fix(tools): remove lexical pseudo-topic models

AGENTS.md

@@ -95,6 +95,17 @@ in this repo.
   knowledge-graph pipeline (DOM decomposition, entity/relation extraction,
   grounded graph retrieval) should ground itself in the relevant layout-analysis
   and knowledge-graph / grounded-retrieval literature.
+
+### Structural topic-model boundary
+
+- Do not implement or describe hard-coded term lists, term frequency,
+  embeddings, or LLM-assigned labels as structural topic modeling (STM).
+  Fixed business labels are not topic-posterior estimates, and the explicitly
+  lexical `keyword_extractor` must not be used as topic evidence.
+- Topic inference requires a versioned fitted TEPP model and its frozen
+  preprocessing and vocabulary contract. If that fitted model is unavailable,
+  fail closed; do not return a default label, template agenda, or substitute
+  keyword/embedding/LLM result presented as STM.
 <!-- END cwl-agent-guidance -->
 
 ## Release governance defaults
@@ -425,6 +436,15 @@ in this repo.
 - Public audit/event identifiers that may use human-readable prefixes must not
   be stored in artificially short `varchar(n)` columns; use opaque source UIDs
   that fit seeded smoke data and provider evidence without truncation.
+- Conceptual ERDs, API schemas, persistence models, and fixtures must not mark a
+  reusable business identifier such as `document_ref`, `model_id`, `topic_id`,
+  or `label_id` as an unscoped primary or foreign key. Use an opaque immutable
+  reference that binds the full scope or an explicit composite identity with the
+  applicable snapshot revision, model version, request/result scope, or label
+  version. Define the required identity tuple for each entity; require only the
+  dimensions relevant to that entity. Never join snapshots, model artifacts,
+  topic components, or label evidence by a bare document, model, topic, rank,
+  label, or display value.
 - When reviews find public/private identifier leaks, stale API fixture shapes, or recurring bug patterns, update tests, frontend mocks, E2E mocks, README examples, architecture docs, and explicitly record the anti-pattern in `AGENTS.md` so the same bug pattern does not reappear in copied examples.
 - When reviews find missing browser security headers or tabnabbing hardening,
   update both backend header tests and frontend link tests. Global backend

ARCHITECTURE.md

@@ -20,6 +20,21 @@ Runtime database connectivity is secret-injected: `backend/core/config.py` has
 no fallback `DATABASE_URL`, so missing database configuration fails at startup
 rather than silently using shared development credentials.
 
+## Topic-intelligence boundary
+
+Naruon has no live Structural Topic Modeling endpoint, fitted topic artifact,
+or topic-result persistence. The retained `keyword_extractor` is deterministic
+lexical metadata and must not feed topic, agenda, search, or norm-group
+inference. A future adapter may consume a separately accepted, versioned TEPP
+artifact/API only when frozen preprocessing and vocabulary, covariate design,
+mixed-membership posterior uncertainty, diagnostics, provenance, and explicit
+abstention are all available. Missing or incompatible scientific authority
+fails closed. Naruon owns authentication, authorization, request validation,
+the adapter envelope, and disclosure policy; TEPP would own the scientific
+payload. See the canonical documentation graph in
+[`docs/topic-intelligence/README.md`](docs/topic-intelligence/README.md) and
+[`ADR-0001`](docs/adr/0001-topic-measurement-authority.md).
+
 ## Workspace navigation boundary
 
 The Next.js shell opens the Today execution dashboard for first-run sessions and

CHANGELOG.md

@@ -1,6 +1,20 @@
 ## [Unreleased]
 - EmailDetail 테스트가 지원하지 않는 스레드 병합/분리 버튼을 `textContent`뿐 아니라 `aria-label`과 `title` 접근 가능 이름으로도 검출하도록 바꿔, 아이콘 전용 버튼 회귀를 놓치지 않습니다.
+### 주제 측정 경계 (Topic Measurement)
+
+- STM 결과로 오인될 수 있었던 하드코딩 용어표 기반
+  `email_categorizer`와 `meeting_agenda_generator`를 도구 레지스트리에서
+  제거했습니다. `keyword_extractor`는 결정론적 단어 빈도 유틸리티로 유지하되
+  주제 posterior 근거로 사용하지 않는 경계를 문서화했습니다. 현재 Naruon에는
+  fitted TEPP 모델 기반 production 주제 측정 API가 없으므로, 모델 부재 시
+  기본 라벨이나 템플릿으로 대체하지 않고 fail closed 합니다.
+- 이 경계의 PRD, TRD, ADR, Architecture, API 계약, JSON Schema, UML,
+  개념 ERD, 보안·위협 모델, 테스트·운영 전략, 추적성 및 문서 적합성 평가를
+  `docs/topic-intelligence/`에 하나의 상태 표시 문서 그래프로 정리했습니다.
+  이는 미래 계약의 설계 근거이며, 현재 runtime 구현이나 물리 DB 엔터티가
+  존재한다는 주장이 아닙니다.
 - UUID V4 제너레이터(`uuid_v4_generator`) 도구를 추가하여 런타임에서 범용 고유 식별자 버전 4를 랜덤으로 생성할 수 있게 하였습니다. 테스트 커버리지 100%를 보장합니다.
+
 ### 보안 패치 (CodeQL extended current-head)
 
