diff --git a/.gitattributes b/.gitattributes
index d91d91d7b8aeecaafe84c071f9666e8eb79f381c..bed0738c7eeb449bca98b5d2f33c89a1ee56349a 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -58,63 +58,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text
-outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/model.glb filter=lfs diff=lfs merge=lfs -text
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json
deleted file mode 100644
index 4e0795941ad6d66c0a4e4093c3fe3cec6b21ed14..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json
+++ /dev/null
@@ -1,378 +0,0 @@
-{
-  "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "game_id": "G045_daily_street_lightlane",
-  "provider": "meshy",
-  "scope": "3d_object_only_not_complete_stage2",
-  "tasks": [
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_001",
-        "asset_name": "cat_resident_player",
-        "name_zh": "猫兽人主角",
-        "role": "character",
-        "input_mode": "image_to_3d",
-        "prompt": "Cat-beastfolk resident player, light-beige fur, teal-green short jacket, deep-blue shorts and orange crossbody satchel, clean symmetrical full-body front-facing T-pose, semi-realistic stylized game character, arms extended, feet visible, plain studio background, one character only, humanoid rig ready, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1045,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "P01,WD03",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png",
-        "sha256": "4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png"
-      },
-      "subject_group": "character",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_002",
-        "asset_name": "street_bicycle",
-        "name_zh": "街区自行车",
-        "role": "vehicle",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood bicycle per reference image: orange low-step frame, two dark wheels with clear axle hierarchy, beige front basket and dark saddle, frame-and-wheels relationship readable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1046,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "vehicle",
-        "spec_ref": "P02,V01",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png",
-        "sha256": "518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_003",
-        "asset_name": "apartment_building",
-        "name_zh": "公寓建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Two-story apartment building per reference image: grey-blue main block with exterior corridor and stairs, orange front door, laundry lines and shared garbage spot, collision-friendly blocky silhouette, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1047,
-        "decimation_target": 8000,
-        "texture_size": 4096,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P03,W01",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png",
-        "sha256": "068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_004",
-        "asset_name": "convenience_store",
-        "name_zh": "便利店建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood convenience store per reference image: teal-green awning sign, warm white doors and windows, visible shelves through glass, side parking spot, original branding-free facade, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1048,
-        "decimation_target": 8000,
-        "texture_size": 4096,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P04,W02",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png",
-        "sha256": "ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_005",
-        "asset_name": "maintenance_toolkit_set",
-        "name_zh": "维修工具组",
-        "role": "prop",
-        "input_mode": "image_to_3d",
-        "prompt": "Street maintenance and odd-job toolkit set per reference image: toolbox, broom, trash picker, folded crate and small lost-item pouch props, everyday readable silhouettes, single kit, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1049,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W03,E04",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png",
-        "sha256": "594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_006",
-        "asset_name": "npc_housekeeper_neighbor",
-        "name_zh": "N01 家务熟手邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1050,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_007",
-        "asset_name": "npc_curious_neighbor",
-        "name_zh": "N02 好奇邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1051,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_008",
-        "asset_name": "npc_stroll_partner",
-        "name_zh": "N03 散步伙伴",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1052,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_009",
-        "asset_name": "npc_illustrator",
-        "name_zh": "N04 插画师",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1053,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q05",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_010",
-        "asset_name": "npc_landlord",
-        "name_zh": "N05 房东",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1054,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q07",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_011",
-        "asset_name": "npc_store_clerk",
-        "name_zh": "N06 店员",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1055,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_012",
-        "asset_name": "environment_delivery_car",
-        "name_zh": "环境车辆",
-        "role": "vehicle",
-        "input_mode": "text_to_3d",
-        "prompt": "Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1056,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "vehicle",
-        "spec_ref": "V05,F04",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_013",
-        "asset_name": "street_lamp_post",
-        "name_zh": "街区路灯",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1057,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P06,WD01,VC02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_014",
-        "asset_name": "riverside_park_bench",
-        "name_zh": "河边公园长椅",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1058,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W04,L06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_015",
-        "asset_name": "street_vending_machine",
-        "name_zh": "商业街售货机",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1059,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    }
-  ]
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/15d2c61a9faa8357f98e7718449d893ee6193aae2b91a55b0a4d0c84bd390cca.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/15d2c61a9faa8357f98e7718449d893ee6193aae2b91a55b0a4d0c84bd390cca.meta.json
deleted file mode 100644
index d283fe7d4d26f0a6a12f288c6b1ce5ab2002c61c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/15d2c61a9faa8357f98e7718449d893ee6193aae2b91a55b0a4d0c84bd390cca.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1055
-  },
-  "model": "meshy-6",
-  "task_id": "01a14306-6d62-71d9-9dd8-c065947db983",
-  "task_ids": [
-    "01a14305-65ed-75d2-b62c-7f0896dbf080",
-    "01a14306-6d62-71d9-9dd8-c065947db983"
-  ],
-  "elapsed_sec": 193.06,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14306-6d62-71d9-9dd8-c065947db983/output/preview.png?Expires=4945708800&Signature=UCb~G85d6PBF0wLrtJXSUMhevYM4GUteeJCFGyBZXHPhZCgHN7bg4w3RNyS6N5FDpJBvioN-puUfutt-crtMWnnP55dwrG68Ce46ezklgqQZlsHmlDfWubWuFZyjX8XXawg~PG9bcp3QUBKf9yk1IYC95J4VF5SHoOZY0cZu6hySiWI41QvxXygyTtbUO-nKOYodkMq5VPagxeGZftJov8xS9piGUS5M-WtcRxomWTVaSrTU-4PmN9mUo2nR43VvzVRVbAlSpn~jl55quZJ5~3VPf6RuH2ngfqQKpdKqqb7epD5l0ks65IeUlPPzcE3UTWq2lTB8759YbWQwywHKWg__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 9990340,
-  "triangles": 8321,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "pygltflib@v1.16.5",
-  "cache_key": "15d2c61a9faa8357f98e7718449d893ee6193aae2b91a55b0a4d0c84bd390cca"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/1d0421ee6a9344421a99a540325ae990a6721287353c895e0e91ee5adf07c503.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/1d0421ee6a9344421a99a540325ae990a6721287353c895e0e91ee5adf07c503.meta.json
deleted file mode 100644
index e1875785fbf6d9ed8545c753815586e07df7168f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/1d0421ee6a9344421a99a540325ae990a6721287353c895e0e91ee5adf07c503.meta.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "should_texture": true,
-    "enable_pbr": true,
-    "image_url": 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"
-  },
-  "model": "meshy-6",
-  "task_id": "01a14303-0e8d-73df-a6dd-8d99ff647f79",
-  "task_ids": [
-    "01a14303-0e8d-73df-a6dd-8d99ff647f79"
-  ],
-  "elapsed_sec": 132.96,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-0e8d-73df-a6dd-8d99ff647f79/output/preview.png?Expires=1792384960&Signature=LMXHwvvvy0vNtsgSKJTbUOPQMDzLo36neSOn8stp5iiCE2gnGIwGezCjMVR-pivEgyxplyDX23dmjp1GjoL3qIfQO~pPTZHYreTDQASytl~rQpoWl1oq5D04LRRAwgk94dGHX9wLcxyVJQKq1iJrjK8-V4W0YgFvQ8JbfZgb9UXrBHFA-UEny576efl5fvt7i-YhU3v1mSI2lp-Nl0ZgM0NvdhNNm1GIB76O1TkrYB1e6IBlYTWcbAW0XlnEkVslo05Ht7pEThCR1SCTJsSptnEFpPBBeGN41D52QRDE2D8-JOvPL257ZBpJpI67nF0HrA-bs4BtkImbi65PzmkCng__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 11651744,
-  "triangles": 7562,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "Khronos glTF Blender I/O v4.4.55",
-  "cache_key": "1d0421ee6a9344421a99a540325ae990a6721287353c895e0e91ee5adf07c503"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/26a93022400fe0ef24f99d483f9a0340da570705cce3afa74bcb7447dcafd06f.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/26a93022400fe0ef24f99d483f9a0340da570705cce3afa74bcb7447dcafd06f.meta.json
deleted file mode 100644
index ac057419315980069d77f5bf045b1e86db8e0d62..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/26a93022400fe0ef24f99d483f9a0340da570705cce3afa74bcb7447dcafd06f.meta.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "should_texture": true,
-    "enable_pbr": true,
-    "image_url": 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hxJ19xfliozO+e4ny9cDMMf3q3ZWPX/92/btVdfzZinzOzceqoMAIDKNZoeAP956L6g2+kZ2Trr6rs1oHd3vfb0Q2GqquF64PZr5XS7wlrDT7+u1KghAxQVGRnWOsLlmkvO0VmTxykqsmZn/2tqxdpNcrqO3eDas/+QDMPQ1ZecpRsuP69Oa6rMzj37tXz1Bhk1aweHzdLV6+UsdmnCmKEntJ0brzhP559+quz2mjVW//PQfYqJjjqh526Mdu89qAiHQ+++8IgsFnqx1MRPi1Zo+KC+J7RfxURF6r+P/UnNmzWtxcpOXlNPHaU+3TufdD0cAACNU6PpAXA8vF6vDqemV+vMQ0ZWjg4cTpXTWTuN5qycXO07lCJncXGtbC8UZ3GxDqYcUZGz4nP07tlFg/v1qnTd3Lx8HUpNk9vtqXQZj8ejrJxc+XxGjWvLKyjUynWbdOqY4TVet7p8PkNH0jN1+Ehatc445hcWKjs3r8L9Ho9HKUcyKnxWufkFOpSaJo+n8veoKl07tdPwwf1ks9kqPFZY5FR+YWG1tuP1enUoNU2paZkyjJp/FqU8JT0REhPiKl0mOzevQl1FzmJt37VPu/YerNaZ8OrsW5Wp7vfQ5XYrK6d6Z9uq+p4cy8+LVqh39y5qmXRivVi6dmqv4YP7VRhy4fX5dCg1TXv3Hw75fg0d0Fu9u3cOuU2Xy6Vdew/qSHpmyMerkpmVo9S0zOPq2ZCdm6edew4oNy//mMtmZGZr/6HUY36H0jOydSQ9U95q9pDweL2Kj4+tduPf5XZrz/5DOpSadszvkLO4WPsOpSgnt+rXl52bp32HUqq1X5Vu81jfeZfLpYMpR465XEFhkXbuOaCMrJqdVS8scmrZmg069ZRhx1zW4/Fo36EU7TuUUuFzsdlsGj64X8gGr2EYysrJrdYxuSb/H12ZjKwc7T+UWuW+4/MZSkvPOuH/r6hM65YtNHxwP8XFhO5ZVfq55hVU/blm5+bJ5Tq+3yClr/FQavVeY0FhUchjaEY1jw1FTv8+XZP3CQBQPxpND4Ca8Hq9ev2DLzX9m1mB/8Pt3qWD7r7xco0aOiBo2W9+/ElvffS1DqWmSZKsVotGDO6v39x8hbp2al/j557z8xK9+v4X2nfgkCTJYrFoSP9euvumy9WnR1e53G5dcMPv1K51sl79z/8F1nvxzU/01Yx5uuy8abr56gsD9//50ee1bedeTX/9Sf28eLkee/YNPfrnuzXrp8Wa+dOvcrs9stvtOmfqBP3hrusDDYw/PfKcUtIz9PazD0uSXn77U33x/Vy98cxD+s/L7/nPwhqGYqKjdMf1l+rSc6cGntPnM/Tmh1/q469mKq+gUNFRkbrigjPUvGkTffjFDL3/0mPHPHu0aOka+XyGxo4cFPLxdz75Vu9/9p3++ae7Ap9JVk6uLr3597LZbPrq7WcVFeU/c75hyw797v/+o3tuvlznTpsor8+ndz75Rp9+PSvwA8bhsOvUMcP021uuVlLzREn+sacP/P0pPfjbm7VmwxZ98vUsXXT2ZP3+zut05pV3aeSQ/jplxCA99fJ7yszOkd1u17WXnK0rLjhdjz77uhYsWSmfz1B8bIweuPP6KseAhvLs/z7QgsUr9cHLjys6yt8LYt+hFP3r+be0ct0mGYahbp3b68+/uUlPvfyeBg/oVWHM/6/L1urR519Teka2JKlDm1Z6+E93qXf3zlqycp3+74kX5Sx2afX6LZpyyW166Pe3a+yIwRVqeey5N/TL0lWSpP+994Xem/69pr/xpGbMW6jnXv1Azz7ye703/QfNX7RMv7vtGl1+/jS5XC49+78P9O2sBYEflIkJ8br5qgt1yblTAtt+5Z3p+vy7OXr96b/ryVfeDdq3br/uEl123jRJ0pRLb1eR0ylJOuuquzRt0in6/Z3XSar+99DpdOnZ1z7QD3MXqLjYraZNEnTbtRdp/+EjWr1ui9567h+BZdMzsvXky+9qwZKV8nq9stlsGjtikH53+9Vq3bLFMT8/r9erX5at0lUXnRXy8dS0TF1914Pq3rm9XvrXX4Iey8zO0eW3/VFD+vfWE3+9V0++/I5+XbZWH//vX4pw+HsBfPj5D3rjw68Cjb3oqEhddfFZuvnKCwIN26vu/LO6dGynf/7xrqDtf/btbL389vTAur26ddJffnuLenTtWOVrWrx8rZ597QPt2e8/PiUmxOuS86bq+kvPOebcBBu37tDjz78ZmIul9DP62323qnmzxKBlU1LS9bu/PanN23ZJkuJjY3TvrVfpnKkTgpab8//t3WdgVMXawPH/lvTeGymQkIQQCL03BRRBBRG7iL1XLK9ee9eLKBYUEBEBpffeQw2QUEII6b23TU82W98PmxyypIHleq/M7wtk97Q9Z06ZZ2aec+gE839eLR13dzdn7r99CndNndRufoqM7Hxeff9LSitUGI0w/aHZ3HHrDdxz26R2t7mlvGzZHS1VaDzcXZj9xMw24/1lRtN1eNWmXWi0WuRyGaOHDOC9154yu94dPBbLwmXryMotkD7rFxnGMw/fTd9ePQHIKyjihbfmMOuuWygoLmXlxl1oNFqUSiWTx4/mlacewMrqUm+Qmrp6vl74K3sOx6DRmNY9MKo3Lz1+P8FB3aTpikrL+PSbJZw8fWl4UFhwEG+91PWxBzgeew69Ts+ooW2vES30BgMLf1nLyk2mbQZwcrTnyQfulHIt1Dc0MvOZNxk7cpBZnplVm3bzy5otqCqrUcjljB8zlMH9Itqu4yru0ZdbsnIT2/Yc5ov3XubTrxdzPikNMJWdl598wOy46g0Glq3ZyupNu83uFWNHDOKldu8Vj0g5C6ZPHs/LT81qs36j0cjXi39j+57D3DntRh67bzobth9gxbptfPLm84SHBHH+YiovvzuX/3v2IS6kZLBhxz6amkzH9fpRQ3ln9hNmxz8mNp4vFywnt7AYmUzGyMH9mHXXLbw3ZwHPPXoP143sOJBuMBhZsX4bKzfuQtUcEFIqlYwZNoAXH78fLw9T8DIpJZPn3/qc1559iOS0bH7buJ2bJ47lzRcflbbh8mvDnVNvZNadN5tdG1Izcvjmp5XEnr0gfebv58ODd9/CzRM6zwchCIIg/GdckwGAj+b9yI59Rxk/eiijhw1Ao9GwatMuZr/zBf9+9yWpghQTd56P5y0mMjyEZx6+Cwc7O5LTs1n82wZefvdL1v88F4X8yjtRXEhO563P5xPS3Z8P/+8ZnB3tycjOZ9GK9cx+Zy5bls3D0tKSnj0COXU6gbqGBqnF4OSZBGrq6ok5fV4KAOh0OmLi4hk9bAByuQytVk9NXT0ffLUIJwd7nn34HhRyOWu27mXjjv1E9gqWbsBlFZWUlFRI29ak0VJTV8/Tb3xKSPcAXn/uIapr61m2eitfLVzOwL4R0oPmvEUrWL15N8MH9WX0sIGoVFWs2boXpUKBqqoawxX0344+HseAPuE4OrTf2ty/TxjfL13N6fgk6YHv3IVkaurqAUhISmVw/8jmfXOBmrp6BvQ1PUiu2bybhcvWMW7EICZPGI2lUsmp+ERWbthJTV0933z0fwBodTpq6upZsnITufnFjBzcjz7hpgf02voGTp27wNFT57jr1htwdXFm5aZdLFm5iT3RMSiUcp5/5F60eh2/rtvBx/N+ZMiA3rg6O11xeaiqrqGguFQa911RWc2Tr3xIo7qJB+68GU93N+LOJfLcvz5Dp9fTPdDXbP6i4nLe/Oxbbr1xLGEh3UnNyGbVpt2889l81iyeQ5C/H888fBdzf1iOr7cH99w2iR6B3drbFK4fNRilQs767fsZPWwAEaHdkctkaJvLxWff/kxJWQXXjxpCcHOr3tuff8+hmNPMuGUCo4b0p1HdxPpt+/jih1+oa2iQhhGoNRpq6up55l+fEBLkz+vPPURNXT2/rNrKVwtXMCgqguAgf5556E427Y4mKSWTJ2fdQY/min1MbDwfz1tMn16m89DezpaU9BzpPNzw85fI5TKMRiOvf/I1MbHx3HT9SKJ6h5KTX8yXC1eYzqNW9cWaunqeePVDSitUzJwxhZ49AsgrLGH52m08Ovt9ln37MW4unR/LsxdSqK6p47oOuv97ebjSI9CPMwnJFJWU4+PlLn134Ggs1TV1jBlumreyqpaC4lLToGJgx/6jfL34N6ZOuo7rRw2mUa1m575jLF6xAXcXJ26bPN5UBkrKcbrsHErJyCIpLZM7br2B8OAgkjOyWb1xF8+88Skrf/hMqtRc7sjJs/zfB1/h5+vNa88+hJO9HdExcfy4fD2FxaW8M/uJDveFqqqa2e/Mxd3NmU/ffB4bKysupGawfO1W/vXpt2bBTIDZ782lT3gIM15+nKrqGn5ZtZVPv1lC/z7hUpK0rXsP8/FXPxIWHMiDd9+KtZUVe6JjmLfoV8orqnnu0bvbbIebqxMP3HkzP/22Cb3BwAN33kxYcFCH2/314hVs3XOYJx6YQUTP7pSpqvhtww7e+vQ7fvn2I3r2CJCmXb99P0qlgicfmIGjkwO7Dxzj0InTLPltE88+cnfzvs/mX598Q5C/L++/+jSuzg5k5BTw068bmP32HLau+BYbays0Wj0FxaUsXLYOW1tbXnnqAexsbdh76ARbdh+kvqGBT/71HGDqnfD8m5+Tmp7NHbdOJDK8J+UVlSxbu43HX/6An79+j4BuvhgMRl59/ytUldW8/fLjeLi4kJ1fyNLVW3jxnTlsWDK3y+FW0cfj6BsR2ul1bNmarSxbu40H7riZQVER1NTWs3bbXj7/bgk+nm4MHxyFwWikoLiUqlYtyOu27uWrhcvx9/Ph/umTkSsUbNy5n5jY+DbruNJ7dHtqausoKC7l2Tc+YVC/3tx92yQqq2tZvm4bb376LV++/yrDB5nuKWs272HBL2sZO3wgkyeMxsrC4tK9oraebz823St0en3zvWIzufnFjBjUj8jmYE5rRqORLxcsZ82WPdw3YwqP3We6V9fW11NQXCoFSnV6AzV19cxb/CvWlpY8PvN2bK1t2bL7IPsOn6BnjwAevOtWAE6dSeDl97/Ex9Od5x69G4VMwe7o4/zfh1+jqqqmoUHd6THduPMA85esZtSQ/txy41isLCw4k5DMivXbqKyqYcGct0zbZDBt07K1W8nJL2T4wCiiIkMBOHziDK9/OI9ufl7SteFgTByLlq+jsKiUt19+HDDlRHnh7X+j0+mZ/eRMuvv7Uq6qZtnarXz05Y8E+vnSp9efl4BZEARB+H2uuQBA7NkL7Nh3lGk3Xc8bzz8sfX7dyMHMePQV5i9ZJT1cHD4Rh0Kh4KsPX8XR3g6AoQP7UFqhYt3WvVSoqq6q6+/hE6cxGo3MeWe2VBkYMqCPVAnNKywlOKgbIwZFERMbT3xiKiMH96Omrp60rBy8vdxJSsuioVGNrY01iSmZNKqbGD4oymw9NpZW/PTl+1IL+YC+vbj3qdc5eTqhywh8ZFgIn775vNTC6OrixEdfLuLU2QsEB3UjO6+QNVv2MHpof+a8O1uabuK44dz71BtXtB+amrTExMXz3CNtH+Bb9A4PwdHBnrMXkqTPzpxPxsPdBVVlDafPJ0kBgNPxiQR085UqDodjTuPt6cYnbz4vBWiGD44iNT2HhItpbdaVX1jCL99+SJC/eQW7QlXN0m8+JDwkCAB/X0+e/ddnNDSqWbN4Dg7NifIsFErm/fgr5y+mMW5E111nO7Lkt41UVFbz7cf/x5ABfQCYcfME5v6wjDVb9rSZvq6hgc/fflFa5+Txo6isrmHXgePkFZUQ4OvNtJuuZ96iX/Fwd2XaTR1nQh82sC9NGi3rt+9nSL/eTLjsbRb19Y2sXPC5VCk+eTqB6ONxTJ10Ha+0agUbM2wAs55/m59XbWL65PE4tRpO0Ds0mM/eesGsbH04t6Vs+TPtpus5fT6JpJRMbrlhnNQCdujEGRQKBV9+cOk8HDawL6XlFazbto8KVRUe7i4cPXWOmNh47pl+Ey8+dp+03sH9ezP7nS9wbVWhX/LbRvKLSpjz7mzGDBsgfR4aHMjsd75g5YadUqWuI4eOx9E9wI+Abr4dTjNlwijOXUgh+lgs90y/Sfp836ET2NnacP3I9rPKRx+NxdLCgtefe1hq6R4zfBD3P/0GMbHnpQBAe5qatMz78DWpknPjdSPwdHVh3o+/smbrbp5+8K4282i1Oj795ic83F1Y+vX7Uob+lnKwfe8R7ps+xay1ubWzCclU1dTy6jOzpBbW4YOjwGBkzZY9qCqrzfb/9aMG8+ozD0p/Ozs5msrCmUS6TfGitr6BL39YRo9AP3788l2pV8QN44bz3L8+Y+Wmndxz26Q2wQxnRwem3XQ967btQ6fTd1rmAQ4ejaNvrxBm3XmL9NmAPr2Y+cy/OBZ7ziwAYDAYWD7/M5wdHUz7ddxwJt/zDMfj4qWycuTkWQwGI/9++0X8/UxvJBgyoA/qpiYW/LKW7NwCeoX2kJZZ36Bm2bcfS79j/OihvPTuF+w/cpLUjFsJDQ5kzZbdJKVm8vpzD5kd9/59e/Hg82/z86otvPvKkxQUFZOWmcuDd90qXeeHDuyDu6szn32zhISkdAb3693hvtBotRyPjeex+2/vYp/F4uftydMPXSpHo4b1Z/pDszkae8503C/T0Kjm+6Vr8PZyZ+k370uB7VtvHMudj79q1vX9au7RnekXGc4Hrz0t/T1sYB/ueeJ1Fi5fJ50bR06cxsvDlU/fesHsXpGWkUPCxdQ2y8wvLGHpNx/QPcAUBG09/M1oNDJn/lLWb9/PrLtuafc8u5xOq+en+e9L18lxIwdyy8wXOHn6PA/edStGo5G5zW/2+PHLd6TAzO23TODRl96/oqERh2NO4+bixL/feVEabjZ8cBTpObmcOpPYZvrcgiKWfPUBId1NAViNVstn3y7Bw92Fn+d90PbasP8I982YQo9APxKSU1FVVvPmi49y643jpGV6e7nz1GsfcSE5TQQABEEQ/gtcczkAdh88DsD9t99k9rmjgz1jhw8iM6dAuqm+8Nh97F79g1TpANNYvaJiU3dUveHqxsc+fPdt7F270KwlUKvVmVr+AJ3eNA52RPMD1NnzyYCp+6HBYOTRe29Dr9cTn2h6MImNT0Qul0kPMy2mTxkvVf7BNNbc0sKCyuqux+LdPW2S2bjZ8ObWs5aWnJYgxu03TzSbLsjfl1GD+13Rfjhx9jxNGg1jh3dcWVbI5Qwb2IektGxp7P2ZhCSGD4yid2gPTp83BQbUag0JSemMbPXQ+cV7L7NywedmvTNq6xuoqKxq95jdcsPYNpV/MHVbbKn8A1JXyUH9IqTKP5i6DAPU1P6xsY7RMXH4+/lIlf8WHXUxt7S0YOxw89bnAD/T76j6k7NN3zv9JrMW8QPHYgG4a+oNZtMpFAomXz+KpiYtZxKSzL67+7bLy5Zp7HpXY/VffPxe9qxpex4WlprOQ11z1+1Dx+MAuOOWiWbzjxzcz+z4Go1Gdh+Mwd/Px6zyDzBiUBRuLk7EJXSevdxoNHIoJq7LgM/1o4diZWXB/qOX3v5QVl5J/MUUxo8eZnaetmZja4VGq2XBsjXS/lHI5axc8Dn/fvelTtfp5+3Z5ppw25TrsbBQSteUy50+f5EKVRVTJ13X5vV8tzZ3yz/dSUZ3W2tTF/jVm3aTkpEtff7YzNvZu3ahWeUfYOLY4WZ/BzaX24qqKsCUib6hUc0dt0yUKv9gGjJ188Sx6PV6zl1o/7dcDRtrKxJTM9i61zRkBMDb0429axdKLbAthgyIlCr/AJYWFvh6e5qV35m338zetQulyj+YemoVFJaY/q837x01alh/syCGTCZjanPFKe6cqXK268Bx7GxtmDrJPJgRHhJEaI9AYuMTm3+L6bjtij5O7LlEqXI6fvRQ9q5d2GnlHyDu7EUaGtVdlmkbGysKS0pZuXEn9Q2NAFhbWbHjt/nSkJ3LxcYnUt/QyF233GA2Dt7O1qZNkOZq7tGdue2m68z+7ubjxaD+ESSnZUk9yea8O5tVC//dwb2ibU+2myeOkSr/rRkMBj779mfWb9/PfTOmXFHlH+CmCaPMgqSuzk54urlI9+q8giKy8woZP3qoWa8MSwsL7px2Q5vltefTN59n3U9zzXLN1Dc0Ul5R1e44/knXjZIq/wCn45OoUFUx7abr214bJo7FaDRyuvnNI/16h7F37UKmTBhtNl3LkEfdH3xbiiAIgvDnuOZ6AGTmmm5En327FNllY0gLi0wV8arqOlydnbC2siIlPZV9h0+QlplLUWkZZeWVGH5nojVra0uyUvJZciiG1IxsikrKKSlXtXn1VzcfLwK6+XK2+QH3TEISvl4e3DhuOHO+X8qZ80kMH9SXuLOJ9OoZjIuTo9n87m5tMy8rlQoMV3DzvbxFzcLCVET0zQ+ueQWmfdTeQ1Drh97OHDoeR0RosFRx7sjwQVHsiY4h4WIaocFBZObkM3PGLXi4ObNs7TbUag3nL6ag0WrNAgB2tjYcOnGaoyfOkp1bQEFJmTT20dKybZH39/Vud/1tXoPV/JDo5uLc7vS/JxliC7VaQ3lFFWOHB7f5zsvD1Ww86KXP3dskOVMqTQ95l1c0/qjL91FufiEKhYKgdsqBj7dp/PzlyefcL8sIbtE8blSv63y/WVtZce5CCvuPnOz0PMwvLMHS0gI/b882ywj08yEhxfTKu6qaWlRV1egNep7912dtplU3aaiq7DyYk5yaRUmZqk0A5nL2traMHT6IvYdOUFquwtPdlX1HTmAwGLnlhtEdzvfofbdzLjHVlAtj7XZCQ4IY2j+S8aOHdjmW28+37XvGra2s8HJ3o1xV1e48LePV9x6K4UyCecW6qTmJXVUngZohAyK58boR7D54nAeefQsvD1cGR0Uyalg/Rg8d0CZ/gI+XeY4FpcJ0bhmbr4XZzduzccdB9h+NNZu2rrm1+EoCml159ekHeeOTb/joy0V88f1S+oT3ZPjAvkwcN7xN7y4fT/c28ysUCrNKlJWVBdn5Bfy8ajOp6Zeu8R0lTOsR0LZHRWBzj5JSVSUAWbmFWCgVPP/W522mLS1XSZVwdzdnnnhgBj+uWM+zb3yKk6M9A/pEMGJwXyaMGdZlXpaDx+MICw40C1C354XH7uXFt+cwb9GvzP95NRGhPRg2sC8Txwzt8B5QUGAKgLR33wi8bJ6ruUd3pr3hToF+vhw/FU+FqgpHe7tW94pzZOfmU1haRoXKdK9oufe15u/X/r1id/QxKTFpYVFZp9vVmmc7b0lQWlhI1+/cwpb91jZAHXCF91tbG2uOnjrLkRNnyczJp7CkVPqN7Qm47PrRci7uiT4uBd5bqJtztrRcG5RKJVpNHUu37CUxOZ3CkjIKS0rRaH5folxBEAThr3HNBQC0OlOinYhW3TBb9AoxtUi2PCitWL+N735ajbOjA4P792bYoL6Edg/kxJnzrN68+6rXvXnXQT77dgn2dnYM7tebQf16E9I9gOS0TH76bZPZtCMH92Xt1n2om5o4ez6JgVERWFpaEtU7nNPnk1CrNVxISefBdl7X1lIJ/D1at7a1R6c3tZK193CkUHadcVuv13P05Fnun9F+q3Zrwwf1RS6XcfZCMg3qJgwGI4OiIvD0cOWn3zaRkJRK7LmL2FhbERUZJs330Vc/snXPIby93Bkc1ZuxIwfSOyyEX9Zsbfe91B0lCrdq5zeaZuhy06+atrn3h0U7+18mkyFrp7OOdTtBgb/K5ftIo9VhaaFsNwdGS2Kwyyt9XZWtjixft435S1bj4uTIoH4R0nkYczrebGiEVqeTggqXk7dq/WrJpu9kby+d8631CumOVQct8y0OxsTi7elGeAcZ+FubfP0o9kTHcOBoLHdPu5G9h07i7+dDn3bGELfw9/Vi7aJ/c/jEGWJizxN3PpGlq7ewdPUW7psxpU0yyNYUivY7dun1Biws2z8GmuZ94u/rLVVAW+sXGU5EWNvg1KV1Kvjgtae5e9qNRB+PI+5cEjsOHGHbvsME+fvy45fvmvXgaC8Q1972BAX44uXe9n30g6N60/1PeKXaiCFRbF42j4NHT3HyzAVOn08i9lwi3/+ylvdfedJsKExH+661HfuO8tFXi7C1sWZQv0gG9I0gpLs/Gdl5LFy2rs30Nu28ArTljQiWFkr0BgN6vR4nx47LKiAlsXz4nmlMGDuMg0diiT13gWOxZzl47BTzl6zmh3+/1eFr6PQGA0dPnjFL3tmRiNBgNv78FdHH4jhx5jxx8ReJT1zHjyvW88Jj93P3tBvbzNOSCd6ynd97eTLHq7lHd6a9dWl1LddZU/n7eN5ituyObnOvWLZ2K3Hn2rlXdHDxb2rS8uJj93H2QjIHj53i4LFTXNfB8J7Wukqs2fJmFsv27rftJMFsz5z5S1m3bR9eHq4M6RfJmOEDiAgNZs3m3RyKOd1m+suDyi3nYoCfT7tBh/6RvegVZjpWGdl5PP7yh2i0WqJ6hzJm+ACCuvnh6GjHy+/OvaLtFQRBEP5611wAwM3VmdSMHO6+bVKbJF/VNXXUNTTg4e6CRqNhwS/rCOzmw9JvPpSytAMcizt31es1Go18u3glnu6uLJ//idnDcEsm7NZGDIpi5cZdxMTFk5qRw53NmdIH94tgwS/rOB53Dq1Wx8hBbcdb/pWcm3sblJZVtNl/mdn5Xc5/9kJyc+K0rsfKuzg5Eh7Sg7MJyTQ0qgno5ou7mzNOjvZYW1mZHtbPJzKkf6RUuczMKWDrnkMMHxzF3PdeNqug/vTrxqv5qf9RtjbWWFpaUFpW0ea7wpKyv/R1kb+Hm4sTF5LTqayuadMDpSVLtL9P25b4q9VyHgb5+7L0mw/Mkpgdiz1rNq2LkwMXGhqpb2hs01U1I+dS2XR2tEepVGJnZ8MzD7ftqltSpkLexevjoo/HMXb4oCt6zdyQgX1wc3Xm4LFTjB7Wn4upGTw5644O5zUYjBSVlmFva8uEMcOYMMZUCU1KyeSdLxawetNunpx5O5aW7QcpCtppgVSrNZRXVjK4f/vdwN2be7UM6BvRpgKn0WgoU1WZdX+/nKqymiaNlojQYCJCTYGCispqFi1fz6adBzgSc4YpEzvu8dBme9xM15ZRQ/pzwzjz4QLqpiYqKqu7TNLYlaYmLeWVlbg1J1W8bfJ4dDodx+NMb8/4beOuNrkwuvLtkpU4Ozvy2w+fmu2vzJz2r40trfyttQwJ8/JwRyGX4+JsOr/aK6vlFVXomiv/NXX11NbV4+ftyay7bmHWXbdQ39DIxp37+XbxKrbsjubFx+9rswyAhItpqKqqu3wtq1aro7RChZOjPVMmjmbKxNEYDEbOJCTx9ufzWbF+W7sBgJa3QBQUlTCwb6/Lfm9Zm2mv5B7dlQpVldlQLdO6SlEqlbi7uJCVW8CW3dHt3ysuC8h3ZdJ1I7hn+k1cP3oop85eYM73yxgY1dvsPv97tFxbS8vblpOMK7jf5heVsG7bPgb3683XH71mNgzg13Xbr2gb3JrPxQF9e0lvbGlx+bVh8a8bqWto4Kev3iMy/NJY/7N/wnAdQRAE4c9zzeUAGNqcOG7voRNmn+t0Oh596T2efv0T5DIZFZU1aLU6IsJ6mFX+NRoNJ89c4Go1NKqprW+gZ/cAs4cCvV5PTFzbLMj9+oRjbWXF0lVb0BsMDIoyZbgfHNUbvV7PkpWbcHNxuqIWyD9Tv+aW9p0Hjpl9nptfeEX7Jfp4HD0C/a54uMCIIVFcSMngxOkEBkWFA6bWm36RYRw+cZrUtGxGDuknTV/SXIHuHxlu9kCnqqzmQnL6Fa3z76CQy+nbK5TElAxpvGSLNZvbJgD8uw3uZzqPduw9Yva53mBg7+GTONjZEtU7/A+vp6KqGp3OdB62rvxrNBpOXVbe+vUxlc0d+46afX7ydILZPrW0tKR/ZBipGTnkFRSZTZuelce0B19g6erNHW5Tdl4hOXlFjL3ChI8KuZxJ14/k/MVUVm/ajVwmY/L4UR1OL5PBg8+/w8fzfjT7vFdYD4YOMJ3/TdqOu9Tm5he2CSruOWR6JeioIQPanWdQv94o5HL2Hz7R5rtFyzcw/aHZZOYUtDOnyc+rNnP7Iy+bvZ/ezcWJW24wJaNreb3jlRrSz5QHY1872zPnu1+4/eGXOxzOcKWKSsuY/tBs1m3bK33W8nq0IH8fGhobr2p5Gq2WyqoagoP8zSr/eoOB46fOtTvP4eOnpR4pLTbvOohMJmNwlKmiPGxAHypUVZy7kGI2XVl5JdMfeYkvFywDIPpYLNMfmk1iSoY0jZ2tDTOm3IBMJqOhseNjEH3clH+kox4CrX/jjEdeZtGy9dJncrmMQVERRIYF09jQhLGdIXJDmu+72/e1vV5s33fY7LMrvUd3Ze9lZaeotIzYs4lEhgdjbW156V7Ru+29IvEq7xXWzTkwvDxceWLmDCpUVXzz429XtYz2hIUESm+/aOkZAqbnhg07DnQ5f0mZaRhWv95hZpX/6po6EpLaJsRtz6C+pmvDvsMn23y3cJnp2tAyhKikTIW1tSW9L+stdOTk2TbzCoIgCH+fay4AMHXSdfh5e/LDL6tZt20fufmFxCem8vJ7X5JbWMzj992OTCbDy8MNN1dnDh0/zaGY0xSVlHPk5Fmefv0TqpvHu6Vm5Erj/rpiZ2tDoL8PJ89eYE90DEUl5Zw4fZ7n3/w3Bc3jGtMz86SHNEsLC4b0701yejYBvt7SeNTQkCAc7e1Iy8xl+MCoK2qB/DONGtyPXmE9WLNlD18tXE5MbDzrtu3j6Tc+7bKLt9Fo5NDx0122MrU2cnAUGo2WrNwCBva99L7owf0jyMjOx2A0mr0FIbRHAJYWFmzacYAzCckUlpSxJzqGJ175EAulEp1WT0pGdodjcv9OD987DYDn3vycrXsOcSz2HJ9+s4Qtu6P/0HF2sLcjIzuPA0dPUV5R9ads6803jMHXy4NFKzawccd+cguLycjO48O5C8krKOLBe25tN29BV+ztTC33a7buJj0rDy93d9xcnIg+FsehE+bnYUu34tSsHNRqDbfeMA4vD1e+WfwbP/22iZjYeJav28Ybn3xjlngM4PGZt4NMxuz3viImNp7cwmIOHD3Fy+/Pxdbahntvn9zhNkYfj8PZ0UEKhl2JKeNHYTAYWbt1D4P7R3b69hCZTMa4EYM5euocy9ZupbCkjMrqGvYfOcmu/cfoHxnWpmWzNSsrC177cB67DhwnJSObLbuj+XrRb3Tz8erwLSBeHq5Mv3k855PS+HDuIpJSM8nIzmPxig38tnEnI4ZEERne8RCAcSMHYzAYePOT70jJyKamto6ktCy+/3k1lpYWDBnYp8N52xMaHMiEMcM4FHOaeYtWkJqRQ2pGDvN+/JXt+48wecIo6a0fv1eAnw/BQd1YtmYbew+doEJVRVl5JcvWbiU1M6dNgsiuWFpY0LNHAGcTktix7yhFJeXEnr3A7He+kHrFZGTnSmP2wVQpfePjr7mQnE5SaiYfffUjMXHnGT96qPR2iQfvmoq1lRVvff4dB4/FkltYzLHYczz/1mcYDEbpdZvDBvbF2sqKz775iTMJyVRW15BXUMTn85dgNBoZM7zj3xN9LO6KemXZ2dowuH8km3dFs2nnAUrKVKgqq9m08wAnTicwZviAdq9V3QP8uGHccM5dSGHO/KVkZOeRmJLOGx9+TUWF+Xj0K71Hd2XZmq2s3LiTrNwCjp46y3NvfIZer+fhu6cB0LN7AJaWFmy87F7x+KsfoVQo0Ov0JKdf/b3izqk30LNHANv2Hib2XNss+1fD2sqKmXfeTH5RCc+/+TkHjp5i/5GTPPevz8jLL+5y/uCgblhbW7J5dzRx8YkUlZax79AJHn/lA8B0T05KyTQLLlzO29ON26aM5/zFVD76chFJaVnStWHlpp2MHNxPqvD3DuuBWq1h/pLVFBSXkpSSyZzvf2Hr7kMA5BWUXFECR0EQBOGvdc0NAbC1sea7z97gzU++Y878pdLn1lZWvPDovVI3VblcxgevPcVbn87ntQ++Akwtz9Mnj+eVpx/ksZff57UPvuLHue/gd4UPou++/ASvfTiPtz+fD5jGzU6ZMJpXnn6Ah198lw++XIiTk730iqMRg6M4fOIMA6IuVXwVcjkDoyI4eCyWEUP+s93/W7Z5/idv8N2SlWzYfoBVm3ZjbW3J9CkTUKvVbNh+AMsOxjUmpWVSWq664pZTgPCQHrg6O1FZXcOAVl1HB0WZWolCuvubVabcXJ35v+cfYs78pTz12keAKdP3o/ffhr+fD//3wTweePYt9q5Z8Ht+/l9qYN9eLPjibT7/dgkffWVq/fX38+Hf77zEM69/goXy942hn3HzRL5fupo3Pv6Gz956getGXnkApiM21lZ88/FrvPHJt3z27c/S50qlkofunsp907vO8dCeyePHsHP/Mb77aRU5uYW8NftxPvi/p3nzk+947f1L5+HtU8bz8lOzePyVD3jt/a/48ct36durJ0u++oAvfviFn37dgN5gwNHBnsfun87xuPNmvQD6RoTy+dsv8snXi3nxnTnS575eHsx9/+V2Ewm2iD4ex+hhA9rNf9CR4CB/woKDSMnI5uaJnb+KE+C5R+4iMyeP+UtWM3/JarPlvPPKE53OO2xAX3x9PHl/7g9SYsqQ7v58+tYLnQZlXnzsPuQyOeu27mVbc6usTCZj7PCBvPnSY51Wugb27cXD90xl6eqtPPDsW9LnNtZWvPnCYwR0kGizM+/MfgIrKwtWbdrNyo27ANN1efKEUbz29ENXvbzLyeUy3n3lSV5570ve+uw7s+/GDBvAo/dPv+plvvniY7z2wZe8P9d0fVHI5UwaP5KfvnqXh154j0+/WYKjg72UZ+GOW24gMSWDR156T1rGhDHDeOulx6S/A/19+Obj/+Pdf//A6x99LX3u5uLEp2++IL1W0NPdlX+98AiffP2TdO0D0zX7kXundfjqvJSMbIpKyxg3svOEli1ef+5hXnz733z6zRKzz6N6h/LSE/d3vG9eeAytVs+6bftYt20fAD17BPDy0w/w/heXrsdXeo/uyitPPcBXC1cwb9GvADjY2fLmS48xtDkY5ebqzOvPPsy/v/+5zb0iwM+X1z74ilnPvcWe1Vd3r1AoFLz+3MM89vL7fPr1T/z2Q9tEo1fjkXum4enmwg9L1/LGx98gk8kY1K83Lz11Px/OXYRlJ7kpnB0dePOFx/h43mKeef1TwLQfH7pnKmHBQcx+dw4PvvgO21d81+EyAF56/D7kMhnrt+1j695L14ZxIwbxrxcfla4Nj828naS0TJav28byddsAU7n4ad57vP3pd2zedRAba0teemLmH9ongiAIwh8jU9VW/v7U5f/DjEYjaZm5FBSX4OjgQESoeVf/FvUNjSSlZaLT6enVs4f0yh5VVTWqyhq6B/iada3rirqpiYupWTSpmwjrGSRlMq6uqaO0XEWQv2+7Cfb+G2m0Wmpr63F0sMfCQsmbn37H8dhzHNyw+O/eNFRV1aSk52BhoTR1+WzuPl5UUk6TRkNgN5//eO+Jq1FdUweAk6M9FZXVTL73GR66eypPzrrjdy2vpraO+gY1nh6uV1Vx7YrBYCQlPYvCkjJsra3pHR6Mo4N91zN2omVcqZuzs/SavA7Pw8pqVFVtz0O1WkN9QwPOTg4oFApmPvsmCrmcpd982GZdCckZ1NbV4+XmSljP7m2Skv1djEYjOflF5BWWoNPp6ObjRXCQ/xVvX1FpGdk5hbi4OBEWHHjF5b28ooqLaZkoFHKCA/3bvg2jE6rKajJzC6isqsHT3ZWePQKuKGFbZ4pKyknNysFSoSQ0OFAaT/5n0Wp1pGfnUVRShrWlFYEBPp0GgLqiVmtISs+kscF0jW8Zx15TV09xSTlBAb7kFZRw71Ov89h903nkvttISsukorKGHoF+Ha5br9eTmJJBRWUV7q6u9OoZ1G4SufqGRjKy8ygpU+Hi7ECPwG5dZsy/WnqDgczsfAqKS5HL5QT6+RDof2XDujJzCsjOK8DT3ZWI0OAOy/OV3qMvN2/RClZu3MXW5d9gY2NNYnI6SqWSXj27t8kNAqZ7RWpGDkql+b2iuLQCdVPTf829wmAwUllVjZ2djTQs4O3P5zP/szcYFNX5Kx6rampJTstCqVTQOyxE2o8lZSoaGhsJ7OZ7RdeVK7k2GAxGUjKyKVdVEeDrLZULjUZDdl4R3l7ufzg3giAIgvDHXLMBAOH3ycjO56tFK7j1hrFmybnUag3TH5lNaI8A5n342t+4hf+7fvptE/EXU/j32y+ajXffse8o789dwFfvv/q39Pr4XxETd55fN+zgkXun0T/yUv6BsvJKpj74IjNunsDsJ0XLk/D3y8jOlwIAv6engdCx1gGAzoba/K94/aOvcXN15tWnZ5l9/sGXC9kbfYJdq75vN7AhCIIgCB3532hqFv5r+Pt6kpaRwxc//EKjWk1Id38qKmtYuWEHlVXV3De947HTQuc83V04eTqB1z/+hnumTsLOzoYLyRksWr6O0OBAhgzovJXnWhcU4MO5C8l89OWPPDnrDrr5eJJfVMqSlRuxsFAw4+bxf/cmCoIgXBVrK0vWbd2LpaUF44YPwmA0cPBoLDv2HeWOWyaKyr8gCIJw1UQAQLgqlpaWzPvoNb5e9CuffP2T9Hk3Hy8++dfzDG7O4CxcvZsnjqGyuoaVG3bx/FufA6bxpGOGDeDVZx7s8p3R1zofTw/mvDOb75asNBvTHRYcxLcfvy4lVROEv5tCIcfR3g7L35EoU+icpaUFjvZ2yGX/jBzHrzwzCwsLC9Zt2ctv63cAYG9ry8wZN5uSmQqCIAjCVRJDAITfraa2jrKKShzs7f4RXS3/W+j1ekrLK2lUN+Hl4SpaeH4HVWU1ldU1uDg54voH3xcvCILwd9NoNBSVVCCXg7enx/9MriBBEAThv48IAAiCIAiCIAiCIAjCNeCf0UdOEARBEARBEARBEIROiQCAIAiCIAiCIAiCIFwDRABAEARBEARBEARBEK4BIgAgCIIgCIIgCIIgCNcAEQAQBEEQBEEQBEEQhGuACAAIgiAIgiAIgiAIwjVABAAEQRAEQRAEQRAE4RogAgCCIAiCIAiCIAiCcA0QAQBBEARBEARBEARBuAaIAIAgCIIgCIIgCIIgXANEAEAQBEEQBEEQBEEQrgEiACAIgiAIgiAIgiAI1wARABAEQRAEQRAEQRCEa4AIAAiCIAiCIAiCIAjCNeAfGQAoV1WRnpVHUUk5er3e7LsmjQadTvc3bdlfS6fXo27S/CXLVjc1obtsX/4vaFCr25SBFkajkQa1GqPR+IfXo9Xq0Gr/mnLVoFZjMBj+kmX/1f7KMvnfpKvrirqpibIK1X9kW/5Xz9V/GnXTn3uv0el0aDTadr/Tajv+ThAEQRAEobV/VACgtr6BH35Zw+ff/czydduYu2AZn367hISLqdI0i1ds4PCJM3/jVv51ziWkMP/nVX/Jsuf+sJz4C6ldTwgkpmRQUFQq/X02IZkyVeVfsl1deW/OAjKy89v9rq6hkbc/m09dXcMfXs+GHfvZuvfQH15Oe96fs8Bsf/4vOZ+Yyrc/rbyiaVVV1cTFX/xT1puamUtWbsGfsqwrsXjFBk6cudDhtrw75weWrNz8H9mWuT8s50xC0n9kXf9NylSVnElI/rs3Q/LD0jUcO3XuT1vekZNnWLpmS7vfbd93hJUbd/5p6xIEQRAE4Z9L+XdvwJ9p086DGI1G3nvlSexsbdBqdWzZE82K9Tt4w88HZyeHv3sTrwnRx2IJC+mOn48nADv2H+HGcSPxcHX5j2/LzNsn4+vl/h9fr3D18gtL2LbnEIOiIv7wsk6dTcDG2oruAX5/wpb9MSkZ2fh5e/L8o/f+R9Y3ffJ4vDzd/iPr+m+Sm1fMzgNHGNAn/O/eFABunjj6P3bPGdy/91/WA0kQBEEQhH+Wf1QAIC0zh6k3XoedrQ0AFhZKpt44joSkNHILi80exvIKisnIzsPa2or+fcKxsrRs9V0RWbmFIJPR3d8Xfz9vAOobGykpraCbjxfnEpPRaPWEhQSaVWyNRiOpGTkUlpTi4uRI77AQLCw63s2VVTVcTM1Ep9Ph6e5GWEggcrmpY0ZCcjo9Av0oKa0gt6AIJwcHonqHSt8bjUZS0nMor6zCu4sH/rIKFXq9EUtLJReS0rGxsaZPrxCsraykaapr6riQkoFWo8HDw5VeId2ldV2uoVFNQlIaanUTPQL98PfzASA9O4+6xkaKS8vILSiivl6NRqsjr7AIXx8PfL08AMgtKCIrpwAbG2siw0OwtbHucNvVTRpS0rNo0miICA2msroWJwd7HB3sSMvMwdnJAQ83VwA0Wi1JqZn0DgtGqVRiZW2NXKGQllVcWk56dj4OdjYE+betHJaWqUjOyEKhUBIZFoyTo730naqqGp1Wj6eHa4fbWlRSTlJaJg72dvSN6GlWrjo71gAFxaWkZeYgQ0ZEeHCHAZPUzFxkMujZPQCAkrIKsnILcHJwIDioG9n5RYT2CDDbF+cvpuHh5oK/nzdNGg0XUzIpV1Xh5eFKZHiItB0Z2fnY2Vrj7WkKmuh0OhJTMgjv2R0rS0supKQT1M2X0opKcvMLcXRwICqiJ4rmfdxS/stUVXh7XHkltLqmjuz8IrQ6PfGJKdLx02i0nE9Ko66unm6+3oR095d+c3FpuTQdQGZuAXKZDLlcRlVVDfWWlqRn5UnztGY0GknPyqWwuAyFUklYcIBUhqpr6iguLad7oB/xianU1zcQ0iOAbj5e0vz1jY1cTMlEp9PTOyy4w99VWq5CpaoCICu3gO4BftJ5nZdfAhgJ79kdnV7PxZQMyiursLOxoXdoD+zt7QBTmWrSaHBxcuR8UirWVlb06x2GWqMh4WIaeoOBqIhQHB1M01vbWKFsPh7pWXk4Ozug0+pISc/G2tqKqN5hWFtdKpfFpeVk5Rbg6uxEoL8v+YUl7e4zMA3puJiaSYWqEhsba3qHhuBgb2v6rWUq6tVq3J2dOJuYwvCBfbGwUFJb38CFpDS0Wh3B3f3x8/Zsd9nqpiZSM3KIDA/hQnI6FZVVdPP1lso5mMpjUmomxeUqXJ2c6BvREwsLJZXVteQVFqHR6ohPTCEyPEQqky1SM3Nxd3Wmtq6eispqKVBQUFxKemYuVlaWRIYFS/u9ZX3nElNpaGgkNDgIvV6PESPdfLzIKygGjNK1DyAhOY3u3Xyxt7fDysoSpdICgJz8IqwsLZHJIDOngOGD+gKm4WpJaZnIZHIiQnvg6uwoLctgMHAxLZOq6joC/S6VvfZYWiiRyWSA6VpfV9+Ip7srCUlpaDRaeoV2l8p3e6qqa0nNzMHK0oLeYcFk5hbS3d9Xunfl5BeRk2e6J/YI9JPOhYZGNWmZOfQOCyY+MY36xkb6hIfg5GhPQlIaldU1dA/oRmC3S/tIr9eTmJJBRWUVXh7uhIcEdXifEQRBEAThz/ePCgA42Nty6mwC3l5u0kOmUqnkvVeeMpsuISmdk2cSCOjmQ3pWLqfOXpBa56KPxbLvyEn6RYaj0+nYuf8okyeMZvTQ/uTlF7N83Ta8PNxwdXZEo9Gxbe8hHr57KqHBQRiNRn5ZvYXcwmJ6dg8gLv4iuw7G8MxDd0kPya1l5RawcPk6IkKDsbez4fDJM/j7+fDgnbcAsGrjLrr5etKobsLVxZmUtBhSM3O4a+qNpu837SIhKZ3QHoEcO3UWK0uLDvfNsVPnyMwpQKvXEdTNl4KiUvYdOsGzj96Dg50teQXFzF+6mvCePXB2sONY7DliPN149N7pbZZVUlbB90vX4OnmgqOjA3sOxTB0YF9uvWEsmdl51Nc3UlRaQXZeERWqSjQaLTkFJQT4lePr5cHO/Uc5euocEaHdqaisZtvewzwxc4bUY6C1qupafvhlDTbWVtjb2bH30EnkMhnjxw5lSL9Itu8/SlRET64bOQSA2roGlq3dxnuvPIWDvZLFv27g0XtvIzQ4kLj4i6zevJuQ7v4Y9AYOHos1W9epswls2HGAiNBgGtVNbNtziJl33ExEaA8Aduw/Sll5JS89cX+7+zg9K4+LqZn0CPQj9lwi+w6d4PnH7sXO1obM3AIWLV9H77AQ7GytOXzyDIHdfHngjpsBOB4bz7a9h4nsFYy6UcOO/Ud56sE7CfL3NVvHgaOnOBRzmicemNG8zRfYuPMAPYMCqKmrQ6lQkFNQzJx3XqKuvpFla7fRv084aVl5TL1hLC7Ojnyz+Ddsbazp5uvNmQtJHDgWy+Mzb8fW2pq9h2II8vdl0vWmAECTRsuytdt4/bmH8HBzZc2mPfh4u9PQqMbN1YWU9BhSMrK4Z9pNAKzevJvzF9NMZTL2LFYWHZfJ1mpqa8nOLUCn1xN/MY2wkCAaGpv49qeV2Nla4+HmQvTxOIK7B3D/7ZOxsbZm3bZ95BeWMmXiaAqLy1jwy1ruv30y1bV1VNXUopAryMhuPwCwbus+ktIz6RMeQm19I1v3HOLBO2+lV2h3svMK2LTrIHa2Nrg6OdHQ2Mj25uPRI8APVVUN3yz+FRsba7zd3Thw9CQdZZHIziugqKQcnV7HmYQkugf4sWrjTnqF9iApNYsxwwbQs0cA3/+8Gq1OR3CgP2mZuWzdc4gXH78fd1dnzpxPIik9C71Bj7eHOxnZeZxJSKasQoWvpwcVVVUcPHaKN557BAsLJb+t38HEccMY0i+S3QePgUxGTW0dfj5eZOTkE3P6PC8+dh8AMXHn2bInmp7dA6iqrsXSQklBcRmfvvl8m99iMBhYsHQNao2GkKAA0rLy2Lr7EC88dh+e7q6cT04jOTWT6tp6FAoFA6N6UVxazsLl6/D39cLa2pqdB48xbsRgbhw3vM3yK6tqWbZ2G8FB3ZDLZCiUCrbvO8qdt97AkP6RaLU6vluyCq1OR4/AblxISmfv4RM88cAMqqpryM4vRqPVEn8xjYjQYC6r/7Nj/1HcXJy4mJpBZHgIA/qEE30slt3RMfQO60FtXQNb9xzikXtvo0dgN7RaHQuXr6O2tp7AAB+OnDyDrY0N/n5ezLh5Isfj4tEb9Nx726XK7W8bdvLQXbcSam/H+m37GdAnnLEjBnHkxGm0Wj0ZOXn4eHkwfFBf4hNT+G3jTiJ69kCrN91L7p56I/0iwzEajSz+bSP5hSX0CPLn0PE4bG2ssbOzabecHTsVT3VNLbPuupXElAxOnU1Er9Pj4+OJSlXFjgNHeeWpWbi7OreZNy0rl6WrtuDv543RYOBQzGnyCkt47ZlZeLi5su/ISQ4djyOqdxg6nY4d+48w9cZxDB8URYWqkmVrtxEaHIhCrqC6tpb9R0/RzceDhoYmrK2t2Lb3CI/PnCEFJb9fuga1WkNANy+Oxcbj4ujA4zNndBooFwRBEAThz/OPuuPeccsN/LJmC18uWI6zk6k1NCQogL4RoWYtXmpNE68+PQulUklWXiHf/bQSVVUNrs6OnLuYxoybJ9Av0tQ65OHuSnxiCqOH9jfN26Rh1JD+DOjbCzCNvdyw4yCvP/cQZxOSySko4pWnZ2FnY4NOr+e7Jas4fOI0UyaMbrO95y+mMWxAH6bddD0AA/r0Yv7Pq9Hp9VILnqWFBU8+cAcymYxjp86xbd9h7pp6I9l5hZxJSGb2EzPx8XJvXtdqoONkcaUVKt547hGcHO3R6/V8+9NKDhw5xdRJpl4SA/tEcMetEwEY3C+SrxatoEmjMWvFBtN496iInkyfMgEw9Zj4evFKRgyK4oZxI0jLzCUspDtjhg0A4GJqJqMG92NgVC8Kiko5dOI0Lz52n9TK/Ov67ew6eIxH7r2tzTbvOHAEbw93Hrz7VmQyGRnZ+Xy/dHUXJaEtrVbH1r2HuWXiGMYMHwjA9r1HyCssAaCurp5NO6OZdYepEgiwJ/o4W/cckgIAE0YPRaPtONFWQ0Mjrzw9C3s7W7RaHXN/WEb08TimTBjN+cRUhg/sy9RJ1wHQPzKc75euQa/X09DYxOZdB7l3+k1E9Q4DYM2WPRw5edYsALD38AmOnTrLU7PuxNvTjbr6BjbtPMjMGaYghdFoZNWmXeQUFJttl5WFJe+9/AQymYxNOw/gaG/HUw/eiUKhQKfXM3fBMg4fP82k60de0b5UKhXMfmImMpmMmLh4Nu+O5u6pk8gtKOb0+SReevx+fL090On1fP/zaqDrhHT+fj6MGzGIDdv3S0GRjTsO4Oftway7TMdeVVXD59/9TGZOAcFB3Zg+eTy/bdxJn4gQ1m/bT/8+YfSNCAVMLZY21lbceN2INuvS6/VcSM3g0XumSb17Vm3axfmLqdKxr6mtZ/qU8fQJ74nRaOSrRStIuJhGjwA/9kQfx8vDjcfvvx2FQiGV//YM6d+HopIKauvrub35fDEtv473XnkSCwslhSVlqKpr+Ndzj2DZHMT74odlpKRn4z6kHwDlqkpee+YhXJ0diTt3kZWbdvLg3bfSJ7wn6iYNb332HTn5Re0GO2rrGnjlqVlYWCjJzC1g/pJVqKqqsVAo2bz7IA/fPY3Q4ECMRiPL122noLis3d9SVlFJWWU1bzz3kNRzaN6iFSSnZ+Hpbmpdzs4v4vH7b5eW9+P29YwYFMXk5utfWmYOi1ZsYMTAvjg42LVZh9FoJDQ4iPGjTAG9Feu2EX8xhSH9Izl66gxNTRpmPzUTSwsLjEYjC35Zy+6Dx7h72iRGD+nPzgNHpPLTnvyCYt58/hHs7e0or6xiV/Rxnpp1p9RCvXHHfrbvO8Jzj9xDzOnzVNfU8erTs7C0tKCurp7PvvsZ/y5a4zuSkZPHC4/fh4erC43qJtZv38/d026if6TpnD90PI4tew7RNyKUCykZZOcW8uozD+Li5IBGo2XugmUdBgAuV1qu4oXH7sPf1wudXs+HX/3IxdRM6ZrcwmAwsH7rPiaMGSIFUfcfPUVOfpE0zfnEVO649Qb69uoJgIuzE/GJqQwfFCVN0ye8JyMGR9Gk0fDmp99ha2PDY/fdDsDC5etJTE4jtEcA0c1B11eemolSqaRBrWbO/KWcTUxmSL/I37VfBUEQBEG4Ov+oAECQvy9vv/Q4OflFpGfnkZaZw5ote9h18BjPP3KvNAQgIvRSt2Gv5gfX+voGXJ0defGxe6lvbCQ9Kw9VdTUXUzLQt8rArlQo6BvRU/q7d1gwB46eokGt5kJKBr7eHhS2Stjm5eFKWlZeu9s7ddI4tFodOflFVFRWk5KRhcFgMGV8bw4ADOgbIXXt9PH2QKPRotPruZCcQXBgN3yax7crFQoGRUVw8sz5DvdPcFA3qUu7QqEgLCSItMxcACZPGI1OpyO3oIiKyhpSM3IwGo3odHpaxU5QNzWRnpVHZHgIaZk50ud2tjakZ+W228LUWmJqBi5OjtTW1VNbVw+Au6sLB4/Htjt9SkYuMyZfL+2D4KBuvyuXQE5+EfX1DYwYfOmhdcjASA4cOwUgHSOlUi79LkcHe0rLVVTX1OHkaC8FLDoSERaMvZ2pp4eFhZK+ET3JKzRVxqfddJ35sU5vOdZGcguKkMllRIaHSMuaOmkcavWl7Pn7j5wkITmdWXfeIg33yMkvQqlQ0KunqdIqk8kY0r9Pm0RoY0cMlPZfUloW148aInWPVioURIaGkFdkHjTozIDIS2XS19sTrVaHTqcnMTmd7gG++Hp7SMseFNWbY7HnrnjZrV1ITmf0sAGkZ+VKn7m6OJKWlUNwUDf69wnnQkoGPyxdi72dDbfdNP6KlqtQKHj/lSeprqkjNSMbVVUN2bmFZhU7SwsL+oSbznOZTIaPlzsN6kbAlORy+uTx0j709/PpsmxcbuTgflKLp6+XB++9/CRlqkrKyispr6iksqraLIN8N29PqXu4S/O/YcFBAFhbWaJUKmhsamp3XX17hbRal2k76+sbUVVVY21tRWhwoPQ7hw2IJCk1s93leHm48f4rT1KuqiIzp4ByVRXlqip0uksBHj9vT2l51TV15BWWMGJwlHROGY2mcpGRkycFWS83sE8v6f8+3p4kJKUDkJyWzYC+4Vg29yqRyWT0iQglJi6+3eW0u+x+EVIX/+S0bGxtrNFoNNL2uTg5cvTUOdNQg7RM+kWGSUEZe3s7QnoEXvG6LhcZHiJdu7LzClA3abCzsZLWbW9nR3VNHeUVVSSmpNM7rAcuzfcsS0sL+kWGSQHLrnh5uOLvayrPSoUCTzcXGhob20ynqqqhTFXJoKhLle+h/SPZse+I9PfsJ2dS39BIWlYulVU1JKVloZDLzJbTpzk4YGVpia2NNWE9gqTvHO1tpbKZkJyOv6+3WYJOL3c30rNyRQBAEARBEP5D/jEBgLr6BopKy+nZPYDuAX50D/Bj4phhVFRWM//nVRw9dY6bJ5paoexajTeXX/Ygs2nnAWLiztPN1wtvD3dcnBwor6yWvreytJSCB4A07rahUU1VdQ21dfXsP2pemfXuYMx4XPxF1m/bh6uLE75e7jg6tE0YZd2q9V3eXOnCaKS2rg77y1qDOhtHD2Bvaz4MwdHenoZG04NZfGIKqzfvwcnJHj8vD1ydndpdRnVNnWn6Cykkture7ePlgY21VbvzmM1fXUd9Q2ObfRTk74fBYDAbC2owGKivb8DOzny7bW07/p0GQ/utzTW1dc0VpUvHzs7m0v6rrq3FaDS22a6ePQKv+BV8rZfXsp2NTaYeA3HnLrJ+e6tj7XjpWFfX1GFna2M2ZtnK0tKs50V+cSnhPYPYd/gkvcOCUSgU1NbVY2trI1XGof0y0Ho51bV1UpltYWGppLGx/cqjQd/2t1tbty6Tpn+NGKmpq8feznzZtjZdl4n2qJuaUDdpSE7LIjvvUmukk4MDDvaX8jL06tmdcxeSCQ4y7+XTGZ1ez/I120jJzCawmw/eHm44txp7Dea/EUAuk2MwGNDpdDQ0qrG/bEiPXRfn3uVaH5Oa2noW/7qByuoaAv198PX0MPuNALatylbL4bZQXn75bn8ggnWr81Iuu3R+1dQ1YG9rXmZtrDv+HbV1DSz+dQOqymoC/L3x9fRs04pvZXXpmlBda7pWxMVfRC6/VLYD/X2x6uRYmW/vpd9VXVOHg735+iwtFKg7CHy0u+zW50J1LRqNtt1zXqPTUVNbh0NzcK2FjVXn5dlg6PiVoq3LZ1V1LTKZjAPH4tqsG0xlwtvDPKhk28mxabsu8+1UdDDGviUIa9fqmmprYy1dU4xGI+u37yP2bCL+vl54ebjj6uxIdU2t+ba1Os9lMhkWFpeNv2hWXV2HXl+IqqqGVjPg6tT+/UYQBEEQhD/fPyYAUFldy4Jf1vLuy0+aVXDcXJzw9/Gmura2k7lN8gqKOXLyLK88NUtqWd998LhZAKBBraZR3SRVdssrqlAoFDjZ2+Ps5IiHu4s0HhpMSZ6aNG3fg97yYHXzxDGMbO7mm5aVS3QHLeGXc3Kwb9OzoKKyqtN5KlTm35erqqReEeu37+eGscMYN3IwYGpd3n/0VNv1NldcJ08cQ49WGdYzc/LxcO84yVQLZycHXJwcebJ5DDuYKhblqso2iaDkcjkO9rZUNQcdwBQUKKswf6WgXn/pobuy9YPlZetVN2loaFRLleTW+8PJ0RGFQs4TM2+XHn7VTRryCoquOJO3qtp83aXllbi7Opm62W7fxy03jJV6IKRl5kjdYR0d7airb0Cn00kBiuS0LErLVdJwhXunT8bT1ZnPv1vKweNxTBg9FCdHe2rr6s0CJ6XlFZ1uo7ODA6pK8+0sKVPh7uYMmCqXulaV/st/U6fLdnQgOSPb7LOKDo5HV6ytrLC2smLs8EH0b5XVPbegCMfmynFdXT1b9xxiYFQvTp9PYkCfcEKbW8U7c/5iGunZebz54mM4NAeXVqzbdkXbpVQqsbO1obLq0jXBaDRS0ervqxUdE4dMJuPdV56Uhv6cb271lsjamfEPcnK0p6a2HqPRKJX5knJVh9MfjjmN3mDg3VeekMrpxbSMDqd3br5WTLvpein5p9FoJD07r8NEgJ1ur5O9ecURKC5T4e7ifNXLAtM1wcba2uxaVN/YSFFxObbW1jjY21NdW282T1V1NW5SLycZOt2lc6W2rsGs10an63Z0BKORR++7TTrmGq2WnLxCXF2dms9T8zL1R8pYR1p6hFXX1Ek9S0rLVRiNpmtqTn4RJ04n8NozD0rDPLbvO9ImAHClnJ0c6BsRyoQxQ6XPCkvKsFC2HzAQBEEQBOHP949Jvevj6YaLkwPrtu2lrrlVw5SRPJvUrBx6dpDVujWdwWBqvbA0PdyWVaiIjU/E2KpVx2g0Su921mi17DtygrDgQCwslET07EHCxXRKy0wP0TW19fy4Yr1ZV/nWyzEYDNK61E0aDjRXuFsevjrTt3coeYXFJKWZuutW19R12RU2r7CE3AJTa2ppuYq4+ET69jKNcdbr9Vg0t45pNFr2HT4BgOGybbG2sqRHgB/Rx2LR6U2t7WcTklm0Yr20n+RyuVnQQy6T09Q8dj48JIiC4lKS07Kldf26fjsnzyS0u809ewRw6Hgc9Q2N6PV6du4/RkOjWvre2cGetMwc9Ho9TRoNB462H0AJ8PPGxcmBPdExgGkc+N7m3wgQEuSPTq/naPOxNRgMbNp5gF0Hj0uVo8SUDGLPJXa4fy8kpzVnBjdlbo9PTGFIv96mY200St2w1U1NUqujESOh3QOxtrYm+vhpwNSbZPOeaDStKhMWCgX29nZMnjCKvYdiKC1XEeDng8Fg4MCxWIxGI1XVtexu/n0d6RvRk6OnzlLfYOoOXFqu4mJKBoObu986OtqTnp2LTqdDq9Wx//DJTpdnvuxQCopKSUw1VQprauuJadX9X6PVcuDYKYpLy9udXyaXo9Xp0TeXq16h3Tly8gwajanspGflMX/Jaun4r9m6F1dnJ+6eOokRg/qyavNuGtSm7+RyWbuBNwCdXodCLpcqXlm5BSSlZdFJ462ZfpFhHIo5I23HqbMXOgw8XQmdTodCIZd6+MSeS6SsQoWxw9SCf47u/n5otFoOHY/DaDRSWVXDvk6Ot06vR6lUSMGm0/FJlJSp2lwjWjg62OHr7cHBo6ekXjQnTiewdNWW3xXQ6BsRRuy5C1IQoLa2njPnL0plV64wlZ8r7bETGhxAdW2dNGRGp9OxdvMeDseYzsOQ7v6cPn9R6vWUmZNPena+NL+Tox25+YU0qNXo9Xp2Rx8z643TmaAAU4b96OOmHgBGo5Ftew+zaVc0SoWCqN5hJKdnSWPxyypUnDmfdEXLvhouTo64uzqzO/o4Or0edVMTm3cfkr5vGd7R0tukpKyC0+cvXtE9qj29Qrtz6mwCtXUN0vLmL1ktBZ5y8os4dDyus0UIgiAIgvAH/WN6ACiVSh65dzor1m/n3S8W4GBvS6O6CUulBWOHD5QeEjsT1M2HyLBg5n6/DBdnRwwGI9eNHMLmXQfZvu8IwYHdsLBQkl9YwgdfLqRR3YS7qzP3TZ8CwMCoXpSpVMxduAwXJyeqamroEx7CmGED26xLLpdz0/UjWbd1H9HH4mhQq7lx7HAKi8v4cfl6nn3knk63tZuPF7feOI4lKzfj7GhPk0bLgD7hZOTkdziPn48nv67fgcFgpKqmloF9ezFsYB9kMhmTrh/F5l0HOXbqLA2NjUwcM5yC4lIWr9jQJuv9vbdPYfnarbw/ZwHW1lY0NKq597abpJ4XPXsEsvPAUerqGrhr2o30DA5kw/Z91NXVc+N1I7hz6g0sW7sVBztb6hoa8PXy4NYbx7W7zdNuup51W/fxwZcLkcvkRIYH4+nuKnVlHjdyMD/9upE3P/sOhVzBdaMGk9ZqzHgLpVLJfbdP4efVWzh7IRmj0ciwAX1ITDFVVh3sbXnorqms3LiTwzFxNDVpsbe35eF7pknLOHshmbLySgb3693utkaEBrN0zRYMBgN19Y2MGtpfapG+8brhrN26l4PHYlsd61J+XL6eZx6+m1l33Mwva7dJFd6w4CDGDW9bboYN7EvsuUTWbNnDMw/dxSP33sa6bfvYd/gElkoLBvXv3WlPkPFjhpJXWMyHXy3C2dERVVU1Y4cNlF61NmboQBatWM9bn89HIZczfvRQqULfFV9vD6ZOuo6lq7a0KpO9pOPR1KRl+94jONq1n0/B38cLmQz+9em3vP3S40yfPJ5fN2zn/bkLsLe1paaunqmTrsPX24NTZy+Qkp7Ni4/fj1wuZ8qEMVxMzWLjjgPcN30yPbsHsnrzbmrrGnhi5gyz9fSLCON4bDyfzFuMnZ0t1taWjB89lN3RMZw4fb7LoSyTrh/JL6u28P7cBTjY2+FgZ2f2qrqrNXpof374ZS0fzfsRgABfH0YMjmLvoRPtvqryz+Jgb8vDd09j/bZ97Io+jqWFBQOjIjh15kK7048c2o/zSal8+NUi5HI5ft6ejBrSjwNHTpn1Bmohk8l44M5bWL5mG+99sQBLSws0TVpmzphyVd3ZWwwf2IfMnHw+/3YJri5O0qv8BvQ19RDx9/NCp9Pxxiff8P6rT3c5JMTDzZX7b5/M2i172XXgKA2NatxdnXn4HlMy0nEjBlFeUckn3yzGycEeoxFpXD3AsAF9OR2fxHtzFmChVDC4X2+cHe07Wp0ZG2srHr5nGivWbycmLh69To+VpSUP3TMVgPCeQYwbOYjvlqzC2dEBrU5LVO8wKq+iR86VkMlkPHzPNFZv3s2bn36LUqFg1ND+pGZkI5cr6BHoR3hId/79/VJTTygjjB81lM27o9l98DgRod27XkkrE8cMo7qmjo+//hFnR0cqq6sZM2wgkWGm/CcZ2XnsORTD2BGD/tTfKQiCIAjCJTJVbeVf28z0H2Y0GqmsrkVVVY2NtRWebq5X/Xqh0jIVOoMeH093ZDIZtfUNpter5RWybO02PvnXc5SWqdAbDHh7urVp9WloVFNWUYmzo4PZe+TbU11TR01tHd6e7lhYKFE3adBqde2+NrA9Levy8nBtM+6ztU07D1Bb38g9t02iqLgMe3s7KcFUi9raeqpqavHydMPSwoImjYamJm2bMeNg2s/llVWoG9V4e3q02cd19Q1YWVpKn9fV1WNtY32pu6tGS0lZOdY21p0m9VM3aVAq5CCTYTSYWtHfm/MD906fLCUb02p1VFRW4+xk3+k+AFMrX1FJOQ72du127W/53sLCAi8PV7NjazAYWLlpF/dNn9zp8kvKVTjY2bXZb10da4PBQLmqChsba6lremcMBgNqjQYbKys0Wi0WSiUX0zLZtPMgb734WKfzVlbXUlVdg7ubS5t16XQ6ylXVODra/a6KWmdl8sTp8zg7OhDes/2Kg06nQ61uMnsXu6rKlFvDy8Ptisf5g2m4jlKukJK4tWYwGCgurcDCQiG9H726pg4bGyspyVxnjEYjZRWV6HR6fLzcr7jltyNarY7isgoc7GxxdnLAYDBIySf/qnekX15+LC0sOH8xlZ0HjvP6cw91up32dra4ODlIPU8cHezMcli01rKvmpo0+Hi5m+Xh+D1ahgy5Oju1ub7qdDoamzRXdP6Y/abScqysLKVu7mbrq62npr4ebw83Vm3ejZWlBTNuNr0tpWVIkp2tjZQA9Gro9XqKSstRyBXt3kvq6uqpqKrB16vtNfbPYDQaaWxqwtrSEr3egFwuQ1VVzeffLeWTN57D0tL0toXSchUGg1Haxrq6epQWyi6vtx2pra1HVV2Dm4vT79pvgiAIgiD8fv+YHgAtZDIZrs6OUsbs38PzsqR97T1MXj5Na7Y21tJrpbri5Ghv9hBrbWV5VZWcq1kXmDJCt7z67HIODnZmSb0uT0TXmkwm67TifvlDnf3lybssLfD363q712/bS01dPY/eOx2ZDI7FxqM3GOjeqsXRwkIpZcbvilKp7PD3d/X94ROnGdCn/czlrefvaHxzV8daLpe3WwHpSJNGy3tzFnD7lPEMHdCH2tp6jp44S+/m1xZ2xsXJoU0AqPVvuNL92Z6OyqS6SUNKejb33d5xAEWpVGJvb35Z+r3nc2fBC7lcLr2toEVXwbrWZDLZVR2rrlhYKM1al+VyuTQm+6/S0Kjm/bkLuWvqjQyKiqC6po4jJ88SEdZx+bl8O2UyWZfb+WfvKwd72w4DpEqlEoerDDBYWHR+Tbj8utiaXC7Hy+P3nysKhYJuPh2/VtDe3q7NtfPPJJPJ+HLBcvpGhHLLxDHNQ9FiCQnylwJnMpmszW/8o9vU2T4VBEEQBOGv9Y/rAfBXSsvMYdXm3bz90uN/96ZctW17j1Df0MBdU2/8uzflqtTU1vPrhu1k5hQgk8lwc3HijltvaLfL8V+tdbK0/xbnLiSzeXc0jeomDAYjfXuFMOOWG64qiPSf8t+4/651p+OT2LrvEGp1E0aDkajIUGbcPPGKekFci9Zu3YuVlSW33jD2796UP01WXiFrtuxGVVmNwWAkOLAbd02b1GGAUBAEQRCE/20iACD8T9DpdOgNhg57JFzr1E0alEqFNMRCEK6GuqkJpVIpys81TKPVIkP2lww1EARBEAThv4cIAAiCIAiCIAiCIAjCNeAf8xpAQRAEQRAEQRAEQRA6JgIAgiAIgiAIgiAIgnANEAEAQRAEQRAEQRAEQbgGiACAIAiCIAiCIAiCIFwDRABAEARBEARBEARBEK4BIgAgCIIgCIIgCIIgCNcAEQAQBEEQBEEQBEEQhGuACAAIgiAIgiAIgiAIwjVABAAEQRAEQRAEQRAE4RogAgCCIAiCIAiCIAiCcA0QAQBBEARBEARBEARBuAaIAIAgCIIgCIIgCIIgXANEAEAQBEEQBEEQBEEQrgEiACAIgiAIgiAIgiAI1wARABAEQRAEQRAEQRCEa4AIAAiCIAiCIAiCIAjCNUAEAARBEARBEARBEAThGiACAIIgCIIgCIIgCIJwDRABAEEQBEEQBEEQBEG4BogAgCAIgiAIgiAIgiBcA0QAQBAEQRAEQRAEQRCuASIAIAiCIAiCIAiCIAjXgP8Hn/ra9jk5/AUAAAAASUVORK5CYII="
-  },
-  "model": "meshy-6",
-  "task_id": "01a14304-2a30-713d-8be6-b82013974916",
-  "task_ids": [
-    "01a14304-2a30-713d-8be6-b82013974916"
-  ],
-  "elapsed_sec": 175.62,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14304-2a30-713d-8be6-b82013974916/output/preview.png?Expires=1792385080&Signature=akkg6rGM-ylhGbFK8Dh3nBNV8lA4FdIjKmA4nYL4dQxeq9oZET9-EOFWYAuN6IutfyV6oRo4kZuRGzpJqCMvgwz5coPt~3jXGQ6i8YBIFersT2YuPBIHFYI7dPBTUE-qsBnVC7080vANCfteNVwBKfd62LDbZ1T8sS0VqWD6M9mDw76Iytj~pgCaRfGsRFj7mqpdcSJVH3m9d~IPb8vsqT0CiKx9xlattIsDMmJkhbjo6SUflOYJc7i3ahkmAGeNBPTDXJLZaoxDWFcvdpBKGzTcDZPmdO4O9~X8qc0sQQdego-ROIW9uw0i4P8ki0GVDjpCd0DHAoLhmLm-c4jh6A__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 9283732,
-  "triangles": 4948,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "Khronos glTF Blender I/O v4.4.55",
-  "cache_key": "26a93022400fe0ef24f99d483f9a0340da570705cce3afa74bcb7447dcafd06f"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/4701904eafbca53a75b854d2e936912a3fbbea4d8276ae967234bb9f037eb056.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/4701904eafbca53a75b854d2e936912a3fbbea4d8276ae967234bb9f037eb056.meta.json
deleted file mode 100644
index 558f715006ebe764b9e1c907fe211c405c763491..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/4701904eafbca53a75b854d2e936912a3fbbea4d8276ae967234bb9f037eb056.meta.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "should_texture": true,
-    "enable_pbr": true,
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"
-  },
-  "model": "meshy-6",
-  "task_id": "01a14303-3706-7240-9c8a-98546a4ff8ee",
-  "task_ids": [
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/644958ba41a9b8a22f0c3b10d08c83789d9f485ae21c2943dbcad8ad89186c6f.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/644958ba41a9b8a22f0c3b10d08c83789d9f485ae21c2943dbcad8ad89186c6f.meta.json
deleted file mode 100644
index 8d16542c5f6138ea5ac1a2828f3f9ae9d9a423d9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/644958ba41a9b8a22f0c3b10d08c83789d9f485ae21c2943dbcad8ad89186c6f.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
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-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/6da08ad2bc9daae428211b9bce1f1bc3581877bb117a2fffd593d8614024da0a.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/6da08ad2bc9daae428211b9bce1f1bc3581877bb117a2fffd593d8614024da0a.meta.json
deleted file mode 100644
index 1db3ece6c651606ee28daf10404f9f572bd2374a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/6da08ad2bc9daae428211b9bce1f1bc3581877bb117a2fffd593d8614024da0a.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
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-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/7bb02b3e76878adff2abe7f2cc47adcc990f58fcd0bba090c02e06166c5e8a16.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/7bb02b3e76878adff2abe7f2cc47adcc990f58fcd0bba090c02e06166c5e8a16.meta.json
deleted file mode 100644
index 64b79fa29e3b261a74b52b310679a9fc747ef17d..0000000000000000000000000000000000000000
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+++ /dev/null
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"
-  },
-  "model": "meshy-6",
-  "task_id": "01a14304-6d85-74b2-8d70-da2271d824cb",
-  "task_ids": [
-    "01a14304-6d85-74b2-8d70-da2271d824cb"
-  ],
-  "elapsed_sec": 190.66,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14304-6d85-74b2-8d70-da2271d824cb/output/preview.png?Expires=1792385114&Signature=qIyaqohNcjMY4~w3LQRAZnI6fc0jp0aPV-uofCjl5S8wRzIlc-FQTUdE9XQAvQ3dkmFgjykqRop-~1sKBrKBHRH95t7JvuRctuWwMo7rOKECIwlDgXCVy4fYDyUymQA33V6TRRaMvooEwh6xiviWj6LG2U31b-5MxBx6mdm9NaWXQRjTUdVeGUxMxzQxT9wmWISmNjYY4R6nR9gpSEuVkAyeGowmszyTb~jCNsgEMaJOCgwKhUs0C~PeS-xZpFo601zt0FMX-4lMk9pj2RT9fZf4VTj-bNpljMV7dnoZJLZvZJ1OGfk5twDMr2iYXElEH8LsmoPLVI2N6l5-aNa0AA__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 8321892,
-  "triangles": 7670,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "Khronos glTF Blender I/O v4.4.55",
-  "cache_key": "7bb02b3e76878adff2abe7f2cc47adcc990f58fcd0bba090c02e06166c5e8a16"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/7f4f94fc01ba0628b1c936ab8eb28d485bb964afbc744d828ed1b4d920038144.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/7f4f94fc01ba0628b1c936ab8eb28d485bb964afbc744d828ed1b4d920038144.meta.json
deleted file mode 100644
index 360c1626c934c167bf25e5acb473c5cf3b25242b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/7f4f94fc01ba0628b1c936ab8eb28d485bb964afbc744d828ed1b4d920038144.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1057
-  },
-  "model": "meshy-6",
-  "task_id": "01a14306-5b3b-71ee-87c0-bf7de2a25093",
-  "task_ids": [
-    "01a14305-a53d-7760-91cf-3661dd1b5925",
-    "01a14306-5b3b-71ee-87c0-bf7de2a25093"
-  ],
-  "elapsed_sec": 167.68,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14306-5b3b-71ee-87c0-bf7de2a25093/output/preview.png?Expires=4945708800&Signature=eoVQfdWv4OMa4hbS0nnatyU3FXec4PwSTXSycWzsaSSklwp4vrjG7TRbsmE6~X1WqnYctAWIXOM~OesMrbCAeKuFQVgwK9MupnSCf1iR4X0aZcary1UgVKGnx7ISLVCudAykGcnIbUid62Z-11QO7iuTFOg9~6KfKtZMRrZxgfkjWuQknvm4wc0Ru5P~7w9bkcg~Pa5W6-v4z8MotNTyeCazC~mm31T7rAO~IUt5t0bNo7Y7NgYD9rhTwy6sEmxxkMmIYYHKZayQD1P0-G8I2E6mPc1EF27wjPVfsE12TuT38TaP3AcwFqDJbkjKl7nMr13Bky66YxZGGkH2-OGp9A__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 8084188,
-  "triangles": 7958,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "pygltflib@v1.16.5",
-  "cache_key": "7f4f94fc01ba0628b1c936ab8eb28d485bb964afbc744d828ed1b4d920038144"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/974a16aba0f80b33455aa399efeca28a600f5ad3c97279ad4b119ffbbcdc9e12.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/974a16aba0f80b33455aa399efeca28a600f5ad3c97279ad4b119ffbbcdc9e12.meta.json
deleted file mode 100644
index f419ff1be69b93379efaddc7396f460106551d66..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/974a16aba0f80b33455aa399efeca28a600f5ad3c97279ad4b119ffbbcdc9e12.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1053
-  },
-  "model": "meshy-6",
-  "task_id": "01a14306-0574-778b-902d-def5e43ebb9c",
-  "task_ids": [
-    "01a14305-3a8c-768a-9b6e-73b7233c2c67",
-    "01a14306-0574-778b-902d-def5e43ebb9c"
-  ],
-  "elapsed_sec": 177.34,
-  "credits": null,
-  "cached": false,
-  "format": "glb",
-  "preview_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14306-0574-778b-902d-def5e43ebb9c/output/preview.png?Expires=4945708800&Signature=dupLoiR4pkbGI4-wk1ShHN9JPsvLuwVnPSL7a99CH2j1sEk~e9vDcEFCj62GvgQE98UokvtsfvFt8UB1eeHIGQJEE1r100y1jAVbUCtdlXjLaDPOortVRh842CniIMq3BKY5TY-OtjBH6ZdVlah9jLpq9K9wup3VqONfbb0Hu6xxDrkGvjahHR6pjQkD-FnUb9y1CXTLzRHITok8KLSbAKnxJoJNGU7PCU7RRW7vNhyfkU8MUEAGBPHgh5hmWw90H0lSZoF~wBoL1jT7u8bVInAeXkyX1zPRDd3ZjssPX54h~QGSVuIHJOpKTCEqRcjcYDsRGFlPyXLpN6vSPDvz7Q__&Key-Pair-Id=K1VGYTHIYLM9UM",
-  "bytes": 8664800,
-  "triangles": 8260,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 4,
-  "images": 4,
-  "nodes": 1,
-  "generator": "pygltflib@v1.16.5",
-  "cache_key": "974a16aba0f80b33455aa399efeca28a600f5ad3c97279ad4b119ffbbcdc9e12"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/9844e9b2b058619a45c5f1326fc9419a9f1b45d810902d0a02592d37f60aeb6b.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/9844e9b2b058619a45c5f1326fc9419a9f1b45d810902d0a02592d37f60aeb6b.meta.json
deleted file mode 100644
index 27cc46622246d24be91a6d6b4dca1f8a46c42f01..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/9844e9b2b058619a45c5f1326fc9419a9f1b45d810902d0a02592d37f60aeb6b.meta.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "should_texture": true,
-    "enable_pbr": true,
-    "image_url": 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"
-  },
-  "model": "meshy-6",
-  "task_id": "01a14303-1478-74cd-8b89-00f9271cac6b",
-  "task_ids": [
-    "01a14303-1478-74cd-8b89-00f9271cac6b"
-  ],
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/a8318bda7b245e48be7f721388e2be9ac3ae8e7e35cd88d7459b93e063b1a57d.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/a8318bda7b245e48be7f721388e2be9ac3ae8e7e35cd88d7459b93e063b1a57d.meta.json
deleted file mode 100644
index 0d99b17f0868a3e1800cc0fb25aa0245b03d6368..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/a8318bda7b245e48be7f721388e2be9ac3ae8e7e35cd88d7459b93e063b1a57d.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/b6c67ee5d2a796bdd4d29b4c7257d4c88a9b2bac5c5b37993445100992c6ec41.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/b6c67ee5d2a796bdd4d29b4c7257d4c88a9b2bac5c5b37993445100992c6ec41.meta.json
deleted file mode 100644
index 9a580ae1b29bc00e5278e676f38e586a9a18405d..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/b6c67ee5d2a796bdd4d29b4c7257d4c88a9b2bac5c5b37993445100992c6ec41.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/db93d9f213f2ed98c7fdda9eeb78bd3e56023d80a0e917f782b982eefa1b5df8.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/db93d9f213f2ed98c7fdda9eeb78bd3e56023d80a0e917f782b982eefa1b5df8.meta.json
deleted file mode 100644
index fedb67a34770d4398e2240a84cfb78dbac2fd8c9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/db93d9f213f2ed98c7fdda9eeb78bd3e56023d80a0e917f782b982eefa1b5df8.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1059
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/e8e5659dc025c4f4950da08f8240760ce5482c484d9ee6e1dbdd4269c062f4d9.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/e8e5659dc025c4f4950da08f8240760ce5482c484d9ee6e1dbdd4269c062f4d9.meta.json
deleted file mode 100644
index 3e80518d1a72353c6defdd59924641b969da9963..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/e8e5659dc025c4f4950da08f8240760ce5482c484d9ee6e1dbdd4269c062f4d9.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1058
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/f088c1a2622700791378807cd364f0eb9c41c9a7ec4a4f2b1056de0fcc684f03.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/f088c1a2622700791378807cd364f0eb9c41c9a7ec4a4f2b1056de0fcc684f03.meta.json
deleted file mode 100644
index 80e4eae641b7d25d4657101db32ab2c24572911b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/f088c1a2622700791378807cd364f0eb9c41c9a7ec4a4f2b1056de0fcc684f03.meta.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "provider": "meshy",
-  "payload": {
-    "ai_model": "meshy-6",
-    "model_type": "standard",
-    "topology": "triangle",
-    "target_polycount": 8000,
-    "should_remesh": true,
-    "mode": "preview",
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "seed": 1050
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-  "model": "meshy-6",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/0c198271c5babadea211de7c31f70cceeac8573c80c99a10632c7fc13b98db76.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/0c198271c5babadea211de7c31f70cceeac8573c80c99a10632c7fc13b98db76.meta.json
deleted file mode 100644
index 701c6c89ab23d4cca89a956c75174297b754a125..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/0c198271c5babadea211de7c31f70cceeac8573c80c99a10632c7fc13b98db76.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "model_seed": 1045,
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/24c4524bd1656984e5b86112f2170cae0a19ea24c6adc6ca793694daa9b82278.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/24c4524bd1656984e5b86112f2170cae0a19ea24c6adc6ca793694daa9b82278.meta.json
deleted file mode 100644
index 98dab686546981e02ce23f28a5a0d4d3cbd69a3a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/24c4524bd1656984e5b86112f2170cae0a19ea24c6adc6ca793694daa9b82278.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/2ffe8850e6e31902cc252a668b1ca806e33610e4096e8cf9f700ecc76dd8ef1c.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/2ffe8850e6e31902cc252a668b1ca806e33610e4096e8cf9f700ecc76dd8ef1c.meta.json
deleted file mode 100644
index 45d6e501d15b827b2606de5410e419101e20d013..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/2ffe8850e6e31902cc252a668b1ca806e33610e4096e8cf9f700ecc76dd8ef1c.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/53b47277e349e5f727c045d3f18876fb5e23c3f6de6557e9bf67dbfa5ac0366d.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/53b47277e349e5f727c045d3f18876fb5e23c3f6de6557e9bf67dbfa5ac0366d.meta.json
deleted file mode 100644
index 315d0ed9d0a940f3f5362f784f7a69a4a7bfa0d2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/53b47277e349e5f727c045d3f18876fb5e23c3f6de6557e9bf67dbfa5ac0366d.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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-    "pbr": true,
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/75e0c5ec72e0b52dbf62c7d650e4fefeceedce6d7fc7334a65f6ab5a6f23166c.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/75e0c5ec72e0b52dbf62c7d650e4fefeceedce6d7fc7334a65f6ab5a6f23166c.meta.json
deleted file mode 100644
index b38bab3529c159c0e36fcc539e1651f39f7f5d37..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/75e0c5ec72e0b52dbf62c7d650e4fefeceedce6d7fc7334a65f6ab5a6f23166c.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8446c37efb2ca76ca7823d2a60e7f33705fe8080d6738602cf10155abfca5205.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8446c37efb2ca76ca7823d2a60e7f33705fe8080d6738602cf10155abfca5205.meta.json
deleted file mode 100644
index cb308ba54f6c7df2aa09a2a50c07778d6a2659bd..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8446c37efb2ca76ca7823d2a60e7f33705fe8080d6738602cf10155abfca5205.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8c0df05de46c22562109d6a0066b37e559ed35be595e20dd61e31c4f142c065b.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8c0df05de46c22562109d6a0066b37e559ed35be595e20dd61e31c4f142c065b.meta.json
deleted file mode 100644
index b2769da9f915316fa0ab7a5c2fd2fdcb5993f883..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/8c0df05de46c22562109d6a0066b37e559ed35be595e20dd61e31c4f142c065b.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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-    "pbr": true,
-    "texture_quality": "standard",
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-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/966bdbe6d16b49c164b0122d852f51bde77de08a483adb922caeea38754a22b3.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/966bdbe6d16b49c164b0122d852f51bde77de08a483adb922caeea38754a22b3.meta.json
deleted file mode 100644
index 3ee0bed8e7e01838ac5d4a60ef4e597babab788c..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,30 +0,0 @@
-{
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-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/bb775f4d59c322071f573c4cf7ff62a01e68d13080d1cc203287342853fc502c.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/bb775f4d59c322071f573c4cf7ff62a01e68d13080d1cc203287342853fc502c.meta.json
deleted file mode 100644
index 8d6e7c6338fec4f65b3740929216156f8e604ace..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/bb775f4d59c322071f573c4cf7ff62a01e68d13080d1cc203287342853fc502c.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
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-    "face_limit": 8000,
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-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "bb775f4d59c322071f573c4cf7ff62a01e68d13080d1cc203287342853fc502c"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/c11cd18fd682c99ff1374d042f8cc3f32a6bd0c834a96297df298782fe129452.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/c11cd18fd682c99ff1374d042f8cc3f32a6bd0c834a96297df298782fe129452.meta.json
deleted file mode 100644
index 78a0a47ff98e848f91a2fe1f4c24b5352b115c86..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/c11cd18fd682c99ff1374d042f8cc3f32a6bd0c834a96297df298782fe129452.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1051,
-    "texture_seed": 1051
-  },
-  "model": "v3.1-20260211",
-  "task_id": "cc80cb88-5b1e-4ce3-bd36-d97c0177e73c",
-  "elapsed_sec": 258.94,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/cc80cb88-5b1e-4ce3-bd36-d97c0177e73c/legacy_mesh.webp?auth_key=1792226132-hreonbey-0-094906be212b69cc4729be3b144371ba",
-  "bytes": 3176296,
-  "triangles": 10440,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "c11cd18fd682c99ff1374d042f8cc3f32a6bd0c834a96297df298782fe129452"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/ccc61504cd8360afd1bb563fb4a9d1af8470f66ed8b6d3344002384edc05b1c5.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/ccc61504cd8360afd1bb563fb4a9d1af8470f66ed8b6d3344002384edc05b1c5.meta.json
deleted file mode 100644
index 0932251451c2bbce92eb5b59a753952d1ec0542f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/ccc61504cd8360afd1bb563fb4a9d1af8470f66ed8b6d3344002384edc05b1c5.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1052,
-    "texture_seed": 1052
-  },
-  "model": "v3.1-20260211",
-  "task_id": "ca05eb37-0729-4aa2-a41d-1bee36e61d6d",
-  "elapsed_sec": 270.54,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ca05eb37-0729-4aa2-a41d-1bee36e61d6d/legacy_mesh.webp?auth_key=1792226253-ggymjkwl-0-4a8b782032e3c3bca69ab126abc85460",
-  "bytes": 2903700,
-  "triangles": 10626,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "ccc61504cd8360afd1bb563fb4a9d1af8470f66ed8b6d3344002384edc05b1c5"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d54636778bae718beec0355405c0fc3d40551a1ac53509369c63bca70fa2217c.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d54636778bae718beec0355405c0fc3d40551a1ac53509369c63bca70fa2217c.meta.json
deleted file mode 100644
index b116ee42fb2169a1c5c3c8409192aa42f345a94d..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d54636778bae718beec0355405c0fc3d40551a1ac53509369c63bca70fa2217c.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1053,
-    "texture_seed": 1053
-  },
-  "model": "v3.1-20260211",
-  "task_id": "703ea0f5-51d7-47e6-a38d-43458e8dfbdb",
-  "elapsed_sec": 229.96,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/703ea0f5-51d7-47e6-a38d-43458e8dfbdb/legacy_mesh.webp?auth_key=1792226262-qhycwuxj-0-4514241b67a03d733bd93c30d7748ed9",
-  "bytes": 2968040,
-  "triangles": 9475,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "d54636778bae718beec0355405c0fc3d40551a1ac53509369c63bca70fa2217c"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d62337eee0b3caba2207ec3686ffd0efb908639bdefd6649e3236e7f0dd62100.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d62337eee0b3caba2207ec3686ffd0efb908639bdefd6649e3236e7f0dd62100.meta.json
deleted file mode 100644
index b040024e8406e1533a45a63fbb08b7838e865533..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d62337eee0b3caba2207ec3686ffd0efb908639bdefd6649e3236e7f0dd62100.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1059,
-    "texture_seed": 1059
-  },
-  "model": "v3.1-20260211",
-  "task_id": "61974b83-4072-4c6d-b0b1-c10a45043a81",
-  "elapsed_sec": 223.81,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/61974b83-4072-4c6d-b0b1-c10a45043a81/legacy_mesh.webp?auth_key=1792226753-fufdoxhm-0-943372fc0062dbe1154de54616cc90db",
-  "bytes": 1497504,
-  "triangles": 7763,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "d62337eee0b3caba2207ec3686ffd0efb908639bdefd6649e3236e7f0dd62100"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d861a2d93592844c36f3e4220ebd9047c60664ae4e43a7d784ff9bec802b5b8b.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d861a2d93592844c36f3e4220ebd9047c60664ae4e43a7d784ff9bec802b5b8b.meta.json
deleted file mode 100644
index e2eb49dd3c96d86c6aee535211cf5333eda9d365..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/d861a2d93592844c36f3e4220ebd9047c60664ae4e43a7d784ff9bec802b5b8b.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1050,
-    "texture_seed": 1050
-  },
-  "model": "v3.1-20260211",
-  "task_id": "62c8741b-2215-47eb-83a4-5d09f4a123e9",
-  "elapsed_sec": 250.75,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62c8741b-2215-47eb-83a4-5d09f4a123e9/legacy_mesh.webp?auth_key=1792226030-kyhbfqdg-0-a780f1c80f1be497631e89a1e7187cfe",
-  "bytes": 3058752,
-  "triangles": 9873,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "d861a2d93592844c36f3e4220ebd9047c60664ae4e43a7d784ff9bec802b5b8b"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/e14eb8c372611bd7a54f2ba0517d81e254cce87b5bd538499ed0c62b6b29c013.meta.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/e14eb8c372611bd7a54f2ba0517d81e254cce87b5bd538499ed0c62b6b29c013.meta.json
deleted file mode 100644
index 70074d2eae889fd2654992704af8049ff9389970..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/tripo/e14eb8c372611bd7a54f2ba0517d81e254cce87b5bd538499ed0c62b6b29c013.meta.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "provider": "tripo",
-  "payload": {
-    "model": "v3.1-20260211",
-    "texture": true,
-    "pbr": true,
-    "texture_quality": "standard",
-    "geometry_quality": "standard",
-    "smart_low_poly": true,
-    "face_limit": 8000,
-    "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-    "model_seed": 1057,
-    "texture_seed": 1057
-  },
-  "model": "v3.1-20260211",
-  "task_id": "9fd46e2c-4479-4558-9ccd-5f13afcaeb27",
-  "elapsed_sec": 228.85,
-  "credits": 30.0,
-  "cached": false,
-  "preview_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/9fd46e2c-4479-4558-9ccd-5f13afcaeb27/legacy_mesh.webp?auth_key=1792226618-iwstkufn-0-d59786764a02f08fd60ae1be43687a79",
-  "bytes": 2272256,
-  "triangles": 8463,
-  "meshes": 1,
-  "materials": 1,
-  "textures": 3,
-  "images": 3,
-  "nodes": 1,
-  "generator": "https://tripo3d.ai",
-  "cache_key": "e14eb8c372611bd7a54f2ba0517d81e254cce87b5bd538499ed0c62b6b29c013"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/frozen_game_inputs.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/frozen_game_inputs.json
deleted file mode 100644
index ae663cb5f4171e31ebb8e269bdee89af72b4f63a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/frozen_game_inputs.json
+++ /dev/null
@@ -1,489 +0,0 @@
-{
-  "schema_version": "game_delivery_view_not_execution_plan_v1",
-  "batch_id": "obj200_meshy_tripo_20261015_v2",
-  "game_id": "G045_daily_street_lightlane",
-  "parent_frozen_plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "parent_frozen_plan_file_sha256": "3c4798c34345ab6f7fc07f850f25deca44051565185a1c8a80058819e1bbd685",
-  "task_count": 15,
-  "parent_plan_original": {
-    "archive": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/provenance/frozen_parent_plan_original.zip",
-    "archive_member": "bench/test_data/outputs/_asset_batches/obj200_meshy_tripo_20261015_v2/plan.json",
-    "original_file_sha256": "3c4798c34345ab6f7fc07f850f25deca44051565185a1c8a80058819e1bbd685",
-    "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d"
-  },
-  "scope": "3d_object_only_not_complete_stage2",
-  "providers": {
-    "meshy": {
-      "model": "meshy-6",
-      "api_base": "https://api.meshy.ai",
-      "options": {
-        "low_poly": false,
-        "texture": true,
-        "enable_pbr": true,
-        "should_remesh": true,
-        "topology": "triangle"
-      },
-      "credits": {
-        "text_preview": 20,
-        "text_refine": 10,
-        "image": 30
-      }
-    },
-    "tripo": {
-      "model": "v3.1-20260211",
-      "api_base": "https://openapi.tripo3d.com/v3",
-      "options": {
-        "low_poly": true,
-        "texture": true,
-        "pbr": true,
-        "texture_quality": "standard",
-        "geometry_quality": "standard"
-      },
-      "credits": {
-        "text": 30,
-        "image": 40
-      }
-    }
-  },
-  "postprocess": {
-    "tool": "gltf-transform-4.2.1_meshoptimizer-0.24.0",
-    "simplify_error": 0.01,
-    "simplify_passes": 1,
-    "triangle_budget": "per_task_decimation_target_at_most_8000",
-    "texture_budget": "per_task_texture_size_downsize_only",
-    "preserve_native_output": true
-  },
-  "runtime_sources": {
-    "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py": "70f8d79c8596908f7edcd7ff68120f558cea6881f23dc2f0e29ef62f2bab75c4",
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-        "name_zh": "猫兽人主角",
-        "role": "character",
-        "input_mode": "image_to_3d",
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-        "name_zh": "街区自行车",
-        "role": "vehicle",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood bicycle per reference image: orange low-step frame, two dark wheels with clear axle hierarchy, beige front basket and dark saddle, frame-and-wheels relationship readable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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-        "task_id": "G045_daily_street_lightlane_obj_003",
-        "asset_name": "apartment_building",
-        "name_zh": "公寓建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Two-story apartment building per reference image: grey-blue main block with exterior corridor and stairs, orange front door, laundry lines and shared garbage spot, collision-friendly blocky silhouette, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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-        "task_id": "G045_daily_street_lightlane_obj_004",
-        "asset_name": "convenience_store",
-        "name_zh": "便利店建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood convenience store per reference image: teal-green awning sign, warm white doors and windows, visible shelves through glass, side parking spot, original branding-free facade, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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-        "task_id": "G045_daily_street_lightlane_obj_005",
-        "asset_name": "maintenance_toolkit_set",
-        "name_zh": "维修工具组",
-        "role": "prop",
-        "input_mode": "image_to_3d",
-        "prompt": "Street maintenance and odd-job toolkit set per reference image: toolbox, broom, trash picker, folded crate and small lost-item pouch props, everyday readable silhouettes, single kit, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1049,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
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-        "task_id": "G045_daily_street_lightlane_obj_006",
-        "asset_name": "npc_housekeeper_neighbor",
-        "name_zh": "N01 家务熟手邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1050,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
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-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
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-        "task_id": "G045_daily_street_lightlane_obj_007",
-        "asset_name": "npc_curious_neighbor",
-        "name_zh": "N02 好奇邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1051,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
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-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
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-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_008",
-        "asset_name": "npc_stroll_partner",
-        "name_zh": "N03 散步伙伴",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1052,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
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-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
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-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_009",
-        "asset_name": "npc_illustrator",
-        "name_zh": "N04 插画师",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1053,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q05",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
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-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
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-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_010",
-        "asset_name": "npc_landlord",
-        "name_zh": "N05 房东",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1054,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q07",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_011",
-        "asset_name": "npc_store_clerk",
-        "name_zh": "N06 店员",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1055,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_012",
-        "asset_name": "environment_delivery_car",
-        "name_zh": "环境车辆",
-        "role": "vehicle",
-        "input_mode": "text_to_3d",
-        "prompt": "Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1056,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "vehicle",
-        "spec_ref": "V05,F04",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_013",
-        "asset_name": "street_lamp_post",
-        "name_zh": "街区路灯",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1057,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P06,WD01,VC02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_014",
-        "asset_name": "riverside_park_bench",
-        "name_zh": "河边公园长椅",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1058,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W04,L06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_015",
-        "asset_name": "street_vending_machine",
-        "name_zh": "商业街售货机",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1059,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    }
-  ],
-  "input_policy": "shared_short_context_and_reference_only",
-  "context_limitations": "Text APIs received shared short context plus asset prompt; image APIs received reference only. Full requirements retained as evidence, not sent to APIs.",
-  "benchmark_metrics": {
-    "C": {
-      "status": "UNKNOWN",
-      "value": null
-    },
-    "Q1": {
-      "status": "UNKNOWN",
-      "value": null
-    },
-    "Q2": {
-      "status": "UNKNOWN",
-      "value": null
-    },
-    "Q3": {
-      "status": "UNKNOWN",
-      "value": null
-    }
-  },
-  "view_sha256": "f4695e9e0784452d592a9cb67f7f21d63c1dbc4145095bd5304e5906cc207241"
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt
deleted file mode 100644
index 4c42962a2dfe5c2e567229606085d70b578d14b3..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt
+++ /dev/null
@@ -1,319 +0,0 @@
-# 《日常街区的光轨穿行》｜街区日常探索机制输入规范
-
-```yaml
-case_id: G045
-base_case_id: G045
-title: 日常街区的光轨穿行
-category: Simulation / Management
-tier: L-Regular
-engine: unity3d
-report_track: Standard
-paired_conditions: [R, N]
-required_real_clients: 1
-fixed_seed: 1045
-spec_version: v3
-status: DRAFT_LOCAL
-mechanism_source: null
-task_spec_version: null
-task_spec_hash: null
-frozen_by: null
-frozen_at: null
-dependency_hashes: null
-```
-
-## 0. 标题、版本与使用说明
-
-- 作品名：《日常街区的光轨穿行》（团队测例 DAILY_STREET_001「日常街区探索」，候选提交名「光轨穿行」）；需求版本：mechanics-v3（2026-10-11 完整性修订：吸收 yyy 反馈——冻结引擎路线为 Unity 2022.3 LTS＋URP（原 three.js Web 推荐与旧版开放式引擎口径废弃，交付口径由 web/index.html 改为 Unity 桌面包）；新增世界设定与创作基线（§15）、演出镜头与反馈冻结（§16）、过场视频 VC01–VC04（§17）、参考图绑定扩充（§18）与分阶段验收/性能诊断（§21）；全部 G/M/W/L/N/E/V/A/Q/X/P/U/S/C 冻结规则与 K01/K02 跨阶段后果原样保留，**不删减任何长流程与跨阶段状态机制**）；运行引擎：**Unity 2022.3 LTS＋URP**——选择理由：第三人称角色、NPC 日程、自行车、存读档与六区域运行闭环在 Unity 路线最完整；目标构建：Windows/macOS 64 位桌面包；参考分辨率 1920×1080（1280×720 最低可用）；摄像机：第三人称跟随（M03 冻结距离 3–7 米）；输入：键鼠；构建启动：Unity 2022.3 打开工程、Build 出桌面可执行文件后直接运行。
-- 文件性质与玩法目标：本文件是给游戏生成模型的输入，要求产出能实际游玩的第三人称 3D 街区探索游戏；玩家扮演原创猫兽人居民，在人类与兽人共同生活的街区中散步、工作、购物、借车、串门、探索、处理麻烦并交付房租；文档存在不等于玩法完成。
-- 整理声明：本文件由团队包 `game_lightlane_example/EXPLORATION_street.txt` 忠实整理为 G045 的 game_spec.md，全部规则编号（G/M/W/L/N/E/V/A/Q/X/P/U/S/C 系）与冻结数值原样保留；团队包 `rubric.txt`（19 父项）与 `metrics.json`（原子断言全集）作为 G045 rubric 的素材基线另行重核；随包 `submission/`、`submission_source.zip` 是一次历史候选提交，不是正确答案，不得从其缺少的能力反向删减需求、调低数量或改变游戏类型（团队稿原约定保留）。
-- 原创边界：人物、地名、对白、故事、徽记与素材使用原创内容；不使用已有动漫或商业游戏名称、具名人物、原作造型、剧情或标志；本测例只借助通用日常模拟与自由探索机制，不能把「像某作品」作为评分条件。images 四张原创图（A01 居民/A02 自行车/A03 公寓/A04 便利店）是概念示意图，不是游戏截图也不是已实现证据，只约束绑定对象的可辨识外观，不要求复刻背景、标注文字或像素，允许无骨骼程序动画。
-- 独立 oracle 数例（引源冻结值，详见 §4/§5/§8 与 T05/T06/T09/T11）：饱腹/精力/卫生每游戏小时 −4/−3/−2；饭团 80 现金/饱腹＋35、普通餐 150/＋65；长椅休息 30 游戏分钟精力＋15 压力−10；信任变化 ＋10/＋8/−12/−15/＋5、阈值 20/40/−20；注意度 撞倒物＋10、骑车撞行人＋20、处理−15、脱离后 −2/真实秒；整理 350/配送 450/清理 300/送稿 650/找物 200；三日毛收入示例 500＋350＋450×3＋650＝3550（需运行可达性验证）。
-- 完整验收范围是下面全部需求。分阶段开发不改变完整范围，也不能把说明、按钮占位或静态动画当作玩法交付。具体实现先保证核心循环与恢复路径，再完善表现。规则优先级：统一口径 C01–C10 ＞ 编号需求 ＞ 外观图绑定 ＞ 示例估算；未给数值用公开且稳定的默认配置，不自行冒充冻结数值。
-- 统一能力边界（yyy 口径）：未明确要求时，不自行扩展复杂联机、AI 对手或其他高耦合玩法；世界背景只用于统一角色、场景、故事和资产提示，不单独增加「世界观质量」评分。
-
-## 1. 总体循环与目标 [G01–G06]
-
-- G01 类型：第三人称三维街区日常探索。可自由平面移动、观察、交互与骑车；不能用只左右移动的躲障跑道替代街区探索。
-- G02 首版六类区域：公寓、便利店、商业街、河边公园、后巷、屋顶；地图约 180m×180m。目标内容长度约 30–45 分钟是设计目标，不以个体游玩时长作为硬性评分阈值。
-- G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。
-- G04 初始第 1 天 08:00，现金 500，未付房租 3000。第 3 天 20:00 为原始截止；交租是独立交易，不能把持有现金显示成已付房租。
-- G05 第 3 天结算：现金足够可支付；不足且房东信任 ≥20 可延期一次到第 4 天 20:00；否则可用公开清理工作抵扣。到期不强制游戏结束。结局三条分支均能到达阶段结算，结算后仍可自由活动。
-- G06 可配置且接入实际规则的参数至少包括移动速度、时间倍率、状态衰减、工作报酬、房租、注意度阈值；平台版本交付 Unity 2022.3 Windows/macOS 桌面包，tune 参数以项目内配置资产（ScriptableObject/JSON config，字段口径沿用 tune/tuneSchema）交付。目录与文件本身不直接获得四维主分。
-
-## 2. 移动与交互 [M01–M08]
-
-- M01 WASD 按相机水平朝向移动；鼠标旋转第三人称视角。步行 3m/s、跑步 5m/s。Shift 跑步，停止跑步 1 真实秒后恢复耐力。跳跃 Space，最高约 0.8m，无无限跳跃。
-- M02 耐力 0–100，初始 100；跑步每真实秒 −12，恢复每真实秒 ＋18，钳制到范围。耗尽转步行，不丧失基础操作能力。
-- M03 相机距离 3–7m，可调整灵敏度；遇墙缩短距离。玩家不能穿过主要建筑碰撞体，镜头不能长期处于不透明墙内。
-- M04 E 交互：最近、有效、距离 ≤2m 且无遮挡的对象；不能隔墙对话或拾取。多个候选按距离、稳定对象 ID 排序，提示与实际对象一致。
-- M05 F 上下车；Tab 背包；M 地图；Esc 暂停与释放鼠标。自行车状态 Space 刹车，不触发步行跳跃。
-- M06 对话、商店、背包、地图与暂停界面期间停止移动并冻结模拟时钟；菜单输入不穿透到世界。首次入场通过明确点击获取鼠标控制，关闭菜单可恢复。
-- M07 用低墙、箱子、窄路形成有碰撞的猫式捷径；只能在声明的路径上攀越，不无条件攀爬全地图。捷径发现记录地图。
-- M08 地图边缘、暂未开放区域用物理/视觉边界约束。提供「回安全点」和「扶正自行车」，不发奖励、不清除债务或任务状态。
-
-## 3. 区域与交付清单 [W01–W08]
-
-- W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。
-- W02 便利店：可进入的货架、收银台、停车位，提供购买、整理工作与配送。
-- W03 商业街：小吃摊、售货机、公告栏、二手摊；提供消费、委托和杂物回收入口。
-- W04 河边公园：步道、桥、长椅与居民，支持休息、找物、观景和约会任务。
-- W05 后巷：箱子、围栏、垃圾桶、施工障碍，存在只能步行通过的窄口。
-- W06 屋顶：通过楼梯/合法平台可达，提供观景点、晾衣区和探索发现。
-- W07 至少 20 个有真实用途的交互点，每点具有唯一对象 ID；重复实例可计数量但不能拿纯装饰充数。各区域有可识别外观与中文名称。
-- W08 公寓到便利店正常步行约 30 真实秒、已知捷径约 18 秒；以相同起终点、不跑步的固定路线验证，分别接受 24–36 秒、14–22 秒。道路适合自行车，窄巷有不同通行限制。
-
-## 4. 时间与生活状态 [L01–L08]
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-- L01 基准 1 真实秒＝1 游戏分钟。新日 08:00 开始；24:00 提示回家，02:00 触发疲劳送回家并跳至当日 08:00（第 N 日 02:00→第 N 日 08:00，不再额外＋1 日）。每天只触发一次该事件。
-- L02 主动睡觉统一进入下一次 08:00；时间跳跃结算 NPC 日程、商店营业与任务期限，日结只执行一次。对话/菜单/暂停时间不计入模拟，不算过期。
-- L03 饱腹/精力/压力/卫生范围 0–100，初始 70/80/25/60。饱腹每游戏小时 −4，精力 −3，卫生 −2；翻垃圾另 −10 卫生；压力由具体事件改变。钳制到范围。
-- L04 饱腹 <20 使耐力恢复减半；精力 <20 使跑步速度 −20%；卫生 <25 使指定 NPC 拒绝陪同。压力 >80 显示烦躁表情与吐槽，不随机改键或禁用移动。边界 20/25/80 使用严格不等号。
-- L05 饭团 80 现金、饱腹＋35；普通餐 150 现金、饱腹＋65。价格/物品/状态在购买或食用时分阶段原子结算，钱不足不能买；每件只吃一次。
-- L06 长椅休息推进 30 游戏分钟，精力＋15、压力−10；洗澡推进 30 游戏分钟、卫生恢复 100。按 C03 处理被动衰减。
-- L07 睡觉精力 100、压力−25、饱腹−15；跳过时段不重复扣被动衰减。卫生不自动恢复。无需生命值或死亡系统。
-- L08 虚构品牌嗜好物 120 现金/包，每包 5 次使用；一次推进 5 游戏分钟、压力−8、卫生−2，按 C03 处理。仅作可选消费，不增加移动、收入或社交成功率；任务不强制消费。允许区/禁止区有明确标识，被发现违反限制后进入邻里响应。
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-## 5. 居民与关系 [N01–N08]
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-- N01 六个原创关键 NPC 及稳定 ID：N01 家务熟手邻居、N02 好奇邻居、N03 散步伙伴、N04 插画师、N05 房东、N06 店员。只使用功能身份与原创对白，没有原作人物的亲属/履历要求。
-- N02 每个关键 NPC 独立信任 −100 到 100，初始 0。状态包括按日程活动、注意玩家、对话/响应、返回活动。
-- N03 每个 NPC 至少两个可观察的日程位置，公布出现时段；离开时显示常见出现时间和最后已知位置。任务 NPC 不得永久失联。时段、坐标是实现配置，不同 NPC 不必完全相同。
-- N04 完成约定＋10，归还借物＋8，失约−12，被发现破坏物品−15，完成道歉/补偿＋5。一次事件一次结算，不无限刷。
-- N05 信任 ≥20 开放一项额外帮助/委托；≥40 允许一种明确的人情帮助；≤−20 暂停借物或陪同。基础购物与主线补救始终保留。每名 NPC 须公开其具体帮助与限制，不临时捏造随机效果。
-- N06 对话提供至少三个有后果的处理类别：正常完成、议价/预付、延期/拒绝。至少一项任务支持不同完成方式。改变选项必须改变真实现金、欠款、期限、关系或任务状态。
-- N07 预付 100 是负债/预付款记录；交付后仅支付剩余报酬，放弃则记入欠款，不能重复接取提款。没有预付的任务不增加该入口。
-- N08 NPC 记得已发生的借用、失约、归还和清理事件，并在相应条件下更新对话；恢复日程不会清空记忆。
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-## 6. 经济与工作 [E01–E08]
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-- E01 整理货架：把 6 件商品按标签放回，350 现金，每日最多 2 次；错放可拿回重放。
-- E02 配送：领取物品、选择路线、交给目标，450 现金，每日最多 2 次；不是在任务面板直接领取奖励。
-- E03 公共清理：找到并分类 8 件垃圾，300 现金，每日最多 1 次；不同于货架整理对象与反馈。
-- E04 送稿剧情工作 650 现金、找物 200 现金，各一次性。杂物每件 20–60 现金，数量与刷新规则公开，不能无限交易同一对象。
-- E05 接取、持有任务物品、交付、领取奖励分离记录，奖励一次；任务物品不可误卖。日次数只在下一游戏日重置。
-- E06 所有交易守恒：购买扣现金，销售移除物品，奖励需完成条件；现金不得为负，债务单列。存档/反复交互/按住按钮不能增发。
-- E07 零现金仍有免费接取的整理/清理工作及主线补救，不能陷入无食物无工作无操作的死局。
-- E08 至少一条正常交租路径：初始 500＋每日一份整理 350 与配送 450×3＋送稿 650＝3550；该算式只是毛收入示例，需把真实期限、路程与必要消费纳入运行可达性验证。
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-## 7. 载具与街道 [V01–V05]
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-- V01 至少一辆可借用自行车；最高速度 8m/s，平滑起步/转向/刹车，保留世界坐标，不收入背包。
-- V02 借车记录所有者与约定归还时点；归还要求有效借用记录且车在指定停车区域。骑过别人的车或站在停车位不自动算归还。
-- V03 上下车检查安全落点，自行车不能穿墙；窄巷要求下车步行。扶正只移到附近合法道路，不清借用状态。
-- V04 逾期归还改变信任并允许解释/申请延期；不应永久移除车或锁死主线。
-- V05 少量环境车辆沿道路循环，玩家/NPC 挡路时减速停车。汽车为环境内容，不要求玩家驾驶汽车；汽车驾驶、战斗、联网均未纳入冻结范围。
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-## 8. 邻里注意与后果 [A01–A07]
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-- A01 街坊注意度 0–100。撞倒物＋10，目击乱扔垃圾＋10，骑车撞行人＋20，目击禁止行为＋10；完成扶正/清理/道歉处理−15。同次事件只结算一次。
-- A02 目击要求距离与无遮挡视线；视距公开为配置，不全图共享位置。距离、遮挡、物品所有者/受影响人独立记录。
-- A03 0–19 正常，20–39 侧目/提醒，40–69 受影响人要求解释/清理，70–100 房东或管理员介入并给补救任务。
-- A04 离开所有目击者视距且 20 真实秒无新事件后，每真实秒 −2 注意度；只有未暂停模拟时间累计，不使用挂机的墙钟时间。
-- A05 响应者只到最后目击位置，再搜索 10 真实秒，不能穿墙或全图跟踪。地图/面板不泄露其未获得的位置。
-- A06 注意度归零不撤销欠款、关系变化或补救任务。赔偿不足转劳动；一次补救不重复加信任。
-- A07 处理选项至少包括解释、道歉/清理、赔偿或劳动；其结果改变事件状态，避免只有文字提示。
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-## 9. 七条原创任务 [Q01–Q07]
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-- Q01 出门买早餐：房间起点→获知租金→可先工作再购买饭团→完成。购买真实扣款并获得/使用物品，解锁基础日常工作。
-- Q02 顺路送货：店员接取→带货沿主街骑车或后巷步行→交给指定居民。期限 120 游戏分钟；超过截止仍可交付，报酬 250，解释原因；等于截止按准时。一次交付一次领取。
-- Q03 借车要记得还：借车→按时归还/获准延期/暂不归还三种处理；信任和后续对话不同，按 V02 验证归还。
-- Q04 房间大整理：8 件杂物分为丢弃、收纳、任务物；发现邻居遗失物，归还触发一次性找物报酬 200；整理后空间视觉状态发生变化。
-- Q05 赶在截稿前：插画师送稿期限 60 游戏分钟；合法捷径或已解锁人情帮助两种方式，准时 650；迟到进入补救委托，稿件不可误卖/丢弃。补救报酬公开为配置，不强行设成原定 650。
-- Q06 河边见面：17:00–19:00（含端点）到场，陪同走三个观景点并触发短对话；错过信任−12，次日可道歉重约一次。开始陪同后日程不得强行带走 NPC 使流程不可达。
-- Q07 房租日：第 3 天 20:00 正常支付/符合条件延期/劳动抵扣三条路径。劳动按每份有效清理工作抵扣 300、至多抵扣剩余欠款，超出部分不变现、不双领现金；欠款归零后结算。
-- 所有任务有未接取、进行中、完成、失败待补救状态；非适用状态不可非法跳转。允许放弃/延期的任务需展示后果。
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-## 10. 条件事件与成长 [X01–X06]
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-- X01 实现五类条件日常事件：房间脏触发抱怨；配送经过公园遇到邀约；遇见未归还车的主人触发催还；清理后房东提及；携带遗失物遇失主可直接归还。
-- X02 同时至多 1 个随机日常事件，两次间隔 ≥90 真实秒，均为未暂停模拟秒。主线交互优先；可拒绝、不强拉角色/相机。满足条件时触发，随机只能用于同等候选的选择。
-- X03 8 个唯一探索发现点：独特场景/物品、原创吐槽、图鉴记录；每点首次发现结算一次，不等同捡金币。
-- X04 成长五类：发现 5 条捷径；3 名居民信任 ≥20；完成 3 类工作；一天生活记录；交租或抵扣阶段纪念。关系、路线、摆件、照片与对话为主要奖励。
-- X05 生活日志准确记录当日行动与收益/后果。阶段结算后的自由活动不重复发主线奖励。
-- X06 路线、时间、报酬、关系与生活状态产生有区别的选择，例如骑车绕路与步行近路、聊天与及时送货、花钱消费与留租金；需至少三个可复现的不同后果案例。
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-## 11. 外观与视听 [P01–P07]
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-- P01 原创低多边形卡通旧街区。主角 A01：浅米色猫兽人、青绿色短外套、深蓝短裤、橙色小挎包、单条弧形尾巴；见 images/A01_resident.png。形象特征仅适用主角，不强迫所有 NPC 同款。
-- P02 自行车 A02：橙色低跨车架、两个深色轮胎、米色前篮、深色坐垫，见 images/A02_bicycle.png；车架与轮子关系清楚。
-- P03 公寓 A03：两层灰蓝楼体、外廊与楼梯、橙色门、晾衣与公共垃圾点，见 images/A03_apartment.png；数量/交互仍以 W 需求为准。
-- P04 便利店 A04：青绿檐招、暖白门窗、可见货架、侧边停车位，见 images/A04_store.png。四图的构图背景/文字不是游戏硬要求，不使用第三方人物参照图。
-- P05 主角至少待机、步行、跑步、跳跃、交互、骑车六状态；耳尾表现疲劳/开心/尴尬。切换自然、无明显断裂或滑步，状态触发语义与自然度分别评测。
-- P06 白天/黄昏/夜间光照变化，夜间道路可见；区域环境声不同。脚步、车铃、收银、门、拾取、任务完成六类音效；首次入场提示启用音频，设置面板提供音量与静音；六类事件音与对应画面表现同帧触发（音画同步按 §16 F08 口径）。
-- P07 美术风格统一、中文清晰，物体/区域剪影可辨；所有素材原创或具可核验使用许可。图是二维构图示意，不要求写实材质、骨骼技术或商业级资产。
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-## 12. UI 与信息 [U01–U05]
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-- U01 HUD：日期时间、现金、已付/未付租金、小地图、当前目标、生活状态、附近交互提示、注意度；低状态突出提醒。
-- U02 日志：期限、线索、关系变化、任务报酬。完成反馈列现金与信任变化。未知日程用常见时段/最后已知位置，不神奇显示所有 NPC 真实状态。
-- U03 地图记录已发现捷径，背包清晰标任务物。UI 显示与真实状态一致。
-- U04 首次入场提供操作说明；暂停提供继续、设置、存档与恢复入口。UI 适配 1920×1080 与 1280×720（冻结口径）；360×640 视口保持布局和按钮可访问为表现层扩展，未要求触屏操控完整三维角色。
-- U05 低/中/高画质档与音量/静音设置，选择有真实效果；计分分别检查入口、可操作、语义、观感，不把一个混合 PASS 广播给四维。
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-## 13. 存档与运行 [S01–S06]
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-- S01 游玩规则、对话与 NPC 状态机本地运行，不依赖实时模型 API 或密钥。依赖可打包，平台预览提供真实入口。
-- S02 手动存档，以及任务结算/睡觉/离开前自动保存；存储失败有可理解反馈，不静默声称成功。
-- S03 保存版本、日期时间、玩家位置、现金/已付租金/债务、背包、任务/奖励记录、信任/事件记忆、探索点、房间状态、自行车位置/归属/借用、日工作次数、延期状态、必要事件冷却。
-- S04 刷新恢复一致状态；存档带版本号，损坏/不兼容提示恢复选项，不静默清空。正常新开局重置局内数据，不串入旧局。
-- S05 提供卡住恢复；睡觉/菜单/上下车/任务分支/读档后可继续操作。恢复保持交易与奖励一次性。
-- S06 在声明的六区域、六关键 NPC、20 交互点规模下至少连续运行 10 分钟，再重复 3 次结算/读档/新局循环，记录错误、帧时间与对象/监听器变化；不擅自新增 60FPS 或大型并发门槛。
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-## 14. 统一口径与冲突处理 [C01–C10]
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-- C01 一种游戏时钟：dt 为未暂停模拟真实秒；游戏分钟增量＝dt×timeScale，timeScale 默认 1（1 真实秒＝1 游戏分钟）；真实一分钟对应 60 游戏分钟。游戏内小时衰减用游戏分钟增量/60，真实秒冷却按 dt 累计而不再乘 60。默认时间倍率只影响游戏日钟，不影响耐力/物理/事件的真实秒参数。
-- C02 初始状态以 G04/L03/M02 为准。分数、跑道连击、躲障、无敌护盾、战斗与联网不属于本测例需求，不因 submission 包含就进入分母；对应项冻结为不适用，不以实现难度决定。
-- C03 休息/洗澡/嗜好等主动短时行动推进的游戏时间按 L03 结算被动衰减一次，再应用主动效果并钳制；睡觉/疲劳送回家用 L07 专用结算，豁免跳过时段的被动衰减。普通状态阈值只应用一次，不每帧叠乘。
-- C04 24:00 统一为次日 00:00；日期按绝对游戏分钟计算。主动睡觉到下一次 08:00，不把 02:00 送回家额外跳一天。只在越过或达到截止第一次处理期限事件；等于截止的交付先结算，再处理过期。同帧多事件顺序固定且声明。
-- C05 使用浮点累计，最终显示可舍入；费用采用整数游戏币，所有明确金额不得浮动。状态/金额计算误差 ≤1e-4 相对量级，计时容差 ≤1 模拟步或 0.1 模拟秒取大者。明显逻辑错误不靠容差豁免。
-- C06 发现、交付、奖励、补偿、关系、延期和日结按唯一事件/任务 ID 去重。可重复工作每次产生新实例 ID，仍受日次数限制；一次性任务不会因为新日重置。
-- C07 三日目标验证包含可支配时间、路程、商店营业、饱腹/精力与消费；不能只凭收入算式宣布可达。延期只一次；劳动抵扣不同时赚现金。房租结算只触发一次。
-- C08 资料/静态字段与实际游戏冲突时，评测以运行行为取证，并保留静态差异诊断；不能通过改名称或 metadata 把跑道证明成街区。
-- C09 未给出的 NPC 时段、视距、交易物品刷新、帮忙细则、补救报酬与随机种子接口列为公开配置。独立评测者按冻结配置建立 oracle，不事后增加唯一正确答案。
-- C10 首版范围全部适用；仅明确排除的汽车驾驶/战斗/联网/触屏全功能控制/真实大模型 NPC 不适用。来源规范与文档完整性放诊断，不另设版权或文档主分。四维评测见 rubric.txt 与 metrics.json（团队包素材基线）。
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-跨阶段后果表（L 档专节；早期状态在后续阶段仍可观察）：
-
-- K01 阶段A（第 1–2 天通过完成约定/归还借物/道歉补偿积累信任，N04 数值落盘）→ 阶段B（信任 ≥20 解锁额外帮助/委托，≥40 解锁人情帮助；N05 阈值差分）→ 阶段C（第 3 天房租不足时，房东信任 ≥20 才可延期一次至第 4 天 20:00，早期信任投资在结局分支仍可观察）；验证方式=跨阶段回溯探针：阶段 A 记录信任快照 → 阶段 B 验证帮助解锁事件 → 阶段 C 验证延期可用性与结局分支。
-- K02 阶段A（第 1–2 天发现并记录猫式捷径，M07 地图落盘；同时完成整理/配送积累现金）→ 阶段B（第 2–3 天赶稿任务 Q05 用合法捷径或人情帮助准时送达获得 650）→ 阶段C（第 3 天交租路径的运行可达性由前三日累计现金、路程与消费共同决定，早期捷径与收入记录在结算仍可观察）；验证方式=阶段 A 捷径/现金快照 → 阶段 B 准时交付事件 → 阶段 C 三日可达性对照（C07 全口径验证）。
-
-## 15. 世界设定与创作基线 [WD01–WD08]
-
-- WD01 世界与时代背景：近现代风格的原创小镇「灯芯巷」——人类与兽人共居的旧街区，房租、零工与人情往来构成普通居民的三日生活；街区的「光轨」是傍晚路灯依次亮起时街上的人流与车灯轨迹。
-- WD02 场景所在地：灯芯巷六区域——公寓、便利店、商业街、河边公园、后巷、屋顶（G02/W01–W06）；地图约 180m×180m，主街适合骑行、窄巷只可步行。
-- WD03 玩家身份与当前任务动机：玩家是初来乍到的猫兽人居民（A01），初始现金 500、未付房租 3000——三天内通过工作、委托与邻里人情攒够房租，融入街区生活。
-- WD04 失败代价与结局表达：房租不足且无条件补救＝搬家纸箱堆上门口（VC04 未续约意象）；正常支付/符合条件延期/劳动抵扣＝灯芯巷夜灯全亮、房间钥匙落定（VC04 结局意象）；结局三条分支均能到达阶段结算，结算后仍可自由活动。
-- WD05 叙事来源：六关键 NPC（N01–N06）＝街区常客的功能身份原创组合；七条任务（Q01–Q07）＝新居民融入生活的自然事件；自行车与公寓、便利店＝街区的基础生活设施。全部原创，不用商业作品命名。
-- WD06 视觉风格与色彩材质：原创低多边形卡通旧街区（P01–P07 承载）；主色＝灯芯暖黄 #E8C86B、街道青灰 #6B7C8A、砖墙赭 #A8705A、夜灯橙 #E89A4B；材质为旧砖、木窗、柏油路与暖色路灯。
-- WD07 环境氛围与声音语言：白天市井人声、黄昏路灯点亮声浪、夜间虫鸣与远处车流为底；音效语言＝脚步、车铃、收银、门、拾取、任务完成（P06/F06 冻结清单）；混音优先级：任务/结算 ＞ 注意度警报 ＞ 六类事件音 ＞ 区域环境。
-- WD08 故事表达方式：VC01 开局交代街区与房租目标 → 过程中日夜转换与条件事件承载生活节奏 → VC04 结局二选一收束；叙事只做包装不改数值。
-
-## 16. 演出、镜头与反馈 [F01–F10]
-
-- F01 视角与镜头：第三人称跟随镜头，距离 3–7 米可调、遇墙缩短（M03 冻结口径）；镜头不能长期处于不透明墙内；1920×1080 下 HUD 不遮挡交互提示，1280×720 保持可读（U01/U04）。
-- F02 关键操作镜头变化：上下车短暂镜头高度过渡（0.4 秒平滑）；交互提示按候选排序高亮最近对象；不设镜头震动冻结项。
-- F03 输入与延迟：按键到画面可见反馈 ≤100 毫秒（桌面 P95，每类输入 ≥30 次采样）；顿帧不改变 C01 时钟口径。
-- F04 环境常驻效果：白天/黄昏/夜间光照过渡（P06）、环境车辆沿路循环（V05）、区域人流点缀、晾衣与橱窗陈设；均为纯表现层。
-- F05 事件反馈表（事件 → 画面表现 → 音效 → 结束条件，全部冻结需求；六类事件音按 P06 口径与画面同帧）：
-
-| 事件 | 画面表现 | 音效 | 结束条件 |
-|---|---|---|---|
-| 移动/跑步 | 步行/跑步动画＋脚步节奏 | footstep（随速度两档） | 移动停止 |
-| 骑行 | 上车过渡＋车铃提示钮 | bicycle_bell | 下车或按铃 |
-| 购物结账 | 收银台金币滚动＋物品入包 | cash_register | 动画收束 |
-| 开关门/进出建筑 | 门开合动画 | door_open | 门闭合 |
-| 拾取/交付 | 物品飞入背包/交出弧线 | pickup | 动画收束 |
-| 任务完成 | 任务卡打勾＋日志条目高亮 | task_complete | 卡片定格 |
-| 注意度升级 | HUD 注意度色阶变化＋目击者感叹标记 | notice_alert | 等级回落或处理完成 |
-| 处理补救 | 处理选项面板确认反馈 | task_complete（处理档） | 面板关闭 |
-| 睡觉日结 | 屏幕渐暗→日期横幅→晨光亮起 | day_advance | 进入次日 08:00 |
-| 房租结算 | 结算面板＋对应 VC04 意象 | rent_settle | 面板定格 |
-
-- F06 关键音效清单（one_shot 8 条＋环境循环 3 条，全部冻结需求）：footstep、bicycle_bell、cash_register、door_open、pickup、task_complete、notice_alert、day_advance（含 rent_settle 档共用）；环境循环：street_day_loop（白天市井）、street_dusk_loop（黄昏路灯与人流）、street_night_loop（夜间虫鸣与远处车流）。无配音需求。
-- F07 VFX 信息性与遮挡约束：注意度色阶、任务卡、交互提示为信息类 VFX，必须清晰可辨；夜间灯光不得遮挡交互对象轮廓；非结算单个全屏遮挡 ≤0.5 秒。
-- F08 音画同步：脚步音与步伐同帧；车铃音与按铃动画同帧；收银音与金币滚动同帧；任务完成音与打勾同帧；日结音与日期横幅同帧。
-- F09 屏幕震动：本作无震动需求（生活模拟固定手感）；表现层自由，不设冻结项。
-- F10 前端界面：标题（开始/继续/设置/退出）→ HUD（U01 全项）→ 背包/地图/日志面板 → 暂停面板（继续/存档/设置/返回）→ 日结/房租结算面板（U02/U03）；关键状态图标＋文字并用；窗口失焦自动暂停（C01 模拟冻结口径）。
-
-## 17. 过场视频 [VC01–VC04]
-
-- VC01 灯芯巷的清晨（Opening，6 秒，1280×720@24fps）
-  - 触发时机：首次由标题点击「开始」后、初始场景操作之前，每局一次
-  - 内容与角色场景：清晨街道、路灯熄灭、便利店开门、猫兽人居民走出公寓、路口人流与车灯开始流动
-  - 资产对应：主角、公寓、便利店、街道与游戏内模型同源
-  - 可跳过：Esc/Enter 跳过 → 直接进入第 1 天 08:00 操作；自然播完 → 进入操作
-  - 状态一致：跳过与自然播完后初始状态一致；播放期间不推进任何游戏时钟
-- VC02 黄昏亮灯（Event，5 秒，1280×720@24fps）
-  - 触发时机：本局首次进入 17:30 黄昏时段时插入一次（画中画、不打断操作），每局最多一次
-  - 内容与角色场景：路灯依次点亮、街灯下人流与自行车流交织、商业街橱窗亮起、主角剪影驻足路口
-  - 资产对应：街道、路灯、自行车与游戏内模型同源
-  - 可跳过：Esc/Enter 跳过 → 立即返回操作视角；自然播完 → 自动返回
-  - 状态一致：不打断操作；播放期间游戏时钟正常推进
-- VC03 房租日结算（Event/Transition，5 秒，1280×720@24fps）
-  - 触发时机：第 3 天 20:00 房租结算成立时插入一次（结算面板之前），每局一次
-  - 内容与角色场景：日历翻到第三日、桌上租金纸袋与硬币、窗外夜灯、房东门口的交互点亮起
-  - 资产对应：公寓、街区与游戏内模型同源
-  - 可跳过：Esc/Enter 跳过 → 直接进入房租结算面板；自然播完 → 进入面板
-  - 状态一致：仅在真实结算点触发；播放期间不推进任何游戏时钟
-- VC04 三日结局（Ending，7 秒，1280×720@24fps，二选一意象共用条目）
-  - 触发时机：房租结算完成、VC03 之后按真实结果择一播放，每局一次
-  - 内容与角色场景：续约意象＝灯芯巷夜灯全亮、房间钥匙落定、纸箱收起；未续约意象＝搬家纸箱堆上门口、夜灯昏暗、主角回望街区
-  - 资产对应：公寓、主角、街区与游戏内模型同源
-  - 可跳过：Esc/Enter 跳过 → 直接进入结局后的自由活动；自然播完 → 进入自由活动
-  - 状态一致：仅在真实结算结果后触发；播放期间不推进任何游戏时钟；跳过不破坏存档状态（S03 全字段保留）
-
-## 18. 参考图绑定 [REF01–REF02]
-
-- REF01 绑定原则：参考图只约束对应资产的造型、配色与比例，不约束街区空间布局（六区域/交互点/路线以本 spec G02/W/M 冻结数值为准）；R 条件提供 images/ 参考图，N 条件仅有文字描述，评测不得因 N 条件未见参考而追加扣分项；四张原创图为概念示意（P01–P04 原约定保留），另两张程序化草图补齐场景与工具组。
-- REF02 绑定表（文件 → 对象）：
-
-| 参考图 | 绑定对象 | 用途 |
-|---|---|---|
-| images/A01_resident.png | 猫兽人主角 | 3D_object image_to_3d（P01） |
-| images/A02_bicycle.png | 自行车 | 3D_object image_to_3d（P02） |
-| images/A03_apartment.png | 公寓建筑 | 3D_object image_to_3d（P03） |
-| images/A04_store.png | 便利店建筑 | 3D_object image_to_3d（P04） |
-| images/ref_scene_street_night_sketch.png | 灯芯巷街区夜景 | 3D_scene image_to_scene |
-| images/ref_toolkit_sketch.png | 维修工具组（找物/杂物/清理道具） | 3D_object image_to_3d |
-
-参考图绑定后须覆盖角色/载具/建筑/场景/工具五类生成输入；yyy 建议的完整地图俯视图、其余关键 NPC 组图、商业街/河边公园/后巷/屋顶场景组图与日夜氛围对照为**待补输入（不阻塞冻结，补齐后原位更新绑定表与 JSONL）**。程序化草图的生成参数与负向提示见 asset_tasks/racing_simulation/ref_images_manifest.jsonl，可按该 manifest 扩散重绘后原位覆盖。
-
-## 19. 范围与正向能力清单 [NR01–NR06]
-
-- NR01 玩法能力：玩家可以在六区域自由移动与观察（M01/M03）、与 20 个交互点交互（M04/W07）、借还自行车（V01–V04）、完成六类工作与七条任务（E/Q）、积累关系与处理邻里注意（N/A）、以三条路径完成房租结算（G05/Q07），全程保留跨日持久状态（K01/K02）。
-- NR02 流程与界面能力：开始/继续、暂停与设置（U04/U05）、手动/自动存档与损坏恢复（S02–S04）、日志与小地图（U02/U03）、日结与房租结算面板全部具备真实入口；结局后可继续自由活动。
-- NR03 演出能力：VC01–VC04 视频、F04 常驻环境、F05 事件反馈表全部行、F06 音效 11 条、日夜光照过渡（P06）与六主角动作状态（P05）。
-- NR04 输入范围：键鼠为完整输入方式（M01/M05/M06）；触屏完整三维控制为表现层扩展，不进入验收分母（U04）。
-- NR05 音频范围：F06 冻结音效清单（8 one_shot＋3 区域环境循环）；无配音需求（P06）。
-- NR06 范围边界（冻结排除项正向表述）：分数/跑道连击/躲障/无敌护盾/战斗系统（C02）、联网/多人/真实大模型 NPC（C10/S01）、玩家汽车驾驶（汽车仅为环境内容，V05）与触屏全功能三维控制（U04）为非需求，不进入分母；submission 缺少的能力不反向删减需求或改变游戏类型（整理声明原约定保留）。
-
-## 20. 复杂度档案
-
-```yaml
-difficulty:
-  tier: L-Regular
-  complexity_profile: {coupling: C2, persistence: P3, recovery: R2, runtime_complexity: B2, hidden_state: H1}
-  dominant_dimensions: [persistence, coupling]
-  rationale: "跨阶段长依赖：信任积累→帮助解锁→房租延期（K01）与捷径发现→准时交付→三日交租可达（K02）两条 A→B→C 链；全量世界/任务/关系/经济状态跨日持久＋手动与自动存档（P3）；时间/状态/经济/注意度多系统耦合（C2）；实时三维移动与载具（B2）；NPC 日程与最后已知位置的信息边界（H1）；存档/读档/新局/卡住恢复多级（R2）"
-  downgrade_boundary: "删去跨日持久与三日目标（单日循环无跨日后果）后降为 M"
-  upgrade_boundary: "加入多人同步或组合状态压力轴并 ≥3 系统相互影响时升 L-Stress"
-  stress_axes: []
-```
-
-## 21. 分阶段验收与性能诊断（local diagnostic，不改变完整验收范围）
-
-- PA01 分阶段验收（yyy 口径，只用于定位失败，不改变最终完整验收与分母）：①基础移动与时钟 → ②六区域与六 NPC → ③任务与经济 → ④自行车与注意度 → ⑤存读档与三日可达性。任一阶段失败不豁免后续阶段的完整验收。
-- PA02 性能与运行成本报告（Report-only）：在声明规模（六区域、六 NPC、20 交互点）下记录帧时间分布、加载耗时与对象/监听器增长；不擅自新增 60FPS 或大型并发门槛（S06 原口径保留）。
-- PA03 短路径 smoke：在 30–45 分钟完整流程中另设 10–15 分钟短路径 smoke（一日工作＋一夜存档＋一次读档）用于快速回归；smoke 结果只作诊断证据，不能替代完整长流程验收。
-- PA04 长流程失败位置报告：评测须报告三日流程中失败/阻断发生的位置（日期/时段/区域/触发交互），以及是否存在状态丢失（存读档后字段 diff）与无法恢复的异常路径（卡死/软锁）；发现问题记录 defect_ids 并以 blocked_by 关联受影响叶子。
-- PA05 Agent feasibility 标记：本测例实现工作量在 G036–G045 中最高（跨日持久化、任务/关系/经济联动、NPC 日程、自行车、存读档、六区域长链），保持玩法规模不删减；feasibility 风险与本节诊断独立报告，不进入四维主分。
-
-## 生成任务明确性补充（2026-10-12）
-
-> 本补充只约束游戏生成输入，不引用 rubric。所有生成器必须以本文件规则和下列 JSONL 任务为准；未列出的装饰细节可以自由发挥，但不得省略已列资产或生命周期。
-
-### 故事与流程最低合同
-- **玩家身份与当前冲突**：以 §1、§8 的角色/世界/冲突为唯一叙事入口；开局必须让玩家知道“我是谁、现在要解决什么问题”。
-- **阶段性目标**：必须有可操作的阶段目标、至少一次状态变化/升级，并在 §7 的胜利、失败、重开入口中闭环；不能只生成一个无结果的场景演示。
-- **结果承接**：胜利/失败都要有明确结果文案或演出，并回到可重开、暂停或标题入口；视频、音频和 VFX 不得推进未完成的玩法状态。
-
-### 生成任务清单（JSONL 冻结口径）
-- scene：1 个；必须生成连贯可游玩的空间，不得出现下半部文字、拼贴面板、物体 lineup 或 UI。
-- object：15 个（text-to-3D 10，image-to-3D 5）；object 任务资产为：cat_resident_player, street_bicycle, apartment_building, convenience_store, maintenance_toolkit_set, npc_housekeeper_neighbor, npc_curious_neighbor, npc_stroll_partner, npc_illustrator, npc_landlord, npc_store_clerk, environment_delivery_car, street_lamp_post, riverside_park_bench, street_vending_machine。每项只允许一个物体；组合物必须拆成独立任务。
-- motion：6 个；覆盖动作：resident_idle, resident_walk, resident_run, resident_jump, resident_interact, resident_ride_cycle。
-- video/audio/mechanic/UI：分别为 4/11/1/1 个任务；若某项为 0，只能是本案例明确的 N/A，不得默默遗漏。
-
-### Video / motion / audio 生命周期
-- Video 每项必须写清 `trigger`、`start_condition`、`duration_sec`、`skippable`、`skip_behavior`、`end_condition` 和进入的下一个游戏状态；播放期间不得偷偷消耗计时或改变战斗/任务数值。
-- Motion 每项必须写清 `source_asset`、`trigger`、`start_condition`、`duration_sec`、`loop`、`end_condition` 和 `cancel_behavior`；动作要与对应输入/机制事件绑定，不可只写“walk animation”。
-- Audio 每项必须写清触发事件、时长、`loop`、`stop_condition`、优先级和 ducking；关键命中/提示音必须与事件帧同步，暂停/结算时按 §9 处理。
-
-### Mechanic / UI / 游玩手感
-- Mechanic JSON 与 §1–§7 配对：每个输入都要有 precondition、state_change、feedback、next_state；必须说明失败/禁用分支。
-- UI JSON 与 §9/F10 配对：HUD、菜单、结算、暂停、错误/禁用态要有 state binding；关键状态不能只用颜色表达。
-- 手感冻结：相机跟随/构图、FOV、碰撞/遮挡、镜头调整与镜头震动必须在 §9 说明；震动反馈必须写明触发、强度和持续时间，并提供关闭设置且不改变判定。
-
-### VFX 时机合同
-每个特效必须写清 `trigger event`、预备/前摇、active window、持续时间、结束条件、被打断/取消条件，以及和相机震动、音效、UI 的同步关系；命中特效、失败特效、胜利特效不能只写“有粒子”。
-
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md
deleted file mode 100644
index 869ae1f54e0eaf8706c0b2865a76c19c00190ee0..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# 优先资产：方法选择与执行边界
-
-核对日期：2026-10-15。本文是候选方法建议和开跑前检查，不是实验结果，也不是已验证的
-API 配置。评测口径以 `GameFactory-3A/eval/asset_evaluation_plan.md` 为准；
-本次没有改动该方案、原始全集或其他五份现有优先生成 JSONL。
-
-## 1. 建议首轮组合
-
-| 类别 | 首轮建议 | 放在哪组 | 选择理由 / 限制 |
-|---|---|---|---|
-| 3D Object | Meshy + Tripo + 腾讯云 Hunyuan3D Pro 商用 API | B1 商用生成 API；逐版本核对开放权重状态 | 三家即可；人物/刚体、T2/I2 分组，保持同输入和低模预算 |
-| 3D Scene | 新增 World Labs Marble World API；保留 WorldPlay/HY-World 2.1 Scene → WorldMirror → 导出，以及 LLM 布局/组合 | Marble 为 B2 商用 direct 候选；另两路为组合方法对照 | Marble 提供世界生成和 mesh 导出；不拿仅视频输出替代 3D 交付 |
-| 自然音 / SFX（看情况吧） | ElevenLabs Sound Effects v2 + Stable Audio 3.0 Large 商用 API | B4 Environment / Foley 子组 | 一个面向音效、一个通用音频基线；先用声音事件和环境音 pilot 验证，不跑成音乐任务 |
-| 中文人物语音 | MiniMax Speech（优先验证 `speech-2.8-hd`）+ ElevenLabs TTS 的固定中文支持版本 | B4 Speech 子组 | 当前 38 条都是中文；固定说话人、情绪映射和完整台词 |
-| CG | Seedance + MiniMax Hailuo（优先验证 `MiniMax-Hailuo-2.3`） | B3；T2V 与 I2V 分开 | 先跑两家即可，不急着加入第三家；按 API 的时长/分辨率能力冻结共同条件 |
-| Motion | MoMask + Puppeteer 对比 LLM 直出；可再加 Meshy Text-to-Motion | 前两路是开放模型链 / 系统对照；Meshy 是 B5 商用 API 候选 | 先满足用户要的两路比较；若 B5 必须有闭源专用生成 API，再加第三路 |
-| T-pose | 同一份冻结准备输入 | 不新增主表 | 两个动作方法共享角色输入，不把图像准备差异算成动作模型收益 |
-
-商用 API 不自动等于闭源模型。特别是 Hunyuan3D 必须登记云端实际版本，不能把本地
-开放权重版本包装成闭源行；MiniMax 的视频实验也要写清 Hailuo 的具体 ID，不能只写品牌。
-MoMask、Puppeteer、WorldPlay、WorldMirror 的源码/模型发布可在官方仓库核对 [R1–R4]。
-上述 API 的能力入口见文末官方来源；账户权限和实际可用版本仍需 pilot 确认。
-
-**规模建议**：B1 三家、CG 两家、音频按两种任务各两家；场景优先补 Marble，而不是
-为了凑三列加入不能导出场景的模型。B2 暂时只有一个确认能力范围的商用 direct 候选，
-如需至少两家闭源 direct API，仍须找到并实测第二家，不能用组合方法假充。
-
-## 2. 为什么按这些方法分组
-
-原方案 §1/§4 要求闭源专用生成 API、开放模型与多模型组合分开，首轮主表不混排。
-因此保留用户提出的系统比较，但不悄悄改掉主表含义：
-
-- B1/B3/B4：按相同任务和输入条件比较商用服务，记录模型的权重开放状态。
-- B2：Marble direct 与 WorldPlay + WorldMirror / LLM composition 分 panel 报告。
-- B5：MoMask 生成人形动作，Puppeteer 在本项目链路中主要承担绑骨/蒙皮准备；
-  LLM 直出是另一条系统方法。不能把它们标为两个闭源 motion API。
-- 如果研究问题改成“各种资产生成方法谁更适合游戏”，可以增加系统对照表，
-  但需要显式修改协议，而不是沿用 `Closed-source method` 表头。
-
-为了隔离动作生成能力，建议先冻结一套人形骨架和白模，两路动作采用同一个映射、
-retargeter、20 fps 和接触检查；角色网格、绑骨差异另在第三阶段端到端比较。
-LLM 直出必须交付轨迹/骨骼动画文件，只有 Blender 脚本、文字或预览视频不算交付成功。
-
-## 3. 与当前优先清单对齐的阻塞项
-
-### 3D Object：低模与上下文能力
-
-现有 200 条保留低模 prompt，且 `decimation_target` 最高 8000。
-建议统一保存原始 API mesh 和固定后处理后的导入 mesh，分别报告；不要只给一家减面，
-也不要把减面后生成的新几何缺陷完全归给原始模型。
-
-Hunyuan3D Pro 的普通模式中 `Prompt` 与图像输入互斥，不能假设 I2 能同时传游戏梗概。
-这类调用按原方案记为 `reference-only` 辅助组；T2 可以拼接冻结的短版梗概。
-其 3.0/3.1 的 LowPoly 支持不同，先确定使用的版本和后处理政策 [R5]。
-其他 I2 API 也逐一核对文本上下文能力，不能只在 judge 阶段补梗概。
-
-不要用 `latest` 当作冻结版本。Meshy 文档还提示其 `lowpoly` 模型类型将在
-2026-10-30 退役；现有清单的 `poly_policy: low_poly_priority` 只是实验政策，
-不等于必须调用这个即将退役的 API 类型 [R6]。
-
-### 3D Scene：交付要有真实场景
-
-69 条现有场景任务要求 `output_format: glb`。WorldPlay 的视频需经 WorldMirror 重建
-及场景导出后，才与场景任务对齐；只生成视频不能记 Delivery 成功。
-
-Marble 的世界接口返回 splats 和 collider mesh，并另提供高质量带纹理 GLB 导出。
-建议正式 B2/第三阶段使用后者，保存碰撞网格和世界尺度信息；不要拿无外观的 collider
-mesh 冒充完整美术场景，也不要把 splat 文件简单改后缀作为 GLB [R7]。
-生成和导出都计入耗时/成本；若导出不可用，保留该任务的未交付状态，不替换成全景图。
-
-LLM composition 固定资产池、资产提供者、坐标系统、规划调用数和修补预算；
-若它额外调用对象生成器，费用和调用全部计入系统方法，不能只记 LLM 的 token 成本。
-
-### Audio：需要按声音任务选方法，不是一种模型跑全部
-
-当前非语音 706 条：623 条 `one_shot`、83 条 `ambient_loop`；语音 38 条，全部中文。
-这是分类字段统计；实际循环要求仍以每条任务的 `loop` / 生命周期字段为准。
-非语音时长为 0.15–45 秒，其中 93 条短于 0.5 秒、281 条短于 1 秒、19 条长于 30 秒。
-
-ElevenLabs SFX API reference 写 0.5–30 秒，能力总览却写 0.1–30 秒，存在官方文档差异；
-开跑前应实测短音边界并保存实际请求/响应，不自行认定所有短音都支持 [R8, R9]。
-Stable Audio 的 API 时长范围也应从具体 endpoint 核对。这里建议验证 3.0 Large 商用服务，
-而不是把已开放权重的 3.0 Medium / Small / Small SFX 当作闭源方法 [R10]。
-若账户仍使用 2.5 API，则固定并单列 2.5 版本，不混写成 3.0 的结果 [R16]。
-共同能力子集用于配对排名；其余任务仍留在第三阶段完整清单中，标注能力缺口。
-
-解码、重采样到要求的 48 kHz / 24 kHz 可采用同一确定性工具并保存原件。
-短音裁切、长音拼接/循环、生成式延长不是无代价格式转换：如要使用，须预先冻结规则，
-单列 pipeline 结果及成本，不能悄悄改任务时长来让 API 通过。
-
-语音不得把“游戏梗概 + 角色介绍”拼进需要念出的台词。检查 API 是否有独立的
-instruction/style 通道；没有时仅可使用冻结的 voice/emotion 参数映射，单列标准 TTS
-条件，不宣称模型直接读取了梗概。MiniMax 的 TTS 参数和 ElevenLabs 版本入口见 [R11, R12]。
-
-### CG：现有任务与 reference-image 协议不一致
-
-194 条中 190 条 `text_to_video`、4 条 `image_to_video`，但当前全集和优先清单都没有
-参考图路径字段。不能用这批数据直接声称完成“194 条 reference-image-to-video”评测。
-
-建议明确选择：保留 T2V 子表、Q1 预声明 N/A；同时为 I2V 冻结真实的共同参考帧，
-补齐路径和来源后再运行 Q1。若要全部改成 I2V，应生成并冻结共享参考图，然后形成新的
-任务版本；不能各视频方法自行画图作为自己的参考。
-
-现有时长 4–12 秒、输出 1280×720/24 fps；Hailuo 2.3 文生接口的原生规格是
-768P 的 6/10 秒或 1080P 的 6 秒，不能把原 JSON 原样发出去 [R13]。
-先冻结共同能力 pilot 子集；非原生任务保留并标注，不隐式拉伸视频来满足事件时长。
-Seedance 使用已接入的实际 model ID，先验证能力矩阵，不将品牌名当成版本。
-
-### Motion：保留非人形与短动作
-
-新增 78 条中，76 条人形、G040 一条 vehicle、G030 一条 mechanical。
-两个非人形任务必须保留用于第三阶段，不能因为 MoMask 主要面向人形就删去。
-按 rig group 单列，不支持时明确记录；需要 procedural 交付时另列方法，不冒充 MoMask 结果。
-
-Meshy Text-to-Motion 的官方接口提供自然语言动作和 FBX/BVH，当前时长要求为
-2–10 秒且按 0.5 秒步长 [R14]。它适合补商用动作 API 对照，但不是现有所有短动作的
-无缝替换。LLM 直出也固定同一骨架、帧数和时长，不只比较好看的视频。
-
-## 4. 最小执行顺序
-
-1. 先为 13 个 L 级游戏准备第三阶段的全部既有资产任务；完整性与模型共同能力子集是两件事。
-2. 选 G003/G025/G040 等 3–5 个游戏做 pilot，覆盖中文语音、RTS 压力、机械/载具边界。
-   每类先挑少量代表任务；这不缩减 L 级正式导入清单。
-3. 冻结游戏梗概、任务/参考输入、适用指标、API 版本、调用次数、超时重试和后处理。
-4. 报 Delivery、C、Q1/Q2/Q3、Coverage 和成本，按游戏宏平均；不挑 best-of，也不将
-   未测能力缺口伪装成 API 生成失败。正式分母政策在开跑前固定。
-5. 第三阶段各 repair 方法共享同一冻结资产包。提前固定每类资产的交付来源，不能按
-   单条 judge 最高分挑选，也不能悄悄用 LLM 补产物来遮盖某个生成方法的失败。
-   若比较资产来源，另设因子并保持 repair 方法与预算一致。
-
-## 官方来源
-
-- [R1: MoMask](https://github.com/EricGuo5513/momask-codes)
-- [R2: Puppeteer](https://github.com/Seed3D/Puppeteer)
-- [R3: HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay)
-- [R4: HunyuanWorld-Mirror](https://github.com/Tencent-Hunyuan/HunyuanWorld-Mirror)
-- [R5: Hunyuan3D Pro API](https://intl.cloud.tencent.com/ind/document/product/1284/75540)
-- [R6: Meshy Text-to-3D](https://docs.meshy.ai/en/api/text-to-3d)
-- [R7: World Labs World API](https://docs.worldlabs.ai/api)
-- [R8: ElevenLabs Sound Effects API](https://elevenlabs.io/docs/api-reference/text-to-sound-effects/convert)
-- [R9: ElevenLabs Sound Effects 能力总览](https://elevenlabs.io/docs/overview/capabilities/sound-effects)
-- [R10: Stable Audio 模型及商用 API 入口](https://stability.ai/stable-audio)
-- [R11: MiniMax TTS](https://platform.minimax.io/docs/api-reference/speech-t2a-http)
-- [R12: ElevenLabs 模型](https://elevenlabs.io/docs/overview/models)
-- [R13: MiniMax Hailuo T2V](https://platform.minimax.io/docs/api-reference/video-generation-t2v)
-- [R14: Meshy Text-to-Motion](https://docs.meshy.ai/en/api/text-to-motion)
-- [R15: Tripo 开发者入口](https://developers.tripo3d.ai/)
-- [R16: Stable Audio 2.5 官方发布与 API 入口](https://stability.ai/news-updates/stability-ai-introduces-stable-audio-25-the-first-audio-model-built-for-enterprise-sound-production-at-scale)
-
-Seedance 的本地封装 `GameFactory-3A/models/gen_cg_video/seedance_model.py` 已登记
-model ID 和官方文档入口；本次网页抓取未能直接读取该火山引擎文档，因此不在本文
-宣称已核对其全部时长/分辨率规格，开跑前仍需 API pilot。
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt
deleted file mode 100644
index 88d3554b41934de90f5e2cbeee1173dbe7b0cbdf..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-# G045_daily_street_lightlane / 3D_object requirement
-
-Source of truth: ../general_requirement.txt
-
-Frozen task inputs:
-- cat_resident_player: Cat-beastfolk resident player, light-beige fur, teal-green short jacket, deep-blue shorts and orange crossbody satchel, clean symmetrical full-body front-facing T-pose, semi-realistic stylized game character, arms extended, feet visible, plain studio background, one character only, humanoid rig ready, Y-up
-- street_bicycle: Neighborhood bicycle per reference image: orange low-step frame, two dark wheels with clear axle hierarchy, beige front basket and dark saddle, frame-and-wheels relationship readable, game-ready PBR, single object, Y-up, pivot at base
-- apartment_building: Two-story apartment building per reference image: grey-blue main block with exterior corridor and stairs, orange front door, laundry lines and shared garbage spot, collision-friendly blocky silhouette, game-ready PBR, Y-up
-- convenience_store: Neighborhood convenience store per reference image: teal-green awning sign, warm white doors and windows, visible shelves through glass, side parking spot, original branding-free facade, game-ready PBR, Y-up
-- maintenance_toolkit_set: Street maintenance and odd-job toolkit set per reference image: toolbox, broom, trash picker, folded crate and small lost-item pouch props, everyday readable silhouettes, single kit, game-ready PBR, Y-up
-- npc_housekeeper_neighbor: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- npc_curious_neighbor: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- npc_stroll_partner: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- npc_illustrator: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- npc_landlord: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- npc_store_clerk: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready
-- environment_delivery_car: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base
-- street_lamp_post: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up
-- riverside_park_bench: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up
-- street_vending_machine: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json
deleted file mode 100644
index 5fc8e3f1648c3bb5a3aca102405c3356fdbe1c2e..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json
+++ /dev/null
@@ -1,651 +0,0 @@
-{
-  "created_at": "2026-10-16T11:10:47+00:00",
-  "parent_plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "original_records_archive": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/provenance/records_before_localization_6d3ea470a1272c8d.zip",
-  "archive_sha256": "e7dad625e3b44b75ece2e9649f869c8142d7356d3399745707aacc5c4011fe1e",
-  "records": [
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json",
-      "before_sha256": "6841aefbc6c3ced1ec44ea0e877eb8b40b2f023a5b7ec05939088b1dc78f11f5",
-      "after_sha256": "6841aefbc6c3ced1ec44ea0e877eb8b40b2f023a5b7ec05939088b1dc78f11f5"
-    },
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/tripo/obj200_meshy_tripo_20261015_v2/asset_task_index.json",
-      "before_sha256": "3a3c49a1f72d0791cfcbfb9d87976e0a38b214ea860fb4fe3eb570f4202ec8dc",
-      "after_sha256": "3a3c49a1f72d0791cfcbfb9d87976e0a38b214ea860fb4fe3eb570f4202ec8dc"
-    },
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/meta.json",
-      "before_sha256": "8f0f216b02c973ad84ebdedecd66e518f753a145bbfc6af06d2fa2e9876cb8fc",
-      "after_sha256": "8f0f216b02c973ad84ebdedecd66e518f753a145bbfc6af06d2fa2e9876cb8fc"
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-      "remove_lighting": true,
-      "consumed_credits": 30
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json
deleted file mode 100644
index eda987881eaa9a0fadcc54ed2fb3e9d4d94989ec..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "submissions": {
-    "78835a4856c7326217beb0e25cddf125b7b2a348b78ef62f876882bf3bf3b8c3": {
-      "endpoint": "/openapi/v1/image-to-3d",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "01a14303-0e8d-73df-a6dd-8d99ff647f79",
-      "response": {
-        "result": "01a14303-0e8d-73df-a6dd-8d99ff647f79"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v1/image-to-3d/01a14303-0e8d-73df-a6dd-8d99ff647f79": {
-      "id": "01a14303-0e8d-73df-a6dd-8d99ff647f79",
-      "type": "image-to-3d",
-      "name": "",
-      "art_style": "",
-      "object_prompt": "",
-      "style_prompt": "",
-      "negative_prompt": "",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "text_style_prompt": "",
-      "image_style_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125636690,
-      "progress": 100,
-      "started_at": 1792125636694,
-      "finished_at": 1792125760795,
-      "expires_at": 1792384960795,
-      "task_error": null,
-      "model_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-0e8d-73df-a6dd-8d99ff647f79/output/model.glb?Expires=1792384960&Signature=FpNTgH5KPSsrVgQ~sqiBv1NWG~BiMUhWDImHIbqSfH6Z1X5nwHfFXXbhIJdPtkO-4-6yaLdRkHXZfC2tcyRXGBlGbCqC6fVjeH9cL8YgrJhgNDsJKCMZHaENg3-MpbVyKF3TkVqnjhzPQxGdCL12iT0DDEYJeE4qzCotA9UXgVDB4vV6y4qjcwAt2I2OoaKFgOBG1kC5Erj9x6Ek-vVHusjflD1VNWQaUULOQU7lCkuPWUx3pFMk-otd3XZ~DhW2eRtwaitYxNQRBTLQ58FzUGj-jANZp7kxZTRDkRYZ7pt8jbvNQRz9YFObmWHnfld1h7N0JSxTcjaxiekcmsg~3w__&Key-Pair-Id=K1VGYTHIYLM9UM",
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-      ],
-      "remove_lighting": true,
-      "consumed_credits": 30
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json
deleted file mode 100644
index 6606fe7d8aba2e764aa13b7673df81d245393c42..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "submissions": {
-    "f2f3eda78134a20d3f3196a519fa5eb915b2e9d7ebb77b98a6bd3faddb55b068": {
-      "endpoint": "/openapi/v1/image-to-3d",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "01a14303-3706-7240-9c8a-98546a4ff8ee",
-      "response": {
-        "result": "01a14303-3706-7240-9c8a-98546a4ff8ee"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v1/image-to-3d/01a14303-3706-7240-9c8a-98546a4ff8ee": {
-      "id": "01a14303-3706-7240-9c8a-98546a4ff8ee",
-      "type": "image-to-3d",
-      "name": "",
-      "art_style": "",
-      "object_prompt": "",
-      "style_prompt": "",
-      "negative_prompt": "",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "text_style_prompt": "",
-      "image_style_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125647015,
-      "progress": 100,
-      "started_at": 1792125647019,
-      "finished_at": 1792125801944,
-      "expires_at": 1792385001944,
-      "task_error": null,
