feat(sleep): 3-way train/val/test split + gate_mode on|off

Data-split refactor (the anti-overfitting foundation the user asked for):
  - TaskRecord gains split∈{train,val,test} and origin∈{real,dream}.
  - assign_splits: real tasks deterministically split into val/test (disjoint);
    DREAM-augmented tasks (origin='dream') NEVER enter val/test — they only go to
    train. val gates updates; test is the final held-out measure.
  - gbrain loader maps its held-out.jsonl -> test, benchmark.jsonl -> train/val,
    so the gbrain held-out stays the true final score.
  - consolidate(): train drives reflect, val gates; adds gate_mode='off' (greedy,
    no hard filter) reporting val movement (greedy_improved/regressed/flat).
  - run_gbrain/transfer/experiment score on test (val fallback); run_gbrain gains
    --gate on|off. Legacy replay/holdout names normalized.

New test proves dream tasks never land in val/test. 21 tests pass; mock
experiment + gate=off both green.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

skillopt/sleep/experiments/run_experiment.py

@@ -42,7 +42,8 @@ from skillopt.sleep.types import TaskRecord
 def _score_holdout(backend, tasks: List[TaskRecord], skill: str, memory: str,
                    metric: str = "mixed", w: float = 0.5) -> float:
     from skillopt.sleep.consolidate import select_gate_score
-    holdout = [t for t in tasks if t.split == "holdout"] or tasks
+    # the persona experiment uses a 2-way split (train/val, no test); score on val
+    holdout = [t for t in tasks if t.split in ("val", "holdout")] or tasks
     pairs = replay_batch(backend, holdout, skill, memory)
     h, s = aggregate_scores(pairs)
     return select_gate_score(h, s, metric, w)