test(cp1): lock determinism + handover diagnostic invariant

tests/grid/test_predator_evade_behavior.py

@@ -0,0 +1,42 @@
+import random
+
+from proteus.grid.difficulty import Difficulty
+from proteus.grid.game import MotiveGridGame
+from proteus.grid.scenario import get_scenario
+
+
+def _build_at_handover():
+    """Build predator_evade and replay the EASY Cut pre-roll to the handover."""
+    scenario = get_scenario("predator_evade")()
+    rng = random.Random(42)
+    cut = scenario.cut_length(Difficulty.EASY)
+    game = MotiveGridGame(scenario, rng, Difficulty.EASY, max_steps=cut + 15)
+    for _ in range(cut):
+        action = scenario.cut_focal_policy(game)
+        game.apply_motive_action(action)
+        scenario.record_focal_move(action)
+    return scenario, game, cut
+
+
+def test_cut_handover_positions_are_deterministic():
+    _, game, _ = _build_at_handover()
+    # EASY layout: focal walks west from (5,3) for 2 steps -> (3,3);
+    # predator chases from (7,3) -> (5,3).
+    assert (game.focal_sprite.x, game.focal_sprite.y) == (3, 3)
+    assert (game.predator_sprite.x, game.predator_sprite.y) == (5, 3)
+
+
+def test_diagnostic_invariant_optimal_differs_from_habit_at_handover():
+    scenario, game, _ = _build_at_handover()
+    optimal = scenario.optimal_action(game)
+    habit = scenario.habit_action(game)
+    assert habit == "left"          # the inertia: keep walking west into the dead-end
+    assert optimal == "up"          # escape that maximizes BFS distance
+    assert optimal != habit         # THE measured divergence
+
+
+def test_full_replay_is_reproducible_across_two_builds():
+    _, g1, _ = _build_at_handover()
+    _, g2, _ = _build_at_handover()
+    assert (g1.focal_sprite.x, g1.focal_sprite.y) == (g2.focal_sprite.x, g2.focal_sprite.y)
+    assert (g1.predator_sprite.x, g1.predator_sprite.y) == (g2.predator_sprite.x, g2.predator_sprite.y)