Local news model: ML-NEWS XAU transformer scores headlines (news_model.py, mode primary)

The owner's DeBERTa-v3-small two-head ONNX model (golden set 72/78, ~72%
agreement with DeepSeek on unseen days) replaces DeepSeek for headline
scoring. DeepSeek keeps event direction, the summary and the dashboard
endpoint.

- news_model.py: vendored ML-NEWS runtime (lazy onnxruntime/tokenizers,
  so the bot starts without them) + load-once, never-raising singleton.
- news_engine: _enrich_headline_scores mode switch (off/fallback/primary),
  _apply_model_sentiment (gold head only: xau_score + XAU entry, never
  USD; not-relevant shows 0.0; never touches _llm_sent_cache),
  _audit_with_deepseek (archive-only labels, deepseek_audit_per_day).
- Owner decision: primary mode writes nothing to logs/newsdata.db except
  audit copies — the model's output is not training data.
- app_config: news_model section (path/threads/mode/deepseek_audit_per_day)
  + NEWS_MODEL_MODE env override; validated at boot.
- news_archive: model rows carry teacher_model xau_transformer@<trained_at>.
- deploy/README-news-model.md (ops for bot + dashboard daemon), unit gets a
  commented NEWS_MODEL_MODE=off line; requirements + onnxruntime, tokenizers.
- test_all.py Module 38 (15 tests, one against the real bundle). Suite 581.
- CLAUDE.md, spec status note, handoff marked implemented.

Books unaffected: news never enters signals; no signal file touched.

news_archive.py

@@ -324,9 +324,17 @@ def build_row(headline: Any, *, teacher_model: Optional[str] = None,
               teacher_prompt_sha1: Optional[str] = None,
               origin: str = "live",
               first_seen: Optional[str] = None,
-              last_seen: Optional[str] = None) -> Optional[Dict[str, Any]]:
+              last_seen: Optional[str] = None,
+              model_teacher: Optional[str] = None) -> Optional[Dict[str, Any]]:
     """Flatten one ``NewsSentiment`` (or an equivalent object) into a row.
 
+    Provenance: an ``llm`` row stores ``teacher_model`` (the DeepSeek
+    model name); a ``model`` row stores ``model_teacher`` (the local
+    scorer's tag, ``xau_transformer@<trained_at>``, news_model.teacher_tag).
+    Both carry ``teacher_prompt_sha1``: for a model row it is the DeepSeek
+    prompt the model was distilled from, which is the truthful lineage.
+    Keyword rows carry neither.
+
     Returns None when the object carries no usable headline text."""
     text = getattr(headline, "headline", None)
     if not isinstance(text, str) or not text.strip():
@@ -349,9 +357,10 @@ def build_row(headline: Any, *, teacher_model: Optional[str] = None,
             getattr(headline, "currencies_mentioned", None)),
         "keywords_json": _json_or_none(getattr(headline, "keywords", None)),
         "score_source": source,
-        "teacher_model": teacher_model if source == "llm" else None,
+        "teacher_model": (teacher_model if source == "llm"
+                          else model_teacher if source == "model" else None),
         "teacher_prompt_sha1": (
-            teacher_prompt_sha1 if source == "llm" else None),
+            teacher_prompt_sha1 if source in ("llm", "model") else None),
         "origin": origin,
         "xau_score": _finite_score(getattr(headline, "xau_score", None)),
     }
@@ -364,7 +373,8 @@ _archives_lock = threading.Lock()
 
 def archive_headlines(headlines: Any, *, teacher_model: Optional[str] = None,
                       teacher_prompt_sha1: Optional[str] = None,
-                      origin: str = "live") -> None:
+                      origin: str = "live",
+                      model_teacher: Optional[str] = None) -> None:
     """Persist a batch of scored headlines if the news_archive config
     block enables it. NEVER raises — a logging failure must not break a
     scan."""
@@ -377,7 +387,7 @@ def archive_headlines(headlines: Any, *, teacher_model: Optional[str] = None,
         for headline in headlines:
             row = build_row(headline, teacher_model=teacher_model,
                             teacher_prompt_sha1=teacher_prompt_sha1,
-                            origin=origin)
+                            origin=origin, model_teacher=model_teacher)
             if row is not None:
                 rows.append(row)
         if not rows: