diff --git a/Game/bots/20260817-0750-gen5-s5-intercepts.json b/Game/bots/20260817-0750-gen5-s5-intercepts.json
deleted file mode 100644
index fe2826ad..00000000
--- a/Game/bots/20260817-0750-gen5-s5-intercepts.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/Game/bots/20260817-1701-gen5-s5-intercepts-retry1.json b/Game/bots/20260817-1701-gen5-s5-intercepts-retry1.json
deleted file mode 100644
index 8061502e..00000000
--- a/Game/bots/20260817-1701-gen5-s5-intercepts-retry1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/Game/bots/20260818-0210-gen5-s5-intercepts-retry2.json b/Game/bots/20260818-0210-gen5-s5-intercepts-retry2.json
deleted file mode 100644
index cf16133e..00000000
--- a/Game/bots/20260818-0210-gen5-s5-intercepts-retry2.json
+++ /dev/null
@@ -1 +0,0 @@
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1.1122229099273682, -1.6883182525634766, -0.6878098845481873, 0.3998478055000305, 0.13158196210861206, -1.165747046470642, -1.006307601928711, 0.4301455318927765, -0.7712000012397766, -1.348528504371643, -1.3477963209152222, -2.224431276321411]], "biases": [-1.1553573608398438, 1.4050331115722656, 0.8777029514312744, 0.30059587955474854, -0.08080991357564926, 0.3529224097728729, 1.188995122909546, 0.7545241117477417, 0.41457659006118774, -1.8497862815856934, 0.19137269258499146, 0.5412134528160095, 1.3274099826812744, 0.3038165867328644, -1.0942692756652832, -0.800201952457428, 0.8641376495361328, 0.5422167778015137, 0.702836275100708, -0.9038190245628357, 2.236013412475586, 2.7624588012695312, 0.502540647983551, -0.7514896392822266, -4.415685653686523, 0.09254661947488785, 0.6916828751564026, 1.427919864654541, 2.204965353012085, -4.229736328125, -0.6481443047523499, 0.6482481360435486], "activation": "linear"}], "action_space": {"type": "multi_discrete", "heads": [{"name": "rot_x", "bins": [-1.0, -0.5, 0.0, 0.5, 1.0]}, {"name": "rot_y", "bins": [-1.0, -0.5, 0.0, 0.5, 1.0]}, {"name": "rot_z", "bins": [-1.0, -0.5, 0.0, 0.5, 1.0]}, {"name": "thrust_x", "bins": [-1.0, -0.5, 0.0, 0.5, 1.0]}, {"name": "thrust_y", "bins": [-0.5, 0.0, 0.45, 0.75, 1.0]}, {"name": "thrust_z", "bins": [-1.0, -0.5, 0.0, 0.5, 1.0]}, {"name": "turbo", "bins": [0.0, 1.0]}]}}
\ No newline at end of file
diff --git a/Game/scripts/ship_ai_controller.gd b/Game/scripts/ship_ai_controller.gd
index 3e478a62..ca34e3ae 100644
--- a/Game/scripts/ship_ai_controller.gd
+++ b/Game/scripts/ship_ai_controller.gd
@@ -46,6 +46,20 @@ extends AIController3D
 # frozen checkpoints keep their original objective; generation 5 handling
 # turns it on while reducing the orientation-agnostic term.
 @export var forward_velocity_to_ball_weight := 0.0
+# Aerial mirror of forward_velocity_to_ball_weight: nose-first closing speed
+# on the ball, active above GROUND_HANDLING_HEIGHT instead of below it (the
+# two are mutually exclusive by altitude, never both active on the same
+# tick). Generation 5's intercepts stage added air_intercept_chance without
+# an airborne equivalent of the term that actually solved ground handling;
+# above 3m the only remaining approach incentive was the generic, orientation
+# -agnostic velocity_to_ball_weight (0.02-0.04), which three consecutive
+# 60M-step attempts (180M cumulative, resuming each time) showed produces
+# zero learnable gradient toward touching an aerial ball at all —
+# productive_air_touch_fraction stayed exactly 0.0 the whole time while every
+# other metric kept improving on the same budget. Uses the full 3D nose
+# vector rather than the planar-only one, since a real aerial requires
+# pitching away from level.
