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