-      "model_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/model.glb?Expires=1792385001&Signature=ndWqKQPLhpPzPaPBld6kdO8EN95YhuO02hQ4OtprNUR8~oqVtVOWnIaKJwTGd2bzvBBfGe9dtWSXvTbtnPRGVUBpwJo1LhsLdtNfDqN3QeBwBpOUO0fJXJ1QYzH6Lsxn0T6xbiheOK8oTOYT0p2PRuDGazrrfrYh0QAnYh~5YPUtHS210yEHrJuybZVE1VeKkpoVUZrmSy30Jv-SuqULMXRosSPl5fqO21PRtjlTu2NZDwmr7Nn3n7AqmKbrtrJCFisc0cInfCqLEDombKee~oHeBR4Df7zAYrDwb-Znx-7QUluhjDTot75OLlBDqQgQ5jtBTuwzu16cE9UJY8aMFQ__&Key-Pair-Id=K1VGYTHIYLM9UM",
-      "model_urls": {
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-        "fbx": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/model.fbx?Expires=1792385001&Signature=dBF2QSSNpo1wGPCrIAQk-S6ct8hwgjPsRLW~kJyHPc0LLScVakHcYaZ7HfaEG-P5wwMD0I~bsaz9orW1-qsPekUVj1GpPeb8xwKurOmwoqfBd2m3W7WzOeiTMtKe3cwaIs~UAWcvegRiY6Kp3IxdS70pruHTvPJjKcrAowJa~I-ovExQqZ9Y2~~QfHGE7e1ZTQLdzWY-mFr-dk4AaSeiHRXmomQzjXFa-pFTHECuBgp7a34HySk2HwzaUGSBxN5Pc6Nxj2YJKRmzUGVIgLtqxaiMQ8EexI57k047MvHSHdB1AUiZTBV6oKEpeYDHD7eFhffRyVr7wwXUGINQ82h9OA__&Key-Pair-Id=K1VGYTHIYLM9UM",
-        "usdz": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/model.usdz?Expires=1792385001&Signature=ZeOFZr~wE3YGNtVPM~pTnclnNiLkXelBjub81uUheoyHgS~vHg63WfgSDMAIzqWMQ428BLIID1QBr0~Kks~R6P5Bo1yKdK0bfiQa4O51n-mng5N14qxMB6~ZhIJATOUTsPtVx0bTwUS~ttJdY2BDo-mhY9bZ~obqb8mrGPr-tH569pXrM9AOgwGDekbeiUFLeZRSQsFI~40EUsQqN2-MHc66K4su9wDp6zPNK2KQCipWaZjlzWYZ4P2MSGqEPmwasrK-0Dj29Qv~-rfn9ulbag8NrD1mLKvPARJvlLs6cnSHMmk55dObKnnRLi5k9tEyR-7PSmi-bLLknqzs79OZeg__&Key-Pair-Id=K1VGYTHIYLM9UM",
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-        "stl": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/model.stl?Expires=1792385001&Signature=Gp-F9dbyRVfoFMovEOQTjTPSBm0AeoQzVWyv4PEJr~dPcr9PX6CUhfQMFFnznO38gCYZBmBrSQpshT1vApDjZtmJ81-G6tK3Yh4cNwEmdxx5vX7ulyfIT3MLwpIH8MfJt5D1kIyPIqox9X5uRFywlzkULOds6O~70BgrMUGt0g~LGgvgxaOMUrX8Xv3Xyc4sMXkRGHZW1wpfWhji9ZVjezElve9FqzGO09iAqRA34znd8sJyvpfEH7ytCKAYb1mxZdmGgyDmNa-nhEWR3m9QT-EtijqQQpK3p3AZa1kvcZixfEwUtV0wHKXdRww-GIYLA9Lwk19jqw4P4p1Leq1~2Q__&Key-Pair-Id=K1VGYTHIYLM9UM"
-      },
-      "thumbnail_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/preview.png?Expires=1792385001&Signature=R1LYV3U-EddRouQF4zLMXCBEZ~E~Ho-CD3pK77MrlOFQnOP3FZiQU~b7F2cAtZH3NN-dsUiF71rdKxvYwRhTifer2MILc~UAhGYlIG525-J~dAoG3LUyoRhWyXJYhv78yIDTFYlQ2m6gvUYb3~0zWQBbogDgXQxyzXD131KC-KWkameWwIYjnKXpaZGUXfo0qjt0hDd0thjfQpFs~yJMkeT-Wkjav1F8StPrm5lE8YqxpWH0kiNPV0-~wxa0GaFb5RQ-mzueFnXzSBo18gpUf9HkZZwQXWh5An5U2zUudINj0nZzvwVHsTrut3kcHCJdsrHjlT73KhFG2UG9c3-uQw__&Key-Pair-Id=K1VGYTHIYLM9UM",
-      "texture_urls": [
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-          "normal": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/texture_0_normal.png?Expires=1792385001&Signature=Q1B8sGUsHf9phmOuXyNVKeSSvpUVIAPBcNPeAP5s7fFqbCwLeEFgSQGLme5gBkwnn5Er3v3KizLDAYuMIy08DYqCrby8zRDc7-2tl3nQKMpE9nkHemNnaY8jtotVsGUobicfcZk~1EvxROefD~rb57gToHMDM9KLKViiK5scAc3OvaFAMNyO3fMWV9B1mlo~7Lof~b3qr0LlVgijd90LGxM9E0wp3f4VUGaaqm7jpwb9pkVS0ti4lni3hcBFy7-c4MINUbOaEUWuPCe9XrsNxKyXuN~8~rcL9VK4s6tc1VhV~rUgEQxMaaO4fYquG2UvSBvs0XHrYeBZ8QEQT5w5tA__&Key-Pair-Id=K1VGYTHIYLM9UM",
-          "emission": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14303-3706-7240-9c8a-98546a4ff8ee/output/texture_0_emission.png?Expires=1792385001&Signature=dfVoiq3SMFmNtAauH5SOs8knKUTBViC8u4Zmdrko1bQBDgl93bO7B2WfdimkPZkGw9kFcxO8NZIPe5GgVIPl8dFkVq7QDPWA0xLKUgiUZVtUO88gRuROD8WTZJet9WNcVP2iJQFhksJ~Q8ZFHmuqjgICN9C98bbzul1LbZNH8DxgdhAnKtwCHekccohGwAR2I2gUDqfFa4AIGdjiptg3TS-pu8-BRATZAygE64JWQXuWQQVVTmPKYorgQxo4MoBl6IlBGJU3ehxkvFHzeLD8OZuSRRrl225jpmInkNhhkU0tDDWcLhyM30QQi8ogDbFZDOxmj6seiVbeYBgZ9zM~NA__&Key-Pair-Id=K1VGYTHIYLM9UM"
-        }
-      ],
-      "remove_lighting": true,
-      "consumed_credits": 30
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json
deleted file mode 100644
index 085e2a5784e345eadd620a9905bb8dfc0bc2c4a6..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "submissions": {
-    "de57d28bbfb5a4f24d981f726150f7e7adf8400e8ee18d9a429806401cc530ac": {
-      "endpoint": "/openapi/v1/image-to-3d",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "01a14304-2a30-713d-8be6-b82013974916",
-      "response": {
-        "result": "01a14304-2a30-713d-8be6-b82013974916"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v1/image-to-3d/01a14304-2a30-713d-8be6-b82013974916": {
-      "id": "01a14304-2a30-713d-8be6-b82013974916",
-      "type": "image-to-3d",
-      "name": "",
-      "art_style": "",
-      "object_prompt": "",
-      "style_prompt": "",
-      "negative_prompt": "",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "text_style_prompt": "",
-      "image_style_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125709170,
-      "progress": 100,
-      "started_at": 1792125709174,
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-      "remove_lighting": true,
-      "consumed_credits": 30
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json
deleted file mode 100644
index 4367348bdd1439c98422198888677e70cb910828..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "submissions": {
-    "5b5623f9e0169b1b76b1380e013c6808685cfc88c004c8c6d3994c1522931b45": {
-      "endpoint": "/openapi/v1/image-to-3d",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "01a14304-6d85-74b2-8d70-da2271d824cb",
-      "response": {
-        "result": "01a14304-6d85-74b2-8d70-da2271d824cb"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v1/image-to-3d/01a14304-6d85-74b2-8d70-da2271d824cb": {
-      "id": "01a14304-6d85-74b2-8d70-da2271d824cb",
-      "type": "image-to-3d",
-      "name": "",
-      "art_style": "",
-      "object_prompt": "",
-      "style_prompt": "",
-      "negative_prompt": "",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "text_style_prompt": "",
-      "image_style_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125726642,
-      "progress": 100,
-      "started_at": 1792125726645,
-      "finished_at": 1792125914010,
-      "expires_at": 1792385114010,
-      "task_error": null,
-      "model_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14304-6d85-74b2-8d70-da2271d824cb/output/model.glb?Expires=1792385114&Signature=RT53NBa3QuGWkTJFM3cVOtoGR7NgBY7vI-OJQqzIyrFsBHDK1I0PwzviDWBYlEzPxUTx54mK8QvrsVpZBhX0rNxs8dUl8izMcwc4biBoJ8UYRuMOaVpkRoeYI68PLxKS0t-PAk3C9TEaH3~KxRM0IIB~~nedfvlz72LUR~KC9VWZO5Fy6-FLygO6~tJEq-JJLhz6PNZiJSsVLX44smFvss3ceJ92droXNYHphaJ7VVuZ9~~k5~FxtSD9zzHB1AOHXvkMZMdUA~3njG0dTHp27YuJwxQNKBHRnjB3www7-s-Sm89OGddxs8enP5VNbgBVwZKXFAD0yKgji0766NcbDg__&Key-Pair-Id=K1VGYTHIYLM9UM",
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-      "remove_lighting": true,
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json
deleted file mode 100644
index 7142b03b23720e4c80e3095aa1015cdd4b4789f9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
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-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14304-c0c0-72a1-ae01-d22c7cbde513",
-      "response": {
-        "result": "01a14304-c0c0-72a1-ae01-d22c7cbde513"
-      }
-    },
-    "31d9ced086156e5618881fe2563a275f675306bbe7d690a486953246ee659676": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14305-b3df-7658-852b-4b5148706db3",
-      "response": {
-        "result": "01a14305-b3df-7658-852b-4b5148706db3"
-      }
-    }
-  },
-  "results": {
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-      "id": "01a14304-c0c0-72a1-ae01-d22c7cbde513",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1050,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125749814,
-      "progress": 100,
-      "started_at": 1792125749820,
-      "finished_at": 1792125804260,
-      "task_error": null,
-      "model_urls": {
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-      "texture_urls": [],
-      "consumed_credits": 20
-    },
-    "/openapi/v2/text-to-3d/01a14305-b3df-7658-852b-4b5148706db3": {
-      "id": "01a14305-b3df-7658-852b-4b5148706db3",
-      "type": "text-to-3d-refine",
-      "mode": "refine",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1050,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125809642,
-      "progress": 100,
-      "started_at": 1792125809649,
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-      "task_error": null,
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-      "remove_lighting": true,
-      "consumed_credits": 10
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json
deleted file mode 100644
index c331c67101503acedd4ed2249e5ebc19214674ff..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "7801071d5b2c4543cbc9d7dc290cae4be3286437673d45422c6852c29656d9fc": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-1c9d-7594-a6ca-c9ba3960bf9e",
-      "response": {
-        "result": "01a14305-1c9d-7594-a6ca-c9ba3960bf9e"
-      }
-    },
-    "1d6cad905716d9d62aec5ff846626353ec4f9c88f721d23c9fafe93bb51a8e2b": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14305-eab1-7254-936a-bbfcd1adeedb",
-      "response": {
-        "result": "01a14305-eab1-7254-936a-bbfcd1adeedb"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v2/text-to-3d/01a14305-1c9d-7594-a6ca-c9ba3960bf9e": {
-      "id": "01a14305-1c9d-7594-a6ca-c9ba3960bf9e",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1051,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125773486,
-      "progress": 100,
-      "started_at": 1792125773490,
-      "finished_at": 1792125819977,
-      "task_error": null,
-      "model_urls": {
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-      },
-      "thumbnail_url": "https://assets.meshy.ai/8f6e5ce2-d8d8-44a6-a7ed-c92b05e979af/tasks/01a14305-1c9d-7594-a6ca-c9ba3960bf9e/output/preview.png?Expires=4945708800&Signature=D3chJrJynVt-edryLmGDphiGboSEIVAY4dO-qAX6HxRNT2fp9Opth2fBwrme5D-lmLYqRBegfSSENAVH4D6nmjtzvOhIWjw~~VlXviMkDkNg~DqNCGUt2WifV0VPHTs5fE-nc5b2dHM547VRLqvpz1EaCvUSpXnlnFMgyWYXZx8PEja9w7TSgUGBql5U1jaHu--Yi7Gjzz7rwD7RxclQn9KX6EscY-lwfORp~x6I2526CSsw7DXg0GWgih7LW1VyYkbWt7B7~TOHTE29dgF2yZYd7ZuCCDc5-XGVznkRGdUksd50qXF88cbL9j6w48uavl3moyjlS5XZr4u7rT1muQ__&Key-Pair-Id=K1VGYTHIYLM9UM",
-      "texture_urls": [],
-      "consumed_credits": 20
-    },
-    "/openapi/v2/text-to-3d/01a14305-eab1-7254-936a-bbfcd1adeedb": {
-      "id": "01a14305-eab1-7254-936a-bbfcd1adeedb",
-      "type": "text-to-3d-refine",
-      "mode": "refine",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1051,
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-      "remove_lighting": true,
-      "consumed_credits": 10
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-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json
deleted file mode 100644
index 6ff32099d0d1d0b8e3a7dcdade08515f17d35196..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "4099af4610d12336bcb907b32c4152c9964106286b55bb655889f5355ffe8638": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-2be4-7682-a244-506aa4911b16",
-      "response": {
-        "result": "01a14305-2be4-7682-a244-506aa4911b16"
-      }
-    },
-    "6ec05e3fd93804f9433efdb730dc353dd203d130820bd1f18327a1819a15ab27": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14306-0b00-72e9-8cb7-c1aa030ad63b",
-      "response": {
-        "result": "01a14306-0b00-72e9-8cb7-c1aa030ad63b"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v2/text-to-3d/01a14305-2be4-7682-a244-506aa4911b16": {
-      "id": "01a14305-2be4-7682-a244-506aa4911b16",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1052,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125777630,
-      "progress": 100,
-      "started_at": 1792125777638,
-      "finished_at": 1792125829051,
-      "task_error": null,
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-      "remove_lighting": true,
-      "consumed_credits": 10
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-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json
deleted file mode 100644
index 21d04fc577d332c135384106327d9bc754b2e831..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "9a9b5686998b9284d3ab4fde603aeada5ccf67f8300321a95df799ad92ae2edd": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-3a8c-768a-9b6e-73b7233c2c67",
-      "response": {
-        "result": "01a14305-3a8c-768a-9b6e-73b7233c2c67"
-      }
-    },
-    "ab4554e809fdc08d83243836baccf01f57463b09de697e97bd005a6c08024d15": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14306-0574-778b-902d-def5e43ebb9c",
-      "response": {
-        "result": "01a14306-0574-778b-902d-def5e43ebb9c"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v2/text-to-3d/01a14305-3a8c-768a-9b6e-73b7233c2c67": {
-      "id": "01a14305-3a8c-768a-9b6e-73b7233c2c67",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1053,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125781392,
-      "progress": 100,
-      "started_at": 1792125781397,
-      "finished_at": 1792125829276,
-      "task_error": null,
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-      "remove_lighting": true,
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-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json
deleted file mode 100644
index 4b2f8f4972d3aaf61686482983b49cce717f30fe..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "5d3980ae1dea18d8a898ec67cf7fefc9e7a055e3dc9b31dc401d0326d3a6a642": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-53a6-76ba-aacd-bfeecd6322a6",
-      "response": {
-        "result": "01a14305-53a6-76ba-aacd-bfeecd6322a6"
-      }
-    },
-    "404b72444f6f46316c0d41ac1d8a7ae383c3a3a4ab77de0dd6ccad5773680834": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14306-36be-7432-a1c2-c5079ff35ab8",
-      "response": {
-        "result": "01a14306-36be-7432-a1c2-c5079ff35ab8"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v2/text-to-3d/01a14305-53a6-76ba-aacd-bfeecd6322a6": {
-      "id": "01a14305-53a6-76ba-aacd-bfeecd6322a6",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1054,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
-      "created_at": 1792125788112,
-      "progress": 100,
-      "started_at": 1792125788119,
-      "finished_at": 1792125841423,
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-      "remove_lighting": true,
-      "consumed_credits": 10
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-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json
deleted file mode 100644
index b15f820db429fc68951d4ff855920df9ee629c68..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "138228236996f4ce1cc4ca628fb9460978db1b27e6f203a33d14d0724064d60b": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-65ed-75d2-b62c-7f0896dbf080",
-      "response": {
-        "result": "01a14305-65ed-75d2-b62c-7f0896dbf080"
-      }
-    },
-    "b1b3250cab838a55578970c00e3027b4ac05843316dbc978fb4b58bfec9c445a": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14306-6d62-71d9-9dd8-c065947db983",
-      "response": {
-        "result": "01a14306-6d62-71d9-9dd8-c065947db983"
-      }
-    }
-  },
-  "results": {
-    "/openapi/v2/text-to-3d/01a14305-65ed-75d2-b62c-7f0896dbf080": {
-      "id": "01a14305-65ed-75d2-b62c-7f0896dbf080",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1055,
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-      "remove_lighting": true,
-      "consumed_credits": 10
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-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json
deleted file mode 100644
index 686465444eadbd2707cfb35cd9552b24efec7645..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
-    "dfeca2b472a710e0b7a8eab7743107957458c4993eef472402c17bcd140b8018": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 20,
-      "status": "created",
-      "remote_id": "01a14305-8ba1-713c-b5f4-b34a1eb0f48a",
-      "response": {
-        "result": "01a14305-8ba1-713c-b5f4-b34a1eb0f48a"
-      }
-    },
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json
deleted file mode 100644
index 35ae8fe67c0a1aed3c4fed4fcbd2b319e290a6d3..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json
deleted file mode 100644
index 51f1c823d0b3d5580610a0bc209047836189d8d7..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
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-      "status": "created",
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-    "89c086d617d7238b03b823ee9c6b1492bad24223fb984b079dd3d455b7e18eb9": {
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-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14308-04b0-7272-a6cd-9b52bc55402c",
-      "response": {
-        "result": "01a14308-04b0-7272-a6cd-9b52bc55402c"
-      }
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-      "type": "text-to-3d-refine",
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-      "remove_lighting": true,
-      "consumed_credits": 10
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json
deleted file mode 100644
index 0b749af429c48a81889782f4c0fb563d8230deea..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-  "submissions": {
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-      "status": "created",
-      "remote_id": "01a14307-5b70-72f0-aee9-007460a98719",
-      "response": {
-        "result": "01a14307-5b70-72f0-aee9-007460a98719"
-      }
-    },
-    "c1d500ae6f19c9f9bfad9ff806403042031bde246ee36d78a718dc3201269c51": {
-      "endpoint": "/openapi/v2/text-to-3d",
-      "reserved_credits": 10,
-      "status": "created",
-      "remote_id": "01a14308-69ca-7231-89be-b1cb32e3f71c",
-      "response": {
-        "result": "01a14308-69ca-7231-89be-b1cb32e3f71c"
-      }
-    }
-  },
-  "results": {
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-      "id": "01a14307-5b70-72f0-aee9-007460a98719",
-      "type": "text-to-3d-preview",
-      "mode": "preview",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1059,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "texture_prompt": "",
-      "texture_image_url": "",
-      "status": "SUCCEEDED",
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-      "progress": 100,
-      "started_at": 1792125921022,
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-      "texture_urls": [],
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-    },
-    "/openapi/v2/text-to-3d/01a14308-69ca-7231-89be-b1cb32e3f71c": {
-      "id": "01a14308-69ca-7231-89be-b1cb32e3f71c",
-      "type": "text-to-3d-refine",
-      "mode": "refine",
-      "name": "",
-      "model_type": "standard",
-      "seed": 1059,
-      "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
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-      "status": "SUCCEEDED",
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-      "progress": 100,
-      "started_at": 1792125987301,
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-      "task_error": null,
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_001.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_001.json
deleted file mode 100644
index 8c7c60206e98c1603441f878472c405e348251fb..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_001.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-  "submissions": {
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-      "endpoint": "/files",
-      "reserved_credits": 0,
-      "status": "created",
-      "remote_id": "file_23fd6556-8121-4262-b7b0-586c2ca8ab45",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "file_token": "file_23fd6556-8121-4262-b7b0-586c2ca8ab45"
-        }
-      }
-    },
-    "19f6b742ab73c3b6f240ccb914f2633a0a23bb9a125781a0f302f4072acdd7b6": {
-      "endpoint": "/generation/image-to-model",
-      "reserved_credits": 40,
-      "status": "created",
-      "remote_id": "941f7de4-72b7-4756-a6c4-5bc21f3fc07a",
-      "response": {
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-        "status": "success",
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-          "task_id": "941f7de4-72b7-4756-a6c4-5bc21f3fc07a"
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-      "reported_credits": 40.0
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-      "status": "success",
-      "data": {
-        "type": "image_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "file": {
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-            "object": {
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-          "texture_quality": "standard",
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-        "task_id": "941f7de4-72b7-4756-a6c4-5bc21f3fc07a",
-        "created_at": "2026-10-16T08:21:18.682631Z",
-        "completed_at": "2026-10-16T08:25:46.274844Z",
-        "credits_consumed": 40.0
-      }
-    }
-  }
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json
deleted file mode 100644
index 803bebf10b0ac89b180441b6926b5e68b0c8d3bc..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-  "submissions": {
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-      "endpoint": "/files",
-      "reserved_credits": 0,
-      "status": "created",
-      "remote_id": "file_f0d142ad-8371-417a-b08f-cbfc1b122d13",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "file_token": "file_f0d142ad-8371-417a-b08f-cbfc1b122d13"
-        }
-      }
-    },
-    "5c58677b9ec82642167dc1687fb1e4dc5fc3d06e91f17825bd6c588ecc3d4b92": {
-      "endpoint": "/generation/image-to-model",
-      "reserved_credits": 40,
-      "status": "created",
-      "remote_id": "f250d2da-3f5c-44e4-bf37-77841bac3d81",
-      "response": {
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-        "status": "success",
-        "data": {
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-          "task_id": "f250d2da-3f5c-44e4-bf37-77841bac3d81"
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-      },
-      "reported_credits": 40.0
-    }
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-      "status": "success",
-      "data": {
-        "type": "image_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "file": {
-            "type": "jpeg",
-            "object": {
-              "key": "tcli_8a234d301b1e472ebe10d73adcd43336/20261016/f250d2da-3f5c-44e4-bf37-77841bac3d81/image.jpeg",
-              "bucket": "cn-openapi"
-            }
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-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1046,
-          "texture_seed": 1046,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
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-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/f250d2da-3f5c-44e4-bf37-77841bac3d81/tripo_pbr_model_f250d2da-3f5c-44e4-bf37-77841bac3d81.glb?auth_key=1792225666-ykagyolb-0-67bb4a6abf664950d1782e4ff247a71c",
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-        },
-        "task_id": "f250d2da-3f5c-44e4-bf37-77841bac3d81",
-        "created_at": "2026-10-16T08:23:39.415991Z",
-        "completed_at": "2026-10-16T08:27:42.440576Z",
-        "credits_consumed": 40.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json
deleted file mode 100644
index cebc5b8e3e7cf3411335bf7d629e89ba29a0bb28..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-  "submissions": {
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-      "endpoint": "/files",
-      "reserved_credits": 0,
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-      "remote_id": "file_45299b5c-ad90-496b-a23a-2e94f69e5a93",
-      "response": {
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-        "status": "success",
-        "data": {
-          "file_token": "file_45299b5c-ad90-496b-a23a-2e94f69e5a93"
-        }
-      }
-    },
-    "6b85809ffb58940bd3a8dd408f82e71e4c9a0cf5e871dcee53a8acc0932f490d": {
-      "endpoint": "/generation/image-to-model",
-      "reserved_credits": 40,
-      "status": "created",
-      "remote_id": "abcf3e83-c2da-4bec-9423-36a607dfab5c",
-      "response": {
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-        "status": "success",
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-          "task_id": "abcf3e83-c2da-4bec-9423-36a607dfab5c"
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-  "results": {
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-      "status": "success",
-      "data": {
-        "type": "image_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "file": {
-            "type": "jpeg",
-            "object": {
-              "key": "tcli_8a234d301b1e472ebe10d73adcd43336/20261016/abcf3e83-c2da-4bec-9423-36a607dfab5c/image.jpeg",
-              "bucket": "cn-openapi"
-            }
-          },
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
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-          "smart_low_poly": true,
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-        "output": {
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-        },
-        "task_id": "abcf3e83-c2da-4bec-9423-36a607dfab5c",
-        "created_at": "2026-10-16T08:25:55.227875Z",
-        "completed_at": "2026-10-16T08:29:32.635359Z",
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-    }
-  }
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json
deleted file mode 100644
index af08c66d6834dd4f0df7078637a95bb6d4a08dcf..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-  "submissions": {
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-      "endpoint": "/files",
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-        "status": "success",
-        "data": {
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-        }
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-      "reserved_credits": 40,
-      "status": "created",
-      "remote_id": "62f31bb7-0e64-4931-912c-244de1c4e503",
-      "response": {
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-      "status": "success",
-      "data": {
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-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "file": {
-            "type": "jpeg",
-            "object": {
-              "key": "tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62f31bb7-0e64-4931-912c-244de1c4e503/image.jpeg",
-              "bucket": "cn-openapi"
-            }
-          },
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1048,
-          "texture_seed": 1048,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "enable_image_autofix": false
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62f31bb7-0e64-4931-912c-244de1c4e503/tripo_pbr_model_62f31bb7-0e64-4931-912c-244de1c4e503.glb?auth_key=1792225872-ilctwycw-0-33b1a8e3abe88839ba797630c66b4665",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62f31bb7-0e64-4931-912c-244de1c4e503/legacy_mesh.webp?auth_key=1792225872-gmcbggas-0-064d39259d92a99ca864ff3a30d9f70c"
-        },
-        "task_id": "62f31bb7-0e64-4931-912c-244de1c4e503",
-        "created_at": "2026-10-16T08:27:50.692889Z",
-        "completed_at": "2026-10-16T08:31:07.522891Z",
-        "credits_consumed": 40.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json
deleted file mode 100644
index 49d52eeb2c9c9dd957f022c121658dee446601a1..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_005.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-  "submissions": {
-    "70d63fb51826da171f45c318e3caf438a22eccd34207049e63a84e58eac50ecf": {
-      "endpoint": "/files",
-      "reserved_credits": 0,
-      "status": "created",
-      "remote_id": "file_1125e812-6445-4973-8d23-2f8cd008758a",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "file_token": "file_1125e812-6445-4973-8d23-2f8cd008758a"
-        }
-      }
-    },
-    "035b474ee68d876edddacce7609d19d580a341d6d5bd6ae616b6c8bfb58d14d9": {
-      "endpoint": "/generation/image-to-model",
-      "reserved_credits": 40,
-      "status": "created",
-      "remote_id": "1470ba93-a649-473f-ae70-2c8ab22972a2",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "image_to_model",
-          "status": "queued",
-          "task_id": "1470ba93-a649-473f-ae70-2c8ab22972a2"
-        }
-      },
-      "reported_credits": 40.0
-    }
-  },
-  "results": {
-    "/tasks/1470ba93-a649-473f-ae70-2c8ab22972a2": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "image_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "file": {
-            "type": "jpeg",
-            "object": {
-              "key": "tcli_8a234d301b1e472ebe10d73adcd43336/20261016/1470ba93-a649-473f-ae70-2c8ab22972a2/image.jpeg",
-              "bucket": "cn-openapi"
-            }
-          },
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1049,
-          "texture_seed": 1049,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "enable_image_autofix": false
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/1470ba93-a649-473f-ae70-2c8ab22972a2/tripo_pbr_model_1470ba93-a649-473f-ae70-2c8ab22972a2.glb?auth_key=1792225982-lkshxwvz-0-f4d9d6e945f46996068ee11096bc58b3",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/1470ba93-a649-473f-ae70-2c8ab22972a2/legacy_mesh.webp?auth_key=1792225982-ysilogmi-0-6d2430a3e4415554ed5d657f6aa75fb4"
-        },
-        "task_id": "1470ba93-a649-473f-ae70-2c8ab22972a2",
-        "created_at": "2026-10-16T08:28:50.235667Z",
-        "completed_at": "2026-10-16T08:32:58.157712Z",
-        "credits_consumed": 40.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json
deleted file mode 100644
index 19aebe86948382f437cc2283470f82500c0a61ab..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_006.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "06f53be5a9a902d99a338a1958e272962b9b5ce98d5aaa2ed0c598beed586a4f": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "62c8741b-2215-47eb-83a4-5d09f4a123e9",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "62c8741b-2215-47eb-83a4-5d09f4a123e9"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/62c8741b-2215-47eb-83a4-5d09f4a123e9": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1050,
-          "texture_seed": 1050,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62c8741b-2215-47eb-83a4-5d09f4a123e9/tripo_pbr_model_62c8741b-2215-47eb-83a4-5d09f4a123e9.glb?auth_key=1792226030-moxuuahl-0-d511ddd9b20685743289f01e36842a08",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62c8741b-2215-47eb-83a4-5d09f4a123e9/legacy_mesh.webp?auth_key=1792226030-kyhbfqdg-0-a780f1c80f1be497631e89a1e7187cfe",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/62c8741b-2215-47eb-83a4-5d09f4a123e9/text2image_62c8741b-2215-47eb-83a4-5d09f4a123e9.jpeg?auth_key=1792226030-ysrycfva-0-d68aa0b0d5aeed5389fbe69a92c711e7"
-        },
-        "task_id": "62c8741b-2215-47eb-83a4-5d09f4a123e9",
-        "created_at": "2026-10-16T08:29:43.351969Z",
-        "completed_at": "2026-10-16T08:33:45.338650Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json
deleted file mode 100644
index 121bc45c346d66d96b10afb2612d6596198f840f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_007.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "df57c019a99f594fba742083c91c71cb1ac231fa3c36d2788e82f76b9d44800a": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "cc80cb88-5b1e-4ce3-bd36-d97c0177e73c",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "cc80cb88-5b1e-4ce3-bd36-d97c0177e73c"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/cc80cb88-5b1e-4ce3-bd36-d97c0177e73c": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1051,
-          "texture_seed": 1051,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/cc80cb88-5b1e-4ce3-bd36-d97c0177e73c/tripo_pbr_model_cc80cb88-5b1e-4ce3-bd36-d97c0177e73c.glb?auth_key=1792226132-zwhlwaal-0-435b31599cb685d635cf742fe29d0842",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/cc80cb88-5b1e-4ce3-bd36-d97c0177e73c/legacy_mesh.webp?auth_key=1792226132-hreonbey-0-094906be212b69cc4729be3b144371ba",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/cc80cb88-5b1e-4ce3-bd36-d97c0177e73c/text2image_cc80cb88-5b1e-4ce3-bd36-d97c0177e73c.jpeg?auth_key=1792226132-qqwebkmv-0-57fbb66086d4aa0de03d1212ffe9bdeb"
-        },
-        "task_id": "cc80cb88-5b1e-4ce3-bd36-d97c0177e73c",
-        "created_at": "2026-10-16T08:31:15.447264Z",
-        "completed_at": "2026-10-16T08:35:30.219476Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json
deleted file mode 100644
index 803230c393c1ec93852904a9204bd523250270c2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_008.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "a391a378d43c6779d6422d95f778b7cd41c4ec6233c52c117bf2c38f1b5df576": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "ca05eb37-0729-4aa2-a41d-1bee36e61d6d",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "ca05eb37-0729-4aa2-a41d-1bee36e61d6d"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/ca05eb37-0729-4aa2-a41d-1bee36e61d6d": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1052,
-          "texture_seed": 1052,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ca05eb37-0729-4aa2-a41d-1bee36e61d6d/tripo_pbr_model_ca05eb37-0729-4aa2-a41d-1bee36e61d6d.glb?auth_key=1792226253-ctmlcvma-0-3302dc10c136a0d487a8c708ee248104",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ca05eb37-0729-4aa2-a41d-1bee36e61d6d/legacy_mesh.webp?auth_key=1792226253-ggymjkwl-0-4a8b782032e3c3bca69ab126abc85460",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ca05eb37-0729-4aa2-a41d-1bee36e61d6d/text2image_ca05eb37-0729-4aa2-a41d-1bee36e61d6d.jpeg?auth_key=1792226253-pnlbkjcs-0-44801e6523f5f8adfdc4a3260cc6ec65"
-        },
-        "task_id": "ca05eb37-0729-4aa2-a41d-1bee36e61d6d",
-        "created_at": "2026-10-16T08:33:06.780504Z",
-        "completed_at": "2026-10-16T08:37:30.585085Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json
deleted file mode 100644
index 8430ca7f9e93601ac90a1af34091a3f97f5b1a91..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_009.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "7e68b7f4a84e51c2f6cfa3d387a0f77d06abd095ae207263c9946541c49c6488": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "703ea0f5-51d7-47e6-a38d-43458e8dfbdb",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "703ea0f5-51d7-47e6-a38d-43458e8dfbdb"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/703ea0f5-51d7-47e6-a38d-43458e8dfbdb": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1053,
-          "texture_seed": 1053,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/703ea0f5-51d7-47e6-a38d-43458e8dfbdb/tripo_pbr_model_703ea0f5-51d7-47e6-a38d-43458e8dfbdb.glb?auth_key=1792226262-cfhluiyq-0-9aca6a81a6f4bf2330b19b7d4df808b9",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/703ea0f5-51d7-47e6-a38d-43458e8dfbdb/legacy_mesh.webp?auth_key=1792226262-qhycwuxj-0-4514241b67a03d733bd93c30d7748ed9",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/703ea0f5-51d7-47e6-a38d-43458e8dfbdb/text2image_703ea0f5-51d7-47e6-a38d-43458e8dfbdb.jpeg?auth_key=1792226262-maopcswg-0-a58e190d2bcd3644c70211fa9bb389b5"
-        },
-        "task_id": "703ea0f5-51d7-47e6-a38d-43458e8dfbdb",
-        "created_at": "2026-10-16T08:33:54.702982Z",
