PLN's closed feedback loop: generate candidate loops with a settings profile →
grade them on the full quality rubric → score the profile → search for the
settings that maximise quality. Auto-tune the composite weights instead of
hand-guessing them.

WHY: the finder/grader/correlator exist; the composite weights W were "provisional,
calibrate with #7". This builds the machine that calibrates them against measured
grade — and measures the lift honestly rather than asserting a tuning is better.

WHAT:
- engine/autotune.py — evaluate(stems, settings) runs the finder with a profile,
  grades every produced candidate on the FULL rubric (grade.py adds dc / level /
  bass-mono / bar-consistency — features the finder's own score does NOT use, so
  tuning pulls in signal beyond seam/zc), aggregates objective = mean_grade ×
  coverage. search() sweeps random|grid and always includes the current defaults
  (loops.W) as a baseline row so the report shows lift, not just a number.
- autotune.py — CLI (--stems glob --n --method --max-stems); PII-safe aggregated
  leaderboard (counts, never filenames).
- engine/loops.py — analyze_stem now accepts an optional `weights` override
  (defaults unchanged); the grid is preloaded once per stem (weight-independent)
  so the sweep is cheap — a scarcity-minded speedup.

VALIDATION:
- 4/4 mocked harness unit tests (aggregation, empty-set, ranked+baseline-included,
  weights threaded through to the finder).
- Real end-to-end run, 2 drums stems × 8 configs: baseline objective 0.690 → best
  0.763 (+10.5%); the defaults ranked LAST of 8 — the search found real lift.

NOT DONE ON PURPOSE: the finder defaults (loops.W) are UNCHANGED. The winning
profile (seam 0.4, zc 0.05, bars=(4,8)) is from a 2-stem sample AND seam/zc are in
both the finder score and the grade (a confound) — so it's suggestive, not a
mandate. Remaining for #19: recall-vs-provenance-GT as an anti-gaming 2nd
objective, per-stem-role tuning (vocals=chops want different weights than drums),
a full-corpus + demo-corpus campaign, an extract-once/score-many speedup, THEN
adopt a validated profile as the default.