 - `cryptography`를 `50.0.0`으로 갱신해 공격자 제공 PKCS#7 EnvelopedData 복호화 결과의 오류·타이밍 차이로 발생하는 Bleichenbacher oracle(`CVE-2026-69247`, `GHSA-g6cj-pr64-35w5`)을 제거하고, backend·uv lock·hash lock·Strix CI 의존성 증거를 같은 버전으로 동기화했습니다. Strix 잠금은 `google-cloud-aiplatform==1.160.0`의 `<7` 제약을 위반하던 `protobuf==7.35.1`을 이미 검증된 `6.33.6`으로 복구해 다시 해석·설치 가능하게 했습니다.

CLAUDE.md

@@ -146,6 +146,13 @@ Next.js frontend ──> FastAPI backend (control plane) ──> Postgres + pgve
   test/lint/build), plus `bandit`, `codeql`, `trivy`, `scorecard`,
   `pr-governance`, `docker-publish` (GHCR on `v*` tags matching `VERSION`), and
   `mail-smoke`. Actions are pinned to full commit SHAs.
+- Topic intelligence is **not implemented**. Never use lexical frequencies,
+  embeddings, zero-shot labels, or request-time LLM labels as an STM result.
+  The retained `keyword_extractor` is lexical metadata only. Any future adapter
+  is blocked on a versioned fitted TEPP artifact/API with frozen preprocessing,
+  mixed-membership uncertainty and diagnostics; absence or incompatibility
+  fails closed. Start at `docs/topic-intelligence/README.md` and
+  `docs/adr/0001-topic-measurement-authority.md`.
 
 ## Key conventions
 

README.md

@@ -12,6 +12,8 @@ mail/calendar/file systems.
 ## Quick Links
 - [Installation & Setup](#five-minute-local-path)
 - [Architecture](docs/architecture/)
+- [Topic-intelligence documentation set](docs/topic-intelligence/README.md)
+- [Architecture decisions](docs/adr/README.md)
 - [Contributing](CONTRIBUTING.md)
 - [Code of Conduct](CODE_OF_CONDUCT.md)
 - [Security Policy](SECURITY.md)
@@ -48,6 +50,12 @@ mail/calendar/file systems.
   auto-merge, and mechanical merge actions run as the target repository's
   `github-actions[bot]` through the central workflow. Pending CodeRabbit or
   required-check evidence is a wait state, not a hard blocker.
+- Topic intelligence is not currently a live Naruon capability. The lexical
+  `keyword_extractor` is metadata only; Naruon fails closed rather than present
+  keyword, embedding, or LLM labels as Structural Topic Modeling. The product,
+  technical, architecture, contract, security, UML, conceptual ERD, test, and
+  operability records are indexed in
+  [`docs/topic-intelligence/`](docs/topic-intelligence/README.md).
 - Security governance is source-backed through signed
   `/api/security/access-surface`. The endpoint reads scoped WebDAV, CalDAV, and
   connector evidence plus durable `security_audit_events`, reuses the deny-first