-        "completed_at": "2026-10-16T08:37:40.653693Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json
deleted file mode 100644
index 20593c56027b4cc93e51320dc60372a2e7570cda..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_010.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "9d5c38fdfb996360be4d09149ddca27bf000dd5d62315f6b35136162fdf67bbb": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "4c4b0e00-6c9e-4d10-8949-4859a8e15ada",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "4c4b0e00-6c9e-4d10-8949-4859a8e15ada"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/4c4b0e00-6c9e-4d10-8949-4859a8e15ada": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1054,
-          "texture_seed": 1054,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c4b0e00-6c9e-4d10-8949-4859a8e15ada/tripo_pbr_model_4c4b0e00-6c9e-4d10-8949-4859a8e15ada.glb?auth_key=1792226387-xmotoxxr-0-458dea0a06d065fb4edc593bfff39a04",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c4b0e00-6c9e-4d10-8949-4859a8e15ada/legacy_mesh.webp?auth_key=1792226387-zxabvddz-0-a27d18698b5beeba07710db3c4ab4129",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c4b0e00-6c9e-4d10-8949-4859a8e15ada/text2image_4c4b0e00-6c9e-4d10-8949-4859a8e15ada.jpeg?auth_key=1792226387-cvcqjrzw-0-bec9f799f23b28108ccff433f984380e"
-        },
-        "task_id": "4c4b0e00-6c9e-4d10-8949-4859a8e15ada",
-        "created_at": "2026-10-16T08:35:35.731747Z",
-        "completed_at": "2026-10-16T08:39:43.600606Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json
deleted file mode 100644
index 6070d73cafb55b524d4687c2c7a4876d873b9373..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_011.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "888146345dd4f58567b36285a125c1bc7f35a7dacdeaca09014f65fa5a6166b4": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "4c5483d0-84d6-4270-93c5-b70b3709a02f",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "4c5483d0-84d6-4270-93c5-b70b3709a02f"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/4c5483d0-84d6-4270-93c5-b70b3709a02f": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1055,
-          "texture_seed": 1055,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c5483d0-84d6-4270-93c5-b70b3709a02f/tripo_pbr_model_4c5483d0-84d6-4270-93c5-b70b3709a02f.glb?auth_key=1792226519-blfdvybq-0-172ff7dc7db8d529d14285b217743153",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c5483d0-84d6-4270-93c5-b70b3709a02f/legacy_mesh.webp?auth_key=1792226519-oqqitsui-0-898bcac8cc0b5df0b69eda2a5e13fb01",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/4c5483d0-84d6-4270-93c5-b70b3709a02f/text2image_4c5483d0-84d6-4270-93c5-b70b3709a02f.jpeg?auth_key=1792226519-yvqppfep-0-a7cff7d8cf24375b9144122b9b89a69a"
-        },
-        "task_id": "4c5483d0-84d6-4270-93c5-b70b3709a02f",
-        "created_at": "2026-10-16T08:37:37.466400Z",
-        "completed_at": "2026-10-16T08:41:56.335235Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json
deleted file mode 100644
index 285ac6ba2120f34bc7b4cd747a78a90c7fd84f3a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_012.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "e295f627725d8b89d86dd8b46f7cd21d57b753b7e3b6449fba35c1d07a9da652": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "ad320eaa-c03c-4fad-8540-80ef962dcb3f",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "ad320eaa-c03c-4fad-8540-80ef962dcb3f"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/ad320eaa-c03c-4fad-8540-80ef962dcb3f": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1056,
-          "texture_seed": 1056,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ad320eaa-c03c-4fad-8540-80ef962dcb3f/tripo_pbr_model_ad320eaa-c03c-4fad-8540-80ef962dcb3f.glb?auth_key=1792226527-tzegvbtp-0-ac4483d84ef7c41bada35e618b900b90",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ad320eaa-c03c-4fad-8540-80ef962dcb3f/legacy_mesh.webp?auth_key=1792226527-fqpzobkq-0-4d73de786317e40fa9538524daaa4bd3",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/ad320eaa-c03c-4fad-8540-80ef962dcb3f/text2image_ad320eaa-c03c-4fad-8540-80ef962dcb3f.jpeg?auth_key=1792226527-tjxofwnt-0-4f40b3cb4c8d50f8b8bf90d2f8897dbf"
-        },
-        "task_id": "ad320eaa-c03c-4fad-8540-80ef962dcb3f",
-        "created_at": "2026-10-16T08:37:45.413985Z",
-        "completed_at": "2026-10-16T08:42:06.132901Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json
deleted file mode 100644
index 8f55e9ecad87149dc467b9ba08348b8836ee6991..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_013.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "99ed6faf224df6c62e5d245a178c5025cddf18ee005f0cef243e9da031b2a236": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "9fd46e2c-4479-4558-9ccd-5f13afcaeb27",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "9fd46e2c-4479-4558-9ccd-5f13afcaeb27"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/9fd46e2c-4479-4558-9ccd-5f13afcaeb27": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1057,
-          "texture_seed": 1057,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/9fd46e2c-4479-4558-9ccd-5f13afcaeb27/tripo_pbr_model_9fd46e2c-4479-4558-9ccd-5f13afcaeb27.glb?auth_key=1792226618-hbvhpnqw-0-1fae29578efe0dedea12971244c4c810",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/9fd46e2c-4479-4558-9ccd-5f13afcaeb27/legacy_mesh.webp?auth_key=1792226618-iwstkufn-0-d59786764a02f08fd60ae1be43687a79",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/9fd46e2c-4479-4558-9ccd-5f13afcaeb27/text2image_9fd46e2c-4479-4558-9ccd-5f13afcaeb27.jpeg?auth_key=1792226618-wzzwquts-0-2d4d3bea5701317d1594286d991c0e9b"
-        },
-        "task_id": "9fd46e2c-4479-4558-9ccd-5f13afcaeb27",
-        "created_at": "2026-10-16T08:39:51.783241Z",
-        "completed_at": "2026-10-16T08:43:34.886263Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json
deleted file mode 100644
index 7608d380c6ef05e80fa5c2d6b680f19c362298ba..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_014.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "00fcdc6b9308217de93b491fb775fa0fcaf694b4688f6d1d3aa1cf5b788c4c78": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "788184c4-785c-4d4d-b076-b835c7d4570a",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "788184c4-785c-4d4d-b076-b835c7d4570a"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/788184c4-785c-4d4d-b076-b835c7d4570a": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1058,
-          "texture_seed": 1058,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/788184c4-785c-4d4d-b076-b835c7d4570a/tripo_pbr_model_788184c4-785c-4d4d-b076-b835c7d4570a.glb?auth_key=1792226741-dwrqgaoh-0-ba2dfa932f2170bf6bb49c4ea8b8d03a",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/788184c4-785c-4d4d-b076-b835c7d4570a/legacy_mesh.webp?auth_key=1792226741-ngyaqixf-0-a5f43cb5b9e6d2d8dc89fe93d799a223",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/788184c4-785c-4d4d-b076-b835c7d4570a/text2image_788184c4-785c-4d4d-b076-b835c7d4570a.jpeg?auth_key=1792226741-ipbivkgn-0-c94dd024134bf32702e6bb6c6a1b0ee9"
-        },
-        "task_id": "788184c4-785c-4d4d-b076-b835c7d4570a",
-        "created_at": "2026-10-16T08:42:03.915917Z",
-        "completed_at": "2026-10-16T08:45:36.867024Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json
deleted file mode 100644
index cbf853581630d0dc2aeecfd105483a8f2be2612b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/tripo/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_015.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "submissions": {
-    "ccafa096665a7be1c8da034850e6312ffae13866edbea51c60e4d1e6c817d431": {
-      "endpoint": "/generation/text-to-model",
-      "reserved_credits": 30,
-      "status": "created",
-      "remote_id": "61974b83-4072-4c6d-b0b1-c10a45043a81",
-      "response": {
-        "code": 0,
-        "status": "success",
-        "data": {
-          "type": "text_to_model",
-          "status": "queued",
-          "task_id": "61974b83-4072-4c6d-b0b1-c10a45043a81"
-        }
-      },
-      "reported_credits": 30.0
-    }
-  },
-  "results": {
-    "/tasks/61974b83-4072-4c6d-b0b1-c10a45043a81": {
-      "code": 0,
-      "status": "success",
-      "data": {
-        "type": "text_to_model",
-        "status": "success",
-        "progress": 100,
-        "input": {
-          "pbr": true,
-          "prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-          "texture": true,
-          "export_uv": true,
-          "face_limit": 8000,
-          "model_seed": 1059,
-          "texture_seed": 1059,
-          "model_version": "v3.1-20260211",
-          "smart_low_poly": true,
-          "texture_quality": "standard",
-          "geometry_quality": "standard",
-          "texture_alignment": "geometry"
-        },
-        "output": {
-          "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/61974b83-4072-4c6d-b0b1-c10a45043a81/tripo_pbr_model_61974b83-4072-4c6d-b0b1-c10a45043a81.glb?auth_key=1792226753-xhzbehae-0-64baa01d68624508777944f062e2d353",
-          "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/61974b83-4072-4c6d-b0b1-c10a45043a81/legacy_mesh.webp?auth_key=1792226753-fufdoxhm-0-943372fc0062dbe1154de54616cc90db",
-          "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/61974b83-4072-4c6d-b0b1-c10a45043a81/text2image_61974b83-4072-4c6d-b0b1-c10a45043a81.jpeg?auth_key=1792226753-mqvrcvaw-0-50a5974afec09c0075037495da8232e2"
-        },
-        "task_id": "61974b83-4072-4c6d-b0b1-c10a45043a81",
-        "created_at": "2026-10-16T08:42:11.464349Z",
-        "completed_at": "2026-10-16T08:45:50.807975Z",
-        "credits_consumed": 30.0
-      }
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png
deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png
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diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/cloud_api.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/cloud_api.py
deleted file mode 100644
index 0553c4d55352bbb3fb5ade7c38c53d246484e33f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/cloud_api.py
+++ /dev/null
@@ -1,557 +0,0 @@
-"""
-models/common/cloud_api.py
-
-Shared plumbing for closed-source ("cloud API") model wrappers — the part of
-Tripo / Meshy / Rodin / ElevenLabs / Kling wrappers that is identical no matter
-what the provider generates.
-
-Contract: `agent_skills/develop_harness/api_model_require.md` R9.
-This module owns R9.6 (submit → poll → download behind a blocking call),
-R9.7 (API key from the environment, at call time), R9.8 (response cache, so a
-re-run is not re-billed) and R9.9 (retry with backoff, retryable vs. terminal).
-
-It knows nothing about 3D, audio or video — only HTTP, money and failure modes.
-
-Dependencies: stdlib at import time; `requests` is imported lazily on first use
-so `test/harness/smoke.py` keeps working on a machine that does not have it.
-
-Usage:
-    from models.common import cloud_api
-
-    key = cloud_api.require_api_key(None, "TRIPO_API_KEY",
-                                    "https://platform.tripo3d.ai/api-keys")
-    client = cloud_api.CloudAPIClient("https://openapi.tripo3d.ai/v3", key)
-    data = client.request("POST", "/generation/text-to-model", json={...})
-"""
-from __future__ import annotations
-
-import hashlib
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from typing import Any, Callable, Iterable, Optional
-
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.NullHandler())
-
-#: HTTP statuses worth retrying — transient by nature.
-RETRYABLE_STATUS: frozenset[int] = frozenset({408, 409, 425, 429, 500, 502, 503, 504})
-
-#: HTTP statuses that will never succeed on retry: bad request, auth, quota.
-TERMINAL_STATUS: frozenset[int] = frozenset({400, 401, 402, 403, 404, 405, 413, 415, 422})
-
-
-# ── Errors ────────────────────────────────────────────────────────────────────
-
-
-class CloudAPIError(RuntimeError):
-    """
-    Base class for every cloud-API failure.
-
-    Attributes:
-        status:   HTTP status code, when the failure came from a response.
-        task_id:  Provider task id, when one had already been created — keep it,
-                  a timed-out task usually finishes anyway and can be fetched
-                  later (`api_model_require.md` R9.6).
-        payload:  Decoded response body, truncated for readability.
-    """
-
-    retryable = False
-
-    def __init__(self, message: str, *, status: Optional[int] = None,
-                 task_id: Optional[str] = None, payload: Any = None):
-        super().__init__(message)
-        self.status = status
-        self.task_id = task_id
-        self.payload = payload
-
-
-class CloudAPIAuthError(CloudAPIError):
-    """401 / 403 — the key is missing, wrong, or lacks the entitlement."""
-
-
-class CloudAPIQuotaError(CloudAPIError):
-    """402 or a provider-specific "insufficient credits" body. Terminal."""
-
-
-class CloudAPIRequestError(CloudAPIError):
-    """400 / 404 / 422 — the request itself is wrong. Terminal."""
-
-
-class CloudAPIServerError(CloudAPIError):
-    """5xx and connection-level failures. Retryable."""
-
-    retryable = True
-
-
-class CloudAPIRateLimitError(CloudAPIServerError):
-    """429 — retryable, honour `Retry-After` when present."""
-
-
-class CloudTaskFailed(CloudAPIError):
-    """The provider accepted the task and then reported failure."""
-
-
-class CloudTaskTimeout(CloudAPIError):
-    """The task did not reach a terminal state within `timeout` seconds."""
-
-
-# ── Credentials ───────────────────────────────────────────────────────────────
-
-
-def require_api_key(value: Optional[str], env_var: str, signup_url: str,
-                    *, who: str = "This model") -> str:
-    """
-    Resolve an API key, failing fast with an actionable message (R1.6 / R9.7).
-
-    Args:
-        value:      Explicit key passed to the constructor, or None.
-        env_var:    Environment variable to fall back to.
-        signup_url: Where a human gets a key.
-        who:        Name used in the error message.
-
-    Returns:
-        The key.
-
-    Raises:
-        CloudAPIAuthError: no key available.
-    """
-    key = value or os.environ.get(env_var)
-    if key:
-        return key.strip()
-    raise CloudAPIAuthError(
-        f"{who} needs an API key and none was found.\n"
-        f"  1. get one at {signup_url}\n"
-        f"  2. export {env_var}=<your key>          # never commit it\n"
-        f"     (or pass api_key=... to the constructor)"
-    )
-
-
-def redact(key: Optional[str]) -> str:
-    """Key fingerprint safe to put in a log line or `meta.json`."""
-    if not key:
-        return "<unset>"
-    return f"{key[:4]}…{key[-2:]} (len {len(key)})"
-
-
-# ── HTTP ──────────────────────────────────────────────────────────────────────
-
-
-def _requests():
-    """Import `requests` lazily with an actionable message (R1.5 / R1.6)."""
-    try:
-        import requests  # noqa: PLC0415
-    except ImportError as e:  # pragma: no cover - environment dependent
-        raise ImportError(
-            "Cloud-API model wrappers need the `requests` package:\n"
-            "    bash scripts/asset_env_setup/cloud_api_install.sh\n"
-            "    # or simply: python -m pip install requests\n"
-            "(only needed for the API backends; the local-weight models and "
-            "test/harness/smoke.py do not import it.)"
-        ) from e
-    return requests
-
-
-def _truncate(payload: Any, limit: int = 600) -> Any:
-    text = payload if isinstance(payload, str) else json.dumps(payload, default=str)
-    return text if len(text) <= limit else text[:limit] + f"… (+{len(text) - limit} chars)"
-
-
-def classify_http_error(status: int, body: Any, *,
-                        task_id: Optional[str] = None) -> CloudAPIError:
-    """
-    Map an HTTP status onto the right exception class (R9.9).
-
-    The body is inspected too: providers happily answer 200/400 with a
-    "not enough credits" payload, which must not be retried.
-    """
-    text = _truncate(body)
-    low = str(text).lower()
-    # Providers phrase "you are out of money" in prose, and the status code alone
-    # does not distinguish it from a bad key: Tripo answers **403** with
-    # "You don't have enough credit to create this task". Matching the body first
-    # is what keeps "wrong key" and "no money" apart for the caller.
-    if any(s in low for s in ("insufficient", "enough credit", "enough credits",
-                              "out of credit", "purchase more credit",
-                              "quota exceeded", "no credits", "balance")):
-        return CloudAPIQuotaError(
-            f"HTTP {status}: out of credits — {text}", status=status,
-            task_id=task_id, payload=body)
-    if status in (401, 403):
-        return CloudAPIAuthError(
-            f"HTTP {status}: authentication rejected — {text}", status=status,
-            task_id=task_id, payload=body)
-    if status == 402:
-        return CloudAPIQuotaError(
-            f"HTTP {status}: payment required — {text}", status=status,
-            task_id=task_id, payload=body)
-    if status == 429:
-        return CloudAPIRateLimitError(
-            f"HTTP {status}: rate limited — {text}", status=status,
-            task_id=task_id, payload=body)
-    if status in TERMINAL_STATUS:
-        return CloudAPIRequestError(
-            f"HTTP {status}: request rejected — {text}", status=status,
-            task_id=task_id, payload=body)
-    if status in RETRYABLE_STATUS or status >= 500:
-        return CloudAPIServerError(
-            f"HTTP {status}: server error — {text}", status=status,
-            task_id=task_id, payload=body)
-    return CloudAPIError(
-        f"HTTP {status}: unexpected response — {text}", status=status,
-        task_id=task_id, payload=body)
-
-
-class CloudAPIClient:
-    """
-    Thin retrying HTTP client for one provider.
-
-    Args:
-        base_url:     Root of the API, without a trailing slash.
-        api_key:      Bearer token.
-        timeout:      Per-request socket timeout in seconds (not the task timeout).
-        max_retries:  Extra attempts after the first one, for retryable failures.
-        backoff:      Base of the exponential backoff, seconds.
-        auth_header:  Header name / value template, for providers that do not
-                      use `Authorization: Bearer <key>`.
-        user_agent:   Sent as-is; helps providers attribute traffic.
-    """
-
-    def __init__(
-        self,
-        base_url: str,
-        api_key: str,
-        *,
-        timeout: int = 60,
-        max_retries: int = 3,
-        backoff: float = 1.5,
-        auth_header: str = "Authorization",
-        auth_template: str = "Bearer {key}",
-        user_agent: str = "3AGameFactory/0.1 (+https://github.com/OpenDCAI/GameFactory-3A)",
-    ):
-        self.base_url = base_url.rstrip("/")
-        self._api_key = api_key
-        self.timeout = timeout
-        self.max_retries = max_retries
-        self.backoff = backoff
-        self.auth_header = auth_header
-        self.auth_template = auth_template
-        self.user_agent = user_agent
-        self._session = None
-
-    # -- session ---------------------------------------------------------------
-
-    @property
-    def session(self):
-        """Lazily created `requests.Session` (kept alive across polls)."""
-        if self._session is None:
-            requests = _requests()
-            self._session = requests.Session()
-            self._session.headers.update({
-                self.auth_header: self.auth_template.format(key=self._api_key),
-                "User-Agent": self.user_agent,
-            })
-        return self._session
-
-    def close(self) -> None:
-        """Idempotent — safe after a failed construction and safe to call twice."""
-        if self._session is not None:
-            try:
-                self._session.close()
-            finally:
-                self._session = None
-
-    # -- requests --------------------------------------------------------------
-
-    def request(
-        self,
-        method: str,
-        path: str,
-        *,
-        json_body: Optional[dict] = None,
-        files: Optional[dict] = None,
-        data: Optional[dict] = None,
-        params: Optional[dict] = None,
-        expect_json: bool = True,
-    ) -> Any:
-        """
-        Perform one API call, retrying retryable failures (R9.9).
-
-        Args:
-            method:      "GET" / "POST" / …
-            path:        Path appended to `base_url`, or a full URL.
-            json_body:   JSON payload.
-            files/data:  multipart payload (`requests` semantics).
-            params:      Query string.
-            expect_json: Parse the response as JSON; otherwise return `bytes`.
-
-        Returns:
-            Parsed JSON (dict / list) or raw `bytes`.
-
-        Raises:
-            CloudAPIError subclass — terminal errors immediately, retryable ones
-            after `max_retries` attempts.
-        """
-        requests = _requests()
-        url = path if path.startswith("http") else f"{self.base_url}/{path.lstrip('/')}"
-
-        last: Optional[CloudAPIError] = None
-        for attempt in range(self.max_retries + 1):
-            try:
-                resp = self.session.request(
-                    method, url, json=json_body, files=files, data=data,
-                    params=params, timeout=self.timeout,
-                )
-            except (requests.ConnectionError, requests.Timeout) as e:
-                last = CloudAPIServerError(f"{method} {url} failed at transport level: {e}")
-            else:
-                if resp.ok:
-                    if not expect_json:
-                        return resp.content
-                    try:
-                        return resp.json()
-                    except ValueError as e:
-                        raise CloudAPIError(
-                            f"{method} {url} returned {resp.status_code} with a "
-                            f"non-JSON body: {_truncate(resp.text)}"
-                        ) from e
-                try:
-                    body: Any = resp.json()
-                except ValueError:
-                    body = resp.text
-                err = classify_http_error(resp.status_code, body)
-                if not err.retryable:
-                    raise err
-                last = err
-                retry_after = resp.headers.get("Retry-After")
-                if retry_after:
-                    try:
-                        time.sleep(min(float(retry_after), 60.0))
-                    except ValueError:
-                        pass
-
-            if attempt < self.max_retries:
-                delay = self.backoff * (2 ** attempt)
-                logger.warning("[cloud_api] %s %s attempt %d/%d failed (%s); retrying in %.1fs",
-                               method, url, attempt + 1, self.max_retries + 1, last, delay)
-                time.sleep(delay)
-
-        assert last is not None
-        raise last
-
-    def download(self, url: str, *, max_bytes: Optional[int] = None) -> bytes:
-        """
-        Fetch a generated artifact. Signed asset URLs are short-lived — call this
-        immediately after the task reports success.
-
-        Args:
-            url: Absolute URL from the task output.
-            max_bytes: Reject anything larger, so a wrong URL cannot fill a disk.
-
-        Returns:
-            The file content.
-        """
-        content = self.request("GET", url, expect_json=False)
-        if max_bytes is not None and len(content) > max_bytes:
-            raise CloudAPIError(
-                f"downloaded artifact is {len(content)} bytes, over the "
-                f"{max_bytes} byte limit: {url}")
-        if not content:
-            raise CloudAPIError(f"downloaded artifact is empty: {url}")
-        return content
-
-
-# ── Polling ───────────────────────────────────────────────────────────────────
-
-
-def poll_task(
-    fetch: Callable[[], dict],
-    *,
-    task_id: str,
-    timeout: float = 300.0,
-    poll_interval: float = 3.0,
-    done_states: Iterable[str],
-    failed_states: Iterable[str],
-    state_key: str = "status",
-    progress_key: str = "progress",
-    verbose: bool = False,
-) -> dict:
-    """
-    Block until a provider task reaches a terminal state (R9.6).
-
-    Args:
-        fetch:         Callable returning the decoded task object.
-        task_id:       Only used for messages — every error carries it.
-        timeout:       Wall-clock budget in seconds.
-        poll_interval: Seconds between polls.
-        done_states:   States meaning success (compared case-insensitively).
-        failed_states: States meaning failure.
-        state_key:     Field holding the state.
-        progress_key:  Field holding 0-100 progress, when the provider sends one.
-        verbose:       Print progress; otherwise only DEBUG logs (R3.7).
-
-    Returns:
-        The task object in its successful terminal state.
-
-    Raises:
-        CloudTaskFailed:  provider reported failure.
-        CloudTaskTimeout: budget exhausted; the task may still finish server-side,
-                          the error carries `task_id` so it can be fetched later.
-    """
-    done = {s.lower() for s in done_states}
-    failed = {s.lower() for s in failed_states}
-    deadline = time.time() + timeout
-    last_progress = -1
-
-    while True:
-        task = fetch()
-        state = str(task.get(state_key, "")).lower()
-        progress = task.get(progress_key)
-
-        if progress != last_progress:
-            last_progress = progress
-            logger.debug("[cloud_api] task %s: %s (%s%%)", task_id, state, progress)
-            if verbose:
-                print(f"[cloud_api] task {task_id}: {state} ({progress}%)")
-
-        if state in done:
-            return task
-        if state in failed:
-            raise CloudTaskFailed(
-                f"task {task_id} ended as {state!r}: {_truncate(task)}",
-                task_id=task_id, payload=task)
-
-        remaining = deadline - time.time()
-        if remaining <= 0:
-            raise CloudTaskTimeout(
-                f"task {task_id} still {state!r} after {timeout:.0f}s. It may still "
-                f"finish server-side — query it with the task id above, or raise "
-                f"`timeout=` on the model.",
-                task_id=task_id, payload=task)
-        time.sleep(min(poll_interval, remaining))
-
-
-# ── Response cache ────────────────────────────────────────────────────────────
-
-
-def request_hash(payload: dict) -> str:
-    """
-    Stable sha256 over a request description (R9.8).
-
-    The payload must contain everything that changes the output — model version,
-    prompt, image bytes hash, every generation parameter, the output format.
-    Never put the API key in it.
-    """
-    canonical = json.dumps(payload, sort_keys=True, ensure_ascii=False, default=str)
-    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()
-
-
-def bytes_hash(data: bytes) -> str:
-    """sha256 of a binary input, e.g. the reference image."""
-    return hashlib.sha256(data).hexdigest()
-
-
-class ResponseCache:
-    """
-    Content-addressed cache of generated artifacts.
-
-    A cloud call costs credits, so an identical request must never be sent twice
-    (R9.8). Layout::
-
-        <cache_dir>/<provider>/<hash>.<ext>        the artifact
-        <cache_dir>/<provider>/<hash>.meta.json    request description + task id
-
-    Args:
-        cache_dir: Root directory. `None` disables caching entirely.
-        provider:  Sub-directory name, so two providers never collide.
-    """
-
-    def __init__(self, cache_dir: Optional[str | Path], provider: str):
-        self.root = Path(cache_dir).expanduser() / provider if cache_dir else None
-        self.provider = provider
-
-    @property
-    def enabled(self) -> bool:
-        return self.root is not None
-
-    def _paths(self, key: str, ext: str) -> tuple[Path, Path]:
-        assert self.root is not None
-        return self.root / f"{key}.{ext}", self.root / f"{key}.meta.json"
-
-    def get(self, key: str, ext: str = "glb") -> Optional[bytes]:
-        """Cached artifact bytes, or None. Performs no network I/O, ever."""
-        if not self.enabled:
-            return None
-        blob, _ = self._paths(key, ext)
-        if blob.is_file() and blob.stat().st_size > 0:
-            logger.info("[cloud_api] cache hit (%s): %s", self.provider, blob.name)
-            return blob.read_bytes()
-        return None
-
-    def put(self, key: str, data: bytes, meta: dict, ext: str = "glb") -> Path:
-        """Store an artifact plus the request that produced it. Returns its path."""
-        if not self.enabled:
-            return Path()
-        blob, meta_path = self._paths(key, ext)
-        blob.parent.mkdir(parents=True, exist_ok=True)
-        blob.write_bytes(data)
-        meta_path.write_text(
-            json.dumps({**meta, "cache_key": key, "bytes": len(data)},
-                       indent=2, ensure_ascii=False, default=str),
-            encoding="utf-8",
-        )
-        return blob
-
-
-# ── Input encoding ────────────────────────────────────────────────────────────
-
-
-def image_to_png_bytes(image: Any) -> bytes:
-    """
-    Normalize an image input to PNG bytes.
-
-    Accepts a `PIL.Image.Image`, raw `bytes` (returned unchanged), or a
-    `np.ndarray`. Paths are rejected on purpose — `model_require.md` R3.2 says a
-    model takes the object, not a path.
-
-    Returns:
-        PNG-encoded bytes (RGBA preserved when present).
-    """
-    if isinstance(image, (bytes, bytearray)):
-        return bytes(image)
-    if isinstance(image, (str, Path)):
-        raise TypeError(
-            "models take an image object, not a path (model_require.md R3.2). "
-            "Open it in the operator: Image.open(path).convert('RGBA')"
-        )
-
-    from io import BytesIO  # noqa: PLC0415
-
-    from PIL import Image as PILImage  # noqa: PLC0415
-
-    if not isinstance(image, PILImage.Image):
-        try:
-            import numpy as np  # noqa: PLC0415
-        except ImportError:
-            np = None  # type: ignore[assignment]
-        if np is not None and isinstance(image, np.ndarray):
-            image = PILImage.fromarray(image)
-        else:
-            raise TypeError(
-                f"unsupported image type {type(image).__name__}; expected "
-                f"PIL.Image.Image, numpy.ndarray or bytes"
-            )
-
-    buf = BytesIO()
-    image.save(buf, format="PNG")
-    return buf.getvalue()
-
-
-def image_to_data_uri(image: Any, mime: str = "image/png") -> str:
-    """PNG data URI (`data:image/png;base64,…`) for providers that take one inline."""
-    import base64  # noqa: PLC0415
-
-    payload = base64.b64encode(image_to_png_bytes(image)).decode("ascii")
-    return f"data:{mime};base64,{payload}"
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py
deleted file mode 100644
index 61d86409097747264cabddb0301edc925f4f1ff7..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py
+++ /dev/null
@@ -1,578 +0,0 @@
-"""
-models/common/glb_utils.py
-
-Dependency-free inspection of binary glTF (`.glb`) buffers.
-
-Why this exists instead of `trimesh.load(...)`: the API wrappers must report the
-triangle count of what they just paid for (`api_model_require.md` R9.8), and
-that number has to be available on a CPU-only box with nothing but the stdlib
-installed. Parsing the JSON chunk of a GLB is enough for counting.
-
-`glb_summary` and `glb_triangle_count` read the JSON chunk only. `read_glb_mesh`
-decodes vertex data as well, because a generated part has to be *composed* with
-primitives rather than merely counted, and that needs its triangles in the same
-coordinate frame as everything else.
-
-Usage:
-    from models.common.glb_utils import glb_summary, read_glb_mesh
-    info = glb_summary(open("model.glb", "rb").read())
-    print(info["triangles"], info["materials"])
-
-    mesh = read_glb_mesh(open("part.glb", "rb").read())
-    print(len(mesh["positions"]), mesh["low"], mesh["high"])
-"""
-from __future__ import annotations
-
-import json
-import math
-import struct
-from typing import Any, Optional, Sequence
-
-GLB_MAGIC = b"glTF"
-
-#: glTF primitive mode 4 == TRIANGLES. Only triangle primitives are counted.
-_MODE_TRIANGLES = 4
-
-#: glTF componentType -> (struct format, byte size). Indices are allowed to be
-#: any of the unsigned types; positions are float in every file worth reading.
-_COMPONENT_TYPES: dict[int, tuple[str, int]] = {
-    5120: ("b", 1),   # BYTE
-    5121: ("B", 1),   # UNSIGNED_BYTE
-    5122: ("h", 2),   # SHORT
-    5123: ("H", 2),   # UNSIGNED_SHORT
-    5125: ("I", 4),   # UNSIGNED_INT
-    5126: ("f", 4),   # FLOAT
-}
-
-#: glTF accessor type -> component count.
-_TYPE_COUNTS: dict[str, int] = {
-    "SCALAR": 1, "VEC2": 2, "VEC3": 3, "VEC4": 4,
-    "MAT2": 4, "MAT3": 9, "MAT4": 16,
-}
-
-
-class GLBParseError(ValueError):
-    """The buffer is not a GLB we can read."""
-
-
-def is_glb(data: bytes) -> bool:
-    """True when `data` starts with the 12-byte GLB header."""
-    return len(data) >= 12 and data[:4] == GLB_MAGIC
-
-
-def glb_json_chunk(data: bytes) -> dict[str, Any]:
-    """
-    Return the parsed JSON chunk of a GLB.
-
-    Args:
-        data: Whole `.glb` file content.
-
-    Returns:
-        The glTF document as a dict.
-
-    Raises:
-        GLBParseError: bad magic, truncated file, or a missing JSON chunk.
-    """
-    if not is_glb(data):
-        raise GLBParseError(
-            f"not a GLB: expected magic {GLB_MAGIC!r}, got {data[:4]!r}"
-        )
-    if len(data) < 20:
-        raise GLBParseError(f"GLB truncated: {len(data)} bytes")
-
-    _magic, version, total_len = struct.unpack_from("<4sII", data, 0)
-    if total_len > len(data):
-        raise GLBParseError(
-            f"GLB header claims {total_len} bytes, buffer holds {len(data)}"
-        )
-
-    offset = 12
-    while offset + 8 <= len(data):
-        chunk_len, chunk_type = struct.unpack_from("<I4s", data, offset)
-        offset += 8
-        chunk = data[offset:offset + chunk_len]
-        offset += chunk_len
-        if chunk_type == b"JSON":
-            try:
-                return json.loads(chunk.decode("utf-8"))
-            except (UnicodeDecodeError, json.JSONDecodeError) as e:
-                raise GLBParseError(f"GLB JSON chunk is not valid JSON: {e}") from e
-    raise GLBParseError(f"GLB (version {version}) has no JSON chunk")
-
-
-def glb_triangle_count(data: bytes) -> Optional[int]:
-    """
-    Triangle count of a GLB, or None when it cannot be determined.
