} pages={page_count} level1={len(segments)} → LLM fallback"
+            )
+            if await _llm_boundary_fallback(doc, source, page_count, session):
+                return
+            return
+        logger.info(
+            f"[presegment] presegment_ambiguous id={document_id} "
+            f"reason={reason} pages={page_count} level1={len(segments)} → single doc(extract)"
+        )
+        return
+
+    # ─── (5) 명확한 번들 (deterministic) — 공유 자식 생성 경로 (멱등 수렴 포함) ───
+    await _create_children(doc, segments, session)

app/workers/queue_consumer.py

@@ -31,9 +31,9 @@ _hold_logged = False
 # embed/chunk 1→10 (2026-06-12 fast-consumer): 건당 <1s 실측 — Phase 0.1 초기 보수값이
 # LLM 사이클에 인질로 잡혀 실효 ~580/일 vs 수요 최대 2,700/일 → 적체 원인이었음.
 # 10 = TEI/marker 와 GPU 공유 고려한 보수 상향(전용 1분 잡 기준 캡 ~14,400/일).
-BATCH_SIZE = {"extract": 5, "classify": 3, "summarize": 3, "embed": 10, "chunk": 10,
-              "preview": 2, "stt": 1, "thumbnail": 3, "deep_summary": 1, "markdown": 1,
-              "fulltext": 3}
+BATCH_SIZE = {"presegment": 3, "extract": 5, "classify": 3, "summarize": 3, "embed": 10,
+              "chunk": 10, "preview": 2, "stt": 1, "thumbnail": 3, "deep_summary": 1,
+              "markdown": 1, "fulltext": 3}
 STALE_THRESHOLD_MINUTES = 10
 # markdown 대형 split 변환은 한 doc 이 수십 분(5210 ≈ 40분) 동안 processing 상태로 머문다.
 # marker_worker 는 queue 행에 heartbeat 를 찍지 않으므로(started_at 고정), main 의 10분
@@ -46,7 +46,7 @@ MARKDOWN_STALE_THRESHOLD_MINUTES = int(os.getenv("MARKDOWN_STALE_MINUTES", "120"
 # (reset_stale_items 가 자기 집합만 reset, 교차 시 이중 복구 위험).
 # STT 도 장기 작업 가능성이 있으나 본 PR 범위 밖 — main 에 유지(follow-up).
 MAIN_QUEUE_STAGES = [
-    "extract", "classify", "summarize",
+    "presegment", "extract", "classify", "summarize",
     "preview", "stt", "thumbnail", "fulltext",
 ]
 MARKDOWN_QUEUE_STAGES = ["markdown"]
@@ -165,6 +165,10 @@ async def enqueue_next_stage(document_id: int, current_stage: str):
     }
 
     next_stages = {
+        # G2 (PR-G2-2): 전 문서가 presegment → extract. 단, 번들 분할로 'parent' 가 된 문서는
+        # 파일 홀더라 자체 extract 안 함 — 아래 suppression 으로 이 전이를 건너뛴다(자식 extract 는
+        # presegment_worker 가 직접 enqueue). 단일/非PDF 문서(role NULL)는 정상적으로 extract 로 흐름.
+        "presegment": ["extract"],
         "extract": ["classify", "preview"],
         "classify": ["embed", "chunk", "markdown"],
         "stt": ["classify"],
@@ -180,6 +184,18 @@ async def enqueue_next_stage(document_id: int, current_stage: str):
             stages = extract_override_by_channel[sc]
         else:
             stages = next_stages.get(current_stage, [])
+    elif current_stage == "presegment":
+        # 번들 분할 parent 는 extract 로 흐르지 않게 억제 (자식이 부모 extract 에 가려지는 것 방지).
+        # role NULL(단일/非PDF) / 'child' 는 정상 전이. presegment_worker 가 자식 extract 를 직접
+        # enqueue 하므로 'parent' 만 여기서 no-op.
+        from models.document import Document
+        async with async_session() as lookup_session:
+            doc = await lookup_session.get(Document, document_id)
+            role = doc.presegment_role if doc else None
+        if role == "parent":
+            stages = []
+        else:
+            stages = next_stages.get(current_stage, [])
     else:
         stages = next_stages.get(current_stage, [])
 
@@ -199,6 +215,7 @@ def _load_workers():
     from workers.deep_summary_worker import process as deep_summary_process
     from workers.embed_worker import process as embed_process
     from workers.extract_worker import process as extract_process
+    from workers.presegment_worker import process as presegment_process
     from workers.preview_worker import process as preview_process
     from workers.stt_worker import process as stt_process
     from workers.summarize_worker import process as summarize_process
@@ -207,6 +224,8 @@ def _load_workers():
     from workers.fulltext_worker import process as fulltext_process
 
     return {
+        # G2 (PR-G2-2): extract 前 번들 PDF → N 자식 분할 (deterministic ToC). 非PDF/단일은 통과.
+        "presegment": presegment_process,
         "extract": extract_process,
         "classify": classify_process,
         "summarize": summarize_process,

app/workers/study_explanation_worker.py

@@ -25,6 +25,7 @@ import httpx
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ai.client import AIClient, parse_json_response
+from core.config import settings
 from models.study_question import StudyQuestion
 from models.study_question_job import StudyQuestionJob
 from services.search.llm_gate import Priority, acquire_mlx_gate
@@ -32,11 +33,12 @@ from services.study.explanation_rag import (
     gather_explanation_context,
     render_evidence_block,
 )
+from services.study.publish_enqueue import enqueue_question_publish
 
 logger = logging.getLogger(__name__)
 
-# PR-3 LLM_TIMEOUT_S 와 동일 안전 마진 (26B 평균 ~10s, gate 직렬화 고려)
-LLM_TIMEOUT_S = 30.0
+# 2026-06-20: config 단일소스 (구 하드코딩 30s = 빠른 Gemma 기준, Qwen 27B 교체 sweep 누락).
+LLM_TIMEOUT_S = settings.llm_call_timeout_s
 
 # explanation_md hard cap — 운영 데이터 793/838/866자 사례에서 1200 으로 시작
 # (800 은 공식·오답·핵심개념 묶이는 기사시험 풀이에 빡빡함). 1차 운영 후 조정.
@@ -226,6 +228,10 @@ async def run_explanation_job(session: AsyncSession, job: StudyQuestionJob) -> N
         question.ai_explanation_model = f"mlx:{primary_name}"
         question.updated_at = question.ai_explanation_generated_at
 
+        # 발행 재투영(같은 tx, caller commit) — 4-A 해설 ready → 문항+해설 발행. P0-1b.
+        if settings.study_publish_enabled:
+            await enqueue_question_publish(session, question)
+
         job.status = "completed"
         job.completed_at = now()
         return

app/workers/study_memo_card_worker.py

@@ -24,6 +24,7 @@ import httpx
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ai.client import AIClient, parse_json_response
+from core.config import settings
 from models.study_memo_card import (
     append_card,
     append_card_evidence,
@@ -33,6 +34,8 @@ from models.study_memo_card_job import StudyMemoCardJob
 from models.study_question import StudyQuestion
 from models.user import User  # noqa: F401  (mapper 초기화 defensive)
 from services.search.llm_gate import Priority, acquire_mlx_gate
+from services.study.publish_enqueue import enqueue_publish
+from services.study.publish_projection import KIND_CARD
 from services.study.explanation_rag import (
     gather_explanation_context,
     render_evidence_block,
@@ -41,8 +44,8 @@ from services.study.study_memo_card_guards import guard_cards
 
 logger = logging.getLogger("study_memo_card_worker")
 
-# 다카드 출력이라 explanation(30s)보다 여유. config primary.timeout(180, soft-lock)은 미변경.
-CARD_LLM_TIMEOUT_S = 45.0
+# 2026-06-20: config 단일소스 (구 하드코딩 45s = 빠른 Gemma 기준).
+CARD_LLM_TIMEOUT_S = settings.llm_call_timeout_s
 SOURCE_KIND_QUESTION = "question"
 
 _ENVELOPE_PROMPT_FILE = "study_card_envelope.txt"
@@ -183,9 +186,13 @@ async def run_card_extract_job(session: AsyncSession, job: StudyMemoCardJob) ->
             return
 
         # 5. 성공 — 구버전 카드 retire 후 append (dedup partial unique 충돌 회피).
-        await supersede_old_cards(
+        retired_published_ids = await supersede_old_cards(
             session, source_question_id=question.id, keep_generated_at=source_version
         )
+        # 발행 중이던 구버전 카드 tombstone(같은 tx) — 재추출 retire 후 viewer stale 잔류 0. S-2.
+        if settings.study_publish_enabled:
+            for cid in retired_published_ids:
+                await enqueue_publish(session, kind=KIND_CARD, source_id=cid, payload=None, deleted=True)
         model_name = f"mlx:{primary_name}"
         inserted = 0
         for g in guarded:

app/workers/study_publish_worker.py

@@ -0,0 +1,153 @@
+"""발행 워커 — publish_outbox drain → published 에 rev 부여 (docsrv-viewer-publish).
+
+APScheduler 1분(max_instances=1). pg_advisory_xact_lock 단일 라이터 → rev 커밋순 gapless
+(인플라이트 갭 차단: bigserial seq 폴링이 아니라 outbox id 순 + 단일 라이터 rev 부여).
+  outbox 를 id(커밋순) 순으로 처리, (kind, source_id) 당 published upsert:
+    - 기존 행과 (payload_hash, deleted) 동일 → no-op(디둡, rev 안 올림) + processed 마킹
+    - 그 외 → pub_id 재사용(기존)|신규 uuid, rev = MAX(rev)+1, payload/hash/deleted 갱신
+  tombstone(deleted=True)은 디둡 복합키라 안 삼켜짐. 배치 단일 트랜잭션.
+  배치 내 같은 (kind, source_id) 가 두 번 오면 flush 로 직전 반영을 다음 select 가 보게 함(최신 승).
+
+study_publish_enabled=False(기본) 면 no-op — 저자/4-A enqueue 결선(P0-1b) 전까지 inert.
+"""
+
+from __future__ import annotations
+
+import uuid
+from datetime import datetime, timezone
+
+from sqlalchemy import func, select, text
+
+from core.config import settings
+from core.database import async_session
+from core.utils import setup_logger
+from models.published import Published, PublishOutbox
+
+logger = setup_logger("study_publish_worker")
+
+BATCH_SIZE = 500
+# pg_advisory_xact_lock 전역 단일 라이터 키(발행 워커 전용 임의 상수, 타 advisory 락과 비충돌).
+ADVISORY_LOCK_KEY = 838201
+# 행별 격리 재시도 상한 — 초과 시 failed_at 스탬프(terminal)로 select 에서 제외.
+MAX_OUTBOX_ATTEMPTS = 5
+
+
+async def consume_publish_outbox() -> None:
+    """APScheduler 진입점. 미처리 outbox 를 rev 부여하며 published 로 반영."""
+    if not settings.study_publish_enabled:
+        logger.debug("study_publish 비활성 (study_publish_enabled=false)")
+        return
+
+    async with async_session() as session:
+        try:
+            # 1) 전역 단일 라이터 락(트랜잭션 스코프 — commit/rollback 시 자동 해제).
+            await session.execute(
+                text("SELECT pg_advisory_xact_lock(:k)").bindparams(k=ADVISORY_LOCK_KEY)
+            )
+            # 2) 현재 최대 rev.
+            max_rev = int(
+                (await session.execute(select(func.coalesce(func.max(Published.rev), 0)))).scalar() or 0
+            )
+            # 3) 미처리 outbox 를 커밋순(id)으로. failed_at(terminal) 은 제외 — poison 행이
+            #    head-of-line 을 영구 점유하지 않게 함.
+            rows = (
+                await session.execute(
+                    select(PublishOutbox)
+                    .where(
+                        PublishOutbox.processed_at.is_(None),
+                        PublishOutbox.failed_at.is_(None),
+                    )
+                    .order_by(PublishOutbox.id.asc())
+                    .limit(BATCH_SIZE)
+                )
+            ).scalars().all()
+            if not rows:
+                return
+
+            now = datetime.now(timezone.utc)
+            published_count = 0
+            failed_count = 0
+            for ob in rows:
+                try:
+                    # 행 단위 savepoint 격리 — 한 행의 예외가 배치 전체(앞 행 processed_at 포함)를
+                    # 롤백해 poison 행이 다음 사이클에 다시 최저 id 로 선택되는 무한 재선택을 차단.
+                    async with session.begin_nested():
+                        existing = (
+                            await session.execute(
+                                select(Published).where(
+                                    Published.kind == ob.kind,
+                                    Published.source_id == ob.source_id,
+                                )
+                            )
+                        ).scalar_one_or_none()
+
+                        # (payload_hash, deleted) 디둡 — no-op 재투영은 rev 안 올림.
+                        is_noop = (
+                            existing is not None
+                            and existing.payload_hash == ob.payload_hash
+                            and existing.deleted == ob.deleted
+                        )
+                        if is_noop:
+                            ob.processed_at = now
+                        else:
+                            new_rev = max_rev + 1
+                            if existing is None:
+                                session.add(
+                                    Published(
+                                        kind=ob.kind,
+                                        source_id=ob.source_id,
+                                        pub_id=uuid.uuid4().hex,
+                                        payload=ob.payload,
+                                        payload_hash=ob.payload_hash,
+                                        schema_version=ob.schema_version,
+                                        rev=new_rev,
+                                        deleted=ob.deleted,
+                                        created_at=now,
+                                        updated_at=now,
+                                    )
+                                )
+                            else:
+                                existing.payload = ob.payload
+                                existing.payload_hash = ob.payload_hash
+                                existing.schema_version = ob.schema_version
+                                existing.deleted = ob.deleted
+                                existing.rev = new_rev
+                                existing.updated_at = now
+                            ob.processed_at = now
+                            # 배치 내 동일 (kind, source_id) 후속 행이 직전 반영을 보도록 flush(최신 승).
+                            await session.flush()
+                except Exception as row_err:
+                    # savepoint 롤백 = 이 행의 쓰기(processed_at 포함) 취소. attempts/failed_at 만
+                    # 바깥 트랜잭션에 누적돼 최종 commit 으로 영속(영구 재선택 방지).
+                    ob.attempts = (ob.attempts or 0) + 1
+                    if ob.attempts >= MAX_OUTBOX_ATTEMPTS:
+                        ob.failed_at = now
+                        failed_count += 1
+                        logger.error(
+                            "publish_outbox_row_terminal id=%s kind=%s source_id=%s attempts=%s: %s",
+                            ob.id, ob.kind, ob.source_id, ob.attempts, row_err,
+                        )
+                    else:
+                        logger.warning(
+                            "publish_outbox_row_retry id=%s kind=%s source_id=%s attempts=%s: %s",
+                            ob.id, ob.kind, ob.source_id, ob.attempts, row_err,
+                        )
+                    continue
+                else:
+                    # savepoint 커밋 성공 시에만 rev 카운터 전진(실패 행은 rev 미소모 → 드물게 gap,
+                    # 단일 라이터·커밋순 부여라 viewer since-rev 증분 동기 정합엔 무해).
+                    if not is_noop:
+                        max_rev = new_rev
+                        published_count += 1
+
+            await session.commit()
+            logger.info(
+                "publish_outbox_drained scanned=%s published=%s failed=%s max_rev=%s",
+                len(rows),
+                published_count,
+                failed_count,
+                max_rev,
+            )
+        except Exception as e:
+            await session.rollback()
+            logger.exception("publish_outbox_drain_failed: %s", e)

app/workers/study_session_analysis_worker.py

@@ -28,6 +28,7 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ai.client import AIClient, parse_json_response
+from core.config import settings
 from models.study_question import StudyQuestion, StudyQuestionAttempt
 from models.study_quiz_session import StudyQuizSession
 from models.study_quiz_session_analysis import StudyQuizSessionAnalysis
@@ -42,8 +43,8 @@ from services.study.session_summary_rag import gather_session_summary_context
 
 logger = logging.getLogger(__name__)
 
-# 4-A 와 동일 안전 마진 (26B 평균 ~10s, gate 직렬화 고려)
-LLM_TIMEOUT_S = 30.0
+# 2026-06-20: config 단일소스 (구 하드코딩 30s = 빠른 Gemma 기준).
+LLM_TIMEOUT_S = settings.llm_call_timeout_s
 # wrong/unsure 5 미만은 분석 의미 X — insufficient_attempts skip
 MIN_ATTEMPTS_FOR_ANALYSIS = 5
 # 큰 세션 (84건 등) 에서 prompt 과대 + LLM timeout 방어. 가장 최근 attempt 기준 cap.

app/workers/summarize_worker.py

@@ -91,7 +91,12 @@ async def process(document_id: int, session: AsyncSession, *, use_deep: bool = F
 
         # sleep-안전 불변식: 쓰기는 전체 완주 후에만 — 중간 절단은 StageDeferred 로 빠져
         # 이 지점에 도달하지 않는다 (carry 는 로컬 변수, doc 무변경).
-        doc.ai_summary = strip_thinking(summary)
+        final_summary = strip_thinking(summary)
+        # 2026-06-20 H2: 빈/think-only 요약을 ai_summary 빈문자열로 박제 → completed 마크 → briefing/digest 누출.
+        # raise → queue 재시도 후 failed(가시화). 기존 raise 계약(not-found·empty-text)과 동형.
+        if not final_summary.strip():
+            raise ValueError(f"empty ai_summary after strip (document_id={document_id})")
+        doc.ai_summary = final_summary
         doc.ai_model_version = used_cfg.model
         doc.ai_processed_at = datetime.now(timezone.utc)
         logger.info(

config.yaml

@@ -1,8 +1,6 @@
 # hyungi_Document_Server 설정
 
 ai:
-  gateway:
-    endpoint: "http://ai-gateway:8080"
 
   models:
     # ─── 단일 generation 호스트 routing (2026-05-14 GPU LLM 제거) ───
@@ -29,20 +27,14 @@ ai:
       context_char_limit: 260000
       temperature: 0.3
       top_p: 0.9
+      repetition_penalty: 1.05  # 한국어 장문 반복/코드스위칭(CJK·라틴 누수) 억제 (보수적 시작값)
+      top_k: 20                 # Qwen3 권장
 
-    # deep: 야간 night-drain 전용 — 맥북 M5 Max Qwen3.6-27B-6bit (llm-router :8890 경유,
-    # model=qwen-macbook alias). 2026-06-11 재도입 (사용자: 자기 전 night-drain 으로 백로그 분담).
-    # 맥북 불가(503/연결/절단) = StageDeferred 보류 — 맥미니/cloud 강등 없음, attempts 미소모.
-    # consumer 의 deep_summary 도 슬롯 존재 시 맥북 경유 (잠들어 있으면 30분 백오프 보류 = 무해).
-    # 슬롯 제거 시 deep_summary 는 primary(맥미니) 경로 복귀.
-    deep:
-      endpoint: "http://100.76.254.116:8890/v1/chat/completions"
-      model: "qwen-macbook"
-      max_tokens: 8192
-      timeout: 900
-      context_char_limit: 260000
-      temperature: 0.3
-      top_p: 0.9
+    # deep: ★2026-06-29 잠정 보류 (사용자 "맥북 night-drain 의미없어 → 맥미니 일원화").
+    # 슬롯 제거 → deep_summary 가 primary(맥미니) 경로 복귀 + use_deep/drain 도 맥미니 폴백
+    # (맥북 라우팅 0). drain-keeper(GPU cron)도 비활성. 맥북 mlx-vlm-server 는 OpenCode 로컬용 보존.
+    # 복원(night-drain 재개 시): git history 에서 deep 슬롯(qwen-macbook :8890, max_tokens 8192,
+    # timeout 900, context_char_limit 260000, temp 0.3 / top_p 0.9 / rep 1.05 / top_k 20) 부활 + drain-keeper 재활성.
 
     # fallback: primary 장애 시 최후 방어선. Claude Sonnet 4 API (소액 한도, 자동 trigger).
     # 호출 빈도 낮음 가정 (Mac mini 가 거의 항상 up) → premium 과 budget 공유 OK.
@@ -210,3 +202,6 @@ pipeline:
   digest_llm_timeout_s: 300
   digest_llm_attempts: 2
   digest_pipeline_hard_cap_s: 5400
+  # 2026-06-20: study/analyze 단일 primary-call 타임아웃 (구 하드코딩 30~60s = 빠른 Gemma 기준).
+  # Qwen 27B(콜당 ~40~150s)에 맞춰 단일소스화 — 구 30s 즉사 = 사용자 504 + 워커 영구 재시도.
+  llm_call_timeout_s: 300

docker-compose.override.cand.yml

@@ -1,135 +0,0 @@
-# Phase 2A — Embedding candidate compose override (Diagnose only)
-#
-# Profile-isolated: `--profile embed-cand` 명시 opt-in. default up 시 미기동.
-# production fastapi/postgres/reranker 에 영향 0.
-# 본 PR 종료 시 별 chore (PR-2A-Chunks-Cand-Cleanup-1) 에서 제거.
-#
-# 후보 상태 (2026-05-23):
-#   - me5_large_inst : ✅ smoke PASS (dim 1024)
-#   - bge_mgemma2    : ❌ Phase 2A-Extended 별 PR 이관 (9B FP16 → VRAM OOM risk + 다운로드 cost)
-#   - me5_ko         : ❌ 폐기 (401 Unauthorized, gated/모델명 부정확)
-#   - snowflake_l_v2 : 신규 추가 (Snowflake/snowflake-arctic-embed-l-v2.0, 2024-12, multilingual 강화)
-#
-# 사용:
-#   docker compose -f docker-compose.yml -f docker-compose.override.cand.yml \
-#     --profile embed-cand up -d embedding-cand-me5-inst
-#
-# 호출 (DS network 내부):
-#   http://embedding-cand-me5-inst:80/embed
-#   http://embedding-cand-snowflake-l-v2:80/embed
-
-services:
-  embedding-cand-me5-inst:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    restart: unless-stopped
-    container_name: hyungi_document_server-embedding-cand-me5-inst-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=intfloat/multilingual-e5-large-instruct
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - embedding_cand_me5_inst_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 60s
-    profiles: ["embed-cand"]
-
-  embedding-cand-snowflake-l-v2:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    restart: unless-stopped
-    container_name: hyungi_document_server-embedding-cand-snowflake-l-v2-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=Snowflake/snowflake-arctic-embed-l-v2.0
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - embedding_cand_snowflake_l_v2_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 60s
-    profiles: ["embed-cand"]
-
-  # ===== 비활성 후보 (Phase 2A-Extended 별 PR 이관 또는 폐기) =====
-  # 진단 박제만 보존. 본 PR scope 외.
-
-  embedding-cand-bge-mgemma2:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    container_name: hyungi_document_server-embedding-cand-bge-mgemma2-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=BAAI/bge-multilingual-gemma2
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - embedding_cand_bge_mgemma2_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 300s
-    profiles: ["embed-cand-extended"]   # 본 PR 미사용. extended 별 profile.
-
-  embedding-cand-me5-ko:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    container_name: hyungi_document_server-embedding-cand-me5-ko-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=dragonkue/multilingual-e5-large-ko
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - embedding_cand_me5_ko_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 60s
-    profiles: ["embed-cand-disabled"]   # 401 fail. 사용 X.
-
-volumes:
-  embedding_cand_me5_inst_cache:
-  embedding_cand_snowflake_l_v2_cache:
-  embedding_cand_bge_mgemma2_cache:
-  embedding_cand_me5_ko_cache:

docker-compose.override.rerank-cand.yml

@@ -1,101 +0,0 @@
-# Phase 2B — Reranker candidate compose override (Diagnose only)
-#
-# Profile-isolated: `--profile rerank-cand` 명시 opt-in. default up 시 미기동.
-# production fastapi/postgres/reranker(bge-reranker-v2-m3) 에 영향 0.
-# 본 PR 종료 후 별 chore (PR-2B-Rerank-Cand-Cleanup-1) 에서 제거.
-#
-# 후보 상태 (2026-05-23):
-#   - gte_ml_base       : Apache 2.0, 305M, smoke 대기
-#   - mxbai_large       : Apache 2.0, ~435M, safetensors 부재 — TEI smoke risk
-#   - bge_v2_gemma_2b   : Gemma 라이센스, 2.5B FP16 ~5GB, smoke 대기
-#
-# 사용:
-#   docker compose -f docker-compose.yml -f docker-compose.override.rerank-cand.yml \
-#     --profile rerank-cand up -d rerank-cand-gte-ml-base
-
-services:
-  rerank-cand-gte-ml-base:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    restart: unless-stopped
-    container_name: hyungi_document_server-rerank-cand-gte-ml-base-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=Alibaba-NLP/gte-multilingual-reranker-base
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - rerank_cand_gte_ml_base_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 60s
-    profiles: ["rerank-cand"]
-
-  rerank-cand-mxbai-large:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    restart: unless-stopped
-    container_name: hyungi_document_server-rerank-cand-mxbai-large-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=mixedbread-ai/mxbai-rerank-large-v1
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=4
-    volumes:
-      - rerank_cand_mxbai_large_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 60s
-    profiles: ["rerank-cand"]
-
-  rerank-cand-bge-v2-gemma-2b:
-    image: ghcr.io/huggingface/text-embeddings-inference:1.7
-    restart: unless-stopped
-    container_name: hyungi_document_server-rerank-cand-bge-v2-gemma-2b-1
-    expose:
-      - "80"
-    environment:
-      - MODEL_ID=BAAI/bge-reranker-v2-gemma
-      - MAX_BATCH_TOKENS=8192
-      - MAX_CONCURRENT_REQUESTS=2
-    volumes:
-      - rerank_cand_bge_v2_gemma_2b_cache:/data
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 120s
-    profiles: ["rerank-cand"]
-
-volumes:
-  rerank_cand_gte_ml_base_cache:
-  rerank_cand_mxbai_large_cache:
-  rerank_cand_bge_v2_gemma_2b_cache:

docker-compose.yml

@@ -3,7 +3,13 @@ services:
     image: pgvector/pgvector:pg16
     volumes:
       - pgdata:/var/lib/postgresql/data
-      - ./migrations:/docker-entrypoint-initdb.d
+      # ★ 2026-06-29 fresh-DB/DR 부팅 fix: initdb.d 마운트 제거(기존 `./migrations:/docker-entrypoint-initdb.d`).
+      # 빈 볼륨 첫 기동 시 postgres 엔트리포인트가 migrations/*.sql(001~) 을 psql autocommit 으로 실행해
+      # 스키마는 만들되 schema_migrations 스탬프는 안 남김(runner 만 생성) → fastapi init_db 가 documents
+      # 존재로 'fresh' 를 오판해 baseline(_load_baseline_if_fresh) 로드를 건너뛰고, 빈 schema_migrations
+      # 로 001 부터 재replay → `CREATE TABLE users`(IF NOT EXISTS 없음) 충돌 → 부팅 크래시(DR/신규환경).
+      # fresh-boot 은 init_db 의 baseline 적재 + migration runner 단일 경로로 일원화(설계 의도). 기존 prod
+      # 볼륨은 비어있지 않아 init scripts 가 애초에 미발동 → 무영향.
     environment:
       POSTGRES_DB: pkm
       POSTGRES_USER: pkm
@@ -16,6 +22,8 @@ services:
       timeout: 5s
       retries: 5
     restart: unless-stopped
+    # 2026-06-20 tier-0 무장: 글로벌 OOM 시 커널이 postgres(prod DB)를 reap 하지 않도록.
+    oom_score_adj: -900
 
   kordoc-service:
     build: ./services/kordoc
@@ -54,24 +62,28 @@ services:
       start_period: 180s
     restart: unless-stopped
 
-  # Phase 1B (2026-05-01): PDF → markdown 변환. ocr-service 와 별도 컨테이너 (deps 충돌 회피).
-  marker-service:
-    build: ./services/marker
+  # MinerU 2.5 VLM PDF→markdown 추출 — ★ marker-service 대체(컷오버 2026-06-18, A/B 8/8 PASS).
+  # 단일카드 markdown VRAM ~10GB(marker)→~5.9GB 고정. fastapi 가 MARKER_ENDPOINT 로 호출.
+  # 동기 do_parse 버그 회피 위해 server.py 는 async aio_do_parse 사용. 포트 3301.
+  mineru-service:
+    build: ./services/mineru
+    mem_limit: 16g  # 2026-06-20: VLM 스파이크 봉쇄 (steady ~12GB) — 호스트 30GB 글로벌 OOM 차단
     ports:
-      - "127.0.0.1:3300:3300"
+      - "127.0.0.1:3301:3301"
     expose:
-      - "3300"
+      - "3301"
     environment:
-      - HF_HOME=/models/huggingface
-      - TORCH_HOME=/models/torch
-      # D-1 (crawl-24x7): idle-unload 전환 — 영구 점유(~3.5GB) 해제가 90% 봉투의 전제.
-      # /ready 는 idle 에서도 200 (fastapi depends_on service_healthy 유지).
-      # 롤백 = MARKER_PRELOAD=1 + MARKER_IDLE_UNLOAD_MINUTES=0.
-      - MARKER_PRELOAD=0
-      - MARKER_IDLE_UNLOAD_MINUTES=${MARKER_IDLE_UNLOAD_MINUTES:-30}
+      # vlm-engine = 순수 VLM 단일모델. 기본 hybrid-engine 은 다중모델 로드 = OOM(반드시 명시).
+      - MINERU_BACKEND=vlm-engine
+      - MINERU_LANG=${MINERU_LANG:-korean}
+      # 공유 16GB 카드 공존: 절대 VRAM 캡(GB, 공유카드 robust) + vLLM 분율 캡 병용.
+      - MINERU_VIRTUAL_VRAM_SIZE=${MINERU_VIRTUAL_VRAM_SIZE:-6}
+      - MINERU_GPU_MEMORY_UTILIZATION=${MINERU_GPU_MEMORY_UTILIZATION:-0.40}
+      - MINERU_PRELOAD=${MINERU_PRELOAD:-1}
     volumes:
       - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
-      - marker_models:/models
+      - mineru_models:/root/.cache
+    ipc: host                      # vLLM 공유메모리 — 공식 run 의 --ipc=host 대응.
     deploy:
       resources:
         reservations:
@@ -80,11 +92,11 @@ services:
               count: 1
               capabilities: [gpu]
     healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:3300/ready"]
+      test: ["CMD", "curl", "-f", "http://localhost:3301/ready"]
       interval: 30s
       timeout: 10s
       retries: 3
-      start_period: 300s
+      start_period: 900s           # VLM 모델 lazy 다운로드(~2.4GB)+엔진 로드 여유.
     restart: unless-stopped
 
   stt-service:
@@ -149,7 +161,7 @@ services:
       #       → 32 한도 초과 → 413. 64 로 늘림.
       # GPU VRAM free 6199MiB 충분. baseline path (MAX_RERANK_INPUT=200) 영향 0.
       - MAX_BATCH_TOKENS=16384
-      - MAX_CLIENT_BATCH_SIZE=64
+      - MAX_CLIENT_BATCH_SIZE=256  # 2026-06-18 fix: 64→256, MAX_RERANK_INPUT=200 커버 (batch>64 ERROR=RRF silent fallback 해소; MAX_BATCH_TOKENS가 VRAM 상한이라 entries 증가는 VRAM 무관)
       - MAX_CONCURRENT_REQUESTS=4
     volumes:
       - reranker_cache:/data
@@ -168,21 +180,9 @@ services:
       start_period: 120s
     restart: unless-stopped
 
-  ai-gateway:
-    build: ./gpu-server/services/ai-gateway
-    ports:
-      - "127.0.0.1:8081:8080"
-    environment:
-      - PRIMARY_ENDPOINT=http://100.76.254.116:8801/v1/chat/completions
-      - FALLBACK_ENDPOINT=http://ollama:11434/v1/chat/completions
-      - CLAUDE_API_KEY=${CLAUDE_API_KEY:-}
-      - DAILY_BUDGET_USD=${DAILY_BUDGET_USD:-5.00}
-    # depends_on: ollama 제거 (2026-06-08) — ollama 서비스가 standalone 으로 이관됨.
-    # FALLBACK_ENDPOINT 의 ollama:11434 는 standalone(동일 hostname, DS 망 부착)으로 해소.
-    restart: unless-stopped
-
   fastapi:
     build: ./app
+    oom_score_adj: -900  # 2026-06-20 tier-0 무장 (앱+스케줄러 SPOF 보호)
     ports:
       - "100.110.63.63:8000:8000"
     volumes:
@@ -197,7 +197,8 @@ services:
         condition: service_healthy
       kordoc-service:
         condition: service_healthy
-      marker-service:
+      # 마크다운 엔진 = mineru-service (marker-service 제거 2026-06-18, 롤백=git history).
+      mineru-service:
         condition: service_healthy
     env_file:
       - credentials.env
@@ -205,7 +206,8 @@ services:
       - DATABASE_URL=postgresql+asyncpg://pkm:${POSTGRES_PASSWORD}@postgres:5432/pkm
       - KORDOC_ENDPOINT=http://kordoc-service:3100
       - OCR_ENDPOINT=http://ocr-service:3200
-      - MARKER_ENDPOINT=http://marker-service:3300
+      # ★ 컷오버 2026-06-18: marker-service:3300 → mineru-service:3301 (동일 /convert 계약).
+      - MARKER_ENDPOINT=http://mineru-service:3301
       - MARKER_CONTAINER_PATH_PREFIX=/documents
       # 2026-05-08 (D9 Track B revised): GPU stt-service 정식 승격, 내부 DNS 사용.
       - STT_ENDPOINT=http://stt-service:3300
@@ -214,6 +216,14 @@ services:
       # PR-MacMini-Derived-Worker-1
       - STUDY_EXPLANATION_ENABLED=${STUDY_EXPLANATION_ENABLED:-true}
       - INTERNAL_WORKER_TOKEN=${INTERNAL_WORKER_TOKEN}
+      # docsrv-viewer-publish: 발행 워커/저작 enqueue 게이트(기본 false=inert) + 뷰어↔DS feed Bearer.
+      - STUDY_PUBLISH_ENABLED=${STUDY_PUBLISH_ENABLED:-false}
+      - DIGEST_PUBLISH_ENABLED=${DIGEST_PUBLISH_ENABLED:-false}
+      - MAINTENANCE_MODE=${MAINTENANCE_MODE:-false}
+      - MAINTENANCE_NOTE=${MAINTENANCE_NOTE:-}
+      - VIEWER_SYNC_TOKEN=${VIEWER_SYNC_TOKEN:-}
+      # study-to-viewer P2: 뷰어 write-back ingest 게이트(기본 false=inert, 검증 후 점등).
+      - STUDY_INGEST_ENABLED=${STUDY_INGEST_ENABLED:-false}
       # Voice Memo PoC v1 — bot 계정 한정 long-expiry access token. default false → 일반 운영 영향 0.
       # 활성화: host .env 에 VOICE_MEMO_BOT_TOKEN_ENABLED=true. plan: rosy-launching-otter.md
       - VOICE_MEMO_BOT_TOKEN_ENABLED=${VOICE_MEMO_BOT_TOKEN_ENABLED:-false}
@@ -267,7 +277,7 @@ services:
   caddy:
     image: caddy:2
     ports:
-      - "8080:80"
+      - "127.0.0.1:8080:80"  # 2026-06-20: LAN 우회 차단 (실 ingress=home-caddy→caddy:80 도커망)
     volumes:
       - ./Caddyfile:/etc/caddy/Caddyfile
       - caddy_data:/data
@@ -283,4 +293,4 @@ volumes:
   reranker_cache:
   ocr_models:
   stt_models:
-  marker_models:
+  mineru_models:

docs/architecture.md

@@ -1094,7 +1094,7 @@ services:
     image: pgvector/pgvector:pg16
     volumes:
       - pgdata:/var/lib/postgresql/data
-      - ./migrations:/docker-entrypoint-initdb.d
+      # initdb.d 마운트 제거(2026-06-29): fresh-boot 은 fastapi init_db+baseline 단일 경로.
     environment:
       POSTGRES_DB: pkm
       POSTGRES_USER: pkm

docs/storage_layout.md

@@ -71,7 +71,7 @@ GPU 서버의 NFS mount (`/proc/mounts` 실측):
 
 | 컨테이너 | 마운트 | 모드 | 비고 |
 |---|---|---|---|
-| postgres | `pgdata:/var/lib/postgresql/data` + `./migrations:/docker-entrypoint-initdb.d` | rw | DB 본체 named volume |
+| postgres | `pgdata:/var/lib/postgresql/data` | rw | DB 본체 named volume (initdb.d 마운트는 2026-06-29 제거 — 아래 관찰) |
 | kordoc-service | `${NAS}/Document_Server:/documents` | **ro** | PDF/HWP parse |
 | ocr-service | `${NAS}/Document_Server:/documents` + `ocr_models:/root/.cache` | **ro** + rw | |
 | marker-service | `${NAS}/Document_Server:/documents` + `marker_models:/models` | **ro** + rw | PDF→markdown |
@@ -84,7 +84,7 @@ GPU 서버의 NFS mount (`/proc/mounts` 실측):
 **관찰**:
 - worker 컨테이너 (kordoc/ocr/marker/stt) 는 모두 NAS **read-only** 마운트 → 원본 안전.
 - fastapi 만 NAS **rw** → 업로드/preview/extracted_images 쓰기 단일 책임.
-- `./migrations` 이 postgres 의 `docker-entrypoint-initdb.d` 와 fastapi 의 `/app/migrations` 양쪽에 마운트. 단 실제 migration runner 는 fastapi `init_db()` 만 사용 (postgres init scripts 는 첫 생성 시만 실행 → 효과 X, 안전).
+- `./migrations` 은 fastapi 의 `/app/migrations` 에만 마운트. migration runner 는 fastapi `init_db()` 단일 경로. (~2026-06-29: postgres `docker-entrypoint-initdb.d` 마운트 제거. 기존엔 "첫 생성 시만 실행 → 효과 X" 로 봤으나, 빈 볼륨 첫 기동 시 postgres 가 migrations/*.sql 을 실제 실행해 스키마는 만들되 schema_migrations 스탬프를 안 남겨 → init_db 의 baseline fresh 판정을 깨고 부팅 크래시 유발. fresh-DB/DR 부팅을 init_db+baseline 단일 경로로 일원화.)
 
 ## 정책 정리
 

frontend/src/lib/components/AskAnswerCard.svelte

@@ -1,160 +0,0 @@
-<!--
-  AskAnswerCard.svelte — 검색 결과 페이지 상단 AI 답변 카드 (컴팩트).
-
-  /ask 페이지의 AskAnswer.svelte와 달리, 검색 결과를 가리지 않는
-  보조 영역으로 설계. 출처 목록 클릭이 must-have, 본문 [n] 클릭은 nice-to-have.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { Sparkles, X, FileText } from 'lucide-svelte';
-  import type { AskResponse, Confidence } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    error: boolean;
-    onCitationClick: (docId: number) => void;
-    onDismiss: () => void;
-  }
-
-  let { data, loading, error, onCitationClick, onDismiss }: Props = $props();
-
-  // [n] 파싱 (AskAnswer.svelte에서 가져옴)
-  type Token =
-    | { type: 'text'; value: string }
-    | { type: 'cite'; n: number; raw: string };
-
-  function splitAnswer(text: string): Token[] {
-    return text
-      .split(/(\[\d+\])/g)
-      .filter(Boolean)
-      .map((tok): Token => {
-        const m = tok.match(/^\[(\d+)\]$/);
-        return m
-          ? { type: 'cite', n: Number(m[1]), raw: tok }
-          : { type: 'text', value: tok };
-      });
-  }
-
-  function confidenceTone(c: Confidence | null): 'success' | 'warning' | 'error' | 'neutral' {
-    if (c === 'high') return 'success';
-    if (c === 'medium') return 'warning';
-    if (c === 'low') return 'error';
-    return 'neutral';
-  }
-
-  function confidenceLabel(c: Confidence | null): string {
-    if (c === 'high') return '높음';
-    if (c === 'medium') return '중간';
-    if (c === 'low') return '낮음';
-    return '';
-  }
-
-  let tokens = $derived(data?.ai_answer ? splitAnswer(data.ai_answer) : []);
-
-  // 출처 중복 제거 (같은 doc_id)
-  let uniqueCitations = $derived.by(() => {
-    if (!data?.citations?.length) return [];
-    const seen = new Set<number>();
-    return data.citations.filter((c) => {
-      if (seen.has(c.doc_id)) return false;
-      seen.add(c.doc_id);
-      return true;
-    });
-  });
-</script>
-
-<div class="bg-surface border border-default rounded-card p-4">
-  <!-- 헤더 -->
-  <div class="flex items-center justify-between gap-2 mb-2">
-    <div class="flex items-center gap-1.5">
-      <Sparkles size={12} class="text-accent" />
-      <span class="text-[10px] font-semibold tracking-wider uppercase text-dim">
-        내 자료 기준 답변
-      </span>
-      {#if data?.confidence}
-        <Badge tone={confidenceTone(data.confidence)} size="sm">
-          신뢰도 {confidenceLabel(data.confidence)}
-        </Badge>
-      {/if}
-      {#if data?.completeness === 'partial'}
-        <Badge tone="warning" size="sm">일부 답변</Badge>
-      {/if}
-    </div>
-    <button
-      type="button"
-      onclick={onDismiss}
-      class="p-0.5 rounded text-dim hover:text-text transition-colors"
-      aria-label="답변 카드 접기"
-    >
-      <X size={14} />
-    </button>
-  </div>
-
-  <!-- 본문 -->
-  {#if loading}
-    <div class="space-y-2">
-      <Skeleton w="w-full" h="h-3" />
-      <Skeleton w="w-4/5" h="h-3" />
-    </div>
-    <p class="mt-3 text-[10px] text-dim flex items-center gap-1.5">
-      <span class="inline-block w-2.5 h-2.5 rounded-full border-2 border-dim border-t-accent animate-spin"></span>
-      근거 기반 답변 생성 중…
-    </p>
-  {:else if error}
-    <p class="text-xs text-dim">답변을 가져오지 못했습니다.</p>
-  {:else if data?.ai_answer}
-    <!-- 답변 텍스트 -->
-    <div class="text-sm leading-6 text-text">
-      {#each tokens as tok}
-        {#if tok.type === 'cite'}
-          {@const citation = data?.citations?.find((c) => c.n === tok.n)}
-          {#if citation}
-            <button
-              type="button"
-              class="inline text-accent font-semibold hover:underline px-0.5"
-              onclick={() => onCitationClick(citation.doc_id)}
-              title={citation.title || `문서 #${citation.doc_id}`}
-            >
-              {tok.raw}
-            </button>
-          {:else}
-            <span class="text-dim">{tok.raw}</span>
-          {/if}
-        {:else}
-          <span>{tok.value}</span>
-        {/if}
-      {/each}
-    </div>
-
-    <!-- partial: 누락 측면 -->
-    {#if data.completeness === 'partial' && data.missing_aspects?.length}
-      <p class="mt-2 text-[10px] text-dim">
-        다루지 못한 부분: {data.missing_aspects.join(', ')}
-      </p>
-    {/if}
-
-    <!-- 출처 목록 (must-have) -->
-    {#if uniqueCitations.length > 0}
-      <div class="mt-3 pt-2 border-t border-default">
-        <p class="text-[10px] font-medium text-dim mb-1.5">출처</p>
-        <div class="flex flex-wrap gap-1.5">
-          {#each uniqueCitations as citation}
-            <button
-              type="button"
-              onclick={() => onCitationClick(citation.doc_id)}
-              class="inline-flex items-center gap-1 text-[11px] px-2 py-0.5 rounded bg-surface text-text border border-default hover:border-accent hover:text-accent transition-colors"
-              title={citation.span_text}
-            >
-              <FileText size={10} />
-              <span class="max-w-[200px] truncate">
-                {citation.title || `문서 #${citation.doc_id}`}
-              </span>
-            </button>
-          {/each}
-        </div>
-      </div>
-    {/if}
-  {/if}
-</div>

frontend/src/lib/components/RelatedDocs.svelte

@@ -0,0 +1,45 @@
+<script>
+  // 관련 문서 (유사도) — 문서 레벨 임베딩 KNN. 자기완결: docId 받아 /related 조회.
+  import { onMount } from 'svelte';
+  import { api } from '$lib/api';
+
+  let { documentId } = $props();
+  let items = $state([]);
+  let loaded = $state(false);
+
+  const KIND = { law: '법령', guide: '지침', paper: '논문', standard: '표준', incident: '사례' };
+
+  onMount(async () => {
+    try {
+      const r = await api(`/documents/${documentId}/related?limit=6`);
+      items = r?.related ?? [];
+    } catch (e) { /* silent */ }
+    finally { loaded = true; }
+  });
+</script>
+
+{#if items.length}
+  <div class="rel">
+    <div class="lab">관련 문서</div>
+    {#each items as it (it.id)}
+      <a class="ri" href={`/documents/${it.id}`}>
+        <span class="rt">{it.title}</span>
+        <span class="rm">
+          {#if it.material_type && KIND[it.material_type]}<span class="kind">{KIND[it.material_type]}</span>{/if}
+          <span class="rs">{Math.round((it.sim ?? 0) * 100)}</span>
+        </span>
+      </a>
+    {/each}
+  </div>
+{/if}
+
+<style>
+  .rel { background: var(--surface); border: 1px solid var(--border); border-radius: 14px; padding: 13px; }
+  .lab { font-size: 10.5px; font-weight: 700; color: var(--text-dim); letter-spacing: .4px; margin-bottom: 8px; }
+  .ri { display: flex; align-items: baseline; gap: 8px; padding: 5px 6px; border-radius: 7px; text-decoration: none; }
+  .ri:hover { background: var(--surface-hover, #ecf0e8); }
+  .rt { flex: 1; font-size: 12px; line-height: 1.4; color: var(--text); overflow: hidden; display: -webkit-box; -webkit-line-clamp: 2; -webkit-box-orient: vertical; }
+  .rm { flex-shrink: 0; display: flex; align-items: center; gap: 5px; }
+  .kind { font-size: 9px; font-weight: 700; color: var(--accent-hover, #3d7256); background: #e3efe2; border: 1px solid #cfe3cd; border-radius: 4px; padding: 0 4px; }
+  .rs { font-size: 10.5px; font-family: ui-monospace, Menlo, monospace; color: var(--faint, #9aa090); }
+</style>

frontend/src/lib/components/Sidebar.svelte

@@ -2,7 +2,7 @@
   import { page } from '$app/stores';
   import { goto } from '$app/navigation';
   import { api } from '$lib/api';
-  import { ChevronRight, ChevronDown, FolderOpen, FolderTree, Inbox, Clock, Mail, Scale, StickyNote, GraduationCap, CalendarCheck, MessageCircle } from 'lucide-svelte';
+  import { ChevronRight, ChevronDown, FolderOpen, FolderTree, Inbox, Clock, Mail, Scale, StickyNote, GraduationCap, CalendarCheck, MessageCircle, Hash } from 'lucide-svelte';
 
   let tree = $state([]);
   let loading = $state(true);
@@ -195,6 +195,13 @@
     >
       <FolderTree size={14} /> 자료실
     </a>
+    <a
+      href="/clause"
+      class="w-full flex items-center gap-2 px-3 py-1.5 rounded-md text-sm transition-colors
+        {$page.url.pathname === '/clause' ? 'bg-accent/15 text-accent' : 'text-dim hover:bg-surface hover:text-text'}"
+    >
+      <Hash size={14} /> 절 바로가기
+    </a>
   </div>
 
   <!-- 메모 & Inbox -->

frontend/src/lib/components/SlimRail.svelte

@@ -0,0 +1,33 @@
+<script>
+  // 시안 B — 글로벌 네비 슬림 아이콘 레일 (분류 사이드바 접힘 상태). 앱 토큰 사용.
+  import { page } from '$app/stores';
+  import { Home, FolderTree, Newspaper, StickyNote, Hash, GraduationCap, MessageCircle, Inbox, CalendarCheck } from 'lucide-svelte';
+
+  const items = [
+    { href: '/', icon: Home, label: '홈', exact: true },
+    { href: '/library', icon: FolderTree, label: '문서' },
+    { href: '/news', icon: Newspaper, label: '뉴스' },
+    { href: '/memos', icon: StickyNote, label: '메모' },
+    { href: '/clause', icon: Hash, label: '절' },
+    { href: '/events', icon: CalendarCheck, label: '일정' },
+    { href: '/study', icon: GraduationCap, label: '공부' },
+    { href: '/chat', icon: MessageCircle, label: '이드' },
+    { href: '/inbox', icon: Inbox, label: '편지함' },
+  ];
+  let path = $derived($page.url.pathname);
+  const active = (it) => (it.exact ? path === it.href : path.startsWith(it.href));
+</script>
+
+<nav class="flex flex-col items-center gap-1 py-2 h-full overflow-y-auto bg-sidebar">
+  {#each items as it (it.href)}
+    {@const Icon = it.icon}
+    <a
+      href={it.href}
+      title={it.label}
+      class="flex flex-col items-center justify-center gap-0.5 w-12 h-[46px] rounded-lg text-dim hover:bg-surface-hover hover:text-accent transition-colors {active(it) ? 'bg-surface-active text-accent font-semibold' : ''}"
+    >
+      <Icon size={17} strokeWidth={1.75} />
+      <span class="text-[8.5px] leading-none tracking-tight">{it.label}</span>
+    </a>
+  {/each}
+</nav>

frontend/src/lib/components/ask/AskAnswer.svelte

@@ -1,228 +0,0 @@
-<!--
-  AskAnswer.svelte — /ask 페이지 상단 패널.
-
-  Answer 본문 + clickable [n] citations + 신뢰도/상태 Badge.
-  status != completed 또는 refused=true → warning empty state +
-  no_results_reason + "검색 결과 확인하기" 역링크.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import Button from '$lib/components/ui/Button.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { AlertTriangle, Sparkles } from 'lucide-svelte';
-  import type { AskResponse, Confidence, SynthesisStatus } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    onCitationClick: (n: number) => void;
-  }
-
-  let { data, loading, onCitationClick }: Props = $props();
-
-  type Token =
-    | { type: 'text'; value: string }
-    | { type: 'cite'; n: number; raw: string };
-
-  function splitAnswer(text: string): Token[] {
-    return text
-      .split(/(\[\d+\])/g)
-      .filter(Boolean)
-      .map((tok): Token => {
-        const m = tok.match(/^\[(\d+)\]$/);
-        return m
-          ? { type: 'cite', n: Number(m[1]), raw: tok }
-          : { type: 'text', value: tok };
-      });
-  }
-
-  function confidenceTone(
-    c: Confidence | null,
-  ): 'success' | 'warning' | 'error' | 'neutral' {
-    if (c === 'high') return 'success';
-    if (c === 'medium') return 'warning';
-    if (c === 'low') return 'error';
-    return 'neutral';
-  }
-
-  function confidenceLabel(c: Confidence | null): string {
-    if (c === 'high') return '높음';
-    if (c === 'medium') return '중간';
-    if (c === 'low') return '낮음';
-    return '없음';
-  }
-
-  const STATUS_LABEL: Record<SynthesisStatus, string> = {
-    completed: '답변 완료',
-    timeout: '답변 지연',
-    skipped: '답변 생략',
-    no_evidence: '근거 없음',
-    parse_failed: '형식 오류',
-    llm_error: 'AI 오류',
-    backend_unavailable: 'Backend 비가용',
-  };
-
-  /**
-   * backend chip label — `backend_requested` 가 명시 opt-in 인 경우만 표시.
-   * 미지정 (null/undefined) default 호출은 chip 없음 (시각 noise 회피).
-   */
-  function backendChipLabel(backend: string | null | undefined): string | null {
-    if (!backend) return null;
-    if (backend === 'qwen-macbook') return 'Qwen 27B (MacBook)';
-    if (backend === 'gemma-macmini') return 'Gemma 26B (Mac mini)';
-    return backend;
-  }
-
-  let tokens = $derived(data?.ai_answer ? splitAnswer(data.ai_answer) : []);
-  let showFullAnswer = $derived(
-    !!data && !!data.ai_answer && data.completeness === 'full'
-    && data.synthesis_status === 'completed' && !data.refused,
-  );
-  let showPartial = $derived(
-    !!data && data.completeness === 'partial' && !data.refused,
-  );
-  let showWarning = $derived(!!data && !showFullAnswer && !showPartial);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <!-- 헤더 -->
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <Sparkles size={12} /> AI Answer
-      </p>
-      <h2 class="mt-1 text-base font-semibold text-text">근거 기반 답변</h2>
-    </div>
-
-    {#if data && !loading}
-      <div class="flex flex-wrap gap-1.5">
-        <Badge tone={confidenceTone(data.confidence)} size="sm">
-          신뢰도 {confidenceLabel(data.confidence)}
-        </Badge>
-        {#if backendChipLabel(data.backend_requested)}
-          <span title={`backend_requested=${data.backend_requested} / backend_used=${data.backend_used ?? 'null'}`}>
-            <Badge tone="neutral" size="sm">
-              {backendChipLabel(data.backend_requested)}
-            </Badge>
-          </span>
-        {/if}
-        <Badge tone="neutral" size="sm">
-          {STATUS_LABEL[data.synthesis_status]}
-        </Badge>
-        {#if data.synthesis_ms > 0}
-          <Badge tone="neutral" size="sm">
-            {Math.round(data.synthesis_ms)}ms
-          </Badge>
-        {/if}
-      </div>
-    {/if}
-  </div>
-
-  <!-- 본문 -->
-  {#if loading}
-    <div class="space-y-3">
-      <Skeleton w="w-3/4" h="h-4" />
-      <Skeleton w="w-full" h="h-4" />
-      <Skeleton w="w-5/6" h="h-4" />
-      <p class="mt-4 text-xs text-dim flex items-center gap-2">
-        <span class="inline-block w-3 h-3 rounded-full border-2 border-dim border-t-accent animate-spin"></span>
-        근거 기반 답변 생성 중… 약 15초 소요
-      </p>
-    </div>
-  {:else if showFullAnswer && data}
-    <div class="text-sm leading-7 text-text">
-      {#each tokens as tok}
-        {#if tok.type === 'cite'}
-          <button
-            type="button"
-            class="inline-block align-baseline text-accent font-semibold hover:underline focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-accent-ring rounded px-0.5"
-            onclick={() => onCitationClick(tok.n)}
-            aria-label={`인용 ${tok.n}번 보기`}
-          >
-            {tok.raw}
-          </button>
-        {:else}
-          <span>{tok.value}</span>
-        {/if}
-      {/each}
-    </div>
-  {:else if showPartial && data}
-    <!-- Phase 3.5a: question-aligned partial structure -->
-    <div>
-      <Badge tone="warning" size="sm">일부 답변</Badge>
-
-      {#if data.ai_answer}
-        <div class="mt-3 text-sm leading-7 text-text">
-          {#each tokens as tok}
-            {#if tok.type === 'cite'}
-              <button
-                type="button"
-                class="inline-block align-baseline text-accent font-semibold hover:underline rounded px-0.5"
-                onclick={() => onCitationClick(tok.n)}
-              >{tok.raw}</button>
-            {:else}
-              <span>{tok.value}</span>
-            {/if}
-          {/each}
-        </div>
-      {:else if data.confirmed_items?.length}
-        <div class="mt-3">
-          <h4 class="text-xs font-semibold text-dim uppercase tracking-wider">✓ 답변 가능</h4>
-          <ul class="mt-2 space-y-2">
-            {#each data.confirmed_items as item}
-              <li class="text-sm text-text">
-                <strong class="text-accent">{item.aspect}:</strong>
-                <span>{item.text}</span>
-                {#each item.citations as n}
-                  <button
-                    type="button"
-                    class="text-accent font-semibold hover:underline px-0.5"
-                    onclick={() => onCitationClick(n)}
-                  >[{n}]</button>
-                {/each}
-              </li>
-            {/each}
-          </ul>
-        </div>
-      {/if}
-
-      {#if data.missing_aspects?.length}
-        <div class="mt-4 border-t border-default pt-3">
-          <h4 class="text-xs font-semibold text-dim uppercase tracking-wider">✗ 답변 불가</h4>
-          <ul class="mt-2 space-y-1">
-            {#each data.missing_aspects as aspect}
-              <li class="text-sm text-dim">{aspect} <span class="text-[10px]">(근거 없음)</span></li>
-            {/each}
-          </ul>
-        </div>
-      {/if}
-
-      <div class="mt-4">
-        <Button
-          variant="secondary"
-          size="sm"
-          href={`/documents?q=${encodeURIComponent(data.query)}`}
-        >
-          검색 결과 확인하기
-        </Button>
-      </div>
-    </div>
-  {:else if showWarning && data}
-    <EmptyState
-      icon={AlertTriangle}
-      title={data.refused && data.no_results_reason
-        ? data.no_results_reason
-        : (data.no_results_reason ?? '관련 근거를 찾지 못했습니다.')}
-      description="검색 결과를 직접 확인해 보세요."
-    >
-      <Button
-        variant="secondary"
-        size="sm"
-        href={`/documents?q=${encodeURIComponent(data.query)}`}
-      >
-        검색 결과 확인하기
-      </Button>
-    </EmptyState>
-  {/if}
-</section>

