feat(wip): soft title tier — sketch/doodle vocab + ring-loud audit (#1474)

Extends WIP title-tagging to lower-precision cues (sketch/doodle/scribble) safely.

- wip_title.py: soft matcher (word-anchored; sketchbook/kadoodle don't trip it);
  WIP_TITLE_SOFT_SOURCE + soft SQL prefilter; apply_wip_image_tags takes a source arg.
- training_data._AUTO_SOURCES += 'wip_title_soft' → the soft tier is PROVISIONAL and
  never trains the wip head (a finished "sketch" can't pollute it). Only the hard
  tier (wip_title) + manual train.
- ImportSettings.wip_soft_title_tagging_enabled (OFF by default, opt-in). Migration 0087.
- importer: hard tier wins, soft is the fallback (source wip_title_soft).
- backfill: refactored into a shared _backfill_wip_tier; hard always, soft when enabled.
- heads.soft_wip_conflict_audit + daily beat: score soft-tagged images against content
  heads, flag ring-loud ones (PresentationReview mode=process) for the review strip —
  the operator's "measure if they got falsely tagged" safety.
- api settings toggle; ImportFiltersForm soft toggle.
- tests: soft matcher pos/neg; soft source not a training positive; audit flags
  ring-loud + spares quiet.

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

backend/app/api/settings.py

@@ -49,6 +49,7 @@ _EDITABLE_FIELDS = (
     "translation_target_lang",
     "translation_min_confidence",
     "wip_title_tagging_enabled",
+    "wip_soft_title_tagging_enabled",
 )
 
 # Per-host external-download toggles — all plain booleans, validated uniformly.
@@ -91,6 +92,7 @@ async def get_import_settings():
         "translation_target_lang": row.translation_target_lang,
         "translation_min_confidence": row.translation_min_confidence,
         "wip_title_tagging_enabled": row.wip_title_tagging_enabled,
+        "wip_soft_title_tagging_enabled": row.wip_soft_title_tagging_enabled,
     })
 
 
@@ -179,6 +181,12 @@ async def update_import_settings():
         return jsonify(
             {"error": "wip_title_tagging_enabled must be a boolean"}
         ), 400
+    if "wip_soft_title_tagging_enabled" in body and not isinstance(
+        body["wip_soft_title_tagging_enabled"], bool
+    ):
+        return jsonify(
+            {"error": "wip_soft_title_tagging_enabled must be a boolean"}
+        ), 400
 
     async with get_session() as session:
         row = await ImportSettings.load(session)

backend/app/celery_app.py

@@ -179,6 +179,11 @@ def make_celery() -> Celery:
                 "schedule": 86400.0,  # auto-tag wip/editor process art (#1464);
                 # no-op unless process_auto_apply_enabled (opt-in)
             },
+            "soft-wip-conflict-audit-daily": {
+                "task": "backend.app.tasks.ml.scheduled_soft_wip_conflict_audit",
+                "schedule": 86400.0,  # flag ring-loud soft-WIP (sketch/doodle) tags
+                # for review (#1474); no-op with no content heads
+            },
             "prune-presentation-reviews-daily": {
                 "task": "backend.app.tasks.ml.prune_presentation_reviews",
                 "schedule": 86400.0,  # retention: drop resolved review flags >30d

backend/app/models/import_settings.py

@@ -126,6 +126,13 @@ class ImportSettings(Base):
     wip_title_tagging_enabled: Mapped[bool] = mapped_column(
         Boolean, nullable=False, default=True, server_default="true",
     )
+    # Soft WIP title tier (#1474): also tag sketch/doodle/scribble titles, but with
+    # a PROVISIONAL source (`wip_title_soft`) that never trains the head, since these
+    # are lower-precision (a finished "sketch" isn't WIP). OFF by default — a lower-
+    # precision tier is opt-in (the ring-loud audit surfaces false positives).
+    wip_soft_title_tagging_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=False, server_default="false",
+    )
 
     @classmethod
     async def load(cls, session) -> ImportSettings:

backend/app/services/importer.py

@@ -47,7 +47,14 @@ from .attachment_store import AttachmentStore
 from .audits import single_color
 from .link_extract import extract_external_links
 from .thumbnailer import Thumbnailer
-from .wip_title import apply_wip_image_tags, matches_wip_title, resolve_wip_tag_id
+from .wip_title import (
+    WIP_TITLE_SOFT_SOURCE,
+    WIP_TITLE_SOURCE,
+    apply_wip_image_tags,
+    matches_soft_wip_title,
+    matches_wip_title,
+    resolve_wip_tag_id,
+)
 
 log = logging.getLogger(__name__)
 
@@ -999,7 +1006,9 @@ class Importer:
         removal sticks. The existing catalogue is covered separately by the
         operator-triggered backfill sweep. Gated by the settings toggle, and
         best-effort: any failure is logged, never allowed to fail the import."""
-        if not self.settings.wip_title_tagging_enabled:
+        hard_on = self.settings.wip_title_tagging_enabled
+        soft_on = self.settings.wip_soft_title_tagging_enabled
+        if not (hard_on or soft_on):
             return
         if record.primary_post_id is None:
             return
@@ -1007,13 +1016,22 @@ class Importer:
             title = self.session.execute(
                 select(Post.post_title).where(Post.id == record.primary_post_id)
             ).scalar_one_or_none()
-            if not matches_wip_title(title):
+            # HARD tier ("WIP"/"work in progress") wins — higher precision, and it
+            # trains the head; SOFT (sketch/doodle, #1474) is the provisional fallback
+            # that never trains (source wip_title_soft).
+            if hard_on and matches_wip_title(title):
+                source = WIP_TITLE_SOURCE
+            elif soft_on and matches_soft_wip_title(title):
+                source = WIP_TITLE_SOFT_SOURCE
+            else:
                 return
             if self._wip_tag_id is _UNSET:
                 self._wip_tag_id = resolve_wip_tag_id(self.session)
             if self._wip_tag_id is None:
                 return
-            apply_wip_image_tags(self.session, [record.id], self._wip_tag_id)
+            apply_wip_image_tags(
+                self.session, [record.id], self._wip_tag_id, source=source
+            )
         except Exception as exc:  # noqa: BLE001 — a tag must never fail an import
             log.warning(
                 "wip-title auto-tag failed for image %s: %s", record.id, exc

backend/app/services/ml/heads.py

@@ -947,6 +947,74 @@ def system_tag_auto_apply_sweep(
     }
 
 
+def soft_wip_conflict_audit(session: Session, dry_run: bool = False) -> dict:
+    """Ring-loud audit for the SOFT WIP-title cohort (#1474). Images auto-tagged
+    `wip` from a low-precision sketch/doodle title (source='wip_title_soft') that ALSO
+    score >= the process conflict threshold on a content head are probably FINISHED
+    art mis-tagged as process — flag them (PresentationReview, mode='process') so the
+    review strip surfaces them ("also looks like <X>", Keep tag / Remove tag). Does
+    NOT remove the tag; the operator decides. No-op when there are no content heads.
+    numpy-only. Returns {n_scanned, n_flagged}."""
+    import numpy as np
+    from sqlalchemy.dialects.postgresql import insert as pg_insert
+
+    from ..wip_title import WIP_TITLE_SOFT_SOURCE, resolve_wip_tag_id
+
+    settings = _settings(session)
+    ver = settings.embedder_model_version
+    conflict_thr = float(settings.process_conflict_threshold)
+    conf = _conflict_heads(session, ver)
+    wip_id = resolve_wip_tag_id(session)
+    if not conf or wip_id is None:
+        return {"n_scanned": 0, "n_flagged": 0}
+    Wc = np.vstack([np.asarray(r.weights, dtype=np.float32) for r in conf])
+    bc = np.asarray([r.bias for r in conf], dtype=np.float32)
+    conf_tag_ids = [r.tag_id for r in conf]
+
+    soft_ids = [iid for (iid,) in session.execute(
+        select(image_tag.c.image_record_id)
+        .where(image_tag.c.tag_id == wip_id)
+        .where(image_tag.c.source == WIP_TITLE_SOFT_SOURCE)
+    )]
+    # Skip images already flagged for this tag (idempotent re-runs).
+    flagged = {iid for (iid,) in session.execute(
+        select(PresentationReview.image_record_id)
+        .where(PresentationReview.tag_id == wip_id)
+    )}
+    soft_ids = [i for i in soft_ids if i not in flagged]
+
+    n_flagged = 0
+    scanned = 0
+    for start in range(0, len(soft_ids), _AUTO_APPLY_CHUNK):
+        chunk = soft_ids[start:start + _AUTO_APPLY_CHUNK]
+        emb = _load_embeddings(session, chunk)
+        cids = [i for i in chunk if i in emb]
+        if not cids:
+            continue
+        scanned += len(cids)
+        Xn = _l2norm(np.vstack([emb[i] for i in cids]).astype(np.float32), np)
+        cprobs = 1.0 / (1.0 + np.exp(-(Xn @ Wc.T + bc)))
+        max_c = cprobs.max(axis=1)
+        arg_c = cprobs.argmax(axis=1)
+        for k in range(len(cids)):
+            if float(max_c[k]) >= conflict_thr:
+                n_flagged += 1
+                if not dry_run:
+                    session.execute(
+                        pg_insert(PresentationReview)
+                        .values(
+                            image_record_id=cids[k], tag_id=wip_id,
+                            conflict_tag_id=conf_tag_ids[int(arg_c[k])],
+                            conflict_score=float(max_c[k]),
+                            mode="process",
+                        )
+                        .on_conflict_do_nothing()
+                    )
+        if not dry_run:
+            session.commit()
+    return {"n_scanned": scanned, "n_flagged": n_flagged}
+
+
 def retract_auto_applied_heads(session: Session) -> int:
     """Soft auto-apply (milestone 139): re-score every standing source='head_auto'
     tag against its CURRENT head and REMOVE the ones now BELOW the head's