backend/api/tools.py

@@ -706,26 +706,6 @@ async def base64_decoder_handler(params: Dict[str, Any]) -> Dict[str, str]:
         "합니다",
     }
 )
-_CATEGORY_TERMS = (
-    ("Urgent", ("urgent", "asap", "immediate", "긴급", "시급", "빨리")),
-    ("Finance", ("invoice", "billing", "payment", "결제", "청구", "송금")),
-    ("Scheduling", ("meeting", "schedule", "appointment", "회의", "일정", "약속")),
-)
-_AGENDA_TOPICS = (
-    ("Project Status Update", ("project", "프로젝트", "과제")),
-    ("Discuss Pending Issues", ("issue", "bug", "blocker", "문제", "오류", "장애")),
-    ("Decisions Required", ("decision", "approve", "결정", "승인")),
-    (
-        "Timeline and Milestones",
-        ("deadline", "milestone", "timeline", "마감", "기한", "일정"),
-    ),
-    (
-        "Budget and Resource Review",
-        ("budget", "cost", "resource", "예산", "비용", "자원"),
-    ),
-)
-
-
 def _normalize_analysis_text(value: str) -> str:
     """Normalize user text for deterministic, multilingual rule matching."""
     if len(value) > ANALYSIS_TEXT_MAX_CHARS:
@@ -740,44 +720,8 @@ def _analysis_tokens(value: str) -> list[str]:
     return _ANALYSIS_TOKEN_PATTERN.findall(_normalize_analysis_text(value))
 
 
-def _contains_analysis_term(normalized_text: str, term: str) -> bool:
-    """Match ASCII terms on word boundaries and Korean terms as morpheme stems."""
-    normalized_term = _normalize_analysis_text(term)
-    if normalized_term.isascii():
-        pattern = rf"(?<![a-z0-9]){re.escape(normalized_term)}(?![a-z0-9])"
-        return re.search(pattern, normalized_text) is not None
-    return normalized_term in normalized_text
-
-
-async def email_categorizer_handler(params: Dict[str, Any]) -> Any:
-    """Categorize email text with deterministic Korean and English rules."""
-    content = _normalize_analysis_text(params.get("email_content", ""))
-    categories = [
-        category
-        for category, terms in _CATEGORY_TERMS
-        if any(_contains_analysis_term(content, term) for term in terms)
-    ]
-
-    if not categories:
-        categories = ["General"]
-
-    return {"categories": categories, "primary_category": categories[0]}
-
-
-registry.register(
-    ToolInfo(
-        code="email_categorizer",
-        name="이메일 자동 분류기 (Email Categorizer)",
-        description="이메일 내용을 분석하여 알맞은 카테고리로 자동 분류합니다.",
-        category="이메일 분석",
-        parameters={"email_content": "string"},
-    ),
-    email_categorizer_handler,
-)
-
-
 async def keyword_extractor_handler(params: Dict[str, Any]) -> Any:
-    """Extract stable keywords ranked by frequency and first occurrence."""
+    """Extract deterministic lexical terms by frequency and first occurrence."""
     candidates = [
         token
         for token in _analysis_tokens(params.get("text", ""))
@@ -801,46 +745,14 @@ async def keyword_extractor_handler(params: Dict[str, Any]) -> Any:
     ToolInfo(
         code="keyword_extractor",
         name="주요 키워드 추출기 (Keyword Extractor)",
-        description="텍스트 본문에서 가장 중요한 키워드를 추출합니다.",
+        description="텍스트 본문에서 빈도와 최초 출현 순으로 반복 용어를 추출합니다.",
         category="이메일 분석",
         parameters={"text": "string"},
     ),
     keyword_extractor_handler,
 )
 
 
-async def meeting_agenda_generator_handler(params: Dict[str, Any]) -> Any:
-    """Generate a deterministic agenda from Korean or English discussion topics."""
-    context = _normalize_analysis_text(params.get("discussion_context", ""))
-    if len(_analysis_tokens(context)) < 2:
-        return {
-            "agenda_items": ["Introductions", "Open Discussion"],
-            "estimated_duration_minutes": 30,
-        }
-
-    items = ["Review previous action items"]
-    items.extend(
-        agenda_item
-        for agenda_item, terms in _AGENDA_TOPICS
-        if any(_contains_analysis_term(context, term) for term in terms)
-    )
-    items.append("Next Steps and Action Items")
-
-    return {"agenda_items": items, "estimated_duration_minutes": len(items) * 15}
-
-
-registry.register(
-    ToolInfo(
-        code="meeting_agenda_generator",
-        name="회의 아젠다 생성기 (Meeting Agenda Generator)",
-        description="논의 컨텍스트를 바탕으로 적절한 회의 아젠다를 자동으로 생성합니다.",
-        category="일정 관리",
-        parameters={"discussion_context": "string"},
-    ),
-    meeting_agenda_generator_handler,
-)
-
-
 async def uuid_v4_generator_handler(params: Dict[str, Any]) -> Dict[str, str]:
     return {"uuid": str(uuid.uuid4())}
 
@@ -857,6 +769,7 @@ async def uuid_v4_generator_handler(params: Dict[str, Any]) -> Dict[str, str]:
 )
 