-
-    Counts every triangle-mode primitive of every mesh once, i.e. the *asset's*
-    triangle budget, not the per-instance count after node duplication.
-
-    Returns:
-        int triangles, or None if the JSON chunk is unreadable.
-    """
-    try:
-        doc = glb_json_chunk(data)
-    except GLBParseError:
-        return None
-
-    accessors = doc.get("accessors", [])
-    total = 0
-    for mesh in doc.get("meshes", []):
-        for prim in mesh.get("primitives", []):
-            if prim.get("mode", _MODE_TRIANGLES) != _MODE_TRIANGLES:
-                continue
-            idx = prim.get("indices")
-            if idx is not None and idx < len(accessors):
-                count = accessors[idx].get("count", 0)
-            else:
-                pos = prim.get("attributes", {}).get("POSITION")
-                count = accessors[pos].get("count", 0) if pos is not None and pos < len(accessors) else 0
-            total += count // 3
-    return total
-
-
-def glb_summary(data: bytes) -> dict[str, Any]:
-    """
-    Cheap description of a GLB, for logs and `meta.json`.
-
-    Returns:
-        dict with keys:
-            bytes (int)            — buffer size
-            triangles (int | None) — see `glb_triangle_count`
-            meshes, materials, textures, images, nodes (int)
-            generator (str | None) — glTF `asset.generator`
-            error (str)            — present only when the JSON chunk is unreadable
-    """
-    out: dict[str, Any] = {"bytes": len(data)}
-    try:
-        doc = glb_json_chunk(data)
-    except GLBParseError as e:
-        out.update({"triangles": None, "error": str(e)})
-        return out
-
-    out.update({
-        "triangles": glb_triangle_count(data),
-        "meshes": len(doc.get("meshes", [])),
-        "materials": len(doc.get("materials", [])),
-        "textures": len(doc.get("textures", [])),
-        "images": len(doc.get("images", [])),
-        "nodes": len(doc.get("nodes", [])),
-        "generator": (doc.get("asset") or {}).get("generator"),
-    })
-    return out
-
-
-# --------------------------------------------------------------------------
-# Vertex decoding
-#
-# Needed to *compose* a generated part with primitives, as opposed to merely
-# reporting on it: composition means putting its triangles into the same
-# coordinate frame as the rest, and that requires reading them.
-# --------------------------------------------------------------------------
-
-
-def glb_binary_chunk(data: bytes) -> bytes:
-    """Return the BIN chunk of a GLB, or b"" when there is none."""
-    if not is_glb(data):
-        raise GLBParseError(f"not a GLB: got magic {data[:4]!r}")
-    offset = 12
-    while offset + 8 <= len(data):
-        chunk_len, chunk_type = struct.unpack_from("<I4s", data, offset)
-        offset += 8
-        if chunk_type == b"BIN\x00":
-            return data[offset:offset + chunk_len]
-        offset += chunk_len
-    return b""
-
-
-def _read_accessor(doc: dict[str, Any], binary: bytes, index: int) -> list[tuple]:
-    """Decode one accessor into a list of component tuples.
-
-    Honours ``byteStride``. Interleaved buffers are normal out of an
-    exporter, and reading one as tightly packed yields positions that are a
-    silent mixture of coordinates and normals — it parses, it renders, it is
-    garbage.
-    """
-
-    accessor = doc["accessors"][index]
-    component_type = accessor["componentType"]
-    if component_type not in _COMPONENT_TYPES:
-        raise GLBParseError(f"unsupported componentType {component_type}")
-    fmt, component_size = _COMPONENT_TYPES[component_type]
-    per_element = _TYPE_COUNTS[accessor["type"]]
-    count = accessor["count"]
-
-    view_index = accessor.get("bufferView")
-    if view_index is None:
-        # A sparse or zero-filled accessor. Legal, and not something a mesh
-        # we are about to weld into another asset should be relying on.
-        return [tuple([0] * per_element)] * count
-
-    view = doc["bufferViews"][view_index]
-    if view.get("buffer", 0) != 0:
-        raise GLBParseError("only single-buffer GLBs are supported")
-    base = view.get("byteOffset", 0) + accessor.get("byteOffset", 0)
-    packed = component_size * per_element
-    stride = view.get("byteStride") or packed
-
-    out: list[tuple] = []
-    element_format = "<" + fmt * per_element
-    for element in range(count):
-        start = base + element * stride
-        if start + packed > len(binary):
-            raise GLBParseError(
-                f"accessor {index} reads past the {len(binary)}-byte buffer"
-            )
-        out.append(struct.unpack_from(element_format, binary, start))
-    return out
-
-
-def _node_matrix(node: dict[str, Any]) -> list[float]:
-    """A node's local transform as a 16-float column-major matrix.
-
-    Handles both an explicit ``matrix`` and a TRS triple, since a file may use
-    either. Ignoring the transform looks like a modelling error: a mesh
-    authored Z-up whose node carries the -90 that stands it upright arrives on
-    its side, and the natural reaction is to rotate it back in the spec —
-    hard-coding a correction for something the file already stated.
-    """
-
-    if "matrix" in node:
-        return [float(value) for value in node["matrix"]]
-
-    tx, ty, tz = [float(value) for value in node.get("translation", (0, 0, 0))]
-    qx, qy, qz, qw = [float(value) for value in node.get("rotation", (0, 0, 0, 1))]
-    sx, sy, sz = [float(value) for value in node.get("scale", (1, 1, 1))]
-
-    # Quaternion to rotation matrix.
-    xx, yy, zz = qx * qx, qy * qy, qz * qz
-    xy, xz, yz = qx * qy, qx * qz, qy * qz
-    wx, wy, wz = qw * qx, qw * qy, qw * qz
-    rotation = (
-        (1 - 2 * (yy + zz), 2 * (xy + wz), 2 * (xz - wy)),
-        (2 * (xy - wz), 1 - 2 * (xx + zz), 2 * (yz + wx)),
-        (2 * (xz + wy), 2 * (yz - wx), 1 - 2 * (xx + yy)),
-    )
-    scale = (sx, sy, sz)
-    return [
-        rotation[0][0] * scale[0], rotation[0][1] * scale[0], rotation[0][2] * scale[0], 0.0,
-        rotation[1][0] * scale[1], rotation[1][1] * scale[1], rotation[1][2] * scale[1], 0.0,
-        rotation[2][0] * scale[2], rotation[2][1] * scale[2], rotation[2][2] * scale[2], 0.0,
-        tx, ty, tz, 1.0,
-    ]
-
-
-def _multiply(a: Sequence[float], b: Sequence[float]) -> list[float]:
-    """Column-major 4x4 product, applying `b` then `a`."""
-    out = [0.0] * 16
-    for column in range(4):
-        for row in range(4):
-            out[column * 4 + row] = sum(
-                a[k * 4 + row] * b[column * 4 + k] for k in range(4)
-            )
-    return out
-
-
-_IDENTITY = [1.0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 1.0]
-
-
-def read_glb_mesh(data: bytes) -> dict[str, Any]:
-    """Decode every triangle of a GLB into one flat, world-space mesh.
-
-    Walks the scene graph and applies each node's accumulated transform, so
-    the result is in the file's own world coordinates rather than in the
-    local frame of whichever mesh happened to be first.
-
-    The node hierarchy is deliberately *not* preserved. A part fetched from
-    a generator is one object as far as the composition is concerned — the
-    thing worth keeping addressable is the spec's part id, which the writer
-    attaches, and a generator's internal node names ("mesh_0", "Object_2")
-    are not names anything downstream can use.
-
-    Returns:
-        dict with ``positions`` and ``normals`` as lists of 3-tuples,
-        ``uvs`` as 2-tuples, ``indices`` as a flat list, ``low``/``high`` as
-        the axis-aligned bounds, and ``images`` / ``material_runs`` carrying
-        whatever textures the file brought with it. Normals are generated
-        per-face when the file omits them.
-
-    Textures come across because a generated part's whole contribution is
-    surface. Dropping them and letting the part inherit the spec's flat PBR
-    factors is consistent but throws away the thing that was paid for: the
-    stippling on a grip, the panel creases and decals on a car shell. That
-    needs three things travelling together — UVs per vertex, the image
-    bytes, and which triangles use which image — so ``material_runs``
-    records index ranges rather than a single material for the part.
-    """
-
-    doc = glb_json_chunk(data)
-    binary = glb_binary_chunk(data)
-    if not binary:
-        raise GLBParseError(
-            "the GLB has no BIN chunk, so its vertex data is external; "
-            "only self-contained GLBs can be composed"
-        )
-
-    positions: list[tuple[float, float, float]] = []
-    normals: list[tuple[float, float, float]] = []
-    uvs: list[tuple[float, float]] = []
-    indices: list[int] = []
-    material_runs: list[dict[str, Any]] = []
-
-    def add_mesh(mesh_index: int, matrix: Sequence[float]) -> None:
-        # The inverse transpose of the upper 3x3, for the normals. A non-uniform
-        # node scale makes the forward matrix wrong for them, and the symptom is
-        # lighting that reads as a modelling error rather than as a bad import.
-        normal_matrix = _inverse_transpose_3x3(matrix)
-
-        for primitive in doc["meshes"][mesh_index].get("primitives", []):
-            if primitive.get("mode", _MODE_TRIANGLES) != _MODE_TRIANGLES:
-                continue
-            attributes = primitive.get("attributes", {})
-            if "POSITION" not in attributes:
-                continue
-
-            local = _read_accessor(doc, binary, attributes["POSITION"])
-            base = len(positions)
-            for x, y, z in local:
-                positions.append((
-                    matrix[0] * x + matrix[4] * y + matrix[8] * z + matrix[12],
-                    matrix[1] * x + matrix[5] * y + matrix[9] * z + matrix[13],
-                    matrix[2] * x + matrix[6] * y + matrix[10] * z + matrix[14],
-                ))
-
-            if "NORMAL" in attributes:
-                for nx, ny, nz in _read_accessor(doc, binary, attributes["NORMAL"]):
-                    tx = normal_matrix[0] * nx + normal_matrix[3] * ny + normal_matrix[6] * nz
-                    ty = normal_matrix[1] * nx + normal_matrix[4] * ny + normal_matrix[7] * nz
-                    tz = normal_matrix[2] * nx + normal_matrix[5] * ny + normal_matrix[8] * nz
-                    length = math.sqrt(tx * tx + ty * ty + tz * tz) or 1.0
-                    normals.append((tx / length, ty / length, tz / length))
-            else:
-                normals.extend([(0.0, 0.0, 0.0)] * len(local))
-
-            # UVs are per-vertex and must stay aligned with positions, so a
-            # primitive without them still contributes placeholders — a short
-            # UV list would silently shift every following part's texturing.
-            if "TEXCOORD_0" in attributes:
-                uvs.extend(
-                    (float(u), float(v))
-                    for u, v in _read_accessor(doc, binary, attributes["TEXCOORD_0"])
-                )
-            else:
-                uvs.extend([(0.0, 0.0)] * len(local))
-
-            if "indices" in primitive and primitive["indices"] is not None:
-                local_indices = [
-                    value[0] for value in
-                    _read_accessor(doc, binary, primitive["indices"])
-                ]
-            else:
-                local_indices = list(range(len(local)))
-
-            run_start = len(indices)
-            indices.extend(base + value for value in local_indices)
-            material_runs.append({
-                "start": run_start,
-                "count": len(local_indices),
-                "material": primitive.get("material"),
-            })
-
-    def walk(node_index: int, parent: Sequence[float]) -> None:
-        node = doc["nodes"][node_index]
-        matrix = _multiply(parent, _node_matrix(node))
-        if node.get("mesh") is not None:
-            add_mesh(node["mesh"], matrix)
-        for child in node.get("children", ()):
-            walk(child, matrix)
-
-    scenes = doc.get("scenes") or []
-    scene = scenes[doc.get("scene", 0)] if scenes else None
-    roots = scene.get("nodes", ()) if scene else range(len(doc.get("nodes", [])))
-    for root in roots:
-        walk(root, _IDENTITY)
-
-    if not positions:
-        raise GLBParseError("the GLB contains no triangle geometry")
-
-    # Fill in any face normals the file left out, so a consumer never has to
-    # ask whether normals are present.
-    _fill_missing_normals(positions, normals, indices)
-
-    low = tuple(min(point[axis] for point in positions) for axis in range(3))
-    high = tuple(max(point[axis] for point in positions) for axis in range(3))
-    return {
-        "positions": positions,
-        "normals": normals,
-        "uvs": uvs,
-        "indices": indices,
-        "triangles": len(indices) // 3,
-        "low": low,
-        "high": high,
-        "material_runs": material_runs,
-        "materials": _read_materials(doc, binary),
-        "has_uvs": any(u != 0.0 or v != 0.0 for u, v in uvs),
-    }
-
-
-def _read_materials(doc: dict[str, Any], binary: bytes) -> list[dict[str, Any]]:
-    """Each material's PBR factors, its base-colour image and its MR image.
-
-    Base colour and metallic-roughness. Not the normal or occlusion maps:
-    those would give the composed asset a texture set the primitives beside it
-    lack, and a car body with a normal map next to a flat-shaded spoiler reads
-    as two assets. Metallic-roughness is different in kind — it is not detail
-    on top of a surface, it is *which surface it is*.
-
-    Taking the map is what makes the factors trustworthy. An exporter that puts
-    those channels in a texture leaves the factors at glTF's default ``1.0`` to
-    multiply it, so the two only mean anything together. This function used to
-    take the colour alone and substitute dielectric defaults (``0.0``/``0.55``)
-    on the grounds that inherited factors would render a polymer grip as
-    sandblasted steel — true, but it threw away the answer to keep from
-    guessing wrong. Plate armour came out as grey plastic: every generated
-    piece of the knight had ``metallicFactor 1.0`` and an MR map in the file,
-    and both were discarded.
-
-    ``factors_from`` is still recorded, because a material with no map at all
-    still has to fall back and a caller needs to know which happened.
-    """
-
-    out: list[dict[str, Any]] = []
-    images = doc.get("images", [])
-    textures = doc.get("textures", [])
-    views = doc.get("bufferViews", [])
-
-    def image_bytes(reference: dict[str, Any]) -> tuple[bytes | None, str | None]:
-        index = reference.get("index")
-        if index is None or index >= len(textures):
-            return None, None
-        image_index = textures[index].get("source")
-        if image_index is None or image_index >= len(images):
-            return None, None
-        image = images[image_index]
-        view_index = image.get("bufferView")
-        if view_index is not None and view_index < len(views):
-            view = views[view_index]
-            start = view.get("byteOffset", 0)
-            return (
-                bytes(binary[start:start + view["byteLength"]]),
-                image.get("mimeType") or "image/png",
-            )
-        if str(image.get("uri", "")).startswith("data:"):
-            # A data URI is legal and some exporters emit one even in a GLB.
-            # Decoded here so the caller never has to care which form was used.
-            import base64
-
-            header, _, payload = image["uri"].partition(",")
-            return (
-                base64.b64decode(payload),
-                header.split(":")[1].split(";")[0] if ":" in header
-                else "image/png",
-            )
-        return None, None
-
-    for material in doc.get("materials", []):
-        pbr = material.get("pbrMetallicRoughness") or {}
-        colour_image, colour_mime = image_bytes(pbr.get("baseColorTexture") or {})
-        mr_image, mr_mime = image_bytes(
-            pbr.get("metallicRoughnessTexture") or {})
-
-        if mr_image is not None:
-            # The map is coming with us, so the factors mean what they say:
-            # they scale it. glTF's default is 1.0 for exactly this case.
-            metallic = float(pbr.get("metallicFactor", 1.0))
-            roughness = float(pbr.get("roughnessFactor", 1.0))
-            provenance = "file"
-        elif (pbr.get("metallicRoughnessTexture") or {}).get("index") is not None:
-            # A map is declared but its bytes could not be read. Falling back
-            # to dielectric defaults, because inheriting factors written to
-            # scale a map we do not have renders a polymer grip as steel.
-            metallic, roughness, provenance = 0.0, 0.55, "assumed"
-        else:
-            metallic = float(pbr.get("metallicFactor", 1.0))
-            roughness = float(pbr.get("roughnessFactor", 1.0))
-            provenance = "file"
-
-        out.append({
-            "name": material.get("name") or "",
-            "baseColor": [
-                float(value)
-                for value in pbr.get("baseColorFactor", [1.0, 1.0, 1.0, 1.0])
-            ],
-            "metallic": metallic,
-            "roughness": roughness,
-            "factors_from": provenance,
-            "image": colour_image,
-            "mimeType": colour_mime,
-            "mrImage": mr_image,
-            "mrMimeType": mr_mime,
-        })
-    return out
-
-
-def _inverse_transpose_3x3(matrix: Sequence[float]) -> list[float]:
-    """Inverse transpose of a column-major 4x4's rotation/scale block."""
-    a = (matrix[0], matrix[1], matrix[2])
-    b = (matrix[4], matrix[5], matrix[6])
-    c = (matrix[8], matrix[9], matrix[10])
-    determinant = (
-        a[0] * (b[1] * c[2] - b[2] * c[1])
-        - a[1] * (b[0] * c[2] - b[2] * c[0])
-        + a[2] * (b[0] * c[1] - b[1] * c[0])
-    )
-    if abs(determinant) < 1e-20:
-        return [1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]
-    # cofactor / determinant, already the inverse transpose.
-    return [
-        (b[1] * c[2] - b[2] * c[1]) / determinant,
-        (b[2] * c[0] - b[0] * c[2]) / determinant,
-        (b[0] * c[1] - b[1] * c[0]) / determinant,
-        (a[2] * c[1] - a[1] * c[2]) / determinant,
-        (a[0] * c[2] - a[2] * c[0]) / determinant,
-        (a[1] * c[0] - a[0] * c[1]) / determinant,
-        (a[1] * b[2] - a[2] * b[1]) / determinant,
-        (a[2] * b[0] - a[0] * b[2]) / determinant,
-        (a[0] * b[1] - a[1] * b[0]) / determinant,
-    ]
-
-
-def _fill_missing_normals(
-    positions: list[tuple[float, float, float]],
-    normals: list[tuple[float, float, float]],
-    indices: list[int],
-) -> None:
-    """Replace zero-length normals with the area-weighted face average."""
-
-    missing = [
-        index for index, normal in enumerate(normals)
-        if normal[0] == 0.0 and normal[1] == 0.0 and normal[2] == 0.0
-    ]
-    if not missing:
-        return
-
-    accumulated = {index: [0.0, 0.0, 0.0] for index in missing}
-    needed = set(missing)
-    for triangle in range(len(indices) // 3):
-        ia, ib, ic = indices[triangle * 3:triangle * 3 + 3]
-        if not (ia in needed or ib in needed or ic in needed):
-            continue
-        pa, pb, pc = positions[ia], positions[ib], positions[ic]
-        e1 = (pb[0] - pa[0], pb[1] - pa[1], pb[2] - pa[2])
-        e2 = (pc[0] - pa[0], pc[1] - pa[1], pc[2] - pa[2])
-        cross = (
-            e1[1] * e2[2] - e1[2] * e2[1],
-            e1[2] * e2[0] - e1[0] * e2[2],
-            e1[0] * e2[1] - e1[1] * e2[0],
-        )
-        for index in (ia, ib, ic):
-            if index in accumulated:
-                for axis in range(3):
-                    accumulated[index][axis] += cross[axis]
-
-    for index, vector in accumulated.items():
-        length = math.sqrt(sum(value * value for value in vector))
-        normals[index] = (
-            (vector[0] / length, vector[1] / length, vector[2] / length)
-            if length > 1e-20 else (0.0, 1.0, 0.0)
-        )
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py
deleted file mode 100644
index 5363310e6271f7c9f8dadc1c0d2a4b595a43517c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py
+++ /dev/null
@@ -1,463 +0,0 @@
-"""
-models/gen_3d_object/meshy_model.py
-
-MeshyModel — wrapper around Meshy's cloud 3D-generation API (image-to-3D and
-text-to-3D), producing GLB / FBX / OBJ / USDZ / STL.
-
-Reference: https://docs.meshy.ai/  (base https://api.meshy.ai)
-
-Second backend for the `gen_3d_object` slot. Its `infer` / `infer_and_save`
-signatures are identical to `TripoModel`'s and positionally identical to
-`Trellis2Model`'s, so `Gen3DObjectOperator` dispatches on class name alone
-(model_require.md R6).
-
-CONTRACT DEVIATIONS (model_require.md targets local-weight models; the rules that
-replace it live in agent_skills/develop_harness/api_model_require.md):
-  C1  R2.1/R2.2 → R9.1  `model_path` carries a *model version id* ("meshy-6").
-  C2  R2.3      → R9.2  `device` accepted for interface parity and ignored.
-  C3  R3.6/R5.5 → R9.3  no @torch.no_grad(); torch is not involved.
-  C4  R4.1-R4.3 → R9.4  `unload()` is a no-op that closes the HTTP session.
-  C5  R3.3/R3.4 → R9.5  `seed` is forwarded for text-to-3D only, where the API
-                        accepts it; image-to-3D has no seed. Reproducibility is
-                        NOT guaranteed server-side and is never faked locally.
-  C6  R3.1      → R9.6  task-based API (submit → poll → download); `infer()`
-                        blocks until completion or `timeout` (default 300 s).
-                        Text-to-3D runs two tasks (preview → refine) when a
-                        texture is requested; both ids are logged.
-  C7  R1.2             uses the documented `infer_and_save(..., output_path)`
-                        exception; the bytes-returning `infer()` exists alongside.
-  C8  (new)     → R9.8  `cache_dir` prevents re-billing an identical request.
-  C9  (new)     → R9.9  `max_retries` + exponential backoff; retryable and
-                        terminal failures are split.
-
-Environment:
-    MESHY_API_KEY   required at the first infer() call, not at construction.
-                    Get one at https://www.meshy.ai/api  (a `msy_dummy_...`
-                    test-mode key returns canned assets without spending credits).
-    MESHY_API_BASE  optional override of the API root.
-    `pip install requests` is needed for the API backends only.
-
-Usage:
-    from models.gen_3d_object.meshy_model import MeshyModel
-    model = MeshyModel(model_path="meshy-6", output_format="fbx")
-    path = model.infer_and_save(image, output_path="out/model.fbx", seed=42)
-"""
-from __future__ import annotations
-
-import logging
-import os
-import time
-from pathlib import Path
-from typing import Any, Optional
-
-from models.common import cloud_api
-from models.common.glb_utils import glb_summary
-
-logger = logging.getLogger(__name__)
-
-SIGNUP_URL = "https://www.meshy.ai/api"
-
-DEFAULT_API_BASE = "https://api.meshy.ai"
-
-#: Short names accepted for `model_path`. Unknown values pass through untouched.
-MODEL_ALIASES: dict[str, str] = {
-    "meshy": "meshy-6",
-    "meshy-6-text-to-3d": "meshy-6",
-    "meshy-6-image-to-3d": "meshy-6",
-    "meshy-5-text-to-3d": "meshy-5",
-    "meshy-5-image-to-3d": "meshy-5",
-}
-
-DEFAULT_MODEL = "meshy-6"
-
-DONE_STATES = ("succeeded", "success", "completed")
-FAILED_STATES = ("failed", "canceled", "cancelled", "expired")
-
-#: Meshy rejects a target_polycount outside this range with a 400 (terminal).
-POLYCOUNT_RANGE = (100, 300_000)
-
-#: Formats the API can return directly, i.e. keys of `model_urls`.
-SUPPORTED_FORMATS = ("glb", "fbx", "obj", "usdz", "stl", "mtl", "3mf")
-
-
-class MeshyModel:
-    """
-    Cloud image-to-3D / text-to-3D through Meshy.
-
-    Args:
-        model_path: [C1] Meshy `ai_model` id ("meshy-6", "meshy-5", "latest"),
-                    or an alias from `MODEL_ALIASES`.
-        device:     [C2] Accepted for interface parity, ignored. "cpu" is fine.
-        api_key:    Explicit key; defaults to `$MESHY_API_KEY`, read at first call.
-        timeout:    [C6] Seconds to wait for one generation task (each stage of
-                    the text-to-3D preview → refine chain gets the full budget).
-        poll_interval: Seconds between status polls.
-        max_retries: [C9] Extra attempts for retryable HTTP failures.
-        cache_dir:  [C8] Directory for the response cache. None disables it.
-        output_format: Any of `SUPPORTED_FORMATS`; picked out of `model_urls`.
-        low_poly:   [B4.3] Sets `model_type="lowpoly"`, i.e. the service produces
-                    a low-poly mesh instead of us decimating one locally.
-        texture / enable_pbr / topology / should_remesh:
-                    Forwarded to the generation endpoints.
-        refine:     Text-to-3D only. Meshy's preview stage returns an untextured
-                    mesh; the refine stage textures it and costs extra credits.
-                    Ignored when `texture=False`.
-        api_base:   Override the API root (defaults to `$MESHY_API_BASE`).
-        verbose:    Print task progress; off by default (R3.7).
-    """
-
-    PROVIDER = "meshy"
-
-    def __init__(
-        self,
-        model_path: str = DEFAULT_MODEL,
-        device: str = "cuda",
-        *,
-        api_key: Optional[str] = None,
-        # Measured: one image-to-3D with PBR textures took 283 s here, and the
-        # Tripo backend took 601 s for the same job. Timing out does not refund
-        # the credits, so the default errs long; the error carries the task_id.
-        timeout: int = 1800,
-        poll_interval: float = 3.0,
-        max_retries: int = 3,
-        cache_dir: Optional[str] = None,
-        output_format: str = "glb",
-        low_poly: bool = False,
-        texture: bool = True,
-        enable_pbr: bool = True,
-        topology: str = "triangle",
-        should_remesh: Optional[bool] = None,
-        refine: bool = True,
-        api_base: Optional[str] = None,
-        http_timeout: int = 60,
-        verbose: bool = False,
-        **model_specific: Any,
-    ):
-        # [CONTRACT-DEVIATION C1] model_path is a version id, not a weight location.
-        # [CONTRACT-DEVIATION C2] device accepted for interface parity, ignored.
-        self.model_path = MODEL_ALIASES.get(str(model_path).lower(), model_path)
-        self.device = device
-
-        # R2.4 — store everything before anything loads.
-        self.api_key = api_key
-        self.timeout = timeout
-        self.poll_interval = poll_interval
-        self.max_retries = max_retries
-        self.cache_dir = cache_dir
-        self.output_format = output_format.lower().lstrip(".")
-        self.low_poly = low_poly
-        self.texture = texture
-        self.enable_pbr = enable_pbr
-        self.topology = topology
-        self.should_remesh = should_remesh
-        self.refine = refine
-        self.api_base = (api_base or os.environ.get("MESHY_API_BASE")
-                         or DEFAULT_API_BASE)
-        self.http_timeout = http_timeout
-        self.verbose = verbose
-        self.model_specific = model_specific
-
-        if self.output_format not in SUPPORTED_FORMATS:
-            raise ValueError(
-                f"output_format={self.output_format!r} is not one of "
-                f"{SUPPORTED_FORMATS}. Meshy returns these directly in model_urls."
-            )
-
-        self._client: Optional[cloud_api.CloudAPIClient] = None
-        self._cache = cloud_api.ResponseCache(cache_dir, self.PROVIDER)
-
-        #: Record of the most recent **real** call — see `TripoModel.last_call_info`.
-        self.last_call_info: dict[str, Any] = {}
-
-    # ── plumbing ──────────────────────────────────────────────────────────────
-
-    @property
-    def client(self) -> cloud_api.CloudAPIClient:
-        """HTTP client, created on first use. [R9.7] The key is resolved here."""
-        if self._client is None:
-            key = cloud_api.require_api_key(
-                self.api_key, "MESHY_API_KEY", SIGNUP_URL, who="MeshyModel")
-            self._client = cloud_api.CloudAPIClient(
-                self.api_base, key,
-                timeout=self.http_timeout,
-                max_retries=self.max_retries,
-            )
-        return self._client
-
-    def unload(self) -> None:
-        """[C4] Close the HTTP session. Idempotent, and safe before any call."""
-        if self._client is not None:
-            self._client.close()
-            self._client = None
-
-    close = unload
-
-    @staticmethod
-    def _task_id_of(resp: Any) -> str:
-        """Meshy answers a creation call with `{"result": "<task id>"}`."""
-        if isinstance(resp, dict):
-            for key in ("result", "id", "task_id"):
-                value = resp.get(key)
-                if isinstance(value, str) and value:
-                    return value
-        raise cloud_api.CloudAPIError(
-            f"Meshy accepted the request but returned no task id: {resp!r}")
-
-    def _fetch_task(self, endpoint: str, task_id: str) -> dict:
-        task = self.client.request("GET", f"{endpoint}/{task_id}")
-        if not isinstance(task, dict):
-            raise cloud_api.CloudAPIError(
-                f"unexpected task payload for {task_id}: {task!r}", task_id=task_id)
-        return task
-
-    def _model_url(self, task: dict) -> str:
-        """URL of the requested format, with an actionable message when absent."""
-        urls = task.get("model_urls") or {}
-        url = urls.get(self.output_format)
-        if isinstance(url, str) and url.startswith("http"):
-            return url
-        raise cloud_api.CloudAPIError(
-            f"task {task.get('id')} finished but has no {self.output_format!r} "
-            f"URL. Available: {sorted(urls)}. Set output_format to one of those.",
-            task_id=task.get("id"), payload=task)
-
-    def _clamp_polycount(self, value: Optional[int]) -> Optional[int]:
-        """
-        Keep `target_polycount` inside the documented range.
-
-        `Gen3DObjectOperator` forwards TRELLIS.2's default of 1_000_000, which
-        Meshy rejects with a terminal 400. Clamping (loudly) keeps the operator
-        interchangeable (R6) instead of making the caller special-case backends.
-        """
-        if value is None:
-            return None
-        low, high = POLYCOUNT_RANGE
-        clamped = max(low, min(int(value), high))
-        if clamped != int(value):
-            logger.warning("[meshy] target_polycount %s clamped to %s (API range %s-%s)",
-                           value, clamped, low, high)
-        return clamped
-
-    # ── inference ─────────────────────────────────────────────────────────────
-
-    def infer(
-        self,
-        image: Any = None,
-        seed: int = 42,
-        decimation_target: Optional[int] = None,
-        texture_size: Optional[int] = None,
-        *,
-        prompt: Optional[str] = None,
-        negative_prompt: Optional[str] = None,
-        image_url: Optional[str] = None,
-        verbose: Optional[bool] = None,
-        **provider_kwargs: Any,
-    ) -> bytes:
-        """
-        Generate one 3D asset and return the model file bytes.
-
-        Args:
-            image: Reference image as `PIL.Image.Image`, `np.ndarray` or PNG
-                   `bytes` — sent inline as a base64 data URI. Not a path (R3.2).
-            seed: [C5] Forwarded for text-to-3D only; image-to-3D has no seed
-                   parameter. Reproducibility is not guaranteed.
-            decimation_target: Target face count → `target_polycount`, clamped to
-                   the API's 100-300000 range.
-            texture_size: Accepted for signature parity with `Trellis2Model` and
-                   **ignored** — Meshy exposes no texture resolution parameter.
-            prompt: Text prompt for text-to-3D. [A4 decision] Same rationale as
-                   `TripoModel`: keeps text-to-3D reachable without a second slot.
-            negative_prompt: Sent as `texture_prompt`'s counterpart is absent in
-                   the API — rejected explicitly rather than silently dropped.
-            image_url: Publicly reachable image URL, used instead of the data URI.
-            verbose: Override the constructor's verbosity for this call.
-            **provider_kwargs: Merged into the request body verbatim.
-
-        Returns:
-            The model file as `bytes`, in `output_format`.
-
-        Raises:
-            CloudAPIAuthError / CloudAPIQuotaError / CloudTaskFailed /
-            CloudTaskTimeout — see `models/common/cloud_api.py`.
-        """
-        verbose = self.verbose if verbose is None else verbose
-        if image is None and image_url is None and not prompt:
-            raise ValueError(
-                "MeshyModel.infer needs an image (`image=` / `image_url=`) or a "
-                "text `prompt=`; got neither.")
-        if negative_prompt:
-            raise ValueError(
-                "Meshy has no negative_prompt parameter. Fold the constraint into "
-                "`prompt`, or use TripoModel which supports it.")
-        if texture_size is not None:
-            logger.debug("[meshy] texture_size=%s ignored; the API has no equivalent",
-                         texture_size)
-
-        polycount = self._clamp_polycount(decimation_target)
-        use_image = image is not None or image_url is not None
-
-        payload: dict[str, Any] = {
-            "ai_model": self.model_path,
-            "model_type": "lowpoly" if self.low_poly else "standard",
-            "topology": self.topology,
-        }
-        if polycount is not None:
-            payload["target_polycount"] = polycount
-        if self.should_remesh is not None:
-            payload["should_remesh"] = self.should_remesh
-
-        if use_image:
-            payload["should_texture"] = self.texture
-            payload["enable_pbr"] = self.enable_pbr
-        else:
-            payload["mode"] = "preview"
-            payload["prompt"] = prompt
-            # [CONTRACT-DEVIATION C5] accepted by the text endpoint only.
-            payload["seed"] = int(seed)
-        payload.update(provider_kwargs)
-
-        # [C8] Everything that changes the output goes into the key; the image is
-        # represented by its hash, and the API key never appears.
-        png = cloud_api.image_to_png_bytes(image) if image is not None else None
-        cache_key = cloud_api.request_hash({
-            "provider": self.PROVIDER,
-            "payload": payload,
-            "image_sha256": cloud_api.bytes_hash(png) if png else None,
-            "image_url": image_url,
-            "refine": self.refine and self.texture and not use_image,
-            "output_format": self.output_format,
-        })
-        cached = self._cache.get(cache_key, ext=self.output_format)
-        if cached is not None:
-            self.last_call_info = {
-                "provider": self.PROVIDER, "cached": True, "cache_key": cache_key,
-                "bytes": len(cached),
-                **(glb_summary(cached) if self.output_format == "glb" else {}),
-            }
-            return cached
-
-        t0 = time.time()
-        task_ids: list[str] = []
-
-        if use_image:
-            endpoint = "/openapi/v1/image-to-3d"
-            payload["image_url"] = (
-                image_url if png is None else cloud_api.image_to_data_uri(image))
-            task = self._submit_and_wait(endpoint, payload, verbose=verbose,
-                                         task_ids=task_ids)
-        else:
-            endpoint = "/openapi/v2/text-to-3d"
-            task = self._submit_and_wait(endpoint, payload, verbose=verbose,
-                                         task_ids=task_ids)
-            if self.texture and self.refine:
-                # [C6] Preview geometry is untextured; refine is a second billed
-                # task that paints it. Skip with texture=False or refine=False.
-                refine_payload = {
-                    "mode": "refine",
-                    "preview_task_id": task_ids[-1],
-                    "ai_model": self.model_path,
-                    "enable_pbr": self.enable_pbr,
-                }
-                task = self._submit_and_wait(endpoint, refine_payload,
-                                             verbose=verbose, task_ids=task_ids)
-
-        data = self.client.download(self._model_url(task))
-        elapsed = time.time() - t0
-
-        info = glb_summary(data) if self.output_format == "glb" else {"bytes": len(data)}
-        self.last_call_info = {
-            "provider": self.PROVIDER,
-            "model": self.model_path,
-            "task_id": task_ids[-1],
-            "task_ids": task_ids,
-            "elapsed_sec": round(elapsed, 2),
-            "credits": task.get("credits_consumed", task.get("credits")),
-            "cached": False,
-            "cache_key": cache_key,
-            "format": self.output_format,
-            # The provider's own render of what it produced. Recording the URL
-            # (rather than downloading it here) keeps the model out of artifact
-            # placement (R1.2) while giving the caller a free visual check —
-            # "the asset exists" and "the asset is what was asked for" are
-            # different claims, and only a picture settles the second one.
-            "preview_url": task.get("thumbnail_url"),
-            **info,
-        }
-        # R9.8 — one line per billed call.
-        logger.info(
-            "[meshy] task=%s model=%s %.1fs credits=%s triangles=%s bytes=%s",
-            task_ids[-1], self.model_path, elapsed, self.last_call_info.get("credits"),
-            info.get("triangles"), len(data),
-        )
-        if verbose:
-            print(f"[meshy] {self.last_call_info}")
-
-        self._cache.put(cache_key, data, {
-            "provider": self.PROVIDER, "payload": payload,
-            **{k: v for k, v in self.last_call_info.items() if k != "cache_key"},
-        }, ext=self.output_format)
-        return data
-
-    def _submit_and_wait(self, endpoint: str, payload: dict, *,
-                         verbose: bool, task_ids: list[str]) -> dict:
-        """Create one task on `endpoint`, poll it to a terminal state, return it."""
-        created = self.client.request("POST", endpoint, json_body=payload)
-        task_id = self._task_id_of(created)
-        task_ids.append(task_id)
-        if verbose:
-            print(f"[meshy] task {task_id} submitted ({endpoint}, "
-                  f"mode={payload.get('mode', 'image')})")
-        return cloud_api.poll_task(
-            lambda: self._fetch_task(endpoint, task_id),
-            task_id=task_id,
-            timeout=self.timeout,
-            poll_interval=self.poll_interval,
-            done_states=DONE_STATES,
-            failed_states=FAILED_STATES,
-            verbose=verbose,
-        )
-
-    def infer_and_save(
-        self,
-        image: Any = None,
-        output_path: Optional[str] = None,
-        seed: int = 42,
-        decimation_target: Optional[int] = None,
-        texture_size: Optional[int] = None,
-        *,
-        prompt: Optional[str] = None,
-        negative_prompt: Optional[str] = None,
-        image_url: Optional[str] = None,
-        verbose: Optional[bool] = None,
-        **provider_kwargs: Any,
-    ) -> str:
-        """
-        [C7] The documented R1.2 exception: run `infer()` and write the result.
-
-        The path comes **in** from the caller and is never derived here.
-
-        Args:
-            output_path: Destination file. Parent directories are created.
-            (everything else: see `infer`)
-
-        Returns:
-            `str(output_path)`.
-        """
-        if not output_path:
-            raise ValueError("output_path is required (model_require.md R1.2)")
-        data = self.infer(
-            image, seed, decimation_target, texture_size,
-            prompt=prompt, negative_prompt=negative_prompt, image_url=image_url,
-            verbose=verbose, **provider_kwargs,
-        )
-        out = Path(output_path)
-        out.parent.mkdir(parents=True, exist_ok=True)
-        out.write_bytes(data)
-        return str(out)
-
-    # ── account ───────────────────────────────────────────────────────────────
-
-    def balance(self) -> dict:
-        """Remaining credits: ``{"balance": int}``. Cheap and unbilled."""
-        resp = self.client.request("GET", "/openapi/v1/balance")
-        return resp if isinstance(resp, dict) else {"balance": resp}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py
deleted file mode 100644
index 483e185ff7ca75d4c4a592fec4e2acd3bcd251aa..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py
+++ /dev/null
@@ -1,532 +0,0 @@
-"""
-models/gen_3d_object/tripo_model.py
-
-TripoModel — wrapper around Tripo3D's cloud 3D-generation API (image-to-3D and
-text-to-3D), producing a GLB.
-
-Reference: https://developers.tripo3d.ai/  (v3 API, base https://openapi.tripo3d.ai/v3)
-
-CONTRACT DEVIATIONS (model_require.md targets local-weight models; the rules that
-replace it live in agent_skills/develop_harness/api_model_require.md):
-  C1  R2.1/R2.2 → R9.1  `model_path` carries a *model version id*
-                        ("v3.1-20260211"), not a weight location.
-  C2  R2.3      → R9.2  `device` accepted for interface parity and ignored.
-  C3  R3.6/R5.5 → R9.3  no @torch.no_grad(); torch is not involved.
-  C4  R4.1-R4.3 → R9.4  `unload()` is a no-op that closes the HTTP session.
-  C5  R3.3/R3.4 → R9.5  `seed` is forwarded as model_seed/texture_seed when the
-                        provider accepts it; server-side reproducibility is NOT
-                        guaranteed and is never faked locally.
-  C6  R3.1      → R9.6  task-based API (submit → poll → download); `infer()`
-                        blocks until completion or `timeout` (default 300 s).
-                        A timeout error carries the `task_id`.
-  C7  R1.2             uses the documented `infer_and_save(..., output_path)`
-                        exception; the bytes-returning `infer()` exists alongside.
-  C8  (new)     → R9.8  `cache_dir` prevents re-billing an identical request.
-  C9  (new)     → R9.9  `max_retries` + exponential backoff; retryable (5xx/429/
-                        transport) and terminal (400/401/402) failures are split.
-
-Environment:
-    TRIPO_API_KEY   required at the first infer() call, not at construction.
-                    Get one at https://platform.tripo3d.ai/api-keys
-    TRIPO_API_BASE  optional override of the API root.
-    `pip install requests` is needed for the API backends only.
-
-Usage:
-    from models.gen_3d_object.tripo_model import TripoModel
-    model = TripoModel(model_path="v3.1-20260211")
-    path = model.infer_and_save(image, output_path="out/model.glb", seed=42)
-    print(model.last_call_info)   # task_id, elapsed, credits, triangles
-"""
-from __future__ import annotations
-
-import logging
-import os
-import time
-from pathlib import Path
-from typing import Any, Optional
-
-from models.common import cloud_api
-from models.common.glb_utils import glb_summary
-
-logger = logging.getLogger(__name__)
-
-#: Where a human gets a key. Quoted verbatim in the fail-fast message (R1.6).
-SIGNUP_URL = "https://platform.tripo3d.ai/api-keys"
-
-DEFAULT_API_BASE = "https://openapi.tripo3d.ai/v3"
-
-#: Short names accepted for `model_path`, mapped to the ids the API expects.
-#: Unknown values are passed through untouched, so a version released after this
-#: file was written still works without a code change.
-MODEL_ALIASES: dict[str, str] = {
-    "tripo": "v3.1-20260211",
-    "tripo-v3.1": "v3.1-20260211",
-    "v3.1": "v3.1-20260211",
-    "tripo-v3.0": "v3.0-20250812",
-    "v3.0": "v3.0-20250812",
-    "tripo-v2.5": "v2.5-20250123",
-    "v2.5": "v2.5-20250123",
-    # P series — natively low-poly, the cheap option for mesh particles (B4.2).
-    "tripo-p1": "P1-20260311",
-    "p1": "P1-20260311",
-}
-
-DEFAULT_MODEL = "v3.1-20260211"
-
-#: Terminal task states. Tripo reports lower-case; compared case-insensitively.
-DONE_STATES = ("success", "succeeded", "finished", "completed")
-FAILED_STATES = ("failed", "cancelled", "canceled", "banned", "expired", "unknown")
-
-#: Output fields that have carried the download URL across API versions.
-_MODEL_URL_KEYS = ("model_url", "pbr_model", "model", "base_model", "glb", "gltf")
-
-#: `face_limit` range the service accepts **while `smart_low_poly` is on**.
-#: Outside it the request is rejected with a terminal 400:
-#:   "when smart_low_poly is enabled, face_limit must be between 500 and 20000
-#:    (triangle mesh), got 30000"
-#: Not in the public docs; found by calling it.
-LOW_POLY_FACE_LIMIT_RANGE = (500, 20_000)
-
-
-class TripoModel:
-    """
-    Cloud image-to-3D / text-to-3D through Tripo3D.
-
-    Interchangeable with `Trellis2Model` in `Gen3DObjectOperator`: the
-    `infer_and_save(image, output_path, seed, decimation_target, texture_size)`
-    call signature is positionally identical (model_require.md R6).
-
-    Args:
-        model_path: [C1] Tripo model version id, or an alias from `MODEL_ALIASES`.
-        device:     [C2] Accepted for interface parity, ignored. "cpu" is fine.
-        api_key:    Explicit key; defaults to `$TRIPO_API_KEY`, read at first call.
-        timeout:    [C6] Seconds to wait for one generation task.
-        poll_interval: Seconds between status polls.
-        max_retries: [C9] Extra attempts for retryable HTTP failures.
-        cache_dir:  [C8] Directory for the response cache. None disables it.
-        output_format: "glb" is produced natively. Any other format needs the
-                    conversion endpoint — see `convert_path`.
-        convert_path: API path of the format-conversion endpoint. Left as None
-                    because it is not pinned down in the public v3 docs; set it
-                    to enable `output_format="fbx"` without touching this file.
-        low_poly:   [B4.3] Ask the service for clean low-poly topology
-                    (`smart_low_poly`) instead of decimating locally.
-        texture / pbr / quad / texture_quality / geometry_quality:
-                    Forwarded verbatim to the generation endpoint.
-        api_base:   Override the API root (defaults to `$TRIPO_API_BASE`).
-        verbose:    Print task progress; off by default (R3.7).
-    """
-
-    #: Provider name, used for cache namespacing and log lines.
-    PROVIDER = "tripo"
-
-    def __init__(
-        self,
-        model_path: str = DEFAULT_MODEL,
-        device: str = "cuda",
-        *,
-        api_key: Optional[str] = None,
-        # Measured: image-to-3D with smart_low_poly took **601 s**, i.e. it would
-        # have died one second past a 600 s budget. Timing out does not refund
-        # the credits — the task finishes server-side and only the download is
-        # lost — so the default errs long. The error carries the task_id.
-        timeout: int = 1800,
-        poll_interval: float = 3.0,
-        max_retries: int = 3,
-        cache_dir: Optional[str] = None,
-        output_format: str = "glb",
-        convert_path: Optional[str] = None,
-        low_poly: bool = False,
-        texture: bool = True,
-        pbr: bool = True,
-        quad: bool = False,
-        texture_quality: str = "standard",
-        geometry_quality: str = "standard",
-        api_base: Optional[str] = None,
-        http_timeout: int = 60,
-        verbose: bool = False,
-        **model_specific: Any,
-    ):
-        # [CONTRACT-DEVIATION C1] model_path is a version id, not a weight location.
-        # [CONTRACT-DEVIATION C2] device accepted for interface parity, ignored.
-        self.model_path = MODEL_ALIASES.get(str(model_path).lower(), model_path)
-        self.device = device
-
-        # R2.4 — every constructor argument lands on self before anything loads,
-        # so unload()/reload round-trips without losing configuration.
-        self.api_key = api_key
-        self.timeout = timeout
-        self.poll_interval = poll_interval
-        self.max_retries = max_retries
-        self.cache_dir = cache_dir
-        self.output_format = output_format.lower().lstrip(".")
-        self.convert_path = convert_path
-        self.low_poly = low_poly
-        self.texture = texture
-        self.pbr = pbr
-        self.quad = quad
-        self.texture_quality = texture_quality
-        self.geometry_quality = geometry_quality
-        self.api_base = (api_base or os.environ.get("TRIPO_API_BASE")
-                         or DEFAULT_API_BASE)
-        self.http_timeout = http_timeout
-        self.verbose = verbose
-        self.model_specific = model_specific
-
-        self._client: Optional[cloud_api.CloudAPIClient] = None
-        self._cache = cloud_api.ResponseCache(cache_dir, self.PROVIDER)
-
-        #: Record of the most recent **real** call: task_id, elapsed_sec, credits,
-        #: triangles, bytes, cached. Operators copy it into meta.json (R9.8).