backend/app/services/ml/training_data.py

@@ -32,7 +32,12 @@ from ...models.tag import image_tag
 # `process_auto` (#1464): wip/editor screenshot applied by the process sweep are
 # ALSO provisional — the head must learn only from title (`wip_title`) + manual
 # labels, never its own auto-applied output, or it would runaway (operator 2026-07-12).
-_AUTO_SOURCES = ("head_auto", "ccip_auto", "ml_auto", "presentation_auto", "process_auto")
+# `wip_title_soft` (#1474): the soft title tier (sketch/doodle) is LOW-precision, so
+# it's provisional too — a finished piece titled "sketch" must not train the wip head.
+_AUTO_SOURCES = (
+    "head_auto", "ccip_auto", "ml_auto", "presentation_auto", "process_auto",
+    "wip_title_soft",
+)
 
 
 def _hygiene_excluded_ids(session: Session) -> set[int]:

backend/app/services/wip_title.py

@@ -27,7 +27,13 @@ from ..models.tag import WIP_SYSTEM_TAG, Tag, image_tag
 
 # image_tag.source stamped on title-heuristic WIP tags — distinct from the other
 # apply sources so provenance stays legible and a future undo can target only these.
+# HARD tier ("WIP"/"work in progress") is high-precision → trains the wip head.
 WIP_TITLE_SOURCE = "wip_title"
+# SOFT tier (sketch/doodle/scribble, #1474) is LOWER-precision — a finished "sketch"
+# is often not WIP. This source is PROVISIONAL (in training_data._AUTO_SOURCES) so it
+# NEVER trains the wip head; a soft-tagged image that also looks like real content is
+# surfaced by the ring-loud audit for review.
+WIP_TITLE_SOFT_SOURCE = "wip_title_soft"
 
 # A standalone "WIP" / "W.I.P" token, or the phrase "work in progress"
 # (space/underscore/hyphen separated). The letter-boundary lookarounds are what
@@ -39,11 +45,20 @@ _WIP_RE = re.compile(
     re.IGNORECASE,
 )
 
-# Coarse SQL prefilter for the backfill sweep — narrows the post scan to rows that
+# Soft tier: sketch / doodle / scribble (+ plurals), letter-boundary anchored so
+# "sketchbook" / "kadoodle" don't trip it. Deliberately conservative — recall is
+# secondary because the soft source doesn't train the head and the ring-loud audit
+# catches false positives.
+_SOFT_WIP_RE = re.compile(
+    r"(?<![A-Za-z])(?:sketch|sketches|doodle|doodles|scribble|scribbles)(?![A-Za-z])",
+    re.IGNORECASE,
+)
+
+# Coarse SQL prefilters for the backfill sweep — narrow the post scan to rows that
 # COULD match before the precise regex confirms. Case-insensitive ILIKE patterns.
-# MUST stay a SUPERSET of _WIP_RE (every regex match contains "wip" or
-# "work…progress") or the sweep would silently miss posts.
+# Each MUST stay a SUPERSET of its regex or the sweep would silently miss posts.
 WIP_TITLE_SQL_PREFILTER = ("%wip%", "%work%progress%")
+SOFT_WIP_TITLE_SQL_PREFILTER = ("%sketch%", "%doodle%", "%scribble%")
 
 # Chunk bulk inserts so a large sweep can't blow past psycopg's 65535-parameter
 # ceiling (3 params/row → ~21k rows max; 5k stays comfortably under).
@@ -51,12 +66,19 @@ _INSERT_CHUNK = 5000
 
 
 def matches_wip_title(title: str | None) -> bool:
-    """True when a post title explicitly marks it work-in-progress."""
+    """True when a post title explicitly marks it work-in-progress (HARD tier)."""
     if not title:
         return False
     return _WIP_RE.search(title) is not None
 
