feat(improve): Phase 3 task 7 — bounded, gated self-improvement loop

Stand up the deterministic self-improvement loop over the ruleset/policy
governance data. Zero-dependency and fully tested offline; detection gate intact.

- 7.1/7.2 bounded levers + validator (improve/validator.py): two levers (rule,
  policy); typed edits make pattern-text and 0-5 severity changes structurally
  impossible; EvolveValidator rejects bad status, unknown rule_ids (loop cannot add
  rules), enabled->disabled jumps (shadow staging mandatory), unresolved CWEs, and
  out-of-bounds K/MIN_SCORE, raising StrictValidationError.
- 7.3 novelty + replay + journal (improve/journal.py): a JSON round journal records
  every edit/lever/outcome; reverted edits are blocked from re-proposal (novelty);
  a replay smoke gate boots the candidate ruleset before re-benchmarking.
- 7.4 benchmark deltas -> rule_signals.json (recall side: miss; precision side: fp).
- 7.5 hard acceptance gate + byte-for-byte revert (improve/evolve.py): a round is
  accepted only if recall >= 90% AND FP < 10% AND project score did not regress AND
  no matched-finding regression; otherwise the ledger is never written (byte-for-byte
  revert). run_round/run_improvement drive rounds to convergence.
- 7.6 auto-shadow precision-draggers (enabled rules with FPs -> shadow, never
  delete), nominate coverage gaps via rule_signals, and keep the project score as the
  reward separate from the recall/FP feasibility gate (the loop cannot raise the
  score by shadowing rules that breach the gate — proven by the revert test).

Design note: the loop's safety machinery is deterministic and benchmark-driven
(the proposer is the per-rule signal); the LLM MetaAgent.evolve is the optional
richer proposer that plugs into this same validated/gated machinery when the
HarnessX extra is present (offline-untestable). This mirrors how DocuHarnessX
realizes self-improvement.

9 new tests (validator, proposer/signals, accept, byte-for-byte revert, convergence);
118 pass total, 2 construction smokes skip without the extra; ruff + mypy clean;
detection gate 93.6%/0% intact. Tasks 7.1-7.6 checked off.

Co-Authored-By: Claude Opus 4.8 <[email protected]>

src/openultrasast/improve/__init__.py

@@ -0,0 +1,30 @@
+"""Bounded, gated self-improvement loop over the ruleset/policy governance data."""
+
+from .evolve import RoundOutcome, build_rule_signals, propose_status_edits, run_improvement, run_round
+from .journal import load_journal, reverted_edit_keys
+from .validator import (
+    VALID_LEVERS,
+    EvolveBounds,
+    EvolveValidator,
+    PolicyConstantEdit,
+    RuleStatusEdit,
+    StrictValidationError,
+    edits_to_ledger,
+)
+
+__all__ = [
+    "EvolveBounds",
+    "EvolveValidator",
+    "PolicyConstantEdit",
+    "RoundOutcome",
+    "RuleStatusEdit",
+    "StrictValidationError",
+    "VALID_LEVERS",
+    "build_rule_signals",
+    "edits_to_ledger",
+    "load_journal",
+    "propose_status_edits",
+    "reverted_edit_keys",
+    "run_improvement",
+    "run_round",
+]