feat(sleep): multi-objective reward (accuracy/tokens/latency) + user preferences

- ReplayResult records per-rollout tokens + latency_ms; replay_one measures them
  (approximated from text length when the backend doesn't track tokens, e.g. mock).
- replay.multi_objective_reward(w_acc, w_tokens, w_latency): weighted reward so a
  skill can be optimized to be cheaper/faster, not only more accurate (cost terms
  normalized vs a reference, default = accuracy-only / backward compatible).
- Backend.preferences (free text) injected into reflect as a prior; build_backend
  attaches it (to the optimizer for dual backends). run_gbrain gains --preferences.

3 new tests (multi-objective ordering, preference injection, cost recording).
29 tests pass; mock gates + 3.8/3.12 compile green.

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

tests/test_sleep_engine.py

@@ -232,6 +232,51 @@ class TestLlmMiner(unittest.TestCase):
         self.assertEqual(make_llm_miner(EmptyBackend())([digest]), [])
 
 
+class TestMultiObjectiveAndPrefs(unittest.TestCase):
+    def test_multi_objective_reward(self):
+        from skillopt.sleep.replay import multi_objective_reward
+        from skillopt.sleep.types import ReplayResult, TaskRecord
+        t = TaskRecord(id="t", project="/p", intent="x")
+        expensive = [(t, ReplayResult(id="t", hard=1.0, tokens=4000, latency_ms=20000))]
+        cheap = [(t, ReplayResult(id="t", hard=1.0, tokens=200, latency_ms=1000))]
+        self.assertEqual(
+            multi_objective_reward(expensive, w_acc=1, w_tokens=0, w_latency=0),
+            multi_objective_reward(cheap, w_acc=1, w_tokens=0, w_latency=0),
+        )
+        re = multi_objective_reward(expensive, w_acc=1, w_tokens=1, w_latency=1)
+        rc = multi_objective_reward(cheap, w_acc=1, w_tokens=1, w_latency=1)
+        self.assertGreater(rc, re)
+
+    def test_preferences_injected_into_reflect(self):
+        from skillopt.sleep.backend import CliBackend
+        from skillopt.sleep.types import TaskRecord, ReplayResult
+        captured = {}
+
+        class CapBackend(CliBackend):
+            name = "cap"
+            def _call(self, prompt, *, max_tokens=1024):
+                captured["prompt"] = prompt
+                return "[]"
+
+        be = CapBackend()
+        be.preferences = "Prefer concise British English."
+        t = TaskRecord(id="t", project="/p", intent="x", reference_kind="rule",
+                       judge={"checks": [{"op": "contains", "arg": "z"}]})
+        be.reflect([(t, ReplayResult(id="t", hard=0.0, fail_reason="failed: contains=z"))],
+                   [], "skill", "", edit_budget=2, evolve_skill=True, evolve_memory=False)
+        self.assertIn("British English", captured["prompt"])
+
+    def test_replay_records_cost(self):
+        from skillopt.sleep.backend import MockBackend
+        from skillopt.sleep.replay import replay_one
+        from skillopt.sleep.types import TaskRecord
+        t = TaskRecord(id="t", project="/p", intent="hello world",
+                       reference_kind="exact", reference="hi")
+        r = replay_one(MockBackend(), t, "some skill text", "")
+        self.assertGreater(r.tokens, 0)
+        self.assertGreaterEqual(r.latency_ms, 0.0)
+
+
 class TestMultiRolloutAndBudget(unittest.TestCase):
     def test_rolloutset_stats(self):
         from skillopt.sleep.rollout import RolloutSet