-        self.last_call_info: dict[str, Any] = {}
-
-    # ── plumbing ──────────────────────────────────────────────────────────────
-
-    @property
-    def client(self) -> cloud_api.CloudAPIClient:
-        """
-        HTTP client, created on first use.
-
-        [C6/R9.7] The API key is resolved here rather than in `__init__`, so the
-        wrapper can be imported and constructed on a machine with no credentials
-        (which is exactly what `test/harness/smoke.py` does).
-        """
-        if self._client is None:
-            key = cloud_api.require_api_key(
-                self.api_key, "TRIPO_API_KEY", SIGNUP_URL, who="TripoModel")
-            self._client = cloud_api.CloudAPIClient(
-                self.api_base, key,
-                timeout=self.http_timeout,
-                max_retries=self.max_retries,
-            )
-        return self._client
-
-    def unload(self) -> None:
-        """[C4] Close the HTTP session. Idempotent, and safe before any call."""
-        if self._client is not None:
-            self._client.close()
-            self._client = None
-
-    close = unload
-
-    @staticmethod
-    def _unwrap(resp: Any) -> dict:
-        """
-        Strip Tripo's `{"code": 0, "data": {...}}` envelope.
-
-        A non-zero `code` on an HTTP 200 is a terminal application error — the
-        same request will fail the same way, so it must not be retried (R9.9).
-        """
-        if not isinstance(resp, dict):
-            raise cloud_api.CloudAPIError(f"unexpected response type {type(resp).__name__}: {resp!r}")
-        code = resp.get("code")
-        if code not in (None, 0):
-            message = resp.get("message") or resp.get("msg") or resp
-            suggestion = resp.get("suggestion")
-            raise cloud_api.CloudAPIRequestError(
-                f"Tripo API error code={code}: {message}"
-                + (f" — {suggestion}" if suggestion else ""),
-                payload=resp,
-            )
-        data = resp.get("data")
-        return data if isinstance(data, dict) else resp
-
-    def _upload_image(self, png: bytes) -> str:
-        """Upload PNG bytes, return the `file_token` used as generation input."""
-        data = self._unwrap(self.client.request(
-            "POST", "/files",
-            files={"file": ("input.png", png, "image/png")},
-        ))
-        token = data.get("file_token") or data.get("image_token") or data.get("token")
-        if not token:
-            raise cloud_api.CloudAPIError(
-                f"upload succeeded but no file_token in the response: {data}")
-        return str(token)
-
-    @staticmethod
-    def _extract_model_url(task: dict) -> str:
-        """Find the artifact URL in a finished task, across output field names."""
-        output = task.get("output") or task.get("result") or {}
-        for key in _MODEL_URL_KEYS:
-            value = output.get(key)
-            if isinstance(value, str) and value.startswith("http"):
-                return value
-            if isinstance(value, dict) and isinstance(value.get("url"), str):
-                return value["url"]
-        raise cloud_api.CloudAPIError(
-            f"task {task.get('task_id')} reported success but carries no model URL. "
-            f"output={output!r}",
-            task_id=task.get("task_id"), payload=task)
-
-    def _fetch_task(self, task_id: str) -> dict:
-        return self._unwrap(self.client.request("GET", f"/tasks/{task_id}"))
-
-    # ── payload ───────────────────────────────────────────────────────────────
-
-    def _clamp_face_limit(self, value: int) -> int:
-        """
-        Keep `face_limit` inside the range the service accepts.
-
-        `Gen3DObjectOperator` forwards TRELLIS.2's default of 1 000 000, and with
-        `low_poly=True` anything outside 500-20 000 is a terminal 400. Clamping
-        (loudly) keeps the backends interchangeable (R6) rather than making the
-        caller special-case each provider.
-        """
-        value = int(value)
-        if not self.low_poly:
-            return value
-        low, high = LOW_POLY_FACE_LIMIT_RANGE
-        clamped = max(low, min(value, high))
-        if clamped != value:
-            logger.warning("[tripo] face_limit %s clamped to %s "
-                           "(smart_low_poly range %s-%s)", value, clamped, low, high)
-        return clamped
-
-    def _build_payload(
-        self,
-        *,
-        prompt: Optional[str],
-        negative_prompt: Optional[str],
-        seed: Optional[int],
-        decimation_target: Optional[int],
-        provider_kwargs: dict,
-    ) -> dict:
-        """Generation parameters shared by the image and text endpoints."""
-        payload: dict[str, Any] = {
-            "model": self.model_path,
-            "texture": self.texture,
-            "pbr": self.pbr,
-            "texture_quality": self.texture_quality,
-            "geometry_quality": self.geometry_quality,
-        }
-        if self.quad:
-            payload["quad"] = True
-        if self.low_poly:
-            # [B4.3] Ask the service for low-poly topology. Decimating a 2M-face
-            # mesh locally wrecks UVs and normals; generating low-poly does not.
-            payload["smart_low_poly"] = True
-        if decimation_target:
-            payload["face_limit"] = self._clamp_face_limit(decimation_target)
-        if prompt:
-            payload["prompt"] = prompt
-        if negative_prompt:
-            payload["negative_prompt"] = negative_prompt
-        if seed is not None:
-            # [CONTRACT-DEVIATION C5] forwarded, but the service does not promise
-            # that the same seed reproduces the same mesh. Never faked locally.
-            payload["model_seed"] = int(seed)
-            payload["texture_seed"] = int(seed)
-        payload.update(provider_kwargs)
-        return payload
-
-    # ── inference ─────────────────────────────────────────────────────────────
-
-    def infer(
-        self,
-        image: Any = None,
-        seed: int = 42,
-        decimation_target: Optional[int] = None,
-        texture_size: Optional[int] = None,
-        *,
-        prompt: Optional[str] = None,
-        negative_prompt: Optional[str] = None,
-        image_url: Optional[str] = None,
-        verbose: Optional[bool] = None,
-        **provider_kwargs: Any,
-    ) -> bytes:
-        """
-        Generate one 3D asset and return the model file bytes.
-
-        [C6] Submits the task, polls until it finishes, downloads the artifact —
-        all inside this call. Typical wall time is 20 s to 4 min.
-
-        Args:
-            image: Reference image as `PIL.Image.Image`, `np.ndarray` or PNG
-                   `bytes`. Not a path (R3.2). Give `image`, `image_url` or
-                   `prompt`; image wins when several are present.
-            seed: [C5] Forwarded as `model_seed` / `texture_seed`. Reproducibility
-                   is not guaranteed server-side.
-            decimation_target: Target face count, forwarded as `face_limit`.
-                   None leaves it to the service.
-            texture_size: Accepted for signature parity with `Trellis2Model` and
-                   **ignored** — Tripo exposes `texture_quality`, not a
-                   resolution. Pass `texture_quality=` to the constructor.
-            prompt: Text prompt for text-to-3D. [A4 decision] The operator slot is
-                   image-to-3D today; accepting `prompt` here keeps text-to-3D
-                   available without a second wrapper.
-            negative_prompt: Text-to-3D only.
-            image_url: Publicly reachable image URL, used instead of uploading.
-            verbose: Override the constructor's verbosity for this call.
-            **provider_kwargs: Merged into the request body verbatim, so a new
-                   API parameter needs no change here.
-
-        Returns:
-            The model file as `bytes` — GLB unless `output_format`/`convert_path`
-            say otherwise.
-
-        Raises:
-            CloudAPIAuthError: no or invalid `TRIPO_API_KEY`.
-            CloudAPIQuotaError: out of credits (terminal, never retried).
-            CloudTaskTimeout: not finished within `timeout`; carries `task_id`.
-            CloudTaskFailed: the service rejected or failed the task.
-        """
-        verbose = self.verbose if verbose is None else verbose
-        if image is None and image_url is None and not prompt:
-            raise ValueError(
-                "TripoModel.infer needs an image (`image=` / `image_url=`) or a "
-                "text `prompt=`; got neither.")
-        if texture_size is not None:
-            # [CONTRACT-DEVIATION C2-style] accepted for parity, ignored.
-            logger.debug("[tripo] texture_size=%s ignored; use texture_quality",
-                         texture_size)
-        if self.output_format != "glb" and not self.convert_path:
-            raise NotImplementedError(
-                f"TripoModel produces GLB; output_format={self.output_format!r} needs "
-                f"Tripo's conversion endpoint, whose path is not pinned in the public "
-                f"v3 docs. Either set convert_path=... on the constructor, use "
-                f"MeshyModel(output_format='fbx') which returns FBX natively, or "
-                f"convert the GLB with engine_adapters/blender/."
-            )
-
-        png = cloud_api.image_to_png_bytes(image) if image is not None else None
-        use_image = png is not None or bool(image_url)
-        payload = self._build_payload(
-            # The image endpoint has no prompt field — sending one is a terminal
-            # 400. An image plus a prompt means "use the image".
-            prompt=None if use_image else prompt,
-            negative_prompt=None if use_image else negative_prompt,
-            seed=seed,
-            decimation_target=decimation_target, provider_kwargs=provider_kwargs,
-        )
-
-        # [C8] Cache key covers everything that changes the output. The image is
-        # represented by its hash, never its bytes, and the key never sees the API key.
-        cache_key = cloud_api.request_hash({
-            "provider": self.PROVIDER,
-            "payload": payload,
-            "image_sha256": cloud_api.bytes_hash(png) if png else None,
-            "image_url": image_url,
-            "output_format": self.output_format,
-        })
-        cached = self._cache.get(cache_key, ext=self.output_format)
-        if cached is not None:
-            self.last_call_info = {
-                "provider": self.PROVIDER, "cached": True, "cache_key": cache_key,
-                "bytes": len(cached), **glb_summary(cached),
-            }
-            return cached
-
-        t0 = time.time()
-        if png is not None:
-            # `input` takes a file_token, a public URL or a previous task id.
-            payload["input"] = self._upload_image(png)
-            endpoint = "/generation/image-to-model"
-        elif image_url:
-            payload["input"] = image_url
-            endpoint = "/generation/image-to-model"
-        else:
-            endpoint = "/generation/text-to-model"
-
-        created = self._unwrap(self.client.request("POST", endpoint, json_body=payload))
-        task_id = str(created.get("task_id") or created.get("id") or "")
-        if not task_id:
-            raise cloud_api.CloudAPIError(
-                f"{endpoint} accepted the request but returned no task_id: {created}")
-        if verbose:
-            print(f"[tripo] task {task_id} submitted ({endpoint})")
-
-        # Some responses already carry a finished task; only poll when they do not.
-        state = str(created.get("status", "")).lower()
-        if state in DONE_STATES:
-            task = created
-        else:
-            task = cloud_api.poll_task(
-                lambda: self._fetch_task(task_id),
-                task_id=task_id,
-                timeout=self.timeout,
-                poll_interval=self.poll_interval,
-                done_states=DONE_STATES,
-                failed_states=FAILED_STATES,
-                verbose=verbose,
-            )
-
-        data = self.client.download(self._extract_model_url(task))
-        elapsed = time.time() - t0
-
-        info = glb_summary(data) if self.output_format == "glb" else {"bytes": len(data)}
-        self.last_call_info = {
-            "provider": self.PROVIDER,
-            "model": self.model_path,
-            "task_id": task_id,
-            "elapsed_sec": round(elapsed, 2),
-            "credits": task.get("credits_consumed", task.get("credits")),
-            "cached": False,
-            "cache_key": cache_key,
-            # The provider's own render — see the note in meshy_model.py.
-            "preview_url": (task.get("output") or {}).get("rendered_image_url"),
-            **info,
-        }
-        # R9.8 — one line per billed call: what it cost and what it produced.
-        logger.info(
-            "[tripo] task=%s model=%s %.1fs credits=%s triangles=%s bytes=%s",
-            task_id, self.model_path, elapsed, self.last_call_info.get("credits"),
-            info.get("triangles"), len(data),
-        )
-        if verbose:
-            print(f"[tripo] {self.last_call_info}")
-
-        self._cache.put(cache_key, data, {
-            "provider": self.PROVIDER, "payload": payload,
-            **{k: v for k, v in self.last_call_info.items() if k != "cache_key"},
-        }, ext=self.output_format)
-        return data
-
-    def infer_and_save(
-        self,
-        image: Any = None,
-        output_path: Optional[str] = None,
-        seed: int = 42,
-        decimation_target: Optional[int] = None,
-        texture_size: Optional[int] = None,
-        *,
-        prompt: Optional[str] = None,
-        negative_prompt: Optional[str] = None,
-        image_url: Optional[str] = None,
-        verbose: Optional[bool] = None,
-        **provider_kwargs: Any,
-    ) -> str:
-        """
-        [C7] The documented R1.2 exception: run `infer()` and write the result.
-
-        The path comes **in** from the caller and is never derived here — the
-        operator owns artifact placement.
-
-        Args:
-            output_path: Destination file. Parent directories are created.
-            (everything else: see `infer`)
-
-        Returns:
-            `str(output_path)`.
-        """
-        if not output_path:
-            raise ValueError("output_path is required (model_require.md R1.2)")
-        data = self.infer(
-            image, seed, decimation_target, texture_size,
-            prompt=prompt, negative_prompt=negative_prompt, image_url=image_url,
-            verbose=verbose, **provider_kwargs,
-        )
-        out = Path(output_path)
-        out.parent.mkdir(parents=True, exist_ok=True)
-        out.write_bytes(data)
-        return str(out)
-
-    # ── account ───────────────────────────────────────────────────────────────
-
-    def balance(self) -> dict:
-        """
-        Remaining credits: ``{"balance": float, "frozen": float}``.
-
-        Cheap and unbilled — call it before a batch to check the budget (C4 note:
-        this is the only method that talks to the API without generating).
-        """
-        return self._unwrap(self.client.request("GET", "/account/balance"))
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py
deleted file mode 100644
index c5b3d362ea28a51bac750f7a3efcd9a6459786db..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py
+++ /dev/null
@@ -1,596 +0,0 @@
-"""
-operators/gen_3d_object/operator.py
-
-Gen3DObjectOperator — accepts a loaded model and processes an input dict into a
-3D output (GLB file on disk).
-
-The operator is intentionally model-agnostic: you inject any object that
-implements the same interface as Trellis2Model.  New model wrappers can be
-swapped in without touching this file.
-
-Output layout — two modes, chosen by whether `output_dir` is given:
-
-  * **per-game (default, `output_dir=None`)** — resolved by
-    `pipeline.common.paths`, so artifacts are grouped by generated game project::
-
-        test_data/outputs/<game_id>/<run_id>/assets/3d_object/<task_id>/
-            ├── model.glb
-            └── meta.json
-
-  * **flat (legacy, `output_dir="..."`)** — writes `<output_dir>/<task_id>.glb`,
-    byte-for-byte the historical behaviour. Existing callers are unaffected.
-
-Usage:
-    from models.gen_3d_object.trellis_2_model import Trellis2Model
-    from operators.gen_3d_object.operator import Gen3DObjectOperator
-
-    model = Trellis2Model(model_path="...")
-
-    # per-game layout
-    op = Gen3DObjectOperator(model=model, run_id="20260731_1032")
-
-    # legacy flat layout (unchanged)
-    op = Gen3DObjectOperator(model=model, output_dir="outputs/3d_object")
-
-    result = op.run({
-        "game_id": "gameA_cyberpunk_shooter",   # optional, inferred when omitted
-        "task_id": "sword_001",
-        "image_path": "path/to/image.png",      # or "image": PIL.Image
-        "seed": 42,
-    })
-    print(result["glb_path"])
-
-Two optional extras, both additive and off unless asked for:
-
-  * naming an ``asset_id`` in the task hands the finished mesh to the
-    three.js adapter — staged, oriented, manifest rewritten;
-  * ``run_art_plan(game_id, image_model)`` generates a whole game's
-    recorded art plan (``funcs/art_plan.py``) that way in one call.
-
-``funcs/asset_pack.py`` covers the other case: three CC0 models,
-downloaded and imported in seconds, for when generating is overkill.
-
-A third route skips inference entirely. Passing ``spec`` instead of an
-image builds the mesh from a declarative part list
-(``funcs/code_asset.py``), which suits anything exactly describable — a
-crate, a sign, a wheel, a rifle. It needs no model and no GPU, states its
-own facing and size instead of leaving them to be guessed, and keeps its
-parts as named glTF nodes so gameplay can still move them::
-
-    op = Gen3DObjectOperator(model=None, run_id="20260826_1400")
-    op.run({
-        "game_id": "gameA_cyberpunk_shooter",
-        "task_id": "crate_001",
-        "spec": {
-            "subject": "wooden supply crate",
-            "units": "metres", "forward": "+z",
-            "asset_type": "prop", "height_metres": 0.6,
-            "parts": [{"id": "body", "kind": "box",
-                       "size": [0.6, 0.6, 0.6], "at": [0, 0.3, 0]}],
-        },
-    })
-
-The two can be mixed, which is usually the best available answer for a
-detailed hard-surface asset. A ``mesh`` part reads a GLB off disk — one
-component generated by a cloud model — and is placed, measured and gated
-exactly like a primitive. What each side contributes is not
-interchangeable: the generator supplies shapes no formula states (a
-moulded grip's finger swells), and the spec supplies the dimensions and
-placement a prompt cannot specify (a rail's 30 mm slot pitch). Spending
-generation on something a lathe states exactly buys an approximation at a
-hundred times the triangles, and the approximation is the part that can no
-longer be adjusted by editing a number — so the ``provenance`` gate
-reports what was delegated and at what cost::
-
-    op.run({
-        "game_id": "gameA_cyberpunk_shooter",
-        "task_id": "rifle_001",
-        "spec": {
-            "subject": "assault rifle",
-            "units": "metres", "forward": "+z",
-            "asset_type": "weapon", "height_metres": 0.24,
-            "parts": [
-                # exact dimensions: stated
-                {"id": "receiver", "kind": "box",
-                 "size": [0.04, 0.048, 0.30], "at": [0, 0.139, -0.03]},
-                # moulded surface: fetched, with a uniform `size` so the
-                # mesh keeps the proportions it was generated with
-                {"id": "grip", "kind": "mesh",
-                 "source": "parts/grip.glb",
-                 "size": [0.116, 0.116, 0.116], "at": [0, 0.045, -0.104]},
-            ],
-        },
-    })
-"""
-from __future__ import annotations
-
-import time
-from pathlib import Path
-from typing import Any, Optional
-
-from PIL import Image
-
-#: Registered task kind for this operator (see pipeline/common/paths.py).
-TASK_KIND = "3d_object"
-
-#: Artifact filename used by the per-game layout.
-GLB_FILENAME = "model.glb"
-
-
-class Gen3DObjectOperator:
-    """
-    Operator for 3D object generation.
-
-    Args:
-        model: A loaded model with an `infer_and_save(image, output_path, ...)` method.
-               Currently supports Trellis2Model; any model with the same interface works.
-        output_dir (str, optional): **Legacy flat mode.** When given, every GLB is
-               written as `<output_dir>/<task_id>.glb`, exactly as before. When
-               omitted (default), the per-game layout under
-               `test_data/outputs/<game_id>/<run_id>/assets/3d_object/<task_id>/`
-               is used instead.
-        run_id (str): Groups all artifacts of one generation run. Per-game mode only.
-        default_game_id (str, optional): Fallback game project for tasks that carry
-               no `game_id` and whose input paths reveal none. Per-game mode only.
-    """
-
-    def __init__(
-        self,
-        model: Any,
-        output_dir: Optional[str] = None,
-        run_id: str = "default",
-        default_game_id: Optional[str] = None,
-    ):
-        self.model = model
-        self.run_id = run_id
-        self.default_game_id = default_game_id
-        # Legacy flat mode is active only when an explicit output_dir is supplied.
-        self.output_dir = Path(output_dir) if output_dir else None
-        if self.output_dir is not None:
-            self.output_dir.mkdir(parents=True, exist_ok=True)
-
-    # --------------------------------------------------------------------------
-
-    def _load_image(self, inp: dict) -> Image.Image:
-        """Return a PIL image from an input dict that has 'image_path' or 'image'."""
-        if "image" in inp and isinstance(inp["image"], Image.Image):
-            return inp["image"]
-        if "image_path" in inp:
-            p = Path(inp["image_path"])
-            if not p.is_absolute() and not p.exists():
-                # resolve relative to repo root (codes_official/)
-                repo_root = Path(__file__).resolve().parents[2]
-                p = repo_root / inp["image_path"]
-            return Image.open(str(p)).convert("RGBA")
-        raise ValueError("Input must contain 'image_path' (str) or 'image' (PIL.Image).")
-
-    # --------------------------------------------------------------------------
-
-    def _artifact_suffix(self) -> str:
-        """
-        File extension for the artifact — GLB unless the injected model says
-        otherwise (the cloud backends can emit FBX / OBJ / USDZ).
-
-        The result dict keeps the ``glb_path`` key whatever the extension is:
-        renaming a returned key breaks `eval.py` and `test/`.
-        """
-        fmt = getattr(self.model, "output_format", None)
-        return str(fmt).lower().lstrip(".") if fmt else "glb"
-
-    def _resolve_out_path(self, inp: dict, task_id: str) -> tuple[str, Path, Path]:
-        """
-        Return ``(game_id, task_dir, glb_path)`` for either output mode.
-
-        Legacy flat mode keeps the historical `<output_dir>/<task_id>.glb`.
-        """
-        suffix = self._artifact_suffix()
-        if self.output_dir is not None:
-            game_id = str(inp.get("game_id") or inp.get("game") or "")
-            return game_id, self.output_dir, self.output_dir / f"{task_id}.{suffix}"
-
-        from pipeline.common import paths
-        game_id = paths.infer_game_id(inp, fallback=self.default_game_id)
-        task_dir = paths.task_output_dir(game_id, TASK_KIND, task_id, run_id=self.run_id)
-        filename = GLB_FILENAME if suffix == "glb" else f"model.{suffix}"
-        return game_id, task_dir, task_dir / filename
-
-    # --------------------------------------------------------------------------
-
-    def run(self, inp: dict) -> dict:
-        """
-        Generate a 3D object from an image prompt, or build one from a spec.
-
-        Args:
-            inp (dict):
-                - image_path (str): path to the reference / concept image
-                  OR image (PIL.Image): pre-loaded image
-                  OR spec (dict): a part spec, which builds the mesh instead
-                  of inferring it (see `funcs/code_asset.py`)
-                - compose (dict): combine_avatar arguments, including caller-defined
-                  socket_definitions, grip_templates and optional slot_definitions
-                - game_id (str, optional): game project this task belongs to;
-                  inferred from `image_path` when omitted
-                - task_id (str, optional): used to name the output file / directory
-                - seed (int, optional): random seed (default 42)
-                - decimation_target (int, optional)
-                - texture_size (int, optional)
-
-        Returns:
-            dict:
-                - task_id (str)
-                - glb_path (str): absolute path to the saved GLB file
-                - elapsed_sec (float)
-                - game_id (str), task_kind (str), output_dir (str)  ← additive
-        """
-        if inp.get("compose") is not None:
-            if inp.get("spec") is not None:
-                raise ValueError("provide compose or spec, not both")
-            return self._run_compose(inp)
-        if inp.get("spec") is not None:
-            return self._run_spec(inp)
-
-        task_id = inp.get("task_id", f"task_{int(time.time())}")
-        seed = inp.get("seed", 42)
-        decimation_target = inp.get("decimation_target", 1_000_000)
-        texture_size = inp.get("texture_size", 4096)
-
-        game_id, task_dir, out_path = self._resolve_out_path(inp, task_id)
-
-        text_mode = inp.get("input_mode") == "text_to_3d"
-        if text_mode and not str(inp.get("prompt") or "").strip():
-            raise ValueError("text_to_3d requires a non-empty prompt")
-        image = None if text_mode else self._load_image(inp)
-        inference_kwargs = {"prompt": inp["prompt"]} if text_mode else {}
-
-        t0 = time.time()
-        glb_path = self.model.infer_and_save(
-            image,
-            output_path=str(out_path),
-            seed=seed,
-            decimation_target=decimation_target,
-            texture_size=texture_size,
-            **inference_kwargs,
-        )
-        elapsed = time.time() - t0
-
-        result = {
-            "task_id": task_id,
-            "glb_path": glb_path,
-            "elapsed_sec": round(elapsed, 2),
-            "game_id": game_id,
-            "task_kind": TASK_KIND,
-            "output_dir": str(task_dir),
-        }
-
-        # Image-to-3D infers a floor under a subject that stands on the edge
-        # of its own crop, and it is never wanted: measured at a fifth of the
-        # triangle budget, and a grey pancake under the character in the
-        # scene. Removing it here means every consumer of this task output
-        # gets a clean mesh, rather than each game discovering the slab.
-        if inp.get("strip_ground_plate", True):
-            from .funcs.mesh_cleanup import strip_ground_plate
-
-            cleanup = strip_ground_plate(glb_path)
-            result["ground_plate"] = cleanup
-            if cleanup.get("applied"):
-                result.setdefault("warnings", []).append(
-                    f"removed an inferred floor: {cleanup['plate_count']} "
-                    f"sheets and {cleanup['debris_count']} loose lumps, "
-                    f"{cleanup['removed_fraction']:.0%} of the mesh"
-                )
-            elif cleanup.get("reason") and "nothing" not in cleanup["reason"]:
-                result.setdefault("warnings", []).append(
-                    f"left the mesh alone: {cleanup['reason']}"
-                )
-
-        # meta.json only in per-game mode, so legacy output dirs stay untouched.
-        if self.output_dir is None:
-            from pipeline.common import paths
-            meta = {
-                **result,
-                "run_id": self.run_id,
-                "prompt": inp.get("prompt"),
-                "source_image": inp.get("image_path"),
-                "seed": seed,
-                "decimation_target": decimation_target,
-                "texture_size": texture_size,
-                "model": type(self.model).__name__,
-            }
-            for key in ("asset_id", "asset_type", "forward_axis",
-                        "scale_hint_metres", "notes"):
-                if inp.get(key) is not None:
-                    meta[key] = inp[key]
-            # Cloud backends expose what the call cost and produced — task id,
-            # credits, triangle count (api_model_require.md R9.8). Recording it
-            # here is what makes a billed run reproducible after the fact.
-            call_info = getattr(self.model, "last_call_info", None)
-            if call_info:
-                meta["model_call"] = dict(call_info)
-            paths.write_task_meta(task_dir, meta)
-
-        # Optional handover to the engine adapter, active only when the task
-        # names an asset. A generated mesh is not part of a game until the
-        # adapter has staged it and recorded which way it faces.
-        if inp.get("asset_id") and self.output_dir is None:
-            from .funcs.asset_import import import_asset
-            result.update(
-                import_asset(
-                    game_id,
-                    task_id,
-                    asset_id=str(inp["asset_id"]),
-                    asset_type=str(inp.get("asset_type") or "prop"),
-                    run_id=self.run_id,
-                    forward_axis=str(inp.get("forward_axis") or ""),
-                    scale_hint_metres=inp.get("scale_hint_metres"),
-                    verified_by=str(inp.get("verified_by") or "heuristic"),
-                    notes=str(inp.get("notes") or ""),
-                    project_hint=str(inp.get("project_hint") or ""),
-                    preview=bool(inp.get("preview", True)),
-                )
-            )
-
-        return result
-
-    # --------------------------------------------------------------------------
-
-    def _run_compose(self, inp: dict) -> dict:
-        """Resolve caller-configured attachments, then build through the spec route."""
-        import json
-        from .funcs.code_asset_templates.human_template.combine import (
-            combine_avatar, write_report,
-        )
-
-        task_id = inp.get("task_id", f"task_{int(time.time())}")
-        _, _, out_path = self._resolve_out_path(inp, task_id)
-        directory = out_path.parent / f"{out_path.stem}_compose"
-        config = dict(inp["compose"])
-        config.setdefault("parts_dir", directory / "parts")
-        started = time.time()
-        combined = combine_avatar(**config)
-        result = self._run_spec({**inp, "task_id": task_id, "spec": combined["spec"]})
-        report_path = write_report(combined["report"], directory / "report.json")
-        sockets_path = directory / "sockets.json"
-        sockets_path.write_text(json.dumps(combined["report"]["sockets"], indent=2),
-                                encoding="utf-8")
-        result["compose"] = {**combined["report"], "report_path": report_path,
-                             "sockets_path": str(sockets_path)}
-        result["elapsed_sec"] = round(time.time() - started, 2)
-        return result
-
-    def _run_spec(self, inp: dict) -> dict:
-        """Build the asset from a declarative spec rather than an image.
-
-        Taken when the task carries a ``spec``. No model is loaded and no
-        image is read, so this path works with ``model=None`` — a crate
-        does not need a GPU, and requiring one to build a box is what makes
-        primitives feel like a defeat rather than a choice.
-
-        Three things come out of the spec that the generated path can only
-        guess at, which is the reason to prefer it wherever it applies:
-
-          * ``forward_axis`` is *stated*, so the import is recorded as
-            ``verified_by="spec"`` rather than ``"heuristic"``. Facing is
-            the one property `orientation_review` exists to establish, and
-            here there is nothing to establish.
-          * ``scale_hint_metres`` is stated, so the mesh has a real size
-            instead of a unit box someone has to scale by eye.
-          * parts keep their ids as glTF node names, so a wheel can be spun
-            after import. A reconstructed mesh is one fused body.
-
-        ``strip_ground_plate`` is deliberately not run: it removes a floor
-        that image-to-3D *invented*, and a spec cannot invent one. Running
-        it here would risk deleting a plinth that was asked for.
-
-        A spec may also carry ``mesh`` parts, which read a GLB off disk —
-        typically one component from a cloud model, composed in where no
-        formula states the shape. Those change one thing here and nothing
-        else: ``verified_by`` drops to ``"spec+generated"``, because a
-        generated part's facing was *asserted* by whoever placed it and not
-        established by anything. Claiming ``"spec"`` for a composition that
-        is largely fetched geometry would put an unverified facing behind the
-        one label that is supposed to mean it was not guessed.
-        """
-        from .funcs.code_asset import MESH_KIND, build_code_asset
-
-        task_id = inp.get("task_id", f"task_{int(time.time())}")
-        game_id, task_dir, out_path = self._resolve_out_path(inp, task_id)
-        # A spec always yields glTF, whatever output_format an injected
-        # model happens to advertise.
-        out_path = out_path.with_suffix(".glb")
-        task_dir.mkdir(parents=True, exist_ok=True)
-
-        t0 = time.time()
-        report = build_code_asset(
-            inp["spec"],
-            str(out_path),
-            revise=inp.get("revise"),
-            strict=bool(inp.get("strict", True)),
-        )
-        elapsed = time.time() - t0
-
-        # Which parts were fetched rather than derived. Carried on the result
-        # because after export the two are indistinguishable, and only one of
-        # them can still be changed by editing a number: a reviewer who wants
-        # the grip 4 mm thicker needs to know that means regenerating it.
-        # `long_axis` travels too, since a regenerated part is a new set of
-        # axes and the rotation that placed the old one will not place it.
-        generated_parts = [
-            {
-                "id": part["id"],
-                "source": part["source"],
-                "long_axis": part.get("long_axis"),
-            }
-            for part in report["spec"]["parts"]
-            if part.get("kind") == MESH_KIND
-        ]
-
-        result: dict[str, Any] = {
-            "task_id": task_id,
-            "glb_path": report["glb_path"],
-            "elapsed_sec": round(elapsed, 2),
-            "game_id": game_id,
-            "task_kind": TASK_KIND,
-            "output_dir": str(task_dir),
-            "source": "code_asset+generated" if generated_parts else "code_asset",
-            "spec_ok": report["ok"],
-            "gates": report["gates"],
-            "triangles": report["triangles"],
-            "bounds": report["bounds"],
-            "part_ids": report["part_ids"],
-            "generated_parts": generated_parts,
-            "stop_reason": report["stop_reason"],
-            "attempts": report["attempts"],
-        }
-        if report["warnings"]:
-            result.setdefault("warnings", []).extend(report["warnings"])
-        if report["failures"]:
-            # Surfaced as warnings on the result *and* kept in `gates`: a
-            # gate failure that only appears in a nested dict is the silent
-            # early return this whole module is shaped against.
-            result.setdefault("warnings", []).extend(
-                f"gate: {message}" for message in report["failures"]
-            )
-
-        if self.output_dir is None:
-            from pipeline.common import paths
-
-            meta = {
-                **result,
-                "run_id": self.run_id,
-                "spec": report["spec"],
-                "forward_axis": report["forward_axis"],
-                "scale_hint_metres": report["scale_hint_metres"],
-                "model": "code_asset",
-            }
-            for key in ("asset_id", "asset_type", "notes"):
-                if inp.get(key) is not None:
-                    meta[key] = inp[key]
-            paths.write_task_meta(task_dir, meta)
-
-        # A failed spec is not handed to the engine. An asset that exists
-        # gets used, so a gate failure has to stop it becoming an asset.
-        if inp.get("asset_id") and self.output_dir is None and report["ok"]:
-            from .funcs.asset_import import import_asset
-
-            result.update(
-                import_asset(
-                    game_id,
-                    task_id,
-                    asset_id=str(inp["asset_id"]),
-                    asset_type=str(
-                        inp.get("asset_type")
-                        or report["spec"].get("asset_type")
-                        or "prop"
-                    ),
-                    run_id=self.run_id,
-                    # Stated by the spec, not inferred from a view.
-                    forward_axis=report["forward_axis"],
-                    scale_hint_metres=report["scale_hint_metres"],
-                    # "spec" means the facing was stated rather than guessed.
-                    # With generated parts in the composition that is only
-                    # half true: the *placement* is stated, but the mesh
-                    # inside it came from a generator with no facing of its
-                    # own, and whoever wrote the rotation asserted one. Said
-                    # distinctly so a reviewer knows an orientation check is
-                    # still worth a look, instead of trusting a label that
-                    # was earned by a different route.
-                    verified_by="spec+generated" if generated_parts else "spec",
-                    notes=str(inp.get("notes") or "")
-                    or (
-                        f"Built from a spec: {report['subject']}."
-                        + (
-                            f" {len(generated_parts)} generated part(s): "
-                            f"{', '.join(part['id'] for part in generated_parts)}."
-                            if generated_parts else ""
-                        )
-                    ),
-                    project_hint=str(inp.get("project_hint") or ""),
-                    preview=bool(inp.get("preview", True)),
-                )
-            )
-        return result
-
-    # --------------------------------------------------------------------------
-
-    def run_art_plan(
-        self,
-        game_id: str,
-        image_model: Any = None,
-        plan: Optional[list[dict]] = None,
-        assets: tuple[str, ...] = (),
-    ) -> list[dict]:
-        """
-        Generate a game's recorded art plan and import each asset.
-
-        The plan lives in `funcs/art_plan.py`; it names a subject, an asset
-        type and a **height in metres**, because a mesh normalised into a
-        unit box has no size of its own. Concept images come from
-        `image_model` unless an entry points at one.
-
-        Facing is recorded as `heuristic`, never as verified: single-image
-        reconstruction puts the input view on a predictable axis, but a
-        guess labelled as checked stops anyone checking. Each import leaves
-        a review sheet for `agent_skills/asset_qa/`.
-        """
-        from .funcs.art_plan import concept_image, plan_for_game
-
-        entries = plan if plan is not None else plan_for_game(game_id)
-        if not entries:
-            raise ValueError(
-                f"No art plan for {game_id!r}; add one to "
-                "operators/gen_3d_object/funcs/art_plan.py"
-            )
-
-        results: list[dict] = []
-        for entry in entries:
-            if assets and entry["asset_id"] not in assets:
-                continue
-            image, provenance, framing = concept_image(entry, image_model)
-            task_dir = Path(
-                self._resolve_out_path(
-                    {"game_id": game_id}, entry["task_id"]
-                )[1]
-            )
-            task_dir.mkdir(parents=True, exist_ok=True)
-            # Keep the concept image beside the mesh it produced: a bad
-            # asset is almost always a bad concept image, and that is not
-            # diagnosable after the fact if the input was thrown away.
-            image.save(task_dir / "concept.png")
-            result = self.run(
-                {
-                    "game_id": game_id,
-                    "task_id": entry["task_id"],
-                    "image": image,
-                    "prompt": entry["full_prompt"],
-                    "seed": entry.get("seed", 42),
-                    "decimation_target": entry["triangles"],
-                    "texture_size": entry["texture"],
-                    "asset_id": entry["asset_id"],
-                    "asset_type": entry["type"],
-                    "forward_axis": entry["faces"],
-                    "scale_hint_metres": entry["height"],
-                    "verified_by": "heuristic",
-                    "notes": entry["notes"]
-                    or f"Generated from: {provenance}. Facing assumed "
-                    "from the input view.",
-                }
-            )
-            result["asset_id"] = entry["asset_id"]
-            result["concept_source"] = provenance
-            result["framing"] = framing
-            if framing.get("touched_border"):
-                # The subject ran off the edge of the generated image, so
-                # part of it was never drawn. Cropping cannot invent it.
-                result.setdefault("warnings", []).append(
-                    "the concept image was cropped at the border, so the "
-                    "mesh is probably truncated: regenerate with another "
-                    "seed"
-                )
-            results.append(result)
-        return results
-
-    # --------------------------------------------------------------------------
-
-    def run_batch(self, inputs: list[dict]) -> list[dict]:
-        """Run a list of input dicts sequentially and return results."""
-        return [self.run(inp) for inp in inputs]
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py
deleted file mode 100644
index 94e8ec7b966e0c84658fd5322dc30f0c769f0f2f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py
+++ /dev/null
@@ -1,444 +0,0 @@
-"""
-pipeline/common/paths.py
-
-Single source of truth for every input / output path in 3AGameFactory.
-
-The benchmark is authored **per game project**, so outputs are organized the same
-way. An artifact is uniquely addressed by ``(game_id, run_id, task_kind, task_id)``::
-
-    test_data/test_samples/<game_id>/<TaskDir>/...                        # inputs (read-only)
-    test_data/outputs/<game_id>/<run_id>/<layer>/<task_kind>/<task_id>/   # outputs
-
-Full output tree::
-
-    test_data/outputs/                         # OUTPUT_ROOT ($AAAGF_OUTPUT_ROOT)
-    └── gameA_cyberpunk_shooter/               # one dir per generated game project
-        ├── latest -> 20260731_1032/           # symlink to the most recent run
-        └── <run_id>/                          # "default", or a timestamp
-            ├── run_meta.json                  # ckpts · seeds · git sha · argv
-            ├── assets/
-            │   ├── 3d_object/<task_id>/       # model.glb        + meta.json
-            │   ├── tpose/<task_id>/           # tpose_fg.png     + meta.json
-            │   ├── 3d_scene/<task_id>/
-            │   ├── motion/<task_id>/
-            │   ├── cg_video/<task_id>/
-            │   └── audio/<task_id>/            # dialogue / sound-effect audio + meta.json
-            ├── mechanic/<task_id>/            # engine project + demo_outputs/ + launch.sh
-            ├── ui/<task_id>/
-            ├── pipeline/<task_id>/            # end-to-end game-slice artifacts
-            └── eval/
-                ├── <task_kind>/<task_id>/     # metrics.json · build.log · judge_log.json
-                └── summary.json               # aggregate over the whole run
-
-The root stays next to the test set so any evaluator finds inputs and outputs side
-by side. Generated artifacts get large — point ``$AAAGF_OUTPUT_ROOT`` at a scratch
-disk to relocate the whole tree without touching code.
-
-This module has **no third-party dependencies** and imports no models, so it is
-safe to import from anywhere (including CPU-only tooling and tests).
-"""
-from __future__ import annotations
-
-import json
-import os
-import subprocess
-import sys
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Optional
-
-# ── Roots ─────────────────────────────────────────────────────────────────────
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-
-TEST_DATA_ROOT = REPO_ROOT / "test_data"
-TEST_SAMPLES_ROOT = TEST_DATA_ROOT / "test_samples"
-
-#: Output root. Override with ``export AAAGF_OUTPUT_ROOT=/scratch/aaagf_outputs``.
-OUTPUT_ROOT = Path(
-    os.environ.get("AAAGF_OUTPUT_ROOT", TEST_DATA_ROOT / "outputs")
-).expanduser()
-
-#: Used when a task carries no game association (ad-hoc single-image demos).
-UNASSIGNED_GAME = "_scratch"
-
-#: Run id used when the caller does not ask for a fresh timestamped one.
-DEFAULT_RUN_ID = "default"
-
-LATEST_LINK = "latest"
-
-
-# ── Task kinds ────────────────────────────────────────────────────────────────
-
-#: task_kind -> layer directory. Layer A = assets, B = mechanic, C = ui.
-TASK_LAYER: dict[str, str] = {
-    # Layer A — asset generation
-    "3d_object": "assets",
-    "tpose": "assets",
-    "3d_scene": "assets",
-    "motion": "assets",
-    "cg_video": "assets",
-    "audio": "assets",
-    # Game design (story / chapters / asset & screen plan) — A-layer diagnostic only
-    "design": "design",
-    # Layer B / C — code generation
-    "mechanic": "mechanic",
-    "ui": "ui",
-    # End-to-end vertical slice
-    "pipeline": "pipeline",
-    # Whitebox-stage game evaluation (stage-1 LLM/Agent whitebox rubric) —
-    # an eval-only kind: results land under ``eval/whitebox/<task_id>/``
-    "whitebox": "whitebox",
-}
-
-#: task_kind -> directory name inside ``test_samples/<game_id>/``.
-TASK_INPUT_DIR: dict[str, str] = {
-    "3d_object": "3D_object",
-    "tpose": "tpose",
-    "3d_scene": "3D_scene",
-    "motion": "motion",
-    "cg_video": "cg_video",
-    "audio": "audio",
-    "design": "design",
-    "mechanic": "mechanic",
-    "ui": "ui",
-    "pipeline": "pipeline",
-    # whitebox case inputs (game_spec/rubric/references) live under
-    # ``test_bench_example/<case>/``; this dir keeps the registration
-    # complete for the shared task-input helpers.
-    "whitebox": "whitebox",
-}
-
-#: task_kind -> per-game task list filename inside ``TASK_INPUT_DIR``.
-TASK_JSONL: dict[str, str] = {
-    "3d_object": "object_tasks.jsonl",
-    "tpose": "tpose_tasks.jsonl",
-    "3d_scene": "scene_tasks.jsonl",
-    "motion": "motion_tasks.jsonl",
-    "cg_video": "cg_tasks.jsonl",
-    "audio": "audio_tasks.jsonl",
-    "design": "design_tasks.jsonl",
-    "mechanic": "mechanic_tasks.jsonl",
-    "ui": "ui_tasks.jsonl",
-    "pipeline": "pipeline_task.jsonl",
-    "whitebox": "whitebox_tasks.jsonl",
-}
-
-#: task_kind -> cross-game aggregate jsonl under ``test_samples/``.
-TASK_COLLECT_JSONL: dict[str, str] = {
-    "3d_object": "3D_object_gen_collect.jsonl",
-    "tpose": "tpose_gen_collect.jsonl",
-    "3d_scene": "3D_scene_gen_collect.jsonl",
-    "motion": "motion_gen_collect.jsonl",
-    "cg_video": "cg_video_collect.jsonl",
-    "audio": "audio_gen_collect.jsonl",
-    "design": "design_collect.jsonl",
-    "mechanic": "mechanic_collect.jsonl",
-    "ui": "ui_collect.jsonl",
-    "pipeline": "pipeline_collect.jsonl",
-    "whitebox": "whitebox_collect.jsonl",
-}
-
-#: Layer-A kinds only — what ``pipeline/assets_gen/`` covers.
-ASSET_TASK_KINDS = tuple(k for k, v in TASK_LAYER.items() if v == "assets")
-
-
-def check_kind(task_kind: str) -> str:
-    """Raise a helpful error for an unregistered task kind."""
-    if task_kind not in TASK_LAYER:
-        raise ValueError(
-            f"Unknown task_kind {task_kind!r}. Register it in "
-            f"pipeline/common/paths.py first. Known: {sorted(TASK_LAYER)}"
-        )
-    return task_kind
-
-
-# ── Input paths ───────────────────────────────────────────────────────────────
-
-def game_input_dir(game_id: str) -> Path:
-    """``test_data/test_samples/<game_id>/``"""
-    return TEST_SAMPLES_ROOT / game_id
-
-
-def task_input_dir(game_id: str, task_kind: str) -> Path:
-    """``test_data/test_samples/<game_id>/<TaskDir>/``"""
-    return game_input_dir(game_id) / TASK_INPUT_DIR[check_kind(task_kind)]
-
-
-def task_jsonl(game_id: str, task_kind: str) -> Path:
-    """``test_data/test_samples/<game_id>/<TaskDir>/<kind>_tasks.jsonl``"""
-    return task_input_dir(game_id, task_kind) / TASK_JSONL[check_kind(task_kind)]
-
-
-def collect_jsonl(task_kind: str) -> Path:
-    """``test_data/test_samples/<kind>_collect.jsonl`` — cross-game task list."""
-    return TEST_SAMPLES_ROOT / TASK_COLLECT_JSONL[check_kind(task_kind)]
-
-
-def resolve_tasks_path(task_kind: str, tasks: str | None = None,
-                       game_id: str | None = None) -> Path:
-    """
-    Standard task-list resolution shared by every ``run.py`` / ``eval.py``:
-
-    explicit ``--tasks``  →  the game's own ``*_tasks.jsonl`` (if non-empty)
-                          →  the cross-game ``*_collect.jsonl``.
-    """
-    if tasks:
-        return resolve_input_path(tasks)
-    if game_id:
-        per_game = task_jsonl(game_id, task_kind)
-        if per_game.exists() and per_game.stat().st_size > 0:
-            return per_game
-    return collect_jsonl(task_kind)
-
-
-def list_games() -> list[str]:
-    """Every game project directory present in the test set."""
-    if not TEST_SAMPLES_ROOT.is_dir():
-        return []
-    return sorted(
-        p.name
-        for p in TEST_SAMPLES_ROOT.iterdir()
-        if p.is_dir() and not p.name.startswith((".", "_"))
-    )
-
-
-def infer_game_id(task: dict, fallback: Optional[str] = None) -> str:
-    """
-    Resolve which game project a task belongs to.
-
-    Priority: explicit ``game_id`` / ``game`` field  →  any path-like field that
-    sits under ``test_samples/<game_id>/``  →  ``fallback``  →  ``UNASSIGNED_GAME``.
-    """
-    for key in ("game_id", "game"):
-        if task.get(key):
-            return str(task[key])
-
-    for value in task.values():
-        if not isinstance(value, str) or "test_samples" not in value:
-            continue
-        parts = Path(value).parts
-        if "test_samples" in parts:
-            idx = parts.index("test_samples")
-            if idx + 1 < len(parts):
-                return parts[idx + 1]
-
-    return fallback or UNASSIGNED_GAME
-
-
-def iter_tasks(tasks_path: str | Path, game_filter: str | None = None):
-    """
-    Yield ``(task_dict, game_id)`` from a jsonl, skipping blanks and comments.
-
-    Every runner should iterate through this so `game_id` resolution and the
-    `--game` filter behave identically across tasks.