 
+def matches_soft_wip_title(title: str | None) -> bool:
+    """True when a title carries a SOFT WIP cue (sketch/doodle/scribble, #1474)."""
+    if not title:
+        return False
+    return _SOFT_WIP_RE.search(title) is not None
+
+
 def resolve_wip_tag_id(session: Session) -> int | None:
     """The seeded ``wip`` system tag's id (migration 0075), or None if absent."""
     return session.execute(
@@ -64,8 +86,10 @@ def resolve_wip_tag_id(session: Session) -> int | None:
     ).scalar_one_or_none()
 
 
-def apply_wip_image_tags(session: Session, image_ids, tag_id: int) -> int:
-    """Attach ``tag_id`` (source='wip_title') to each image id, idempotently —
+def apply_wip_image_tags(
+    session: Session, image_ids, tag_id: int, *, source: str = WIP_TITLE_SOURCE
+) -> int:
+    """Attach ``tag_id`` (stamped with ``source``) to each image id, idempotently —
     never disturbs an existing tag or its source. Returns the number of image_tag
     rows newly inserted. Does NOT commit.
 
@@ -92,7 +116,7 @@ def apply_wip_image_tags(session: Session, image_ids, tag_id: int) -> int:
         session.execute(
             pg_insert(image_tag)
             .values([
-                {"image_record_id": iid, "tag_id": tag_id, "source": WIP_TITLE_SOURCE}
+                {"image_record_id": iid, "tag_id": tag_id, "source": source}
                 for iid in to_insert
             ])
             .on_conflict_do_nothing(index_elements=["image_record_id", "tag_id"])

backend/app/tasks/maintenance.py

@@ -1047,6 +1047,41 @@ def cleanup_old_download_events() -> int:
         return result.rowcount or 0
 
 
+def _backfill_wip_tier(session, tag_id, prefilter, matcher, source) -> int:
+    """One keyset-paginated pass over posts whose title matches a WIP tier, applying
+    `tag_id` (stamped `source`) to their images. Shared by the hard + soft tiers
+    (#1458 / #1474). Coarse `prefilter` (ILIKE superset) narrows the scan; the precise
+    `matcher` confirms. Idempotent-additive (ON CONFLICT DO NOTHING). Returns the row
+    count newly applied."""
+    from ..models import Post
+    from ..models.image_provenance import ImageProvenance
+    from ..services.wip_title import apply_wip_image_tags
+
+    applied = 0
+    last_id = 0
+    while True:
+        rows = session.execute(
+            select(Post.id, Post.post_title)
+            .where(Post.id > last_id)
+            .where(Post.post_title.is_not(None))
+            .where(or_(*[Post.post_title.ilike(p) for p in prefilter]))
+            .order_by(Post.id.asc())
+            .limit(WIP_BACKFILL_PAGE)
+        ).all()
+        if not rows:
+            break
+        last_id = rows[-1][0]
+        match_ids = [pid for pid, title in rows if matcher(title)]
+        if match_ids:
+            image_ids = session.execute(
+                select(ImageProvenance.image_record_id)
+                .where(ImageProvenance.post_id.in_(match_ids))
+            ).scalars().all()
+            applied += apply_wip_image_tags(session, image_ids, tag_id, source=source)
+            session.commit()
+    return applied
+
+
 @celery.task(
     name="backend.app.tasks.maintenance.backfill_wip_title_tags",
     # Coarse-prefiltered scan over posts; the candidate set is small on a typical
@@ -1054,32 +1089,32 @@ def cleanup_old_download_events() -> int:
     soft_time_limit=1800, time_limit=2100,
 )
 def backfill_wip_title_tags() -> int:
-    """Scan EXISTING posts for explicit WIP titles and apply the `wip` system tag
-    to their images — the operator-triggered back-catalogue catch-up for
-    title-based WIP tagging (task #1458). New imports are tagged live by the
-    importer; this covers everything already in the library.
+    """Scan EXISTING posts for WIP titles and apply the `wip` system tag to their
+    images — the operator-triggered back-catalogue catch-up (task #1458 hard tier +
+    #1474 soft tier). New imports are tagged live by the importer; this covers the
+    existing library.
 
-    Keyset-paginated over posts (restart-safe). A coarse SQL prefilter narrows to
-    titles that COULD match; the precise regex (matches_wip_title) confirms.
-    Idempotent-additive (ON CONFLICT DO NOTHING) — never disturbs an existing tag.
+    HARD tier ("WIP"/"work in progress") always runs (the operator triggered the
+    scan); the SOFT tier (sketch/doodle, provisional source) runs only when
+    wip_soft_title_tagging_enabled, AFTER hard so a title matching both keeps the
+    trained hard tag (ON CONFLICT DO NOTHING). Keyset-paginated, restart-safe.
 