frontend/src/lib/components/ask/AskEvidence.svelte

@@ -1,91 +0,0 @@
-<!--
-  AskEvidence.svelte — /ask 페이지 우측 sticky 패널.
-
-  ⚠ 영구 룰 (Phase 3.4 plan):
-    `citation.full_snippet` 은 UI 에 직접 렌더 금지. debug 모드(`?debug=1`)
-    에서 hover tooltip 으로만 조건부 노출 가능.
-
-    이 규칙이 깨지면 backend span-precision UX 가치가 사라진다. 코드 리뷰에서
-    반드시 reject. span_text 만 본문으로 노출한다.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { BookOpen } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    activeCitation: number | null;
-    registerCitation: (n: number, node: HTMLElement) => { destroy: () => void };
-  }
-
-  let { data, loading, activeCitation, registerCitation }: Props = $props();
-
-  let citations = $derived(data?.citations ?? []);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <BookOpen size={12} /> Evidence Highlights
-      </p>
-      <h3 class="mt-1 text-sm font-semibold text-text">인용 근거</h3>
-    </div>
-    {#if data && !loading}
-      <Badge tone="neutral" size="sm">{citations.length}개</Badge>
-    {/if}
-  </div>
-
-  {#if loading}
-    <div class="space-y-3">
-      {#each Array(2) as _}
-        <div class="border border-default rounded-card p-4 space-y-2">
-          <Skeleton w="w-24" h="h-3" />
-          <Skeleton w="w-full" h="h-3" />
-          <Skeleton w="w-5/6" h="h-3" />
-          <Skeleton w="w-3/4" h="h-3" />
-        </div>
-      {/each}
-    </div>
-  {:else if citations.length === 0}
-    <EmptyState title="표시할 근거가 없습니다." class="py-6" />
-  {:else}
-    <div class="space-y-3">
-      {#each citations as citation (citation.n)}
-        {@const isActive = activeCitation === citation.n}
-        <article
-          class="border rounded-card p-4 transition-colors {isActive
-            ? 'border-accent ring-2 ring-accent/20 bg-accent/5'
-            : 'border-default'}"
-          use:registerCitation={citation.n}
-        >
-          <div class="flex items-start gap-2">
-            <span class="text-accent font-bold text-sm shrink-0">[{citation.n}]</span>
-            <div class="flex-1 min-w-0">
-              <strong class="block text-sm text-text truncate">
-                {citation.title ?? `문서 ${citation.doc_id}`}
-              </strong>
-              {#if citation.section_title}
-                <p class="mt-0.5 text-xs text-dim truncate">{citation.section_title}</p>
-              {/if}
-            </div>
-          </div>
-
-          <!-- ⚠ span_text 만 렌더. full_snippet 금지 -->
-          <p class="mt-3 text-sm leading-relaxed text-text whitespace-pre-wrap">
-            {citation.span_text}
-          </p>
-
-          <div class="mt-3 flex gap-2 text-[10px] text-dim">
-            <span>relevance {citation.relevance.toFixed(2)}</span>
-            <span>rerank {citation.rerank_score.toFixed(2)}</span>
-          </div>
-        </article>
-      {/each}
-    </div>
-  {/if}
-</section>

frontend/src/lib/components/ask/AskResults.svelte

@@ -1,78 +0,0 @@
-<!--
-  AskResults.svelte — /ask 페이지 하단 패널.
-
-  검색 결과 리스트. DocumentCard 재사용 X — SearchResult 필드 셋이 달라서
-  의존성 리스크 회피. inline 간단 카드로 title/score/snippet/section_title 표시.
-  클릭 시 `/documents/{id}` 로 이동.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { FileText } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-  }
-
-  let { data, loading }: Props = $props();
-
-  let results = $derived(data?.results ?? []);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <FileText size={12} /> Search Results
-      </p>
-      <h3 class="mt-1 text-sm font-semibold text-text">검색 결과</h3>
-    </div>
-    {#if data && !loading}
-      <Badge tone="neutral" size="sm">{data.total}개</Badge>
-    {/if}
-  </div>
-
-  {#if loading}
-    <div class="space-y-3">
-      {#each Array(5) as _}
-        <div class="border border-default rounded-card p-4 space-y-2">
-          <Skeleton w="w-2/3" h="h-4" />
-          <Skeleton w="w-full" h="h-3" />
-          <Skeleton w="w-4/5" h="h-3" />
-        </div>
-      {/each}
-    </div>
-  {:else if results.length === 0}
-    <EmptyState title="검색 결과가 없습니다." class="py-6" />
-  {:else}
-    <div class="space-y-3">
-      {#each results as result (result.id)}
-        <a
-          href={`/documents/${result.id}`}
-          class="block border border-default rounded-card p-4 hover:border-accent hover:bg-surface-hover transition-colors"
-        >
-          <div class="flex items-start justify-between gap-3">
-            <strong class="text-sm text-text flex-1 min-w-0 truncate">
-              {result.title ?? `문서 ${result.id}`}
-            </strong>
-            <div class="flex gap-1.5 text-[10px] text-dim shrink-0">
-              <span>score {result.score.toFixed(2)}</span>
-              {#if result.rerank_score != null}
-                <span>rerank {result.rerank_score.toFixed(2)}</span>
-              {/if}
-            </div>
-          </div>
-          {#if result.section_title}
-            <p class="mt-1 text-xs text-dim truncate">{result.section_title}</p>
-          {/if}
-          {#if result.snippet}
-            <p class="mt-2 text-xs text-dim line-clamp-2">{result.snippet}</p>
-          {/if}
-        </a>
-      {/each}
-    </div>
-  {/if}
-</section>

frontend/src/lib/types/ask.ts

@@ -1,84 +0,0 @@
-/**
- * Phase 3.4: `/api/search/ask` 응답 타입.
- *
- * Backend Pydantic 모델 (`app/api/search.py::AskResponse`) 과 1:1 매칭.
- * 필드 변경 시 양쪽 동시 수정 필수.
- */
-
-export type SynthesisStatus =
-  | 'completed'
-  | 'timeout'
-  | 'skipped'
-  | 'no_evidence'
-  | 'parse_failed'
-  | 'llm_error'
-  | 'backend_unavailable';
-
-export type Confidence = 'high' | 'medium' | 'low';
-
-export interface Citation {
-  n: number;
-  chunk_id: number | null;
-  doc_id: number;
-  title: string | null;
-  section_title: string | null;
-  /** LLM이 추출한 50~300자 핵심 span. UI에서 이것만 노출. */
-  span_text: string;
-  /**
-   * 원본 800자 window.
-   *
-   * ⚠ UI 기본 경로에서 절대 렌더 금지. debug 모드에서 hover tooltip 용도로만
-   * 조건부 노출 가능. full_snippet을 보여주면 backend span-precision UX
-   * 가치가 사라진다 (plan §Evidence 표시 규칙).
-   */
-  full_snippet: string;
-  relevance: number;
-  rerank_score: number;
-}
-
-export interface SearchResult {
-  id: number;
-  title: string | null;
-  ai_domain: string | null;
-  ai_summary: string | null;
-  file_format: string;
-  score: number;
-  snippet: string | null;
-  match_reason: string | null;
-  chunk_id: number | null;
-  chunk_index: number | null;
-  section_title: string | null;
-  rerank_score: number | null;
-}
-
-export type Completeness = 'full' | 'partial' | 'insufficient';
-
-export interface ConfirmedItem {
-  aspect: string;
-  text: string;
-  citations: number[];
-}
-
-export interface AskResponse {
-  results: SearchResult[];
-  ai_answer: string | null;
-  citations: Citation[];
-  synthesis_status: SynthesisStatus;
-  synthesis_ms: number;
-  confidence: Confidence | null;
-  refused: boolean;
-  no_results_reason: string | null;
-  query: string;
-  total: number;
-  /** Phase 3.5a */
-  completeness: Completeness;
-  covered_aspects: string[] | null;
-  missing_aspects: string[] | null;
-  confirmed_items: ConfirmedItem[] | null;
-  /**
-   * PR-MacBook-RAG-Backend-1: backend dispatcher metadata.
-   * backend 미지정 호출은 둘 다 null (기존 호출자 호환). 명시 opt-in 시만 채워짐.
-   */
-  backend_requested?: string | null;
-  backend_used?: string | null;
-}

frontend/src/lib/utils/isQuestion.ts

@@ -1,61 +0,0 @@
-/**
- * 검색 쿼리가 "질문형"인지 판별 (규칙 기반).
- * true이면 검색 결과 페이지에서 /api/search/ask 병렬 호출.
- *
- * false positive: /ask가 refused=true 반환하면 카드 숨김 → 무해.
- * false negative: 기존 검색 결과만 표시 → 무해.
- */
-export function isQuestion(q: string): boolean {
-  const trimmed = q.trim();
-  if (trimmed.length === 0) return false;
-
-  // 1. ?로 끝나면 단일 단어라도 허용 (왜?, 절차?)
-  if (trimmed.endsWith('?')) return true;
-
-  // ? 없으면 단일 단어 / 4자 미만 제외 (키워드 보호)
-  if (trimmed.length < 4) return false;
-  if (trimmed.split(/\s+/).length < 2) return false;
-
-  // 2. 한국어 질문 어미
-  const KO_ENDINGS = [
-    '인가요', '인가', '인지', '있나요', '있나',
-    '할까요', '할까', '될까요', '될까',
-    '뭐야', '뭔가', '뭘까', '뭔지', '뭐지', '뭐죠',
-    '는지', '은지', '일까',
-    '어때', '어떤가',
-    '됩니까', '합니까', '입니까', '나요', '까요',
-    '주세요', '알려줘', '설명해', '비교해',
-  ];
-  for (const ending of KO_ENDINGS) {
-    if (trimmed.endsWith(ending)) return true;
-  }
-
-  // 3. 한국어 질문 시작어
-  const KO_STARTS = [
-    '어떻게', '왜', '무엇', '무슨', '뭐가', '누가',
-    '어디', '언제', '몇', '얼마나', '어떤', '어느',
-  ];
-  for (const start of KO_STARTS) {
-    if (trimmed.startsWith(start)) return true;
-  }
-
-  // 4. 영어 질문 시작어 (대소문자 무시)
-  const EN_STARTS = [
-    'what', 'how', 'why', 'when', 'where', 'who', 'which',
-    'is', 'are', 'do', 'does', 'can', 'could', 'should', 'would',
-    'explain', 'describe', 'compare', 'tell me',
-  ];
-  const lower = trimmed.toLowerCase();
-  for (const start of EN_STARTS) {
-    if (lower.startsWith(start + ' ')) return true;
-  }
-
-  // 5. 의미 패턴 (끝 단어)
-  const SEMANTIC_ENDINGS = ['차이', '비교', '설명', '요약', '정리', '방법', '절차'];
-  const lastWord = trimmed.split(/\s+/).pop() || '';
-  for (const pat of SEMANTIC_ENDINGS) {
-    if (lastWord === pat || lastWord.endsWith(pat)) return true;
-  }
-
-  return false;
-}

frontend/src/routes/+layout.svelte

@@ -11,6 +11,7 @@
   import { queueOverview } from '$lib/stores/queueOverview';
   import { MACHINE_STATE_LABEL, machineChipClass } from '$lib/utils/queueDisplay';
   import Sidebar from '$lib/components/Sidebar.svelte';
+  import SlimRail from '$lib/components/SlimRail.svelte';
   import SystemStatusDot from '$lib/components/SystemStatusDot.svelte';
   import QueueDrawer from '$lib/components/QueueDrawer.svelte';
   import QuickMemoButton from '$lib/components/QuickMemoButton.svelte';
@@ -21,7 +22,7 @@
   const PUBLIC_PATHS = ['/login', '/setup', '/__styleguide'];
   const NO_CHROME_PATHS = ['/login', '/setup', '/__styleguide'];
   // /news = 풀스크린 브리핑 → 데스크탑 상시 사이드바 없음
-  const NO_SIDEBAR_PATHS = ['/news'];
+  const NO_SIDEBAR_PATHS = ['/news', '/book'];  // /book = 책 몰입(글로벌 분류 트리 숨김, 상단 네비 유지)
 
   // toast 의미 토큰 매핑 (A-8 B3)
   const TOAST_CLASS = {
@@ -198,8 +199,8 @@
       <!-- 메인: 데스크탑 상시 사이드바 + 콘텐츠 -->
       <div class="flex-1 min-h-0 flex">
         {#if showSidebar}
-          <aside class="hidden lg:block shrink-0 overflow-hidden transition-[width] duration-200 ease-out {sidebarCollapsed ? 'w-0 border-r-0' : 'w-sidebar border-r border-default'}">
-            <Sidebar />
+          <aside class="hidden lg:block shrink-0 overflow-hidden transition-[width] duration-200 ease-out {sidebarCollapsed ? 'w-14 border-r border-default' : 'w-sidebar border-r border-default'}">
+            {#if sidebarCollapsed}<SlimRail />{:else}<Sidebar />{/if}
           </aside>
         {/if}
         <main class="flex-1 min-w-0 overflow-auto">

frontend/src/routes/ask/+page.svelte

@@ -1,305 +0,0 @@
-<!--
-  /ask — Phase 3.4 Ask Pipeline Frontend.
-
-  URL-driven: `/ask?q=<encoded>[&backend=<alias>]` 가 진입점.
-  $effect 로 (q, backend) 변화 감지 → `/api/search/ask` 호출 →
-  3-panel 렌더 (Answer / Evidence / Results).
-
-  중복 호출 방지: lastKey (q+backend) 가드.
-
-  Backend selector (PR-3 of DS AI routing policy, 2026-05-23,
-  [[document-server-ai-routing-policy]]) — PR-DocSrv-Web-Ask-Selector-1 확장:
-    - `auto` (기본, URL param 없음 → router 의 rule + LLM triage)
-    - `mac-mini-default` (명시, Mac mini gemma-4-26b)
-    - `qwen-macbook` (명시, M5 Max Qwen3.6-27B; "This device" 토글 on 시만)
-    - `claude-cloud` (명시, 503 scaffold; VITE_ENABLE_CLOUD_BACKEND_DEV=true 시만)
-    - "This device" 토글: localStorage `ds_device_self_label = 'macbook-m5-max' | null`.
-      source IP 의존 0 (PR-0 round 2 발견: caddy 2-hop + X-Forwarded-For 미설정 →
-      DS 가 보는 source IP = LAN gateway, 신뢰 불가).
-    - 503 + error_reason ∈ {macbook_unavailable, provider_not_configured, router_*}
-      시 자동 fallback 금지. UI 가 친절한 메시지 + "Mac mini default 로 재요청" 버튼.
-    - legacy URL `?backend=default|gemma-macmini` 는 그대로 받아서 mac-mini-default 와 동등 처리.
--->
-<script lang="ts">
-  import { page } from '$app/stores';
-  import { goto } from '$app/navigation';
-  import { api, type ApiError } from '$lib/api';
-  import { addToast } from '$lib/stores/toast';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Button from '$lib/components/ui/Button.svelte';
-  import AskAnswer from '$lib/components/ask/AskAnswer.svelte';
-  import AskEvidence from '$lib/components/ask/AskEvidence.svelte';
-  import AskResults from '$lib/components/ask/AskResults.svelte';
-  import { Sparkles, Search, AlertCircle } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  type BackendChoice = 'auto' | 'mac-mini-default' | 'qwen-macbook' | 'claude-cloud';
-
-  function parseBackend(raw: string | null): BackendChoice {
-    if (raw === 'qwen-macbook') return 'qwen-macbook';
-    if (raw === 'mac-mini-default') return 'mac-mini-default';
-    if (raw === 'claude-cloud') return 'claude-cloud';
-    // legacy aliases (?backend=default | gemma-macmini) → mac-mini-default 와 동등
-    if (raw === 'default' || raw === 'gemma-macmini') return 'mac-mini-default';
-    return 'auto';
-  }
-
-  // Build-time feature flag — Claude Cloud UI 노출 여부 (default false).
-  // VITE_ENABLE_CLOUD_BACKEND_DEV=true npm run build 시만 활성. 운영 토글 X
-  // (build-time 한계). DS runtime feature flag API migrate 는 후속 PR.
-  const CLOUD_DEV_ENABLED = import.meta.env.VITE_ENABLE_CLOUD_BACKEND_DEV === 'true';
-  const DEVICE_LABEL_KEY = 'ds_device_self_label';
-  const DEVICE_LABEL_M5_MAX = 'macbook-m5-max';
-
-  // ── state ───────────────────────────────────────────
-  let queryInput = $state('');
-  let selectedBackend = $state<BackendChoice>('auto');
-  let data = $state<AskResponse | null>(null);
-  let loading = $state(false);
-  let backendUnavailable = $state(false);
-  let backendUnavailableMessage = $state('');
-  // "I am on MacBook M5 Max" 토글. mount 시 localStorage 에서 복원.
-  let isMacBookM5Max = $state(false);
-
-  $effect(() => {
-    if (typeof window === 'undefined') return;
-    const stored = window.localStorage.getItem(DEVICE_LABEL_KEY);
-    isMacBookM5Max = stored === DEVICE_LABEL_M5_MAX;
-  });
-
-  function toggleMacBookM5Max() {
-    isMacBookM5Max = !isMacBookM5Max;
-    if (typeof window === 'undefined') return;
-    if (isMacBookM5Max) {
-      window.localStorage.setItem(DEVICE_LABEL_KEY, DEVICE_LABEL_M5_MAX);
-    } else {
-      window.localStorage.removeItem(DEVICE_LABEL_KEY);
-      // 토글 off 시 qwen-macbook 선택돼 있었으면 auto 로 복귀 (선택권 박탈 X 명시 신호).
-      if (selectedBackend === 'qwen-macbook') {
-        selectedBackend = 'auto';
-      }
-    }
-  }
-
-  // 중복 호출 방지 가드 (hydration + reactive trigger 이중 발동 방지)
-  let lastKey = '';
-
-  // citation scroll 연동: Answer 가 [n] 클릭 → Evidence 카드로 이동 + highlight
-  const citationNodes = new Map<number, HTMLElement>();
-  let activeCitation = $state<number | null>(null);
-
-  function registerCitation(n: number, node: HTMLElement) {
-    citationNodes.set(n, node);
-    return {
-      destroy() {
-        citationNodes.delete(n);
-      },
-    };
-  }
-
-  function scrollToCitation(n: number) {
-    activeCitation = n;
-    const node = citationNodes.get(n);
-    node?.scrollIntoView({ behavior: 'smooth', block: 'center' });
-  }
-
-  // ── URL 빌더: backend !== 'auto' 일 때만 param 추가 ─────
-  function buildAskUrl(q: string, backend: BackendChoice): string {
-    const params = new URLSearchParams({ q });
-    if (backend !== 'auto') params.set('backend', backend);
-    return `/ask?${params.toString()}`;
-  }
-
-  // ── submit (URL-driven, back 자동) ──────────────────
-  function submit() {
-    const q = queryInput.trim();
-    if (!q) return;
-    goto(buildAskUrl(q, selectedBackend));
-  }
-
-  function handleKeydown(e: KeyboardEvent) {
-    if (e.key === 'Enter' && !e.isComposing) {
-      e.preventDefault();
-      submit();
-    }
-  }
-
-  // 503 후 "Mac mini default 로 재요청" — auto 로 reset (param 명시 제거).
-  function retryWithMacMiniDefault() {
-    const q = $page.url.searchParams.get('q') ?? queryInput.trim();
-    if (!q) return;
-    goto(`/ask?q=${encodeURIComponent(q)}`);
-  }
-
-  // PR-3 of routing policy — error_reason → 친절 메시지 매핑.
-  // silent fallback 금지 ([[feedback_no_silent_fallback_explicit_opt_in]]):
-  // 모든 503 case 는 명시 표시, 다른 backend 자동 재호출 X.
-  function friendlyErrorMessage(reason: string | undefined, detail: string): string {
-    switch (reason) {
-      case 'macbook_unavailable':
-        return 'MacBook M5 Max 가 응답하지 않습니다. 깨우거나 (launchctl start) Mac mini default 로 다시 요청하세요.';
-      case 'provider_not_configured':
-        return 'Claude Cloud 백엔드는 아직 구성되지 않았습니다 (2026-06-15 이후 별 PR 활성화 예정).';
-      default:
-        if (reason && reason.startsWith('router_')) {
-          return `라우터 호출 실패 (${reason}). Mac mini default 로 다시 요청하거나 잠시 후 재시도하세요.`;
-        }
-        if (reason && reason.startsWith('upstream_')) {
-          return `Upstream backend 가 응답하지 않습니다 (${reason}). 잠시 후 재시도하세요.`;
-        }
-        return detail || '답변 생성 실패';
-    }
-  }
-
-  // ── API 호출 ───────────────────────────────────────
-  async function runAsk(q: string, backend: BackendChoice) {
-    loading = true;
-    activeCitation = null;
-    backendUnavailable = false;
-    backendUnavailableMessage = '';
-    const path = backend !== 'auto'
-      ? `/search/ask?q=${encodeURIComponent(q)}&backend=${encodeURIComponent(backend)}&limit=10`
-      : `/search/ask?q=${encodeURIComponent(q)}&limit=10`;
-    try {
-      data = await api<AskResponse>(path);
-    } catch (err) {
-      const apiErr = err as ApiError;
-      if (apiErr.status === 503) {
-        backendUnavailable = true;
-        backendUnavailableMessage = friendlyErrorMessage(apiErr.errorReason, apiErr.detail);
-        data = null;
-      } else {
-        addToast('error', apiErr.detail || '답변 생성 실패');
-        data = null;
-      }
-    } finally {
-      loading = false;
-    }
-  }
-
-  // ── URL → runAsk (중복 가드) ────────────────────────
-  $effect(() => {
-    const q = $page.url.searchParams.get('q') ?? '';
-    const backend = parseBackend($page.url.searchParams.get('backend'));
-    queryInput = q;
-    selectedBackend = backend;
-    if (!q) {
-      data = null;
-      loading = false;
-      backendUnavailable = false;
-      lastKey = '';
-      return;
-    }
-    const key = `${q}|${backend}`;
-    if (key === lastKey) return;
-    lastKey = key;
-    runAsk(q, backend);
-  });
-</script>
-
-<svelte:head>
-  <title>질문 - PKM</title>
-</svelte:head>
-
-<div class="h-full overflow-auto">
-  <!-- 상단 검색바 (sticky) -->
-  <div class="sticky top-0 z-10 bg-bg/80 backdrop-blur border-b border-default px-4 py-3">
-    <div class="flex flex-wrap items-center gap-2 max-w-[1680px] mx-auto">
-      <div class="relative flex-1 min-w-0">
-        <Search
-          size={14}
-          class="absolute left-3 top-1/2 -translate-y-1/2 text-dim pointer-events-none"
-        />
-        <input
-          data-search-input
-          type="text"
-          bind:value={queryInput}
-          onkeydown={handleKeydown}
-          placeholder="질문을 입력하세요 (/ 키로 포커스)"
-          class="w-full pl-9 pr-3 py-2 bg-surface border border-default rounded-lg text-text text-sm focus:border-accent outline-none"
-        />
-      </div>
-      <label
-        class="flex items-center gap-1.5 text-xs text-dim cursor-pointer select-none"
-        title="이 디바이스가 MacBook M5 Max 인 경우 체크 — This device (qwen-macbook) 옵션 활성화됩니다. localStorage 저장."
-      >
-        <input
-          type="checkbox"
-          checked={isMacBookM5Max}
-          onchange={toggleMacBookM5Max}
-          class="accent-accent"
-        />
-        <span>This is M5 Max</span>
-      </label>
-      <select
-        bind:value={selectedBackend}
-        title="Backend 선택 — silent fallback 0 정책 (선택한 backend 만 시도, 실패 시 503)."
-        class="py-2 px-2 bg-surface border border-default rounded-lg text-text text-xs focus:border-accent outline-none min-w-0 max-w-[42vw] truncate"
-      >
-        <option value="auto">Auto (router)</option>
-        <option value="mac-mini-default">Mac mini (default)</option>
-        <option
-          value="qwen-macbook"
-          disabled={!isMacBookM5Max}
-          title={isMacBookM5Max
-            ? 'MacBook M5 Max Qwen3.6-27B (직접 호출)'
-            : 'Check "This is M5 Max" toggle to enable'}
-        >
-          {isMacBookM5Max ? 'This device (Qwen MacBook)' : 'This device (unavailable)'}
-        </option>
-        <option
-          value="claude-cloud"
-          disabled={!CLOUD_DEV_ENABLED}
-          title={CLOUD_DEV_ENABLED
-            ? 'Claude Cloud (dev mode — returns 503 until activation PR)'
-            : 'Cloud backend not configured yet'}
-        >
-          Claude Cloud {CLOUD_DEV_ENABLED ? '(dev)' : '(unavailable)'}
-        </option>
-      </select>
-    </div>
-  </div>
-
-  <!-- 본문 -->
-  <div class="max-w-[1680px] mx-auto p-4">
-    {#if backendUnavailable}
-      <div class="py-16">
-        <EmptyState
-          icon={AlertCircle}
-          title="Backend 가 응답하지 않습니다"
-          description={backendUnavailableMessage}
-        >
-          <Button variant="primary" size="sm" onclick={retryWithMacMiniDefault}>
-            Mac mini (default) 로 재요청
-          </Button>
-        </EmptyState>
-      </div>
-    {:else if !queryInput && !loading && !data}
-      <div class="py-16">
-        <EmptyState
-          icon={Sparkles}
-          title="근거 기반 답변을 받아보세요"
-          description="질문을 입력하면 문서에서 근거를 찾아 인용 기반 답변을 생성합니다."
-        />
-      </div>
-    {:else}
-      <div class="grid gap-4 lg:grid-cols-[1.2fr_0.9fr] items-start">
-        <!-- 좌: Answer + Results -->
-        <div class="flex flex-col gap-4 min-w-0">
-          <AskAnswer {data} {loading} onCitationClick={scrollToCitation} />
-          <AskResults {data} {loading} />
-        </div>
-
-        <!-- 우: Evidence (lg 이상 sticky) -->
-        <div class="lg:sticky lg:top-20 lg:self-start min-w-0">
-          <AskEvidence
-            {data}
-            {loading}
-            {activeCitation}
-            {registerCitation}
-          />
-        </div>
-      </div>
-    {/if}
-  </div>
-</div>

frontend/src/routes/book/[id]/+page.svelte

@@ -0,0 +1,281 @@
+<script>
+  // ASME/법령 절-KB — 코드북·공부 리더 (r2). parent 표준/법령을 한 권의 책처럼.
+  // 좌 인덱스(Part/章→절/조) · 중 본문(MarkdownDoc=공식·표·이미지) · breadcrumb·이전다음·양방향 백링크.
+  import { onMount, tick } from 'svelte';
+  import { page } from '$app/stores';
+  import { goto } from '$app/navigation';
+  import { api } from '$lib/api';
+  import MarkdownDoc from '$lib/components/MarkdownDoc.svelte';
+
+  let parentId = $state(null);
+  let parentTitle = $state('');
+  let clauses = $state([]);
+  let selectedId = $state(null);
+  let clauseDoc = $state(null);
+  let links = $state(null);
+  let expanded = $state({});
+  let loading = $state(false);
+  let q = $state('');
+
+  // 공부도구 (노트/형광펜/암기카드) — clause_study
+  let studyItems = $state([]);
+  let studyOpen = $state(false);
+  let noteDraft = $state('');
+  const KLABEL = { note: '노트', highlight: '형광펜', card: '암기카드' };
+  async function loadStudy(id) {
+    try { const r = await api(`/documents/${id}/study`); studyItems = r?.items ?? []; }
+    catch { studyItems = []; }
+  }
+  async function addStudy(kind, payload) {
+    if (!selectedId) return;
+    try { await api(`/documents/${selectedId}/study`, { method: 'POST', body: JSON.stringify({ kind, payload }) }); await loadStudy(selectedId); }
+    catch (e) { console.warn(e); }
+  }
+  function selText() { return (typeof window !== 'undefined' && window.getSelection ? window.getSelection().toString() : '').trim(); }
+  function addNote() { const t = noteDraft.trim(); if (!t) return; addStudy('note', { text: t }); noteDraft = ''; }
+  function addHighlight() { const s = selText(); if (!s) { studyOpen = true; alert('본문에서 형광펜 칠할 부분을 먼저 드래그하세요'); return; } addStudy('highlight', { text: s }); studyOpen = true; }
+  function addCard() {
+    const s = selText();
+    const code = links?.clause_code ?? selMeta?.clause_code ?? '';
+    addStudy('card', { cue: `${code} ${strip(clauseDoc?.title, code)}`.trim(), fact: s || (clauseDoc?.md_content ?? clauseDoc?.extracted_text ?? '').replace(/[#*>]/g, '').slice(0, 280).trim() });
+    studyOpen = true;
+  }
+  async function delStudy(id) {
+    try { await api(`/documents/${selectedId}/study/${id}`, { method: 'DELETE' }); await loadStudy(selectedId); } catch {}
+  }
+
+  let parts = $derived.by(() => {
+    const out = [], idx = {};
+    for (const c of clauses) {
+      const p = c.clause_part || '·';
+      if (!(p in idx)) { idx[p] = out.length; out.push({ part: p, items: [] }); }
+      out[idx[p]].items.push(c);
+    }
+    return out;
+  });
+  let visibleParts = $derived.by(() => {
+    const term = q.trim().toLowerCase();
+    if (!term) return parts;
+    return parts.map(g => ({ part: g.part, items: g.items.filter(c =>
+      (c.clause_code || '').toLowerCase().includes(term) || (c.title || '').toLowerCase().includes(term)) }))
+      .filter(g => g.items.length);
+  });
+  let selMeta = $derived(clauses.find((c) => c.id === selectedId) || null);
+  const strip = (t, c) => (t || '').replace(c || '', '').replace(/^[(\s)]+|[(\s)]+$/g, '').trim();
+
+  async function loadBook() {
+    const r = await api(`/documents/${parentId}/clauses`);
+    parentTitle = r?.parent_title ?? '';
+    clauses = r?.clauses ?? [];
+    const e = {};
+    for (const c of clauses) e[c.clause_part || '·'] = true;
+    expanded = e;
+  }
+  async function loadClause(id) {
+    if (!id) return;
+    loading = true;
+    selectedId = id;
+    try {
+      const [d, l] = await Promise.all([api(`/documents/${id}`), api(`/documents/${id}/backlinks`)]);
+      clauseDoc = d; links = l;
+      loadStudy(id);
+      const sel = clauses.find((c) => c.id === id);
+      if (sel) expanded = { ...expanded, [sel.clause_part || '·']: true };
+      goto(`/book/${parentId}?c=${id}`, { replaceState: true, keepFocus: true, noScroll: true });
+      await tick(); window.scrollTo({ top: 0 });
+    } finally { loading = false; }
+  }
+  onMount(async () => {
+    parentId = Number($page.params.id);
+    await loadBook();
+    const c = Number($page.url.searchParams.get('c'));
+    await loadClause(c && clauses.find((x) => x.id === c) ? c : clauses[0]?.id);
+  });
+</script>
+
+<div class="book">
+  <!-- top bar -->
+  <div class="bar">
+    <span class="brand">절-KB</span>
+    <span class="crumbs">{parentTitle} {#if selMeta}<b class="sep">›</b> {selMeta.clause_part} <b class="sep">›</b> <b>{links?.clause_code ?? selMeta.clause_code}</b>{/if}</span>
+    <div class="search"><input placeholder="절·조 번호 또는 키워드" bind:value={q} /></div>
+    <div class="tools"><span class="tool on">읽기</span><span class="tool">형광펜</span><span class="tool">노트</span><span class="tool">암기카드</span></div>
+  </div>
+
+  <div class="main">
+    <!-- left index -->
+    <aside class="idx">
+      <a class="btitle" href={`/documents/${parentId}`}>{parentTitle || '표준'}</a>
+      <div class="bmeta">절 {clauses.length} · 한 권의 책처럼 탐색</div>
+      {#each visibleParts as g (g.part)}
+        <div class="parttab" role="button" tabindex="0" onclick={() => (expanded = { ...expanded, [g.part]: !expanded[g.part] })}>
+          <span class="bar2"></span><span class="pname">{g.part}</span><span class="ct">{g.items.length}</span>
+        </div>
+        {#if expanded[g.part] || q.trim()}
+          {#each g.items as c (c.id)}
+            <div class="ci" class:on={c.id === selectedId} role="button" tabindex="0" onclick={() => loadClause(c.id)}>
+              <span class="no">{c.clause_code}</span><span class="tt">{strip(c.title, c.clause_code)}</span>
+            </div>
+          {/each}
+        {/if}
+      {/each}
+    </aside>
+
+    <!-- reader -->
+    <section class="read">
+      <div class="col">
+        {#if clauseDoc}
+          <div class="studybar">
+            <button class="sbtn" title="선택 형광펜" onclick={addHighlight}>▰</button>
+            <button class="sbtn" class:on={studyOpen} title="노트/공부" onclick={() => (studyOpen = !studyOpen)}>✎</button>
+            <button class="sbtn" title="암기카드 추가" onclick={addCard}>＋</button>
+            {#if studyItems.length}<span class="scount">{studyItems.length}</span>{/if}
+          </div>
+          <div class="kicker"><span class="pth">{selMeta?.clause_part}</span></div>
+          <div class="h-no">{links?.clause_code ?? selMeta?.clause_code}</div>
+          <h1 class="h-title">{strip(clauseDoc.title, links?.clause_code ?? '')}</h1>
+
+          <div class="flow">
+            <button class="fl" disabled={!links?.prev} onclick={() => loadClause(links?.prev?.id)}>← {links?.prev?.clause_code ?? ''}</button>
+            <button class="fl next" disabled={!links?.next} onclick={() => loadClause(links?.next?.id)}>{links?.next?.clause_code ?? ''} →</button>
+          </div>
+
+          {#key clauseDoc.id}
+            <div class="docbody">
+              <MarkdownDoc documentId={clauseDoc.id} mdContent={clauseDoc.md_content ?? clauseDoc.extracted_text} mdStatus={null} class="prose prose-base max-w-none" />
+            </div>
+          {/key}
+
+          {#if links && (links.forward.length || links.back.length)}
+            <section class="conn">
+              {#if links.forward.length}
+                <div><h4>이 절이 참조 <span>{links.forward.length}</span></h4>
+                  <div class="chiprow">{#each links.forward as f}
+                    {#if f.doc_id}<button class="ref" onclick={() => loadClause(f.doc_id)}>{f.code}</button>
+                    {:else}<span class="ref dg" title="외부/미분해">{f.code}</span>{/if}
+                  {/each}</div></div>
+              {/if}
+              {#if links.back.length}
+                <div><h4>이 절을 참조 <span>{links.back.length}</span></h4>
+                  <div class="chiprow">{#each links.back as b}<button class="ref" onclick={() => loadClause(b.doc_id)}>{b.code}</button>{/each}</div></div>
+              {/if}
+            </section>
+          {/if}
+
+          {#if studyOpen}
+            <section class="study">
+              <div class="slab">공부 — 노트 · 형광펜 · 암기카드{#if studyItems.length} <span>{studyItems.length}</span>{/if}</div>
+              <div class="noteadd">
+                <textarea bind:value={noteDraft} placeholder="이 절에 노트…" rows="2"></textarea>
+                <button class="nbtn" onclick={addNote}>노트 저장</button>
+              </div>
+              {#if studyItems.length}
+                <ul class="slist">
+                  {#each studyItems as it (it.id)}
+                    <li class="sitem">
+                      <span class="skind k-{it.kind}">{KLABEL[it.kind] ?? it.kind}</span>
+                      <span class="stext">{it.payload?.text ?? it.payload?.cue ?? ''}</span>
+                      <button class="sdel" title="삭제" onclick={() => delStudy(it.id)}>×</button>
+                    </li>
+                  {/each}
+                </ul>
+              {:else}
+                <p class="shint">본문을 드래그한 뒤 형광펜(▰)/암기카드(＋), 또는 위에 노트를 적으세요.</p>
+              {/if}
+            </section>
+          {/if}
+
+          <div class="pager">
+            <button class="pg" disabled={!links?.prev} onclick={() => loadClause(links?.prev?.id)}>
+              <div class="d">← 이전</div><div class="t"><span class="pno">{links?.prev?.clause_code ?? '—'}</span> {strip(links?.prev?.title, links?.prev?.clause_code)}</div></button>
+            <button class="pg next" disabled={!links?.next} onclick={() => loadClause(links?.next?.id)}>
+              <div class="d">다음 →</div><div class="t"><span class="pno">{links?.next?.clause_code ?? '—'}</span> {strip(links?.next?.title, links?.next?.clause_code)}</div></button>
+          </div>
+        {:else}
+          <p class="empty">{loading ? '불러오는 중…' : '왼쪽에서 절을 선택하세요'}</p>
+        {/if}
+      </div>
+    </section>
+  </div>
+</div>
+
+<style>
+  :global(body) { background: var(--bg); }
+  .book { --paper:#fbfcf9; --serif:"Iowan Old Style","Palatino Linotype","Noto Serif KR",Georgia,serif;
+    display:flex; flex-direction:column; min-height:100vh; }
+  .bar { display:flex; align-items:center; gap:14px; height:50px; padding:0 18px; background:var(--paper); border-bottom:1px solid var(--border); }
+  .brand { font-weight:700; font-size:13.5px; color:var(--text); }
+  .crumbs { color:var(--text-dim); font-size:12.5px; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; max-width:46%; }
+  .crumbs b { color:var(--text); font-weight:600; } .crumbs .sep { color:#c8d6c0; margin:0 5px; }
+  .search { margin-left:auto; }
+  .search input { width:280px; background:var(--surface); border:1px solid var(--border); border-radius:9px; padding:7px 12px; font-size:13px; color:var(--text); outline:none; }
+  .search input:focus { border-color:var(--accent); }
+  .tools { display:flex; gap:2px; }
+  .tool { font-size:12px; color:var(--text-dim); padding:6px 10px; border-radius:8px; border:1px solid transparent; cursor:pointer; }
+  .tool:hover { background:var(--surface); } .tool.on { background:#ecf0e8; border-color:var(--border); color:var(--accent-hover); font-weight:600; }
+
+  .main { display:flex; align-items:flex-start; flex:1; }
+  .idx { width:264px; flex-shrink:0; align-self:stretch; border-right:1px solid var(--border);
+    background:linear-gradient(180deg,#f6f8f3,#f1f4ec); padding:16px 10px 30px 16px; position:sticky; top:0; max-height:100vh; overflow:auto; }
+  .btitle { display:block; font-family:var(--serif); font-size:15.5px; font-weight:600; color:var(--text); text-decoration:none; line-height:1.32; }
+  .btitle:hover { text-decoration:underline; }
+  .bmeta { font-size:11px; color:#9aa090; margin:3px 0 14px; }
+  .parttab { display:flex; align-items:center; gap:8px; margin:11px 0 4px; padding:3px 4px; border-radius:6px; cursor:pointer;
+    font-size:11px; font-weight:700; letter-spacing:.5px; color:var(--text-dim); text-transform:uppercase; }
+  .parttab:hover { background:#fff; } .parttab .bar2 { width:3px; height:12px; border-radius:2px; background:var(--domain-engineering); }
+  .parttab .pname { flex:1; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; } .parttab .ct { color:#9aa090; font-weight:600; letter-spacing:0; }
+  .ci { display:flex; gap:9px; align-items:baseline; padding:4px 9px; border-radius:7px; cursor:pointer; line-height:1.4; }
+  .ci .no { font-family:ui-monospace,Menlo,monospace; font-size:11px; color:var(--accent); font-weight:600; min-width:52px; white-space:nowrap; }
+  .ci .tt { font-size:12.5px; color:var(--text-dim); overflow:hidden; text-overflow:ellipsis; }
+  .ci:hover { background:#fff; }
+  .ci.on { background:#fff; box-shadow:inset 3px 0 0 var(--accent), 0 1px 2px rgba(35,41,31,.05); }
+  .ci.on .no { color:var(--accent-hover); font-weight:700; } .ci.on .tt { color:var(--text); font-weight:600; }
+
+  .read { flex:1; min-width:0; padding:34px 40px 80px; }
+  .col { max-width:680px; margin:0 auto; position:relative; }
+  .studybar { position:absolute; right:-30px; top:4px; display:flex; flex-direction:column; gap:6px; }
+  .sbtn { width:34px; height:34px; border-radius:9px; border:1px solid var(--border); background:var(--paper); color:var(--text-dim); font-size:13px; cursor:pointer; }
+  .sbtn:hover { background:var(--surface); color:var(--accent-hover); }
+  .kicker { margin-bottom:5px; } .kicker .pth { font-size:11.5px; color:#9aa090; font-weight:600; letter-spacing:.3px; }
+  .h-no { font-family:ui-monospace,Menlo,monospace; font-size:13px; color:var(--accent); font-weight:700; letter-spacing:.5px; }
+  .h-title { font-family:var(--serif); font-size:26px; line-height:1.24; font-weight:600; margin:2px 0 14px; letter-spacing:-.2px; color:var(--text); }
+  .flow { display:flex; justify-content:space-between; gap:8px; margin-bottom:18px; }
+  .flow .fl { font-size:11.5px; color:var(--text-dim); background:var(--surface); border:1px solid var(--border); border-radius:8px; padding:5px 11px; cursor:pointer; }
+  .flow .fl:hover:not(:disabled) { background:#ecf0e8; } .flow .fl:disabled { opacity:.35; cursor:default; }
+  .docbody { font-size:15.5px; }
+  .docbody :global(.prose) { color:#2a3024; line-height:1.78; }
+  .docbody :global(.prose h1), .docbody :global(.prose h2), .docbody :global(.prose h3) { font-family:var(--serif); }
+  .docbody :global(a) { color:var(--accent-hover); }
+  .conn { margin-top:34px; padding-top:18px; border-top:1px solid var(--border); display:grid; grid-template-columns:1fr 1fr; gap:22px; }
+  .conn h4 { font-size:11px; font-weight:700; color:var(--text-dim); letter-spacing:.4px; margin:0 0 9px; } .conn h4 span { color:#9aa090; font-weight:500; }
+  .chiprow { display:flex; flex-wrap:wrap; gap:5px; }
+  .ref { font-family:ui-monospace,Menlo,monospace; font-size:11.5px; font-weight:600; color:var(--accent-hover); background:#eef4ec; border:1px solid #d9e6d8; border-radius:6px; padding:2px 8px; cursor:pointer; }
+  .ref:hover { background:#e2efe0; } .ref.dg { color:#9aa090; background:var(--surface); border-color:var(--border); cursor:default; }
+  .pager { display:flex; gap:10px; margin-top:30px; }
+  .pg { flex:1; text-align:left; border:1px solid var(--border); border-radius:11px; padding:11px 14px; background:var(--paper); cursor:pointer; }
+  .pg.next { text-align:right; } .pg:hover:not(:disabled) { border-color:#cfd7c6; background:#fff; } .pg:disabled { opacity:.4; cursor:default; }
+  .pg .d { font-size:10.5px; color:#9aa090; } .pg .t { font-size:12.5px; color:var(--text-dim); font-weight:600; margin-top:1px; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; }
+  .pg .pno { font-family:ui-monospace,Menlo,monospace; color:var(--accent); }
+  .empty { color:#9aa090; text-align:center; padding:80px 0; }
+  .sbtn.on { background:#ecf0e8; color:var(--accent-hover,#3d7256); border-color:var(--border); }
+  .scount { font-size:9px; font-weight:700; color:#fff; background:var(--accent,#4f8a6b); border-radius:8px; padding:1px 5px; text-align:center; }
+  .study { margin-top:24px; padding:14px; border:1px solid var(--border); border-radius:12px; background:var(--surface); }
+  .slab { font-size:11px; font-weight:700; color:var(--text-dim); letter-spacing:.3px; margin-bottom:9px; }
+  .slab span { color:var(--accent-hover,#3d7256); }
+  .noteadd { display:flex; gap:8px; align-items:flex-end; margin-bottom:10px; }
+  .noteadd textarea { flex:1; resize:vertical; border:1px solid var(--border); border-radius:8px; padding:7px 9px; font-size:12.5px; font-family:inherit; color:var(--text); background:var(--paper,#fbfcf9); outline:none; }
+  .noteadd textarea:focus { border-color:var(--accent); }
+  .nbtn { flex-shrink:0; font-size:12px; color:#fff; background:var(--accent,#4f8a6b); border:0; border-radius:8px; padding:8px 12px; cursor:pointer; }
+  .nbtn:hover { background:var(--accent-hover,#3d7256); }
+  .slist { list-style:none; margin:0; padding:0; display:flex; flex-direction:column; gap:5px; }
+  .sitem { display:flex; align-items:baseline; gap:8px; padding:6px 8px; border-radius:8px; background:var(--paper,#fbfcf9); border:1px solid var(--border); }
+  .skind { flex-shrink:0; font-size:9.5px; font-weight:700; border-radius:4px; padding:1px 6px; }
+  .k-note { color:#3d7256; background:#e3efe2; border:1px solid #cfe3cd; }
+  .k-highlight { color:#8a6306; background:#faf3e2; border:1px solid #ecdca3; }
+  .k-card { color:#1d4ed8; background:#eef4fc; border:1px solid #d7e4f7; }
+  .stext { flex:1; font-size:12px; line-height:1.5; color:var(--text); white-space:pre-wrap; word-break:break-word; }
+  .sdel { flex-shrink:0; background:none; border:0; color:var(--faint,#9aa090); cursor:pointer; font-size:14px; }
+  .sdel:hover { color:var(--error,#c0392b); }
+  .shint { font-size:11.5px; color:var(--faint,#9aa090); margin:0; }
+  @media(max-width:820px){ .idx{display:none} .read{padding:24px 18px} .conn{grid-template-columns:1fr} .studybar{position:static;flex-direction:row} .crumbs{max-width:30%} .search input{width:150px} }
+</style>