+@export var air_approach_weight := 0.0
 @export var ball_velocity_to_goal_weight := 0.004
 # Per-tick penalty scaled by distance to the ball (full value at the arena's
 # far diagonal, 0 on top of the ball). Run04 lesson: with idling worth a flat
@@ -357,6 +371,21 @@ func _physics_process(delta):
 			reward += forward_velocity_to_ball_weight * forward_speed * facing_ball \
 				* approach_uprightness * handling_ground_factor
 
+	# Aerial shaping: nose-first 3D closing speed on the ball (see
+	# air_approach_weight). Mirrors the ground block above but with the full
+	# nose vector instead of the planar one, and no uprightness multiplier —
+	# a genuine aerial approach requires pitching away from level, so paying
+	# only while upright would oppose the exact behaviour this rewards.
+	if air_approach_weight > 0.0 and ship.global_position.y >= GROUND_HANDLING_HEIGHT \
+			and to_ball.length_squared() > 0.0001:
+		var nose_forward := -ship.global_transform.basis.z
+		if nose_forward.length_squared() > 0.0001:
+			nose_forward = nose_forward.normalized()
+			var to_ball_dir := to_ball.normalized()
+			var air_facing_ball: float = maxf(nose_forward.dot(to_ball_dir), 0.0)
+			var air_closing_speed: float = maxf(ship.linear_velocity.dot(to_ball_dir), 0.0) / ship.max_speed
+			reward += air_approach_weight * air_closing_speed * air_facing_ball
+
 	# Dense penalty: distance to the ball, so idling far away bleeds reward
 	# instead of scoring a safe zero (see ball_distance_penalty).
 	if ball_distance_penalty > 0.0:
diff --git a/Game/scripts/training_mode.gd b/Game/scripts/training_mode.gd
index 29993087..e13b79f4 100644
--- a/Game/scripts/training_mode.gd
+++ b/Game/scripts/training_mode.gd
@@ -265,7 +265,7 @@ const TRAINING_MODE_OVERRIDES := [
 const SHIP_AI_OVERRIDES := [
 	"ball_touch_reward", "ball_touch_cooldown_ticks", "ball_touch_direction_floor",
 	"velocity_to_ball_weight", "ball_velocity_to_goal_weight", "ball_distance_penalty",
-	"forward_velocity_to_ball_weight", "wall_contact_penalty", "tilt_penalty",
+	"forward_velocity_to_ball_weight", "air_approach_weight", "wall_contact_penalty", "tilt_penalty",
 	"ground_tilt_penalty", "non_forward_penalty", "grounded_upright_reward",
 	"speed_reward_weight", "time_penalty", "airborne_penalty",
 ]
@@ -323,6 +323,7 @@ func _ai_default(name: String) -> Variant:
 		"ball_touch_direction_floor": return 0.3
 		"velocity_to_ball_weight": return 0.02
 		"forward_velocity_to_ball_weight": return 0.0
+		"air_approach_weight": return 0.0
 		"ball_velocity_to_goal_weight": return 0.004
 		"ball_distance_penalty": return 0.002
 		"wall_contact_penalty": return 0.0025
diff --git a/TRAINING.md b/TRAINING.md
index 4b293fe0..87341100 100644
--- a/TRAINING.md
+++ b/TRAINING.md
@@ -639,6 +639,29 @@ marking anything as passing, so the run dies immediately with `RuntimeError:
 No passing generation-5 stage index 0`. Note that retry2 already clears Stage
 5's own 0.75 goal-rate floor; the 0.80 Stage-4 figure was always the stricter
 of the two.