-    Deliberately NOT scheduled as a beat: a periodic re-run would re-apply to
-    matching posts and silently undo a manual WIP removal, so it stays an explicit
-    operator action (Settings → "Scan existing posts for WIP titles"). Returns the
-    number of image-tag rows newly applied.
+    Deliberately NOT scheduled as a beat: a periodic re-run would re-apply to matching
+    posts and silently undo a manual WIP removal, so it stays an explicit operator
+    action (Settings → "Scan existing posts for WIP titles"). Returns rows applied.
     """
-    from ..models import Post
-    from ..models.image_provenance import ImageProvenance
+    from ..models import ImportSettings
     from ..services.wip_title import (
+        SOFT_WIP_TITLE_SQL_PREFILTER,
+        WIP_TITLE_SOFT_SOURCE,
+        WIP_TITLE_SOURCE,
         WIP_TITLE_SQL_PREFILTER,
-        apply_wip_image_tags,
+        matches_soft_wip_title,
         matches_wip_title,
         resolve_wip_tag_id,
     )
 
     SessionLocal = _sync_session_factory()
-    applied = 0
-    last_id = 0
     with SessionLocal() as session:
         tag_id = resolve_wip_tag_id(session)
         if tag_id is None:
@@ -1087,30 +1122,16 @@ def backfill_wip_title_tags() -> int:
                 "backfill_wip_title_tags: no `wip` system tag present; nothing to do"
             )
             return 0
-        like_a, like_b = WIP_TITLE_SQL_PREFILTER
-        while True:
-            rows = session.execute(
-                select(Post.id, Post.post_title)
-                .where(Post.id > last_id)
-                .where(Post.post_title.is_not(None))
-                .where(or_(
-                    Post.post_title.ilike(like_a),
-                    Post.post_title.ilike(like_b),
-                ))
-                .order_by(Post.id.asc())
-                .limit(WIP_BACKFILL_PAGE)
-            ).all()
-            if not rows:
-                break
-            last_id = rows[-1][0]
-            match_ids = [pid for pid, title in rows if matches_wip_title(title)]
-            if match_ids:
-                image_ids = session.execute(
-                    select(ImageProvenance.image_record_id)
-                    .where(ImageProvenance.post_id.in_(match_ids))
-                ).scalars().all()
-                applied += apply_wip_image_tags(session, image_ids, tag_id)
-                session.commit()
+        settings = ImportSettings.load_sync(session)
+        applied = _backfill_wip_tier(
+            session, tag_id, WIP_TITLE_SQL_PREFILTER, matches_wip_title,
+            WIP_TITLE_SOURCE,
+        )
+        if settings.wip_soft_title_tagging_enabled:
+            applied += _backfill_wip_tier(
+                session, tag_id, SOFT_WIP_TITLE_SQL_PREFILTER, matches_soft_wip_title,
+                WIP_TITLE_SOFT_SOURCE,
+            )
     if applied:
         log.info("backfill_wip_title_tags: applied wip to %d image(s)", applied)
     return applied

backend/app/tasks/ml.py

@@ -629,6 +629,24 @@ def scheduled_process_auto_apply() -> str:
     return f"applied={result['n_applied']} flagged={result['n_flagged']}"
 
 
+@celery.task(
+    name="backend.app.tasks.ml.scheduled_soft_wip_conflict_audit",
+    soft_time_limit=1800, time_limit=2100,
+)
+def scheduled_soft_wip_conflict_audit() -> str:
+    """Ring-loud audit over the SOFT WIP-title cohort (#1474) — flag sketch/doodle
+    auto-tags that ALSO look like real content for review. No-op when there are no
+    content heads; idempotent (already-flagged images skipped). Runs regardless of
+    the process-sweep toggle, since soft-title tags come from the importer, not that
+    sweep. Wall-clock bounded by the task time limits."""
+    from ..services.ml.heads import soft_wip_conflict_audit
+
+    SessionLocal = _sync_session_factory()
+    with SessionLocal() as session:
+        result = soft_wip_conflict_audit(session)
+    return f"scanned={result['n_scanned']} flagged={result['n_flagged']}"
+
+
 @celery.task(name="backend.app.tasks.ml.prune_presentation_reviews")
 def prune_presentation_reviews() -> str:
     """Retention (rule 89): drop RESOLVED presentation-review flags older than 30