frontend/src/routes/clause/+page.svelte

@@ -0,0 +1,73 @@
+<script>
+  // 절(clause) 바로가기 — ASME 절 식별자(예: UG-79)로 크로스-doc 위치를 조회해 읽기뷰로 이동 (U-1).
+  // 절은 in_corpus=false(의미검색 비활성)라 일반 검색으론 안 잡히므로 라벨 정확지목 전용 진입점.
+  import { api } from '$lib/api';
+  import { goto } from '$app/navigation';
+
+  let label = $state('');
+  let hits = $state([]);
+  let loading = $state(false);
+  let searched = $state(false);
+  let error = $state('');
+
+  async function lookup() {
+    const q = label.trim();
+    if (!q) return;
+    loading = true;
+    error = '';
+    try {
+      const res = await api(`/documents/clause-lookup?label=${encodeURIComponent(q)}`);
+      hits = res?.hits ?? [];
+      searched = true;
+    } catch (e) {
+      error = '조회에 실패했습니다.';
+      hits = [];
+    } finally {
+      loading = false;
+    }
+  }
+</script>
+
+<div class="mx-auto max-w-3xl px-6 py-10">
+  <h1 class="mb-1 text-2xl font-bold text-base">절 바로가기</h1>
+  <p class="mb-6 text-sm text-dim">
+    ASME 절 식별자(예: <code class="text-accent">UG-79</code>, <code class="text-accent">PG-5</code>)로 문서·위치를 찾아 이동합니다.
+  </p>
+
+  <form onsubmit={(e) => { e.preventDefault(); lookup(); }} class="mb-6 flex gap-2">
+    <input
+      bind:value={label}
+      placeholder="절 식별자 (UG-79, PG-5.6, A-1 …)"
+      autocomplete="off"
+      class="flex-1 rounded-lg border border-default bg-surface px-4 py-2.5 text-base outline-none focus:border-accent"
+    />
+    <button
+      type="submit"
+      disabled={loading || !label.trim()}
+      class="rounded-lg bg-accent px-5 py-2.5 font-medium text-white hover:bg-accent-hover disabled:opacity-50"
+    >
+      {loading ? '조회 중…' : '찾기'}
+    </button>
+  </form>
+
+  {#if error}
+    <p class="text-sm text-accent">{error}</p>
+  {:else if searched && hits.length === 0}
+    <p class="text-sm text-dim">'{label}' 에 해당하는 절을 찾지 못했습니다. (절은 분해된 코드 문서에만 존재합니다)</p>
+  {:else if hits.length > 0}
+    <div class="space-y-2">
+      {#if hits.length > 1}
+        <p class="text-xs text-dim">{hits.length}개 문서에 존재 — 에디션/부록을 선택하세요.</p>
+      {/if}
+      {#each hits as hit (hit.chunk_id)}
+        <button
+          onclick={() => goto(`/documents/${hit.doc_id}?section=${hit.chunk_id}`)}
+          class="block w-full rounded-lg border border-default bg-surface px-4 py-3 text-left transition hover:border-accent hover:bg-surface-hover"
+        >
+          <div class="font-medium text-base">{hit.section_title}</div>
+          <div class="mt-0.5 text-xs text-dim">{hit.doc_title}</div>
+        </button>
+      {/each}
+    </div>
+  {/if}
+</div>

frontend/src/routes/documents/+page.svelte

@@ -8,7 +8,7 @@
   import { goto } from '$app/navigation';
   import { api } from '$lib/api';
   import { addToast } from '$lib/stores/toast';
-  import { X, Plus, Trash2, Tag, FolderTree, Sparkles, ArrowUpDown } from 'lucide-svelte';
+  import { X, Plus, Trash2, Tag, FolderTree, ArrowUpDown } from 'lucide-svelte';
   import MarkdownStatusBadge from '$lib/components/MarkdownStatusBadge.svelte';
   import { isMdStatusVisible } from '$lib/utils/mdStatus';
   import UploadDropzone from '$lib/components/UploadDropzone.svelte';
@@ -22,9 +22,7 @@
   import { useIsXl } from '$lib/composables/useMedia.svelte';
   import { useListKeyboardNav } from '$lib/composables/useListKeyboardNav.svelte';
   import { pLimit } from '$lib/utils/pLimit';
-  import { isQuestion } from '$lib/utils/isQuestion';
   import { domainBgClass, domainLabel } from '$lib/utils/domainSlug';
-  import AskAnswerCard from '$lib/components/AskAnswerCard.svelte';
 
   const FORMATS = ['pdf', 'hwp', 'hwpx', 'md', 'docx', 'xlsx', 'png', 'jpg'];
 
@@ -48,30 +46,6 @@
   let searchResults = $state(null);
   let selectedDoc = $state(null);
 
-  // 이드 답변 상태 (질문형 검색)
-  let askData = $state(null);
-  let askLoading = $state(false);
-  let askError = $state(false);
-  let askDismissed = $state(false);
-
-  async function askSearch(q) {
-    askLoading = true; askError = false; askData = null;
-    try {
-      askData = await api(`/search/ask?q=${encodeURIComponent(q)}&limit=10`);
-    } catch {
-      askError = true; askData = null;
-    } finally {
-      askLoading = false;
-    }
-  }
-
-  let showAskCard = $derived(
-    !askDismissed && (
-      askLoading ||
-      (askData && !askData.refused && askData.ai_answer && askData.synthesis_status === 'completed')
-    )
-  );
-
   // 인스펙터(우측) 토글 — xl+ inline, < xl Drawer.
   const isXl = useIsXl();
   let inspectorOpen = $state(
@@ -145,7 +119,6 @@
     selectedDoc = null;
     selectedIds = new Set();
     kbIndex = 0;
-    askData = null; askLoading = false; askError = false; askDismissed = false;
     if (ui.isDrawerOpen('meta')) ui.closeDrawer();
     if (urlQ) doSearch(urlQ, urlMode);
     else loadDocuments();
@@ -191,7 +164,6 @@
 
   async function doSearch(q, mode) {
     loading = true;
-    if (isQuestion(q)) askSearch(q);
     try {
       const data = await api(`/search/?q=${encodeURIComponent(q)}&mode=${mode}&limit=50`);
       searchResults = data.results;
@@ -406,13 +378,6 @@
         <option value="trgm">부분매칭</option>
         <option value="vector">의미검색</option>
       </select>
-      {#if searchQuery.trim()}
-        <a
-          href={`/ask?q=${encodeURIComponent(searchQuery.trim())}`}
-          class="flex items-center px-2 py-1.5 rounded-lg border border-default text-dim hover:text-accent hover:border-accent transition-colors"
-          title="이 쿼리로 AI 답변"
-        ><Sparkles size={14} /></a>
-      {/if}
     </div>
 
     <!-- 필터 칩 row -->
@@ -483,19 +448,6 @@
       {/if}
     </div>
 
-    <!-- AI 답변 (질문형 검색) — 목록 상단 고정, 아래로 목록 스크롤 -->
-    {#if showAskCard}
-      <div class="px-3 py-2 shrink-0 border-b border-default max-h-[55vh] overflow-y-auto">
-        <AskAnswerCard
-          data={askData}
-          loading={askLoading}
-          error={askError}
-          onCitationClick={(docId) => goto(`/documents/${docId}`)}
-          onDismiss={() => { askDismissed = true; }}
-        />
-      </div>
-    {/if}
-
     <!-- 선택 toolbar -->
     {#if selectionCount > 0}
       <div class="flex flex-wrap items-center gap-2 px-3 py-2 shrink-0 bg-accent/10 border-y border-accent/30">

frontend/src/routes/documents/[id]/+page.svelte

@@ -16,6 +16,7 @@
   import Skeleton from '$lib/components/ui/Skeleton.svelte';
   import HandwriteCanvas from '$lib/components/HandwriteCanvas.svelte';
   import MarkdownDoc from '$lib/components/MarkdownDoc.svelte';
+  import RelatedDocs from '$lib/components/RelatedDocs.svelte';
   import { renderDocMarkdown } from '$lib/utils/docMarkdown';
   import MarkdownStatusBadge from '$lib/components/MarkdownStatusBadge.svelte';
   import NoteEditor from '$lib/components/editors/NoteEditor.svelte';
@@ -130,6 +131,9 @@
   let manageOpen = $state(false);
   // 기본 선택 = 첫 본문 Part 의 첫 절(front-matter TOC 가 아니라 실제 내용으로 진입, front-matter 접힘 유지).
   let defaultSelId = $derived.by(() => {
+    // 딥링크 진입: ?section=<chunk_id> 가 outline 에 있으면 그 절로 (/clause 절 바로가기 → 해당 절 표시).
+    const deep = Number($page.url.searchParams.get('section'));
+    if (deep && outline.some((it) => it.section.chunk_id === deep)) return deep;
     if (treeGroups) {
       const body = treeGroups.find((g) => !g.isFrontMatter);
       if (body && body.items.length) return body.items[0].section.chunk_id;
@@ -318,6 +322,7 @@
 <!-- ════ 우 슬림 레일 (시안 카드 스타일) ════ -->
 {#snippet rail()}
   <div style="display:flex;flex-direction:column;gap:11px;font-size:14px;">
+    <RelatedDocs documentId={doc.id} />
     {#if doc.ai_tldr || doc.ai_summary}
       <div style="background:#f4f7f1;border:1px solid #dde3d6;border-radius:14px;padding:13px;">
         <div style="font-size:10.5px;font-weight:700;color:#697061;letter-spacing:.4px;margin-bottom:7px;">TL;DR</div>

frontend/src/routes/study/+page.svelte

@@ -1,13 +1,58 @@
 <script>
-  // /study — 학습 hub.
-  // 주제로 보기(퀴즈·복습·통계) / 자료 학습 / 필사 세션 / 암기카드 검수.
+  // /study — 학습 hub + 데일리 랜딩('오늘의 공부' 대시보드).
+  // 상단 = 이론 홈(진도·오늘의 개념·복습 due, 재노출 트리거). 하단 = 기존 모드 진입.
   import { onMount } from 'svelte';
   import { api } from '$lib/api';
-  import { BookOpen, PenLine, GraduationCap, FolderKanban, Layers, Repeat, Flag, Inbox, Activity } from 'lucide-svelte';
+  import { addToast } from '$lib/stores/toast';
+  import { BookOpen, PenLine, GraduationCap, FolderKanban, Layers, Repeat, Flag, Inbox, Activity, CalendarCheck, Target } from 'lucide-svelte';
 
   let cardReviewCount = $state(0);
   let questionFlagCount = $state(0);
+
+  // 오늘의 공부 (이론 홈)
+  let curriculum = $state(null);
+  let todayConcepts = $state([]);
+  let weakConcepts = $state([]);        // 약점 개념(관련 기출 정답률 낮음)
+  let dashLoading = $state(true);
+
+  let readPct = $derived(
+    curriculum && curriculum.total ? Math.round((curriculum.read / curriculum.total) * 100) : 0
+  );
+
+  async function loadDashboard() {
+    dashLoading = true;
+    try {
+      const [cur, today] = await Promise.all([
+        api('/study/curriculum'),
+        api('/study/today-concepts?limit=6'),
+      ]);
+      curriculum = cur;
+      todayConcepts = today?.concepts ?? [];
+    } catch {
+      // 코어 대시보드 실패해도 허브 나머지는 동작 (조용히)
+    } finally {
+      dashLoading = false;
+    }
+    // 약점 개념 = 비차단(신규 엔드포인트 실패해도 코어 대시보드 블랙아웃 방지)
+    try {
+      const weak = await api('/study/concepts/weakness-map?limit=5');
+      weakConcepts = weak?.weak ?? [];
+    } catch {}
+  }
+
+  async function markRead(doc) {
+    try {
+      await api(`/study/concepts/${doc.doc_id}/read`, { method: 'POST' });
+      todayConcepts = todayConcepts.filter((c) => c.doc_id !== doc.doc_id);
+      addToast('success', `회독: ${doc.title}`);
+      loadDashboard(); // 진도 갱신
+    } catch {
+      addToast('error', '회독 처리 실패');
+    }
+  }
+
   onMount(async () => {
+    loadDashboard();
     try {
       const r = await api('/study-cards/needs-review/count');
       cardReviewCount = r?.count ?? 0;
@@ -27,6 +72,80 @@
     <p class="text-sm text-dim mt-1">주제별 퀴즈·복습(SRS)·통계 / 학습 자료 회독 / 손글씨 필사 세션.</p>
   </header>
 
+  <!-- 오늘의 공부 (이론 홈 대시보드 = 데일리 트리거) -->
+  <section class="mb-5 rounded-lg border border-default bg-surface p-4 md:p-5">
+    <div class="flex items-center gap-2 mb-3">
+      <CalendarCheck size={18} class="text-accent" />
+      <h2 class="text-base font-semibold text-text">오늘의 공부</h2>
+      {#if curriculum}
+        <span class="ml-auto text-xs text-dim">이론 회독 <span class="text-text font-medium">{curriculum.read}</span> / {curriculum.total} ({readPct}%)</span>
+      {/if}
+    </div>
+
+    {#if dashLoading}
+      <p class="text-xs text-dim">불러오는 중…</p>
+    {:else}
+      {#if curriculum}
+        <div class="h-2 rounded-full bg-bg overflow-hidden mb-3">
+          <div class="h-full bg-accent" style="width: {readPct}%"></div>
+        </div>
+        <div class="flex flex-wrap gap-x-4 gap-y-1 mb-4 text-xs text-dim">
+          {#each curriculum.subjects as s}
+            <span>{s.subject} <span class="text-text">{s.read}/{s.total}</span></span>
+          {/each}
+        </div>
+
+        <div class="flex flex-wrap gap-2 mb-4">
+          <a
+            href="/study/topics/{curriculum.topic_id}/review-queue"
+            class="flex items-center gap-1.5 rounded border border-default px-3 py-1.5 text-xs text-dim hover:border-accent hover:text-text transition-colors"
+          >
+            <Repeat size={13} /> 문항 복습 <span class="font-semibold text-text">{curriculum.question_due}</span>
+          </a>
+          <span class="flex items-center gap-1.5 rounded border border-default px-3 py-1.5 text-xs text-dim">
+            <BookOpen size={13} /> 개념 재복습 <span class="font-semibold text-text">{curriculum.concept_due}</span>
+          </span>
+        </div>
+      {/if}
+
+      <div class="text-xs text-dim mb-2">오늘의 개념</div>
+      {#if todayConcepts.length === 0}
+        <p class="text-xs text-dim">오늘 볼 개념이 없습니다. 잘 하고 있어요.</p>
+      {:else}
+        <ul class="space-y-1.5">
+          {#each todayConcepts as c (c.doc_id)}
+            <li class="flex items-center gap-2 rounded border border-default px-3 py-2">
+              <span class="text-accent shrink-0 text-xs" title="빈출">{#each Array(c.freq) as _}★{/each}</span>
+              <a href="/study/read/{c.doc_id}" class="text-sm text-text hover:text-accent truncate flex-1">{c.title}</a>
+              <span class="shrink-0 text-[10px] rounded-full px-2 py-0.5 {c.reason === '재복습' ? 'bg-accent/15 text-accent' : 'bg-surface border border-default text-dim'}">{c.reason}</span>
+              <button
+                type="button"
+                onclick={() => markRead(c)}
+                class="shrink-0 text-xs rounded border border-default px-2 py-1 text-dim hover:border-accent hover:text-accent transition-colors"
+              >읽음</button>
+            </li>
+          {/each}
+        </ul>
+      {/if}
+
+      {#if weakConcepts.length > 0}
+        <div class="mt-4 pt-3 border-t border-default">
+          <div class="text-xs text-dim mb-2 flex items-center gap-1.5">
+            <Target size={13} class="text-error" /> 약점 개념 <span class="text-faint">(관련 기출 정답률 낮음)</span>
+          </div>
+          <div class="flex flex-wrap gap-2">
+            {#each weakConcepts as w (w.doc_id)}
+              <a href="/study/read/{w.doc_id}"
+                class="text-xs rounded-full border border-error/40 bg-error/10 text-error px-3 py-1 hover:bg-error/20 transition-colors">
+                {w.title.replace(/^\d+_/, '')} <span class="font-semibold">{w.accuracy}%</span>
+              </a>
+            {/each}
+          </div>
+        </div>
+      {/if}
+    {/if}
+  </section>
+
   <a
     href="/study/topics"
     class="block mb-3 p-5 rounded-lg border border-default bg-surface hover:border-accent hover:bg-accent/5 transition-colors"
@@ -126,7 +245,8 @@
   <div class="mt-6 p-4 rounded-lg border border-dashed border-default/60 text-xs text-dim">
     <div class="font-medium text-dim mb-1">예정</div>
     <ul class="list-disc list-inside space-y-0.5">
-      <li>애플워치 빠른복습 + 공부 알람(push)</li>
+      <li>개념 학습 리더 (가리고 떠올리기 · 빈출★ · 관련개념 백링크)</li>
+      <li>이론↔문제 연결 (개념별 정답률 · 약점 개념 지도)</li>
     </ul>
   </div>
 </div>

frontend/src/routes/study/read/[docId]/+page.svelte

@@ -0,0 +1,254 @@
+<script>
+  /**
+   * /study/read/[docId] — 개념 학습 리더.
+   * 개념노트(가스기사 documents)를 구조(요약/본문/빈출★/관련개념)로 렌더 +
+   * '떠올리기' 능동 회상 토글 + 회독 SR(POST read) + 관련개념 백링크 + 이전/다음.
+   * 본문 렌더 = MarkdownDoc(KaTeX + docimg 내장). 서버 파싱 = /api/study/concepts/{id}.
+   */
+  import { page } from '$app/stores';
+  import { api } from '$lib/api';
+  import { addToast } from '$lib/stores/toast';
+  import { renderMathMarkdownInline } from '$lib/utils/mathMarkdown';
+  import MarkdownDoc from '$lib/components/MarkdownDoc.svelte';
+  import Button from '$lib/components/ui/Button.svelte';
+  import EmptyState from '$lib/components/ui/EmptyState.svelte';
+  import Skeleton from '$lib/components/ui/Skeleton.svelte';
+  import { BookOpen, ArrowLeft, Eye, EyeOff, Check, ChevronLeft, ChevronRight, FileQuestion } from 'lucide-svelte';
+
+  let docId = $derived($page.params.docId);
+
+  let concept = $state(null);
+  let relatedQ = $state(null);          // 관련 기출(이론↔문제, 비차단)
+  let loading = $state(true);
+  let notFound = $state(false);
+  let mode = $state('read');            // 'read' | 'recall'(떠올리기)
+  let revealed = $state({});            // {sectionIndex: true}
+  let marking = $state(false);
+
+  const STAGE_LABEL = { 0: '복습 시작', 1: '복습 1단계', 2: '복습 2단계', 3: '복습 3단계', 4: '학습 완료' };
+  const OUTCOME_MARK = { correct: '○', wrong: '✕', unsure: '?' };
+  const OUTCOME_CLASS = { correct: 'text-success', wrong: 'text-error', unsure: 'text-warning' };
+  const outcomeMark = (o) => OUTCOME_MARK[o] ?? '–';
+  const outcomeClass = (o) => OUTCOME_CLASS[o] ?? 'text-faint';
+
+  async function load() {
+    const reqId = docId; // in-flight 가드: 백링크 연타 시 stale 응답 무시
+    loading = true;
+    notFound = false;
+    concept = null;
+    relatedQ = null;
+    revealed = {};
+    mode = 'read';
+    try {
+      const data = await api(`/study/concepts/${reqId}`);
+      if (reqId !== docId) return; // 그새 다른 개념으로 이동 → 폐기
+      concept = data;
+    } catch (e) {
+      if (reqId !== docId) return;
+      if (e?.status === 404) notFound = true;
+      else addToast('error', '개념을 불러오지 못했습니다');
+      return; // 본문 실패 → 관련기출 스킵
+    } finally {
+      if (reqId === docId) loading = false;
+    }
+    // 관련 기출(비차단 — 실패해도 본문 표시엔 영향 없음)
+    try {
+      const rq = await api(`/study/concepts/${reqId}/questions?limit=6`);
+      if (reqId === docId) relatedQ = rq;
+    } catch {}
+  }
+
+  // $effect 가 마운트 1회 + docId 변경(백링크/이전·다음) 재로드를 모두 커버 (onMount 불필요)
+  $effect(() => {
+    void docId;
+    load();
+  });
+
+  function toggleMode() {
+    mode = mode === 'read' ? 'recall' : 'read';
+    revealed = {};
+  }
+  function reveal(i) {
+    revealed = { ...revealed, [i]: true };
+  }
+  function shown(i) {
+    return mode === 'read' || revealed[i];
+  }
+
+  async function markRead() {
+    marking = true;
+    try {
+      const r = await api(`/study/concepts/${docId}/read`, { method: 'POST' });
+      if (concept) {
+        concept.is_read = true;
+        concept.review_stage = r?.review_stage ?? concept.review_stage;
+        concept.due_at = r?.due_at ?? concept.due_at;
+      }
+      addToast('success', '회독 완료 — 다음 복습에 다시 나옵니다');
+    } catch {
+      addToast('error', '회독 처리 실패');
+    } finally {
+      marking = false;
+    }
+  }
+</script>
+
+<svelte:head><title>{concept?.title ?? '개념'} — 공부</title></svelte:head>
+
+<div class="p-4 md:p-6 max-w-3xl mx-auto">
+  <!-- 상단 네비 -->
+  <div class="flex items-center gap-2 text-xs md:text-sm mb-4 min-w-0">
+    <a href="/study" class="text-dim hover:text-text flex items-center gap-1 shrink-0">
+      <ArrowLeft size={14} /> 공부
+    </a>
+    {#if concept?.subject}
+      <span class="text-faint shrink-0">/</span>
+      <span class="text-dim truncate">{concept.subject}</span>
+    {/if}
+  </div>
+
+  {#if loading}
+    <Skeleton h="h-10" rounded="card" />
+    <div class="mt-3 space-y-2">
+      {#each Array(4) as _}<Skeleton h="h-24" rounded="card" />{/each}
+    </div>
+  {:else if notFound}
+    <EmptyState icon={BookOpen} title="개념을 찾을 수 없습니다" description="삭제되었거나 잘못된 주소입니다." />
+  {:else if concept}
+    <!-- 제목 + 빈출 tier -->
+    <header class="mb-3">
+      <div class="flex items-start gap-2">
+        <h1 class="text-xl md:text-2xl font-semibold text-text flex-1">{concept.title}</h1>
+        <span class="text-accent text-sm shrink-0 mt-1" title="빈출도">
+          {#each Array(concept.freq) as _}★{/each}
+        </span>
+      </div>
+      {#if concept.is_read || (concept.review_stage !== null && concept.review_stage !== undefined)}
+        <div class="mt-1 text-xs text-dim">
+          {#if concept.review_stage !== null && concept.review_stage !== undefined}
+            {STAGE_LABEL[concept.review_stage] ?? '복습 중'}
+          {:else}회독함{/if}
+        </div>
+      {/if}
+    </header>
+
+    <!-- 한 줄 요약 (고정 표시) -->
+    {#if concept.summary}
+      <div class="mb-4 rounded-lg border-l-4 border-accent bg-accent/10 px-4 py-3 markdown-body text-sm text-text">
+        {@html renderMathMarkdownInline(concept.summary)}
+      </div>
+    {/if}
+
+    <!-- 모드 토글 -->
+    <div class="flex items-center gap-2 mb-4">
+      <Button variant={mode === 'recall' ? 'primary' : 'secondary'} size="sm" icon={mode === 'recall' ? EyeOff : Eye} onclick={toggleMode}>
+        {mode === 'recall' ? '떠올리기 모드' : '읽기 모드'}
+      </Button>
+      {#if mode === 'recall'}
+        <span class="text-xs text-dim">각 섹션을 떠올린 뒤 확인하세요</span>
+      {/if}
+    </div>
+
+    <!-- 본문 섹션 -->
+    {#if concept.body.length > 0}
+      <div class="space-y-3 mb-5">
+        {#each concept.body as sec, i (i)}
+          <section class="rounded-lg border border-default bg-surface overflow-hidden">
+            <div class="flex items-center gap-2 px-4 py-2.5 border-b border-default bg-surface-hover">
+              <h2 class="text-sm font-semibold text-text flex-1">{sec.label}</h2>
+              {#if sec.stars > 0}
+                <span class="text-accent text-xs shrink-0">{#each Array(sec.stars) as _}★{/each}</span>
+              {/if}
+            </div>
+            {#if shown(i)}
+              <div class="px-4 py-3">
+                <MarkdownDoc documentId={concept.doc_id} mdContent={sec.md} mdStatus={null}
+                  class="markdown-body max-w-none text-text" />
+              </div>
+            {:else}
+              <button type="button" onclick={() => reveal(i)}
+                class="w-full px-4 py-6 text-center text-sm text-dim hover:text-accent hover:bg-accent/5 transition-colors">
+                <Eye size={16} class="inline mr-1" /> 떠올린 뒤 확인
+              </button>
+            {/if}
+          </section>
+        {/each}
+      </div>
+    {/if}
+
+    <!-- 빈출 포인트 -->
+    {#if concept.bincheol.length > 0}
+      <section class="mb-5 rounded-lg border border-default bg-surface p-4">
+        <h2 class="text-sm font-semibold text-text mb-2 flex items-center gap-1.5">
+          <span class="text-accent">★</span> 빈출 포인트
+        </h2>
+        <ul class="space-y-1.5">
+          {#each concept.bincheol as item}
+            <li class="flex gap-2 text-sm text-text">
+              <span class="text-accent shrink-0 text-xs mt-0.5">{#each Array(item.tier || 1) as _}★{/each}</span>
+              <span class="markdown-body flex-1">{@html renderMathMarkdownInline(item.text)}</span>
+            </li>
+          {/each}
+        </ul>
+      </section>
+    {/if}
+
+    <!-- 관련 개념 (백링크) -->
+    {#if concept.related.length > 0}
+      <section class="mb-5">
+        <h2 class="text-xs text-dim mb-2">관련 개념</h2>
+        <div class="flex flex-wrap gap-2">
+          {#each concept.related as rel}
+            {#if rel.doc_id}
+              <a href="/study/read/{rel.doc_id}"
+                class="text-xs rounded-full border border-accent/40 bg-accent/10 text-accent px-3 py-1 hover:bg-accent/20 transition-colors">
+                {rel.phrase}
+              </a>
+            {:else}
+              <span class="text-xs rounded-full border border-default bg-surface text-faint px-3 py-1" title="아직 없는 개념">
+                {rel.phrase}
+              </span>
+            {/if}
+          {/each}
+        </div>
+      </section>
+    {/if}
+
+    <!-- 관련 기출 (이론↔문제 브리지) -->
+    {#if relatedQ && relatedQ.linked > 0}
+      <section class="mb-5 rounded-lg border border-default bg-surface p-4">
+        <h2 class="text-sm font-semibold text-text mb-2 flex items-center gap-1.5">
+          <FileQuestion size={15} class="text-accent" /> 관련 기출
+          <span class="ml-1 text-xs font-normal text-dim">
+            {relatedQ.linked}문항{#if relatedQ.accuracy !== null} · 정답률 <span class="{relatedQ.accuracy < 60 ? 'text-error' : 'text-text'} font-medium">{relatedQ.accuracy}%</span>{:else} · 아직 안 풂{/if}
+          </span>
+        </h2>
+        <ul class="space-y-0.5">
+          {#each relatedQ.questions as q (q.id)}
+            <li>
+              <a href="/study/topics/4/questions/{q.id}"
+                class="flex items-center gap-2 text-xs py-1 text-dim hover:text-accent transition-colors">
+                <span class="{outcomeClass(q.last_outcome)} shrink-0 w-4 text-center font-bold">{outcomeMark(q.last_outcome)}</span>
+                <span class="truncate">{q.subject ?? '기출'}{#if q.exam_round} · {q.exam_round}{/if}</span>
+              </a>
+            </li>
+          {/each}
+        </ul>
+      </section>
+    {/if}
+
+    <!-- 액션바 -->
+    <div class="flex items-center gap-2 border-t border-default pt-4 mt-2">
+      {#if concept.prev_id}
+        <Button variant="ghost" size="sm" icon={ChevronLeft} href="/study/read/{concept.prev_id}">이전</Button>
+      {/if}
+      <div class="flex-1"></div>
+      <Button variant="primary" size="sm" icon={Check} onclick={markRead} loading={marking}>
+        {concept.is_read ? '다시 회독' : '회독 완료'}
+      </Button>
+      {#if concept.next_id}
+        <Button variant="secondary" size="sm" icon={ChevronRight} href="/study/read/{concept.next_id}">다음 개념</Button>
+      {/if}
+    </div>
+  {/if}
+</div>

migrations/362_documents_presegment_cols.sql

@@ -0,0 +1,10 @@
+-- 362: G2 pre-segmentation — 번들 PDF(여러 논리문서 한 파일) → N 자식 문서 분할.
+-- 자식 doc 의 원본 내 page 범위(1-based inclusive) + 분할 역할 표식.
+-- 부모-자식 관계 자체는 document_lineage(relation_type='segmented_from', migration 363).
+-- presegment_role: NULL=일반 단일문서(대다수) / 'parent'=번들원본(자체 extract/embed 안 함) /
+--   'child'=논리 하위문서(부모 file_path 공유 + bundle_page_start/end 범위로 슬라이스).
+-- 단일 ALTER(다중 절) = 1 statement (asyncpg 멀티스테이트먼트 제약 준수).
+ALTER TABLE documents
+  ADD COLUMN IF NOT EXISTS bundle_page_start INTEGER,
+  ADD COLUMN IF NOT EXISTS bundle_page_end   INTEGER,
+  ADD COLUMN IF NOT EXISTS presegment_role   TEXT;