+
+**Stage 5 (`intercepts`) blocked after its own three attempts on 2026-08-18**,
+all on the same single floor: `rollout/productive_air_touch_fraction` stayed
+exactly 0.0 across a continuous 180M-step lineage (retries resume the
+previous attempt's checkpoint, so this is one training run, not three), while
+`air_touch_fraction` sat at noise level (0.00008 → 0.00006 → 0.00006) and
+`goal_rate`/`upright_fraction`/`forward_motion_fraction` all kept improving on
+the same budget — a dead-flat metric next to ones that keep moving, the same
+missing-mechanism signature as Stage 4's original plateau, not a slow-learning
+one. The cause: `forward_velocity_to_ball_weight`, the term that actually
+taught ground pursuit, is hard-gated below `GROUND_HANDLING_HEIGHT` and does
+nothing in the air, so Stage 5's `air_intercept_chance` was asking for aerial
+pursuit with only the generic, orientation-agnostic `velocity_to_ball_weight`
+(0.04) to learn it from. `air_approach_weight` (`ship_ai_controller.gd`) adds
+the airborne mirror — nose-first 3D closing speed on the ball, active above
+`GROUND_HANDLING_HEIGHT`, no uprightness multiplier since a real aerial
+requires pitching away from level — set to 0.15 to match
+`forward_velocity_to_ball_weight`'s proven magnitude, and folded into
+`HANDLING_REWARD_FLAGS` so Stage 6 inherits it too. The three blocked attempts
+were deleted and Stage 5 restarts from Stage 4's checkpoint with the new term,
+same reasoning as every previous mechanism change: don't resume a policy
+shaped by an absent term into one where it now exists.
+
 Stage 6's `league` opponent mode samples a historical exported policy at each
 episode reset. Each later stage preserves the preceding shaping and adds one
 new difficulty.
diff --git a/training/checkpoints/20260817-0750-gen5-s5-intercepts/final.zip b/training/checkpoints/20260817-0750-gen5-s5-intercepts/final.zip
deleted file mode 100644
index 8fc3043c..00000000
Binary files a/training/checkpoints/20260817-0750-gen5-s5-intercepts/final.zip and /dev/null differ
diff --git a/training/checkpoints/20260817-1701-gen5-s5-intercepts-retry1/final.zip b/training/checkpoints/20260817-1701-gen5-s5-intercepts-retry1/final.zip
deleted file mode 100644
index 20be3205..00000000
Binary files a/training/checkpoints/20260817-1701-gen5-s5-intercepts-retry1/final.zip and /dev/null differ
diff --git a/training/checkpoints/20260818-0210-gen5-s5-intercepts-retry2/final.zip b/training/checkpoints/20260818-0210-gen5-s5-intercepts-retry2/final.zip
deleted file mode 100644
index 0b6758ad..00000000
Binary files a/training/checkpoints/20260818-0210-gen5-s5-intercepts-retry2/final.zip and /dev/null differ
diff --git a/training/generation5.py b/training/generation5.py
index 6baaa11b..68b58f28 100644
--- a/training/generation5.py
+++ b/training/generation5.py
@@ -151,9 +151,40 @@ STANDING_ARGS = ["--ent-coef", "0.01", "--entropy-floor"]
 # floor, and air-drill-chance goes to 0. The reward terms already built
 # are left exactly as they were — they should finally pull in a direction
 # the ship can go.
+#
+# Round 7 (2026-08-18): Stage 4 closed by human override (see TRAINING.md).
+# Stage 5 (intercepts) then blocked all three attempts on the same single
+# floor every time — rollout/productive_air_touch_fraction stayed exactly
+# 0.0 across a continuous 180M-step lineage (each retry resumes the
+# previous attempt's checkpoint, not a fresh run), while air_touch_fraction
+# sat at noise level (0.00008 -> 0.00006 -> 0.00006) and goal_rate/
+# upright_fraction/forward_motion_fraction all kept improving on the same
+# budget. A dead-flat metric across that much continued training, next to
+# metrics that keep moving, is the missing-mechanism signature from Round 6
+# again, not a slow-learning one: forward_velocity_to_ball_weight -- the
+# term that actually solved ground handling -- is hard-gated to
+# ship.global_position.y < GROUND_HANDLING_HEIGHT and does nothing in the
+# air, so air_intercept_chance (added for Stage 5) was asking for aerial
+# pursuit with only the generic, orientation-agnostic velocity_to_ball_
+# weight (0.04) to learn it from -- the same class of gap as Stage 4's
+# missing ground-tilt/non-forward pressure before those were added.
+#
+# air_approach_weight (ship_ai_controller.gd) is the airborne mirror:
+# nose-first 3D closing speed on the ball, active above
+# GROUND_HANDLING_HEIGHT instead of below it (mutually exclusive with
+# forward_velocity_to_ball_weight by altitude), with no uprightness
+# multiplier since a real aerial requires pitching away from level. Set to
+# 0.15 to match forward_velocity_to_ball_weight's proven-effective
+# magnitude; added to HANDLING_REWARD_FLAGS (not just Stage 5's flags) so
+# it also carries into Stage 6, which reuses these flags and its own
+# air_intercept_chance. Stage 5 restarts from Stage 4's checkpoint rather
+# than continuing retry2's, same reasoning as every previous mechanism
+# change in this file: don't resume a policy shaped by an absent term into
+# one where it now exists.
 HANDLING_REWARD_FLAGS = [
     "--velocity-to-ball-weight", "0.04",
     "--forward-velocity-to-ball-weight", "0.15",
+    "--air-approach-weight", "0.15",
     "--ball-distance-penalty", "0.01",
     "--ball-touch-reward", "0.7",
     "--ball-velocity-to-goal-weight", "0.06",
diff --git a/training/generation5_state.json b/training/generation5_state.json
index d012a72a..fd242014 100644
--- a/training/generation5_state.json
+++ b/training/generation5_state.json
@@ -1,7 +1,7 @@
 {
   "stage_index": 1,
-  "attempt": 2,
-  "status": "blocked",
+  "attempt": 0,
+  "status": "in_progress",
   "log": [
     {
       "stage_index": 0,
@@ -258,254 +258,6 @@
         ],
         "reason": "Human override. Only miss was the training goal-rate floor, at 0.7731 vs 0.80; every evaluation gate passed (65-22-13 vs promoted/easy.json, 87% non-draw, 12.6% physical-side imbalance) and the goal rate improved monotonically across all three attempts (0.537 -> 0.683 -> 0.773). Also promoted to Game/bots/promoted/medium.json on the same evidence. Stage 5 resumes from this checkpoint."
       }
-    },
-    {
-      "stage_index": 1,
-      "stage_number": 5,
-      "stage_name": "intercepts",
-      "experiment": "20260817-0750-gen5-s5-intercepts",
-      "attempt": 0,
-      "telemetry_tail": {
-        "rollout/air_touch_fraction": 7.999999821186065e-05,
-        "rollout/airborne_fraction": 0.24998261865973473,
-        "rollout/ep_len_mean": 106.38228009033203,
-        "rollout/ep_rew_mean": 10.283133600234985,
-        "rollout/forward_motion_fraction": 0.3337678092420101,
-        "rollout/goal_rate": 0.7703999981880189,
-        "rollout/grounded_upright_fraction": 0.24178000067174435,
-        "rollout/mean_altitude": 3.19321390914917,
-        "rollout/productive_air_touch_fraction": 0.0,
-        "rollout/upright_fraction": 0.7911632860898972,
-        "rollout/vertical_thrust_mean": 0.03387699818262831
-      },
-      "telemetry_failures": [
-        "rollout/productive_air_touch_fraction=0.0000 < 0.0050"
-      ],
-      "evaluation_goal_failures": [],
-      "side_balance_failures": [
-        "20260816-2126-gen5-s4-handling-retry2.json: physical_side_imbalance=0.290 > 0.200"
-      ],
-      "eval": {
-        "timestamp": "2026-08-17T16:01:22+00:00",
-        "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260817-0750-gen5-s5-intercepts.json",
-        "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
-        "seed": 1,
-        "episodes": 100,
-        "wins_a": 51,
-        "wins_b": 32,
-        "draws": 17,
-        "side_results": {
-          "a_team_0": {
-            "wins_a": 18,
-            "wins_b": 23,
-            "draws": 9
-          },
-          "a_team_1": {
-            "wins_a": 33,
