presentation_auto_apply_sweep fires banner/editor-screenshot heads at the FLAT
presentation threshold (source=presentation_auto). Two guards: (1) hard-skip any
image already carrying a human/confirmed content tag — you valued it, so the model
can't bury it; (2) if an auto-hide ALSO scores >= presentation_conflict_threshold
on a content head, hide it but record a PresentationReview row (conflict tag +
score) for the Hidden view.
_auto_apply_heads now excludes system tags, so a graduated wip/banner can't fire
via the content path (and wip never auto-applies at all). presentation_auto added
to _AUTO_SOURCES so auto-hidden chrome never self-trains. Tests: applies,
hard-skip valued, conflict-flag, disabled no-op, ignores wip, content-path
excludes system. Settings UI + scheduling land next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Makes auto-apply truly "soft" for heads: _ids_with_tag (head positives) and
_eligible_tag_ids (graduation count) now count human-applied + operator-confirmed
tags only, via a shared _AUTO_SOURCES (head_auto/ccip_auto/ml_auto) exclusion.
Unconfirmed auto-applied tags no longer train the head that judges them, so a
misfire can't reinforce itself and the retraction sweep can actually drop it.
Confirming a tag (TagPositiveConfirmation) promotes it to a positive AND protects
it from retraction. sklearn-free tests. CCIP reference exclusion is the companion
piece, next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Step 2 of milestone #128. _hygiene_excluded_ids (training_data.py) is the
one shared predicate: images carrying any system tag are dropped from
every OTHER concepts head training — not positives (a rough wip tagged
as a character drags the head toward generic-sketch) and not rejection
or sampled negatives (a wip OF character X is not evidence against X).
A system tags own head trains on them unfiltered; that is what makes
auto-flagging banners work. Selection is split out of train_head as the
sklearn-free head_training_ids so CI (no sklearn) can pin the behavior.
CCIP: reference prototypes skip hygiene-tagged images — a faceless wip
figure region must never become an identity reference — and the ref
cache signature now counts hygiene applications, since tagging an image
wip changes the reference set without touching character/region counts.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding +
trained head' spine; the operator accepted the tagging system and dropped the
harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint,
tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry,
TagEvalRun model + table (migration 0073), and its tests.
The eval's data loaders + metric helpers were NOT eval-specific — the nightly
heads trainer runs on them — so they moved verbatim to
services/ml/training_data.py (heads.py import updated; behavior unchanged).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
bvandeusen