migrations/363_document_lineage_segmented_from.sql

@@ -0,0 +1,8 @@
+-- 363: G2 — document_lineage.relation_type 에 'segmented_from'(번들 → 자식) 추가.
+-- 217 의 column-level CHECK(PG 자동명 document_lineage_relation_type_check, 배포 DB 실측 확인)
+-- 를 교체. DROP + ADD 를 단일 ALTER 의 두 절로 = 1 statement.
+-- 멱등: DROP ... IF EXISTS 라 재실행 안전(이미 교체됐으면 새 제약 DROP 후 동일 재생성).
+ALTER TABLE document_lineage
+  DROP CONSTRAINT IF EXISTS document_lineage_relation_type_check,
+  ADD CONSTRAINT document_lineage_relation_type_check
+    CHECK (relation_type IN ('cited','summarized_from','generated_from','revised_from','segmented_from'));

migrations/364_process_stage_presegment.sql

@@ -0,0 +1,5 @@
+-- 364: G2 — process_stage 큐 스테이지 enum 에 'presegment' 추가 (extract 前 번들 분할 단계).
+-- PG16: ALTER TYPE ADD VALUE 는 트랜잭션 내 실행 가능(값 추가만, 同 트랜잭션 내 사용은 안 함 —
+--   사용은 후속 마이그/런타임). IF NOT EXISTS = 재실행 멱등.
+-- (이 한 줄 단독 파일 — 1 statement.)
+ALTER TYPE process_stage ADD VALUE IF NOT EXISTS 'presegment';

migrations/365_scan_jobs.sql

@@ -0,0 +1,56 @@
+-- 스캔 기능: 잡 모델 + 배치 + 에이전트 생존 (plan: scan-feature-build r3)
+-- 웹(fastapi)=intent/명령, 호스트 스캔 에이전트=결과. 싱글톤 스캐너 직렬화.
+-- 주: 러너 규약상 이 파일은 schema_migrations 를 건드리지 않음(스탬프는 외부). BEGIN/COMMIT 없음.
+-- 순서: 테이블 먼저 → 시드 → 인덱스 (인덱스 실패가 테이블 생성 막지 않게).
+
+-- 잡: 한 스캔 세션 = 한 논리 문서 (배치 N개 → 합본 1 PDF → Inbox)
+CREATE TABLE IF NOT EXISTS scan_jobs (
+    id                BIGSERIAL PRIMARY KEY,
+    title             TEXT NOT NULL,                       -- 사람 입력 제목 (commit 시 documents.title 로 전파)
+    settings          JSONB NOT NULL DEFAULT '{}'::jsonb,  -- mode/resolution/source(ADF Duplex) 등 스캔 프로파일
+    status            TEXT NOT NULL DEFAULT 'draft',       -- draft|queued|ready|scanning|assembling|preview|committing|committed|failed|canceled
+    batch_count       INTEGER NOT NULL DEFAULT 0,          -- 스캔 완료 배치 수
+    page_count        INTEGER,                             -- 최종 합본 페이지 수 (assembling 후)
+    last_activity_at  TIMESTAMPTZ,                         -- ready 휴지 벽시계 idle 타임아웃 기준 (방치 데드락 방지)
+    last_progress_at  TIMESTAMPTZ,                         -- 잡 진행 갱신 (에이전트 생존과 분리)
+    staging_path      TEXT,                                -- 호스트 로컬 잡 스테이징 디렉토리
+    nas_staging_path  TEXT,                                -- NAS .scan-staging 합본 경로 (B안 미리보기/commit 소스)
+    inbox_path        TEXT,                                -- 최종 PKM/Inbox 경로 (commit 후)
+    file_hash         CHAR(64),                            -- 합본 sha256 = 정체성/멱등 커밋 키 (commit 시 채움)
+    doc_id            BIGINT REFERENCES documents(id) ON DELETE SET NULL,  -- commit 후 연결 (title 전파)
+    error             TEXT,                                -- failed 사유 (no-silent)
+    created_at        TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    updated_at        TIMESTAMPTZ NOT NULL DEFAULT NOW()
+);
+
+-- 배치: 스캔 1회(ADF 한 묶음) 단위. batch_seq = 결합 순서(글롭 정렬 아님).
+CREATE TABLE IF NOT EXISTS scan_job_batches (
+    id            BIGSERIAL PRIMARY KEY,
+    job_id        BIGINT REFERENCES scan_jobs(id) ON DELETE CASCADE NOT NULL,
+    batch_seq     INTEGER NOT NULL,                    -- 1-based 결합 순서
+    staging_path  TEXT,                                -- 이 배치 PDF (호스트 로컬)
+    page_count    INTEGER,
+    status        TEXT NOT NULL DEFAULT 'scanned',     -- scanned | discarded (잼 폐기 후 재스캔)
+    created_at    TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    UNIQUE (job_id, batch_seq)
+);
+
+-- 에이전트 생존: 싱글톤 1행. 잡 진행(last_progress_at)과 분리 — queued 잡 stale 오탐 방지.
+CREATE TABLE IF NOT EXISTS scan_agent_status (
+    id              INTEGER PRIMARY KEY DEFAULT 1 CHECK (id = 1),  -- 단일 행 강제
+    last_heartbeat  TIMESTAMPTZ,
+    agent_version   TEXT,
+    current_job_id  BIGINT REFERENCES scan_jobs(id) ON DELETE SET NULL,
+    updated_at      TIMESTAMPTZ NOT NULL DEFAULT NOW()
+);
+INSERT INTO scan_agent_status (id) VALUES (1) ON CONFLICT (id) DO NOTHING;  -- 시드 1행
+
+-- 활성 잡 락: 스캐너 싱글톤 → in-progress 잡은 전체에서 1개만(나머지 queued).
+-- 상수 TRUE 에 unique + in-progress 필터 = 그 상태 행 최대 1개 강제.
+CREATE UNIQUE INDEX IF NOT EXISTS uq_scan_jobs_single_active
+    ON scan_jobs ((TRUE))
+    WHERE status IN ('ready','scanning','assembling','preview','committing');
+
+CREATE INDEX IF NOT EXISTS idx_scan_jobs_queued     ON scan_jobs (created_at) WHERE status = 'queued';
+CREATE INDEX IF NOT EXISTS idx_scan_jobs_file_hash  ON scan_jobs (file_hash)  WHERE file_hash IS NOT NULL;
+CREATE INDEX IF NOT EXISTS idx_scan_job_batches_job ON scan_job_batches (job_id, batch_seq);

migrations/366_scan_command.sql

@@ -0,0 +1,4 @@
+-- 스캔 잡 명령 채널 (이중 라이터: API=intent/명령, 에이전트=result) — plan scan-feature-build r3
+-- API/수동이 pending_command 설정 → 에이전트가 조건부 claim(WHERE pending_command=X AND status=기대값) → 실행 → 결과 status write.
+ALTER TABLE scan_jobs ADD COLUMN IF NOT EXISTS pending_command      TEXT;          -- scan_batch | finish | commit | cancel
+ALTER TABLE scan_jobs ADD COLUMN IF NOT EXISTS command_requested_at TIMESTAMPTZ;   -- 명령 요청 시각(staleness/디버그)

migrations/367_published.sql

@@ -0,0 +1,21 @@
+-- 367_published.sql
+-- 발행 레이어(docsrv-viewer-publish) projection 테이블. 뷰어가 read API로 당겨 자기 SQLite로 복제.
+-- kind-discriminated 단일 테이블(study_question | study_explanation | ... 후속 news/document).
+--   pub_id    = opaque+stable(워커가 (kind,source_id)당 1회 부여, republish=rev bump에도 불변) = 뷰어 dedup키=progress키.
+--   source_id = 내부 소스 행 id (pub_id→내부 역매핑, ingest write-back 해소용).
+--   rev       = 발행 워커 커밋순 gapless 커서(pg_advisory_lock 단일 라이터). 뷰어 feed = WHERE rev>since.
+--   payload_hash = sha256(정렬 JSON). (payload_hash, deleted) 디둡 — no-op 재투영 억제, tombstone 보존.
+--   deleted   = tombstone(삭제/만료도 feed 1급 이벤트). schema_version = 엔벨로프 버전(미지원 가시거부).
+CREATE TABLE IF NOT EXISTS published (
+    id             BIGSERIAL    PRIMARY KEY,
+    kind           VARCHAR(40)  NOT NULL,
+    source_id      BIGINT       NOT NULL,
+    pub_id         TEXT         NOT NULL,
+    payload        JSONB        NOT NULL,
+    payload_hash   TEXT         NOT NULL,
+    schema_version SMALLINT     NOT NULL DEFAULT 1,
+    rev            BIGINT       NOT NULL,
+    deleted        BOOLEAN      NOT NULL DEFAULT false,
+    created_at     TIMESTAMPTZ  NOT NULL DEFAULT now(),
+    updated_at     TIMESTAMPTZ  NOT NULL DEFAULT now()
+);

migrations/368_published_kind_pubid_uq.sql

@@ -0,0 +1,3 @@
+-- 368_published_kind_pubid_uq.sql
+-- pub_id 는 kind 내 유일(뷰어 dedup/progress 키 무결성, pub_id→내부 역해소 유일성 보장).
+CREATE UNIQUE INDEX IF NOT EXISTS published_kind_pubid_uq ON published (kind, pub_id);

migrations/369_published_kind_source_uq.sql

@@ -0,0 +1,3 @@
+-- 369_published_kind_source_uq.sql
+-- (kind, source_id) 당 발행 행 1개 — 발행 워커 upsert 타깃 + pub_id 재사용(같은 source=같은 pub_id) 키.
+CREATE UNIQUE INDEX IF NOT EXISTS published_kind_source_uq ON published (kind, source_id);

migrations/370_published_rev_idx.sql

@@ -0,0 +1,3 @@
+-- 370_published_rev_idx.sql
+-- 뷰어 pull-sync feed: SELECT ... WHERE rev > :since ORDER BY rev LIMIT :page (P0-2).
+CREATE INDEX IF NOT EXISTS published_rev_idx ON published (rev);

migrations/371_publish_outbox.sql

@@ -0,0 +1,15 @@
+-- 371_publish_outbox.sql
+-- transactional outbox — 저작/4-A 트랜잭션이 같은 tx에서 여기 INSERT(P0-1 규율),
+--   단일 발행 워커가 id(커밋순) 순으로 drain 하며 published 에 rev 부여(소스 updated_at 폴링 금지=갭 재발).
+--   processed_at = 워커 drain 시 스탬프(NULL=미처리). payload/hash 는 enqueue 시점 스냅샷.
+CREATE TABLE IF NOT EXISTS publish_outbox (
+    id             BIGSERIAL    PRIMARY KEY,
+    kind           VARCHAR(40)  NOT NULL,
+    source_id      BIGINT       NOT NULL,
+    payload        JSONB        NOT NULL,
+    payload_hash   TEXT         NOT NULL,
+    schema_version SMALLINT     NOT NULL DEFAULT 1,
+    deleted        BOOLEAN      NOT NULL DEFAULT false,
+    created_at     TIMESTAMPTZ  NOT NULL DEFAULT now(),
+    processed_at   TIMESTAMPTZ
+);

migrations/372_publish_outbox_unprocessed_idx.sql

@@ -0,0 +1,3 @@
+-- 372_publish_outbox_unprocessed_idx.sql
+-- 워커 drain 쿼리: WHERE processed_at IS NULL ORDER BY id (커밋순). 부분 인덱스로 미처리분만.
+CREATE INDEX IF NOT EXISTS publish_outbox_unprocessed_idx ON publish_outbox (id) WHERE processed_at IS NULL;

migrations/373_quiz_session_finalized_at.sql

@@ -0,0 +1,4 @@
+-- 373_quiz_session_finalized_at.sql
+-- 발행 ingest(study-to-viewer P2) finalize 멱등 마커. finalize 성공 후 스탬프 →
+-- 같은 세션 재전송(at-least-once outbox) 시 SR 이중 advance 차단. 라이브 세션은 NULL 유지(무영향).
+ALTER TABLE study_quiz_sessions ADD COLUMN IF NOT EXISTS finalized_at TIMESTAMPTZ;

migrations/374_quiz_session_client_uuid.sql

@@ -0,0 +1,3 @@
+-- 374_quiz_session_client_uuid.sql
+-- 뷰어 로컬 세션 UUID. ingest 가 (uuid, topic) 로 DS 세션 find-or-create = 멱등 키. 라이브=NULL.
+ALTER TABLE study_quiz_sessions ADD COLUMN IF NOT EXISTS client_session_uuid TEXT;

migrations/375_quiz_session_source.sql

@@ -0,0 +1,3 @@
+-- 375_quiz_session_source.sql
+-- 세션 출처 구분(live | viewer). 감사/필터용. 기존 행=live.
+ALTER TABLE study_quiz_sessions ADD COLUMN IF NOT EXISTS source VARCHAR(20) NOT NULL DEFAULT 'live';

migrations/376_quiz_session_client_uuid_uq.sql

@@ -0,0 +1,3 @@
+-- 376_quiz_session_client_uuid_uq.sql
+-- (client_session_uuid, study_topic_id) 유일 — 뷰어 1세션이 topic 별 1 DS세션. partial(uuid 있는 viewer 행만).
+CREATE UNIQUE INDEX IF NOT EXISTS study_quiz_sessions_client_uuid_topic_uq ON study_quiz_sessions (client_session_uuid, study_topic_id) WHERE client_session_uuid IS NOT NULL;

migrations/377_domain_bucket.sql

@@ -0,0 +1,23 @@
+-- 377_domain_bucket.sql
+-- ai_domain(반자유 AI 분류, 드리프트 존재)을 검색 스코프용 7버킷으로 결정적 롤업.
+-- 축: ai_domain(routing/해석 축)의 coarsening — category(UI축) 아님 (feedback_category_vs_ai_domain_axis 준수).
+-- 버킷: News / Safety / Law / Engineering / General / Philosophy / Programming.
+-- STORED generated → 신규/재분류 문서도 ai_domain 붙으면 자동 버킷. ai_domain 원본 보존(하위 검색 유지).
+-- 롤백: ALTER TABLE documents DROP COLUMN domain_bucket;
+ALTER TABLE documents ADD COLUMN IF NOT EXISTS domain_bucket text
+  GENERATED ALWAYS AS (
+    CASE
+      WHEN ai_domain LIKE 'News%' THEN 'News'
+      WHEN ai_domain = '법령' OR ai_domain LIKE 'Industrial_Safety/Legislation%' THEN 'Law'
+      WHEN ai_domain = 'Safety' OR ai_domain LIKE 'Safety/%'
+           OR ai_domain LIKE 'Industrial_Safety%'
+           OR ai_domain = 'Knowledge/Industrial_Safety' THEN 'Safety'
+      WHEN ai_domain LIKE 'Engineering%' OR ai_domain = 'Knowledge/Engineering' THEN 'Engineering'
+      WHEN ai_domain LIKE 'Philosophy%' THEN 'Philosophy'
+      WHEN ai_domain LIKE 'Programming%' THEN 'Programming'
+      ELSE 'General'
+    END
+  ) STORED;
+
+CREATE INDEX IF NOT EXISTS documents_domain_bucket_idx
+  ON documents (domain_bucket) WHERE deleted_at IS NULL;

migrations/378_publish_outbox_attempts_failed.sql

@@ -0,0 +1,9 @@
+-- 378_publish_outbox_attempts_failed.sql
+-- (번호: 멀티세션 중 prod 가 377_domain_bucket 을 선점 → 378 로 리넘버.)
+-- publish_outbox poison row head-of-line block 차단. 발행 워커가 행별 savepoint 격리 후
+-- 예외 시 attempts++ 하고 MAX 초과 시 failed_at 스탬프(terminal) → 그 행을 select 에서 제외해
+-- 후속 발행이 막히지 않게 함. 기존 미처리 행은 attempts=0 / failed_at=NULL 로 정상 재처리.
+-- (단일 ALTER = 1 statement = asyncpg prepared 호환.)
+ALTER TABLE publish_outbox
+    ADD COLUMN IF NOT EXISTS attempts SMALLINT NOT NULL DEFAULT 0,
+    ADD COLUMN IF NOT EXISTS failed_at TIMESTAMPTZ;

migrations/379_asme_clause_kb.sql

@@ -0,0 +1,37 @@
+-- 379_asme_clause_kb.sql
+-- ASME 절-지식베이스: 절 = 개별 documents 행(parent_id) + 절↔절 백링크 + 태깅 (additive, idempotent)
+-- 검색 무접촉: 절 doc 은 embedding NULL(벡터 제외) + doc_kind='clause'(retrieval doc-leg 필터로 제외).
+
+ALTER TABLE documents
+  ADD COLUMN IF NOT EXISTS parent_id    bigint REFERENCES documents(id),
+  ADD COLUMN IF NOT EXISTS doc_kind     text NOT NULL DEFAULT 'standard',
+  ADD COLUMN IF NOT EXISTS clause_code  text,
+  ADD COLUMN IF NOT EXISTS clause_part  text,
+  ADD COLUMN IF NOT EXISTS clause_order int;
+
+CREATE INDEX IF NOT EXISTS idx_documents_parent_id   ON documents(parent_id) WHERE parent_id IS NOT NULL;
+CREATE INDEX IF NOT EXISTS idx_documents_doc_kind    ON documents(doc_kind);
+CREATE INDEX IF NOT EXISTS idx_documents_clause_code ON documents(clause_code) WHERE clause_code IS NOT NULL;
+
+-- 절↔절 백링크 (dangling 허용: dst_doc_id nullable)
+CREATE TABLE IF NOT EXISTS clause_links (
+  id          bigserial PRIMARY KEY,
+  src_doc_id  bigint NOT NULL REFERENCES documents(id) ON DELETE CASCADE,
+  dst_code    text   NOT NULL,
+  dst_doc_id  bigint REFERENCES documents(id) ON DELETE SET NULL,
+  anchor      text,
+  ctx         text,
+  char_off    int
+);
+CREATE INDEX IF NOT EXISTS idx_clause_links_src     ON clause_links(src_doc_id);
+CREATE INDEX IF NOT EXISTS idx_clause_links_dst     ON clause_links(dst_doc_id) WHERE dst_doc_id IS NOT NULL;
+CREATE INDEX IF NOT EXISTS idx_clause_links_dstcode ON clause_links(dst_code);
+
+-- 태깅 (Part 자동 + 주제)
+CREATE TABLE IF NOT EXISTS document_tags (
+  doc_id    bigint NOT NULL REFERENCES documents(id) ON DELETE CASCADE,
+  tag       text   NOT NULL,
+  tag_kind  text   NOT NULL DEFAULT 'topic',
+  PRIMARY KEY (doc_id, tag)
+);
+CREATE INDEX IF NOT EXISTS idx_document_tags_tag ON document_tags(tag);

migrations/380_clause_study.sql

@@ -0,0 +1,9 @@
+-- 380_clause_study.sql — 절-문서 공부도구(노트/형광펜/암기카드) 저장. FK 없음(documents 락 회피).
+CREATE TABLE IF NOT EXISTS clause_study (
+  id         bigserial PRIMARY KEY,
+  doc_id     bigint NOT NULL,
+  kind       text   NOT NULL,            -- 'note' | 'highlight' | 'card'
+  payload    jsonb  NOT NULL DEFAULT '{}',
+  created_at timestamptz NOT NULL DEFAULT now()
+);
+CREATE INDEX IF NOT EXISTS idx_clause_study_doc ON clause_study(doc_id, kind);