-            "wins_b": 9,
-            "draws": 8
-          }
-        },
-        "physical_team_wins": {
-          "team_0": 27,
-          "team_1": 56
-        },
-        "win_rate_a": 0.51
-      },
-      "evals": [
-        {
-          "timestamp": "2026-08-17T16:01:22+00:00",
-          "model_a": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260817-0750-gen5-s5-intercepts.json",
-          "model_b": "/home/jcreek/ai-training/CosmicClash/Game/bots/20260816-2126-gen5-s4-handling-retry2.json",
-          "seed": 1,
-          "episodes": 100,
-          "wins_a": 51,
-          "wins_b": 32,
-          "draws": 17,
-          "side_results": {
-            "a_team_0": {
-              "wins_a": 18,
-              "wins_b": 23,
-              "draws": 9
-            },
-            "a_team_1": {
-              "wins_a": 33,
-              "wins_b": 9,
-              "draws": 8
-            }
-          },
-          "physical_team_wins": {
-            "team_0": 27,
-            "team_1": 56
-          },
-          "win_rate_a": 0.51
-        }
-      ],
-      "decision": "fail"
-    },
-    {
-      "stage_index": 1,
-      "stage_number": 5,
-      "stage_name": "intercepts",
-      "experiment": "20260817-1701-gen5-s5-intercepts-retry1",
-      "attempt": 1,
-      "telemetry_tail": {
-        "rollout/air_touch_fraction": 5.9999998658895494e-05,
-        "rollout/airborne_fraction": 0.25157142809033395,
-        "rollout/ep_len_mean": 95.91548010253906,
-        "rollout/ep_rew_mean": 10.333840370178223,
-        "rollout/forward_motion_fraction": 0.3461968092918396,
-        "rollout/goal_rate": 0.8227999938726425,
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     }
   ]
 }
diff --git a/training/logs/20260817-0750-gen5-s5-intercepts_0/events.out.tfevents.1786949423.ai.1699129.0 b/training/logs/20260817-0750-gen5-s5-intercepts_0/events.out.tfevents.1786949423.ai.1699129.0
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diff --git a/training/logs/20260818-0210-gen5-s5-intercepts-retry2_0/events.out.tfevents.1787015440.ai.2183925.0 b/training/logs/20260818-0210-gen5-s5-intercepts-retry2_0/events.out.tfevents.1787015440.ai.2183925.0
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diff --git a/training/train.py b/training/train.py
index f926e6fd..b2780785 100644
--- a/training/train.py
+++ b/training/train.py
@@ -355,6 +355,11 @@ def parse_args():
         "--forward-velocity-to-ball-weight", type=float, default=None,
         help="Low-altitude dense reward for nose-led planar approach toward the ball",
     )
+    curriculum.add_argument(
+        "--air-approach-weight", type=float, default=None,
+        help="Aerial mirror of forward-velocity-to-ball-weight: high-altitude dense reward for "
+        "nose-led 3D closing speed toward the ball",
+    )
     curriculum.add_argument(
         "--ball-distance-penalty", type=float, default=None,
         help="Overrides ShipAIController.ball_distance_penalty (dense per-tick cost scaled by distance to the ball)",
@@ -419,6 +424,7 @@ def _curriculum_kwargs(args) -> dict:
         "ai_grounded_upright_reward": args.grounded_upright_reward,
         "ai_velocity_to_ball_weight": args.velocity_to_ball_weight,
         "ai_forward_velocity_to_ball_weight": args.forward_velocity_to_ball_weight,
+        "ai_air_approach_weight": args.air_approach_weight,
         "ai_ball_distance_penalty": args.ball_distance_penalty,
         "ai_ball_touch_reward": args.ball_touch_reward,
         "ai_airborne_penalty": args.airborne_penalty,