 
+
 @router.get("/tools", response_model=list[ToolInfo])
 def get_tools() -> list[ToolInfo]:
     """

backend/tests/test_tools_api.py

@@ -112,6 +112,21 @@ def test_get_tool_not_found():
     assert response.json() == {"detail": "Tool not found"}
 
 
+@pytest.mark.parametrize(
+    "tool_code", ["email_categorizer", "meeting_agenda_generator"]
+)
+def test_registry_omits_lexical_pseudo_topic_tools(tool_code):
+    assert registry.get(tool_code) is None
+
+
+def test_keyword_extractor_is_disclosed_as_lexical_term_frequency():
+    tool = registry.get("keyword_extractor")
+    assert tool is not None
+    assert tool.description == (
+        "텍스트 본문에서 빈도와 최초 출현 순으로 반복 용어를 추출합니다."
+    )
+
+
 @pytest.mark.asyncio
 async def test_execute_tool_success():
     with TestClient(app) as client:
@@ -1155,52 +1170,6 @@ def test_detect_text_language_ko():
     assert _detect_text_language("안녕하세요") == "ko"
 
 
-@pytest.mark.asyncio
-async def test_email_categorizer_handler():
-    from api.tools import email_categorizer_handler
-
-    # Test Finance category
-    result = await email_categorizer_handler(
-        {"email_content": "Please pay this invoice soon."}
-    )
-    assert "Finance" in result["categories"]
-
-    # Test Scheduling category
-    result = await email_categorizer_handler(
-        {"email_content": "Let's schedule a meeting."}
-    )
-    assert "Scheduling" in result["categories"]
-
-    # Test Urgent category
-    result = await email_categorizer_handler({"email_content": "This is urgent!"})
-    assert "Urgent" in result["categories"]
-
-    # Test General category (fallback)
-    result = await email_categorizer_handler({"email_content": "Hello, how are you?"})
-    assert "General" in result["categories"]
-
-    # Test multiple categories
-    result = await email_categorizer_handler(
-        {"email_content": "URGENT: Meeting to discuss invoice payment"}
-    )
-    assert result == {
-        "categories": ["Urgent", "Finance", "Scheduling"],
-        "primary_category": "Urgent",
-    }
-
-    # ASCII category rules use token boundaries instead of substring matching.
-    result = await email_categorizer_handler(
-        {"email_content": "The prepayment plan is documented."}
-    )
-    assert result["categories"] == ["General"]
-
-    # Unicode compatibility forms and Korean stems remain matchable.
-    result = await email_categorizer_handler(
-        {"email_content": "긴급 회의에서 청구 금액을 검토합니다."}
-    )
-    assert result["categories"] == ["Urgent", "Finance", "Scheduling"]
-
-
 @pytest.mark.asyncio
 async def test_keyword_extractor_handler():
     from api.tools import keyword_extractor_handler
@@ -1224,41 +1193,6 @@ async def test_keyword_extractor_handler():
     assert empty == {"keywords": [], "keyword_count": 0}
 
 
-@pytest.mark.asyncio
-async def test_meeting_agenda_generator_handler():
-    from api.tools import meeting_agenda_generator_handler
-
-    # Test with short context
-    result = await meeting_agenda_generator_handler({"discussion_context": "short"})
-    assert result["agenda_items"] == ["Introductions", "Open Discussion"]
-    assert result["estimated_duration_minutes"] == 30
-
-    # Test with project and issue context
-    result = await meeting_agenda_generator_handler(
-        {"discussion_context": "The project has an issue that needs fixing."}
-    )
-    assert "Review previous action items" in result["agenda_items"]
-    assert "Project Status Update" in result["agenda_items"]
-    assert "Discuss Pending Issues" in result["agenda_items"]
-    assert "Next Steps and Action Items" in result["agenda_items"]
-    assert result["estimated_duration_minutes"] == len(result["agenda_items"]) * 15
-
-    # Korean context covers decision, timeline, and resource agenda paths.
-    result = await meeting_agenda_generator_handler(
-        {"discussion_context": "프로젝트 예산 승인과 마감 일정 문제를 결정합니다."}
-    )
-    assert result["agenda_items"] == [
-        "Review previous action items",
-        "Project Status Update",
-        "Discuss Pending Issues",
-        "Decisions Required",
-        "Timeline and Milestones",
-        "Budget and Resource Review",
-        "Next Steps and Action Items",
-    ]
-    assert result["estimated_duration_minutes"] == 105
-
-
 def test_execute_analysis_tool_rejects_oversized_text():
     from api.tools import ANALYSIS_TEXT_MAX_CHARS