-    """
-    with open(tasks_path) as f:
-        for lineno, line in enumerate(f, 1):
-            line = line.strip()
-            if not line or line.startswith("//"):
-                continue
-            try:
-                task = json.loads(line)
-            except json.JSONDecodeError as e:
-                raise ValueError(f"{tasks_path}:{lineno} is not valid JSON: {e}") from e
-            game_id = infer_game_id(task)
-            if game_filter and game_id != game_filter:
-                continue
-            yield task, game_id
-
-
-# ── Output paths ──────────────────────────────────────────────────────────────
-
-def new_run_id(prefix: str = "") -> str:
-    """Timestamped run id, e.g. ``20260731_103200`` or ``qwen3b_20260731_103200``."""
-    stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    return f"{prefix}_{stamp}" if prefix else stamp
-
-
-def game_output_dir(game_id: str) -> Path:
-    """``test_data/outputs/<game_id>/``"""
-    return OUTPUT_ROOT / game_id
-
-
-def run_dir(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path:
-    """``test_data/outputs/<game_id>/<run_id>/`` — root of one generation run."""
-    return game_output_dir(game_id) / run_id
-
-
-def task_output_dir(
-    game_id: str,
-    task_kind: str,
-    task_id: str,
-    run_id: str = DEFAULT_RUN_ID,
-    create: bool = True,
-) -> Path:
-    """
-    ``test_data/outputs/<game_id>/<run_id>/<layer>/[<task_kind>/]<task_id>/``
-
-    One directory per task, so an artifact ships alongside its preview,
-    intermediates and ``meta.json`` without filename mangling.
-    """
-    check_kind(task_kind)
-    layer = TASK_LAYER[task_kind]
-    base = run_dir(game_id, run_id) / layer
-    # assets/ is further split by kind; mechanic/ ui/ pipeline/ *are* the kind.
-    if layer == "assets":
-        base = base / task_kind
-    path = base / task_id
-    if create:
-        path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-def eval_output_dir(
-    game_id: str,
-    task_kind: str,
-    task_id: str,
-    run_id: str = DEFAULT_RUN_ID,
-    create: bool = True,
-) -> Path:
-    """``test_data/outputs/<game_id>/<run_id>/eval/<task_kind>/<task_id>/``"""
-    check_kind(task_kind)
-    path = run_dir(game_id, run_id) / "eval" / task_kind / task_id
-    if create:
-        path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-def eval_summary_path(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path:
-    """``test_data/outputs/<game_id>/<run_id>/eval/summary.json``"""
-    return run_dir(game_id, run_id) / "eval" / "summary.json"
-
-
-def results_summary_path(game_id: str, task_kind: str,
-                         run_id: str = DEFAULT_RUN_ID) -> Path:
-    """``test_data/outputs/<game_id>/<run_id>/<task_kind>_results_summary.json``"""
-    check_kind(task_kind)
-    return run_dir(game_id, run_id) / f"{task_kind}_results_summary.json"
-
-
-def run_meta_path(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path:
-    """``test_data/outputs/<game_id>/<run_id>/run_meta.json``"""
-    return run_dir(game_id, run_id) / "run_meta.json"
-
-
-# ── Run bookkeeping ───────────────────────────────────────────────────────────
-
-def _git_sha() -> Optional[str]:
-    try:
-        out = subprocess.run(
-            ["git", "-C", str(REPO_ROOT), "rev-parse", "--short", "HEAD"],
-            capture_output=True, text=True, timeout=5,
-        )
-        return out.stdout.strip() or None
-    except Exception:
-        return None
-
-
-def update_latest_link(game_id: str, run_id: str) -> None:
-    """Repoint ``<game_id>/latest`` at ``run_id`` (best effort, never fatal)."""
-    if run_id == LATEST_LINK:
-        return
-    link = game_output_dir(game_id) / LATEST_LINK
-    try:
-        link.parent.mkdir(parents=True, exist_ok=True)
-        if link.is_symlink() or link.exists():
-            link.unlink()
-        link.symlink_to(run_id, target_is_directory=True)
-    except OSError:
-        pass  # filesystem without symlink support — not fatal
-
-
-def write_run_meta(
-    game_id: str,
-    run_id: str = DEFAULT_RUN_ID,
-    *,
-    task_kind: Optional[str] = None,
-    extra: Optional[dict[str, Any]] = None,
-) -> Path:
-    """Record how a run was produced, so results stay reproducible."""
-    path = run_meta_path(game_id, run_id)
-    path.parent.mkdir(parents=True, exist_ok=True)
-
-    meta: dict[str, Any] = {
-        "game_id": game_id,
-        "run_id": run_id,
-        "task_kind": task_kind,
-        "created_at": datetime.now().isoformat(timespec="seconds"),
-        "git_sha": _git_sha(),
-        "argv": sys.argv,
-    }
-    if extra:
-        meta.update(extra)
-
-    if path.exists():  # merge with an earlier task_kind in the same run
-        try:
-            prev = json.loads(path.read_text(encoding="utf-8"))
-            history = prev.pop("history", [])
-            history.append(prev)
-            meta["history"] = history
-        except (json.JSONDecodeError, OSError):
-            pass
-
-    path.write_text(json.dumps(meta, indent=2, ensure_ascii=False), encoding="utf-8")
-    return path
-
-
-def write_task_meta(task_dir: str | Path, meta: dict[str, Any]) -> Path:
-    """Write ``<task_dir>/meta.json`` next to the artifacts."""
-    d = Path(task_dir)
-    d.mkdir(parents=True, exist_ok=True)
-    path = d / "meta.json"
-    from .record_paths import portable_value
-    path.write_text(json.dumps(portable_value(meta, d), indent=2, ensure_ascii=False, default=str),
-                    encoding="utf-8")
-    return path
-
-
-def write_results_summary(results: list[dict], task_kind: str,
-                         run_id: str = DEFAULT_RUN_ID) -> list[Path]:
-    """
-    Group ``run()`` results by ``game_id`` and write one summary per game,
-    refresh ``run_meta.json`` and the ``latest`` symlink.
-
-    Returns the summary paths written.
-    """
-    check_kind(task_kind)
-    grouped: dict[str, list[dict]] = {}
-    for r in results:
-        grouped.setdefault(r.get("game_id") or UNASSIGNED_GAME, []).append(r)
-
-    written = []
-    for game_id, items in grouped.items():
-        path = results_summary_path(game_id, task_kind, run_id)
-        path.parent.mkdir(parents=True, exist_ok=True)
-        path.write_text(json.dumps(items, indent=2, ensure_ascii=False, default=str),
-                        encoding="utf-8")
-        write_run_meta(game_id, run_id, task_kind=task_kind,
-                       extra={"num_tasks": len(items)})
-        update_latest_link(game_id, run_id)
-        written.append(path)
-    return written
-
-
-# ── Misc ──────────────────────────────────────────────────────────────────────
-
-def resolve_input_path(path_like: str | Path) -> Path:
-    """Resolve a possibly repo-relative input path (jsonl paths are repo-relative)."""
-    if str(path_like).replace("\\", "/").startswith("bench/"):
-        from .record_paths import resolve_path
-        return resolve_path(path_like)
-    p = Path(path_like)
-    if p.is_absolute() or p.exists():
-        return p
-    return REPO_ROOT / p
-
-
-def rel_to_repo(path: str | Path) -> str:
-    """Repo-relative string if possible, else the absolute path — for logs / meta."""
-    p = Path(path).resolve()
-    try:
-        return str(p.relative_to(REPO_ROOT))
-    except ValueError:
-        return str(p)
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py
deleted file mode 100644
index 21084bcfe232636debebeb9d1b9df5da1e2909da..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Portable workspace paths for persisted records; resolve only when doing I/O."""
-from __future__ import annotations
-
-from pathlib import Path
-import re
-
-BENCH = Path(__file__).resolve().parents[3]
-_PREFIXES = ("test_data/", "GameFactory-3A/", "scripts/", "prompts/", "config/", "reports/")
-_ROOT = BENCH.as_posix()
-_INLINE_ROOT = re.compile(re.escape(_ROOT).replace("/", r"[\\/]"), re.IGNORECASE)
-
-
-def resolve_path(value: str | Path) -> Path:
-    text = str(value).replace("\\", "/")
-    if text == "bench" or text.startswith("bench/"):
-        path = (BENCH.parent / text).resolve()
-        path.relative_to(BENCH.resolve())
-        return path
-    path = Path(value)
-    return path if path.is_absolute() else BENCH / path
-
-
-def portable_path(value: str | Path, base: Path | None = None) -> str:
-    original = str(value)
-    text = original.replace("\\", "/")
-    if re.match(r"^[A-Za-z][A-Za-z0-9+.-]*:", text) and not re.match(r"^[A-Za-z]:/", text):
-        return original  # URLs, API URIs and dependency descriptors retain their meaning.
-    if text.casefold() == _ROOT.casefold() or text.casefold().startswith(_ROOT.casefold() + "/"):
-        return "bench" + text[len(_ROOT):]
-    if text == "bench" or text.startswith("bench/"):
-        path = resolve_path(text)
-        suffix = path.relative_to(BENCH.resolve()).as_posix()
-        return "bench" if suffix == "." else "bench/" + suffix
-    if text.startswith(_PREFIXES):
-        path = (BENCH / text).resolve()
-        path.relative_to(BENCH.resolve())
-        return "bench/" + path.relative_to(BENCH.resolve()).as_posix()
-    if base is not None and "/" in text and "\n" not in text and not text.startswith("/"):
-        candidate = base / text
-        try:
-            if candidate.exists():
-                return "bench/" + candidate.resolve().relative_to(BENCH.resolve()).as_posix()
-        except (OSError, ValueError):
-            pass
-    return original
-
-
-def portable_text(value: str) -> str:
-    """Convert workspace paths embedded in log/error text, leaving URLs intact."""
-    pattern = re.compile(r"(?:https?://[^\s<>]+)|(" + _INLINE_ROOT.pattern + r"(?:[\\/][^\r\n\"'<>|;,)]*)?)", re.IGNORECASE)
-    return pattern.sub(lambda match: portable_path(match.group(1)) if match.group(1) else match.group(0), value)
-
-
-def portable_value(value, base: Path | None = None):
-    if isinstance(value, dict):
-        return {portable_path(key) if isinstance(key, str) else key: portable_value(item, base)
-                for key, item in value.items()}
-    if isinstance(value, (list, tuple)):
-        return [portable_value(item, base) for item in value]
-    if isinstance(value, Path):
-        return portable_path(value, base)
-    if isinstance(value, str):
-        result = portable_path(value, base)
-        return portable_text(result)
-    return value
-
-
-def runtime_value(value):
-    """Hydrate a verified frozen plan for runtime use; never use before hashing."""
-    if isinstance(value, dict):
-        return {str(resolve_path(key)) if isinstance(key, str) and (key == "bench" or key.startswith("bench/")) else key:
-                runtime_value(item) for key, item in value.items()}
-    if isinstance(value, list):
-        return [runtime_value(item) for item in value]
-    if isinstance(value, str) and (value == "bench" or value.startswith("bench/")):
-        return str(resolve_path(value))
-    return value
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md
deleted file mode 100644
index bd5f2cb297be9a913eaf0cb8aae96779b20cbcd9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md
+++ /dev/null
@@ -1,95 +0,0 @@
-# GameFactory-3A Stage 2 — asset generation
-
-你正在执行同一个游戏测例的第二阶段资产生成。**开始前必须先阅读 `{SETTING_OVERVIEW}`，并严格遵循其中的 asset refinement、asset QA 和 validation 流程。**
-
-## 测例输入
-
-- 游戏测例：`{GAME_ID}`
-- 测例目录：`{CASE_ROOT}`
-- GameFactory-3A 根目录：`{FRAMEWORK_ROOT}`
-- 总入口文档：`{SETTING_OVERVIEW}`
-- 目标引擎：`{ENGINE}`
-- 完整游戏需求：`{GENERAL_REQUIREMENT}`
-- 当前 run 根目录：`{RUN_ROOT}`
-- runner 控制面（冻结索引、prompt/packet/log）：`{GENERATION_CONTROL_ROOT}`
-- 第一阶段白模 run：`{WHITEBOX_RUN_ROOT}`
-- 第一阶段白模 workspace：`{WHITEBOX_WORKSPACE}`
-- 第一阶段资产接口：`{ASSET_SLOTS}`
-- 第一阶段计划/TODO：`{GENERATION_PLAN}`
-- 外部冻结的资产任务索引：`{ASSET_TASK_INDEX}`
-- 资产输出根目录：`{ASSET_OUTPUT_ROOT}`
-- 当前阶段 pipeline task 目录：`{STAGE_WORKSPACE}`
-- 资产 QA 证据根：`{RUN_ROOT}/eval/<task_kind>/<task_id>/`
-
-请读取游戏完整梗概和玩法，再读取 `{ASSET_TASK_INDEX}` 中列出的**每一条**资产任务、对应的 requirement、JSONL 和参考图片。**每一个资产生成任务都必须使用完整游戏梗概作为上下文**，不能只把孤立的资产 prompt 交给生成流程。
-
-## 本阶段任务
-
-1. 根据第一阶段的任务拆解、TODO 和 `asset_slots.json` 确认资产用途、生命周期、比例、朝向、挂点和风格；
-2. 按 `setting_overview.md` 路由到对应的 T-pose、3D object、3D scene、Motion、Audio、CG video 或其他资产 Skill；
-3. 逐项生成冻结任务索引中的所有资产，并执行对应的结构/视觉 QA；
-4. 保持第一阶段定义的 asset ID 和游戏/UI/mechanic contract 不变；
-5. 记录资产来源、配置、参考图、QA 和后续导入要求；
-6. 生成完整的资产 manifest，供第三阶段导入使用。
-
-## 限制
-
-- 不修改第一阶段核心游戏机制和需求；
-- 不擅自删除、合并或跳过资产任务；
-- 资产失败必须记录为失败，不能伪造成功；
-- 不将资产生成 QA 当成游戏已经完成；
-- 本阶段默认不执行 Agent repair。
-
-## 输出位置和 Layer 规则
-
-资产必须按框架标准布局写入：
-
-- `{ASSET_OUTPUT_ROOT}/tpose/<task_id>/`；
-- `{ASSET_OUTPUT_ROOT}/3d_object/<task_id>/`；
-- `{ASSET_OUTPUT_ROOT}/3d_scene/<task_id>/`；
-- `{ASSET_OUTPUT_ROOT}/motion/<task_id>/`；
-- `{ASSET_OUTPUT_ROOT}/audio/<task_id>/`；
-- `{ASSET_OUTPUT_ROOT}/cg_video/<task_id>/`。
-
-每个任务目录至少应有资产文件、`meta.json`、预览（若适用）和 QA 证据。资产 manifest 和阶段产物说明写入 `{ASSET_MANIFEST}`（位于标准 `pipeline/<task_id>/` task 目录）；冻结索引只读自 `{ASSET_TASK_INDEX}`，runner 控制文件写入 `{GENERATION_CONTROL_ROOT}`。每个资产任务的 QA 证据写入 `{RUN_ROOT}/eval/<task_kind>/<task_id>/`。至少生成：
-
-- `asset_manifest.json`；
-- `asset_generation_report.json`；
-- `asset_task_index.json`（由外部入口生成，不得删改任务）；
-- 失败任务及其失败原因。
-
-不要写入 case 输入目录、不要覆盖白模 run，也不要创建 `pipeline/assets/` stage 目录；真实资产必须按 `assets/<task_kind>/<task_id>/` 放置，不能把所有文件平铺在 pipeline task 目录。
-
-## 已确认的优先 3D Object 双服务对比
-
-当用户明确选择优先 200 条对象任务的 Meshy / Tripo API 对比时，使用
-`scripts/asset/generate_3d_comparison.py` 与 `config/asset_3d_comparison.json`，
-具体命令和费用见 `scripts/asset/README_3d_comparison.md`。这是跨游戏的对象生成实验；
-各游戏只包含 3D Object 子集，不能将其标为六类资产齐全的完整 Stage 2。
-
-- 输入以 `test_data/priority_gen/3d_object_priority_lowpoly_collect.jsonl` 的全部 200 条为准，
-  保留原 task ID、prompt、seed、参考图及最高 8000 三角面的既定政策。
-- 完整游戏需求保留在冻结输入包；两家文生调用使用完全相同的短梗概和原资产 prompt，
-  合计不超过 800 字符。API 未接收完整正文这一事实必须记录，不能宣称正文已送入模型。
-- 两家图生均使用同一冻结参考图，列入 `reference_only` 辅助组；不要为了补文字上下文
-  悄悄改图、另画参考图、改变原任务的输入模式或仅给一家添加贴图提示词。
-- 每家分别运行全部 200 条。按人物/刚体、文生/图生及上下文条件分组；Hunyuan3D Pro
-  仅保留后续接入口，本轮不启用、不消耗第三家额度。
-- 固定具体模型版本，不使用 `latest`；Meshy 本轮使用 standard + remesh，
-  不依赖将于 2026-10-30 退役的 `model_type=lowpoly`。
-- 原始 API mesh 写为 `model.glb`，统一后处理的导入 mesh 写为 `import/model.glb`。
-  两家使用同一固定工具、参数与贴图上限；分别记录原始和后处理 QA、耗时及缺陷。
-  减面未达到预算应如实记录失败，不增加生成次数、不换模型补齐或挑选 best-of。
-- 付费调用前需要用户确认预算并在环境中配置密钥。提交后立刻落盘任务 ID；
-  已知远端任务只续查结果，提交结果不确定时停下核对，不自动重发付费任务。
-- 本轮按游戏级共享资产目录落盘，在任务目录下加厂商一级：
-  `test_data/outputs/<game_id>/assets/3d_object/<task_id>/<provider>/`，
-  `<provider>` 为 `meshy` / `tripo`，不使用 `asset_comparison` 输出目录。
-  原始 `model.glb`、`import/model.glb`、`meta.json`、服务端预览均放在厂商目录内；
-  QA 写入 `test_data/outputs/<game_id>/eval/3d_object/<task_id>/<provider>/`。
-  manifest/阶段报告按游戏、批次、厂商放在
-  `test_data/outputs/<game_id>/pipeline/<game_id>_pipeline/<batch_id>/<provider>/`，
-  冻结输入、计费回执与跨游戏预览保存在 `test_data/outputs/_asset_batches/<batch_id>/`。
-  不覆盖不同批次已有的厂商资产，也不覆盖白模工程。
-  结构检查通过不等于视觉 QA 通过；C、Q1/Q2/Q3 的正式评分缺少证据时保持 UNKNOWN。
-  第三阶段的资产来源需要另行固定，不能按逐条最高评分混选两家产物。
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py
deleted file mode 100644
index d2d6fbd911b70a3dec2b7e42bcde3d3703347ec9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""Durable submit receipts for the comparison runner; no automatic POST retry.
-
-Uses the framework's HTTP plumbing. A lost create response is ambiguous: retain
-the credit reservation and require reconciliation instead of buying a new task.
-The runner holds one batch lock while using this client.
-"""
-from __future__ import annotations
-
-import json
-import math
-import os
-from pathlib import Path
-from typing import Any
-
-from models.common import cloud_api
-from pipeline.common.record_paths import portable_value, portable_text
-
-
-def read_json(path: Path, default=None):
-    return json.loads(path.read_text(encoding="utf-8")) if path.is_file() else default
-
-
-def write_json(path: Path, value: Any, *, portable: bool = True) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    temporary = path.with_name(path.name + ".tmp")
-    with temporary.open("w", encoding="utf-8", newline="\n") as stream:
-        json.dump(portable_value(value, path.parent) if portable else value,
-                  stream, ensure_ascii=False, indent=2)
-        stream.write("\n")
-        stream.flush()
-        os.fsync(stream.fileno())
-    os.replace(temporary, path)
-
-
-def safe_error(exc: Exception) -> str:
-    value = str(exc)
-    for name in ("MESHY_API_KEY", "TRIPO_API_KEY"):
-        secret = os.environ.get(name)
-        if secret:
-            value = value.replace(secret, "[REDACTED]")
-    return portable_text(value)[:1600]
-
-
-class BudgetExceeded(RuntimeError):
-    pass
-
-
-class AmbiguousSubmission(RuntimeError):
-    pass
-
-
-def reserved_credits(directory: Path) -> int:
-    return sum(
-        int(item["reserved_credits"])
-        for path in directory.glob("*.json")
-        for item in read_json(path, {}).get("submissions", {}).values()
-        if item["status"] != "rejected"
-    )
-
-
-class JournalClient:
-    """Replay successful create receipts and query the same remote task on resume."""
-
-    def __init__(self, base: str, key: str, journal: Path, cap: int,
-                 prices: dict[str, int], provider: str, transport=None, resume_refused=False):
-        self.journal = journal
-        self.cap = cap
-        self.prices = prices
-        self.provider = provider
-        self.resume_refused = resume_refused
-        # GET is safe to retry. A billed POST gets exactly one transport attempt.
-        self.reader = transport or cloud_api.CloudAPIClient(base, key, max_retries=3)
-        self.writer = transport or cloud_api.CloudAPIClient(base, key, max_retries=0)
-        self.public = None
-
-    def _cost(self, endpoint: str, body: dict) -> int:
-        if endpoint == "/files":
-            return 0
-        if self.provider == "meshy":
-            if endpoint == "/openapi/v2/text-to-3d":
-                return self.prices["text_refine" if body.get("mode") == "refine" else "text_preview"]
-            if endpoint == "/openapi/v1/image-to-3d":
-                return self.prices["image"]
-        elif endpoint in ("/generation/text-to-model", "/generation/image-to-model"):
-            return self.prices["text" if "text-to" in endpoint else "image"]
-        raise ValueError(f"Unpriced POST endpoint: {endpoint}")
-
-    def request(self, method: str, endpoint: str, **kwargs):
-        state = read_json(self.journal, {"submissions": {}, "results": {}})
-        if method != "POST":
-            result = self.reader.request(method, endpoint, **kwargs)
-            if method == "GET" and isinstance(result, dict):
-                state["results"][endpoint] = result
-                payload = result.get("data", result)
-                if isinstance(payload, dict):
-                    remote_id = payload.get("task_id") or payload.get("id")
-                    reported = payload.get("credits_consumed", payload.get("credits"))
-                    for record in state["submissions"].values():
-                        if remote_id and record.get("remote_id") == remote_id and isinstance(reported, (int, float)):
-                            record["reported_credits"] = reported
-                            record["reserved_credits"] = max(record["reserved_credits"], math.ceil(reported))
-                write_json(self.journal, state)
-            return result
-
-        # Hash binary inputs; never persist image bytes or credentials in receipts.
-        descriptor = {"endpoint": endpoint, "body": kwargs.get("json_body"),
-                      "files": {name: cloud_api.bytes_hash(value[1])
-                                for name, value in (kwargs.get("files") or {}).items()}}
-        fingerprint = cloud_api.request_hash(descriptor)
-        previous = state["submissions"].get(fingerprint)
-        if previous:
-            if previous["status"] == "created":
-                return previous["response"]
-            if previous["status"] == "rejected" and not (self.resume_refused and previous.get("resource_refusal")):
-                raise cloud_api.CloudAPIRequestError(previous["error"])
-            if previous["status"] != "rejected":
-                raise AmbiguousSubmission(
-                    f"Unconfirmed submission {fingerprint} in {self.journal}. "
-                    "Reconcile its remote task ID; no automatic resubmission.")
-
-        cost = self._cost(endpoint, kwargs.get("json_body") or {})
-        used = reserved_credits(self.journal.parent)
-        if used + cost > self.cap:
-            raise BudgetExceeded(f"{self.provider}: {used} reserved + {cost} exceeds cap {self.cap}")
-        record = {"endpoint": endpoint, "reserved_credits": cost, "status": "submitting"}
-        if previous:
-            record["rejection_history"] = [*previous.get("rejection_history", []),
-                {k: v for k, v in previous.items() if k != "rejection_history"}]
-        state["submissions"][fingerprint] = record
-        write_json(self.journal, state)  # commit before the request can incur a charge
-        try:
-            response = self.writer.request(method, endpoint, **kwargs)
-        except cloud_api.CloudAPIError as exc:
-            # These are explicit refusals. 5xx/timeouts may have created a task.
-            if exc.status in (400, 401, 402, 403, 404, 405, 413, 415, 422, 429):
-                record.update(status="rejected", error=safe_error(exc), resource_refusal=isinstance(
-                    exc, (cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError, cloud_api.CloudAPIRateLimitError)))
-            else:
-                record.update(status="uncertain", error=safe_error(exc))
-            write_json(self.journal, state)
-            if record["status"] == "uncertain":
-                raise AmbiguousSubmission(f"Unconfirmed POST {fingerprint}; {safe_error(exc)}") from exc
-            raise
-
-        rejected = isinstance(response, dict) and response.get("code") not in (None, 0)
-        envelope = response.get("data", response) if isinstance(response, dict) else {}
-        remote_id = (envelope.get("task_id") or envelope.get("id") or envelope.get("result")
-                     or envelope.get("file_token") or envelope.get("token")) if isinstance(envelope, dict) else None
-        if rejected:
-            rejection = cloud_api.classify_http_error(400, response)
-            record.update(status="rejected", error=safe_error(rejection), resource_refusal=isinstance(
-                rejection, (cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError)))
-        elif not remote_id:
-            record.update(status="uncertain", error="Response contained no remote task/file ID")
-        else:
-            record.update(status="created", remote_id=remote_id, response=response)
-        write_json(self.journal, state)
-        if rejected:
-            raise rejection
-        if record["status"] == "uncertain":
-            raise AmbiguousSubmission(f"Unconfirmed POST {fingerprint}: missing task/file ID")
-        return response
-
-    def download(self, url: str, **kwargs) -> bytes:
-        # Signed CDN downloads must not receive the provider's Authorization key.
-        if not url.startswith("https://"):
-            raise ValueError("Artifact download must use HTTPS")
-        if self.public is None:
-            self.public = cloud_api.CloudAPIClient("", "", max_retries=3)
-            self.public.session.headers.pop("Authorization", None)
-        return self.public.download(url, **kwargs)
-
-    def close(self):
-        self.reader.close()
-        if self.writer is not self.reader:
-            self.writer.close()
-        if self.public is not None:
-            self.public.close()
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py
deleted file mode 100644
index 37fd64a94489f8fea40f24af84e5237413a00fc1..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py
+++ /dev/null
@@ -1,603 +0,0 @@
-#!/usr/bin/env python3
-"""Prepare, run and resume the frozen Meshy/Tripo 200-object comparison.
-
-Prepare/status are offline. Run requires explicit spending caps and API keys.
-Native generation outputs remain unmodified. A shared postprocessor produces
-separate import meshes; visual acceptance is a separate review.
-"""
-from __future__ import annotations
-
-import argparse
-from collections import Counter, defaultdict
-from contextlib import contextmanager
-from datetime import datetime, timezone
-import hashlib
-import io
-import json
-import math
-import os
-from pathlib import Path
-import re
-import shutil
-import struct
-import subprocess
-import sys
-
-BENCH = Path(__file__).resolve().parents[2]
-FRAMEWORK = BENCH / "GameFactory-3A"
-sys.path.insert(0, str(FRAMEWORK))
-from pipeline.common import paths
-from pipeline.common.record_paths import portable_value, resolve_path, runtime_value
-from models.common import cloud_api
-from models.common.glb_utils import glb_json_chunk, glb_summary
-from cloud_journal import (AmbiguousSubmission, BudgetExceeded, JournalClient,
-                           read_json, reserved_credits, safe_error, write_json)
-
-DEFAULT_CONFIG = BENCH / "config/asset_3d_comparison.json"
-IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_-]{0,160}$")
-RUNTIME_FILES = [Path(__file__), Path(__file__).with_name("cloud_journal.py"),
-                 FRAMEWORK / "pipeline/common/record_paths.py",
-                 FRAMEWORK / "pipeline/common/paths.py",
-                 BENCH / "prompts/stage2_assets.md",
-                 FRAMEWORK / "operators/gen_3d_object/operator.py",
-                 FRAMEWORK / "models/gen_3d_object/meshy_model.py",
-                 FRAMEWORK / "models/gen_3d_object/tripo_model.py",
-                 FRAMEWORK / "models/common/cloud_api.py",
-                 FRAMEWORK / "models/common/glb_utils.py",
-                 Path(__file__).with_name("mesh_tools") / "process_mesh.mjs",
-                 Path(__file__).with_name("mesh_tools") / "package-lock.json"]
-MESH_TOOL = Path(__file__).with_name("mesh_tools") / "process_mesh.mjs"
-
-
-def now():
-    return datetime.now(timezone.utc).isoformat(timespec="seconds")
-
-
-def digest(path: Path):
-    return hashlib.sha256(path.read_bytes()).hexdigest()
-
-
-def checked_id(value: str):
-    if not IDENTIFIER.fullmatch(value):
-        raise ValueError(f"Invalid path identifier: {value!r}")
-    return value
-
-
-def bench_path(value: str):
-    result = resolve_path(value).resolve()
-    result.relative_to(BENCH.resolve())
-    return result
-
-
-def batch_directory(config: dict, batch_id: str):
-    return bench_path(config["output_root"]) / "_asset_batches" / checked_id(batch_id)
-
-
-@contextmanager
-def exclusive_batch(directory: Path):
-    """OS lock releases on process exit, including an interrupted run."""
-    directory.mkdir(parents=True, exist_ok=True)
-    with (directory / "batch.lock").open("a+b") as stream:
-        stream.seek(0, 2)
-        if stream.tell() == 0:
-            stream.write(b"0")
-            stream.flush()
-        stream.seek(0)
-        if os.name == "nt":
-            import msvcrt
-            msvcrt.locking(stream.fileno(), msvcrt.LK_NBLCK, 1)
-        else:
-            import fcntl
-            fcntl.flock(stream, fcntl.LOCK_EX | fcntl.LOCK_NB)
-        try:
-            yield
-        finally:
-            stream.seek(0)
-            if os.name == "nt":
-                msvcrt.locking(stream.fileno(), msvcrt.LK_UNLCK, 1)
-            else:
-                fcntl.flock(stream, fcntl.LOCK_UN)
-
-
-def game_directory(plan: dict, game: str):
-    return Path(plan["output_root"]) / checked_id(game)
-
-
-def pipeline_directory(plan: dict, provider: str, game: str):
-    return (game_directory(plan, game) / "pipeline" / (game + "_pipeline")
-            / checked_id(plan["batch_id"]) / checked_id(provider))
-
-
-def generation_index(plan: dict, provider: str, game: str):
-    return (game_directory(plan, game) / ".generation/3d_object" / checked_id(provider)
-            / checked_id(plan["batch_id"]) / "asset_task_index.json")
-
-
-def short_context(text: str, limit: int):
-    """Declared deterministic excerpts, not a claim that the model read the brief."""
-    title = re.search(r"^title:\s*(.+)$", text, re.M)
-    lines = [line.strip() for line in text.splitlines() if line.strip()]
-    title_text = title.group(1) if title else next((s.lstrip("# ") for s in lines if s.startswith("#")), "")
-    world = next((line for line in lines if re.match(r"-\s*W01\b", line)), "")
-    loop = next((line for line in lines if any(word in line for word in ("核心循环：", "核心循环:", "核心玩法：", "核心玩法:"))), "")
-    if not loop:
-        loop = next((line for line in lines if re.match(r"-\s*S01\b", line)), "")
-    if not loop:
-        heading = re.search(r"^##[^\n]*(?:核心循环|一句话定位)[^\n]*\n([^#]+)", text, re.M)
-        loop = heading.group(1) if heading else ""
-    pieces = [re.sub(r"\s+", " ", value).strip()[:size]
-              for value, size in ((title_text, 70), (world, 110), (loop, 110)) if value]
-    value = " | ".join(pieces)
-    if not value:
-        raise ValueError("Cannot extract game context; provide a reviewed short context")
-    return value[:limit]
-
-
-def frozen_copy(source: Path, destination: Path):
-    if destination.exists() and digest(destination) != digest(source):
-        raise ValueError(f"Frozen input changed; use a new batch ID: {destination}")
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    if not destination.exists():
-        shutil.copyfile(source, destination)
-    return {"path": str(destination.resolve()), "sha256": digest(destination),
-            "source": str(source.resolve())}
-
-
-def node_process(arguments: list[str], timeout: int):
-    node = shutil.which("node")
-    if not node:
-        raise ValueError("Node.js is required for the fixed mesh pipeline")
-    environment = os.environ.copy()
-    for key in ("MESHY_API_KEY", "TRIPO_API_KEY"):
-        environment.pop(key, None)
-    if os.name == "nt":
-        # Conda's DLLs can shadow sharp's libvips dependencies in child processes.
-        environment["PATH"] = str(Path(node).parent) + os.pathsep + str(Path(os.environ["SystemRoot"]) / "System32")
-    return subprocess.run([node, str(MESH_TOOL), *arguments], capture_output=True,
-                          text=True, encoding="utf-8", timeout=timeout, env=environment)
-
-
-def mesh_runtime():
-    result = node_process(["--check"], timeout=60)
-    if result.returncode:
-        raise ValueError("Mesh postprocessor unavailable; run npm ci in scripts/asset/mesh_tools. " + result.stderr[-500:])
-    return json.loads(result.stdout)
-
-
-def subject_group(task: dict):
-    role = task.get("role", "")
-    return "character" if task.get("rig", "none") != "none" or role in (
-        "character", "player", "event_figure", "npc", "creature", "unit_special") or role.startswith("enemy") else "rigid"
-
-
-def prepare(config: dict, batch_id: str):
-    if config["input_policy"] != "shared_short_context_and_reference_only" or not config["input_policy_confirmed"]:
-        raise ValueError("The input protocol must be confirmed before preparing this comparison")
-    directory = batch_directory(config, batch_id)
-    plan_file = directory / "plan.json"
-    if plan_file.exists():
-        raise ValueError(f"Batch is already frozen: {plan_file}. Use status/run or a new batch ID.")
-    source = bench_path(config["tasks"])
-    tasks = [json.loads(line) for line in source.read_text(encoding="utf-8-sig").splitlines() if line.strip()]
-    if len(tasks) != config["expected_tasks"]:
-        raise ValueError(f"Expected {config['expected_tasks']} tasks; got {len(tasks)}")
-    for task in tasks:
-        for provider in config["providers"]:
-            output = (bench_path(config["output_root"]) / checked_id(task["game_id"])
-                      / "assets/3d_object" / checked_id(task["task_id"]) / checked_id(provider))
-            if output.is_dir() and any(p.name != ".gitkeep" for p in output.iterdir()):
-                raise ValueError(f"Provider directory already contains assets; resume its batch: {output}")
-    seen = set()
-    frozen_tasks = []
-    documents = {}
-    tool_runtime = mesh_runtime()
-    for task in tasks:
-        game = checked_id(task["game_id"])
-        task_id = checked_id(task["task_id"])
-        identity = (game, task_id)
-        if identity in seen:
-            raise ValueError(f"Duplicate task: {identity}")
-        seen.add(identity)
-        case = BENCH / "test_data/test_samples" / game
-        if game not in documents:
-            requirement = case / "general_requirement.txt"
-            full_text = requirement.read_text(encoding="utf-8")
-            doc_root = directory / "inputs" / game
-            documents[game] = {
-                "general_requirement": frozen_copy(requirement, doc_root / "general_requirement.txt"),
-                "object_requirement": frozen_copy(case / "3D_object/requirement.txt", doc_root / "object_requirement.txt"),
-                "context_excerpt": short_context(full_text, config["context_excerpt_max_chars"]),
-                "context_method": "deterministic title/world/core-loop excerpts; full brief retained locally",
-            }
-        mode = task["input_mode"]
-        prompt = None
-        reference = None
-        if mode == "text_to_3d":
-            # Never shorten the asset prompt. Both providers receive this exact string.
-            prompt = f"Game context (excerpt): {documents[game]['context_excerpt']}\nAsset: {task['prompt']}"
-            if len(prompt) > config["shared_prompt_max_chars"]:
-                raise ValueError(f"Shared prompt exceeds limit: {task_id} ({len(prompt)} chars)")
-        elif mode == "image_to_3d":
-            source_image = bench_path(task["image_path"])
-            reference = frozen_copy(source_image, directory / "references" / (digest(source_image) + source_image.suffix.lower()))
-        else:
-            raise ValueError(f"Unsupported input mode: {mode}")
-        if not 500 <= task["decimation_target"] <= 8000:
-            raise ValueError(f"Unexpected triangle target: {task_id}")
-        frozen_tasks.append({"task": task, "shared_prompt": prompt, "reference": reference,
-                             "subject_group": subject_group(task),
-                             "context_condition": "shared_short_context" if prompt else "reference_only"})
-    output_root = bench_path(config["output_root"])
-    plan = {"schema_version": "asset_3d_comparison_plan_v1", "batch_id": batch_id,
-            "created_at": now(), "scope": "3d_object_only_not_complete_stage2",
-            "config": config, "output_root": str(output_root), "directory": str(directory),
-            "source": frozen_copy(source, directory / "inputs/source_tasks.jsonl"),
-            "method_reference": frozen_copy(bench_path(config["method_reference"]), directory / "inputs/method_selection.md"),
-            "documents": documents, "tasks": frozen_tasks,
-            "runtime_sources": {str(p.resolve()): digest(p) for p in RUNTIME_FILES},
-            "estimated_credits": {}, "recommended_credit_caps": {},
-            "qa_policy": "basic structural checks + pending visual review; no automatic acceptance",
-            "texture_policy": "preserve native output; same deterministic downsize for both import meshes",
-            "postprocess": config["postprocess"], "mesh_runtime": tool_runtime,
-            "deferred_providers": {"hunyuan3d_pro": {"status": "not_enabled", "adapter_contract":
-                "infer_and_save(image, output_path, seed, decimation_target, texture_size, *, prompt); requires pinned cloud version and input capability check"}}}
-    for provider, settings in config["providers"].items():
-        prices = settings["credits"]
-        estimate = sum((prices["text_preview"] + prices["text_refine"] if provider == "meshy" else prices["text"])
-                       if item["shared_prompt"] else prices["image"] for item in frozen_tasks)
-        plan["estimated_credits"][provider] = estimate
-        plan["recommended_credit_caps"][provider] = (estimate * 11 + 9) // 10
-    plan["plan_sha256"] = cloud_api.request_hash(portable_value(plan))
-    write_json(plan_file, plan)
-    write_json(directory / "asset_task_index.json", {"plan_sha256": plan["plan_sha256"],
-        "task_count": len(frozen_tasks), "tasks": frozen_tasks})
-    for provider in config["providers"]:
-        for game in documents:
-            write_json(generation_index(plan, provider, game), {
-                "plan_sha256": plan["plan_sha256"], "game_id": game, "provider": provider,
-                "scope": plan["scope"], "tasks": [item for item in frozen_tasks if item["task"]["game_id"] == game]})
-    refresh_reports(plan)
-    return plan
-
-
-def load_plan(config: dict, batch_id: str):
-    plan = read_json(batch_directory(config, batch_id) / "plan.json")
-    if not plan:
-        raise ValueError("Prepare this batch first")
-    claimed = plan["plan_sha256"]
-    if cloud_api.request_hash({k: v for k, v in plan.items() if k != "plan_sha256"}) != claimed:
-        raise ValueError("Frozen plan was modified")
-    return runtime_value(plan)
-
-
-def validate_inputs(plan: dict):
-    if mesh_runtime() != plan["mesh_runtime"]:
-        raise ValueError("Mesh runtime differs from the frozen version")
-    for filename, expected in plan["runtime_sources"].items():
-        if digest(Path(filename)) != expected:
-            raise ValueError(f"Runtime changed after freeze: {filename}; prepare a new batch")
-    files = [plan["source"], plan["method_reference"]]
-    files.extend(doc[field] for doc in plan["documents"].values()
-                 for field in ("general_requirement", "object_requirement"))
-    files.extend(item["reference"] for item in plan["tasks"] if item["reference"])
-    for entry in files:
-        if digest(Path(entry["path"])) != entry["sha256"]:
-            raise ValueError(f"Frozen input changed: {entry['path']}")
-
-
-def basic_qa(filename: Path, task: dict):
-    data = filename.read_bytes()
-    summary = glb_summary(data)
-    checks = {"parseable_glb": "error" not in summary}
-    if checks["parseable_glb"]:
-        doc = glb_json_chunk(data)
-        checks["glb_v2_exact_length"] = struct.unpack_from("<II", data, 4) == (2, len(data))
-        checks["positive_triangle_count"] = bool(summary["triangles"])
-        checks["triangle_budget"] = 0 < summary["triangles"] <= task["decimation_target"]
-        checks["materials_present"] = summary["materials"] > 0
-        checks["textures_present"] = summary["textures"] > 0 and summary["images"] > 0
-        primitives = [p for m in doc.get("meshes", []) for p in m.get("primitives", [])]
-        checks["uv_present"] = bool(primitives) and all("TEXCOORD_0" in p.get("attributes", {}) for p in primitives)
-        checks["self_contained"] = all("uri" not in b or b["uri"].startswith("data:") for b in doc.get("buffers", [])) and all(
-            "bufferView" in image or str(image.get("uri", "")).startswith("data:") for image in doc.get("images", []))
-    return {"status": "PASS" if all(checks.values()) else "FAIL", "checks": checks,
-            "summary": summary, "triangle_target": task["decimation_target"],
-            "requested_texture_size": task.get("texture_size"),
-            "texture_budget_verified": False, "visual_status": "PENDING",
-            "limitations": ["No visual, orientation, scale, rigging or engine acceptance yet",
-                            "Native texture resolution retained; task texture_size is not enforced",
-                            "Basic checks do not validate every accessor, topology defect or PBR channel"]}
-
-
-def process_import_mesh(source: Path, task: dict):
-    destination = source.parent / "import/model.glb"
-    result = node_process([str(source), str(destination), str(task["decimation_target"]),
-                           str(task["texture_size"])], timeout=300)
-    if result.returncode:
-        raise RuntimeError("Postprocessing failed: " + result.stderr[-1200:])
-    report = json.loads(result.stdout)
-    return destination, report
-
-
-def task_directories(plan: dict, provider: str, task: dict):
-    root = game_directory(plan, task["game_id"])
-    task_id, provider = checked_id(task["task_id"]), checked_id(provider)
-    output = root / "assets/3d_object" / task_id / provider
-    evaluation = root / "eval/3d_object" / task_id / provider
-    return output, evaluation
-
-
-def refresh_reports(plan: dict):
-    summary = {"batch_id": plan["batch_id"], "plan_sha256": plan["plan_sha256"],
-               "updated_at": now(), "expected_assets": len(plan["tasks"]) * len(plan["config"]["providers"]),
-               "estimated_credits": plan["estimated_credits"], "providers": {}}
-    for provider in plan["config"]["providers"]:
-        by_game = defaultdict(list)
-        for item in plan["tasks"]:
-            task = item["task"]
-            output, evaluation = task_directories(plan, provider, task)
-            meta = read_json(output / "meta.json", {})
-            by_game[task["game_id"]].append({"task_id": task["task_id"], "asset_name": task["asset_name"],
-                "task_kind": "3d_object", "status": meta.get("status", "prepared"),
-                "artifact": str(output / "import/model.glb"), "native_artifact": str(output / "model.glb"),
-                "meta": str(output / "meta.json"), "native_structural_qa": meta.get("native_structural_qa"),
-                "import_structural_qa": meta.get("import_structural_qa"),
-                "native_delivered": meta.get("native_delivered", False),
-                "import_delivered": meta.get("import_delivered", False),
-                "qa": str(evaluation / "qa.json"), "visual_status": "PENDING",
-                "context_condition": item["context_condition"], "subject_group": item["subject_group"],
-                "input_mode": task["input_mode"], "error": meta.get("error")})
-        all_entries = [entry for entries in by_game.values() for entry in entries]
-        summary["providers"][provider] = {"task_count": len(all_entries),
-            "status_counts": dict(Counter(entry["status"] for entry in all_entries)),
-            "reserved_credits": reserved_credits(Path(plan["directory"]) / "submissions" / provider),
-            "visual_accepted": 0, "method_group": plan["config"]["method_group"],
-            "benchmark_metrics": {name: {"status": "UNKNOWN", "value": None}
-                                  for name in ("C", "Q1", "Q2", "Q3")},
-            "subgroups": {key: dict(Counter(e[key] for e in all_entries))
-                          for key in ("subject_group", "input_mode", "context_condition")},
-            "game_macro_delivery": sum(sum(e["status"] == "delivered" for e in entries) / len(entries)
-                                       for entries in by_game.values()) / len(by_game),
-            "game_macro_native_delivery": sum(sum(e["native_delivered"] for e in entries) / len(entries)
-                                               for entries in by_game.values()) / len(by_game),
-            "native_structural_pass": sum(e["native_structural_qa"] == "PASS" for e in all_entries),
-            "import_structural_pass": sum(e["import_structural_qa"] == "PASS" for e in all_entries),
-            "delivery_metric_status": "NOT_RUN" if all(e["status"] == "prepared" for e in all_entries) else "PARTIAL"}
-        for game, entries in by_game.items():
-            pipeline = pipeline_directory(plan, provider, game)
-            manifest = {"game_id": game, "provider": provider, "batch_id": plan["batch_id"],
-                "plan_sha256": plan["plan_sha256"], "scope": plan["scope"], "assets": entries,
-                "full_context": plan["documents"][game], "status": "pending_visual_review"}
-            write_json(pipeline / "asset_manifest.json", manifest)
-            write_json(pipeline / "asset_generation_report.json", {
-                "task_count": len(entries), "status_counts": dict(Counter(e["status"] for e in entries)),
-                "failures": [e for e in entries if e["error"]], "visual_accepted": 0})
-            paths.write_task_meta(pipeline, {"game_id": game, "task_id": game + "_pipeline",
-                "task_kind": "pipeline", "run_id": plan["batch_id"], "status": "comparison_only",
-                "provider": provider, "scope": plan["scope"]})
-    write_json(Path(plan["directory"]) / "asset_generation_report.json", summary)
-    return summary
-
-
-def make_model(plan: dict, provider: str, task: dict, cap: int):
-    from models.gen_3d_object.meshy_model import MeshyModel
-    from models.gen_3d_object.tripo_model import TripoModel
-    settings = plan["config"]["providers"][provider]
-    cls = MeshyModel if provider == "meshy" else TripoModel
-    control = Path(plan["directory"])
-    model = cls(model_path=settings["model"], device="cpu", api_base=settings["api_base"],
-                cache_dir=str(control / "cache" / task["game_id"] / task["task_id"]),
-                timeout=1800, poll_interval=5, **settings["options"])
-    key = cloud_api.require_api_key(None, provider.upper() + "_API_KEY", "See the comparison README")
-    journal = control / "submissions" / provider / (task["game_id"] + "__" + task["task_id"] + ".json")
-    model._client = JournalClient(settings["api_base"], key, journal, cap, settings["credits"], provider,
-                                  resume_refused=True)
-    return model
-
-
-def download_preview(model, output: Path):
-    from PIL import Image
-    url = model.last_call_info.get("preview_url")
-    if not url:
-        return {"status": "unavailable"}
-    try:
-        data = model.client.download(url)
-        with Image.open(io.BytesIO(data)) as preview:
-            preview.verify()
-            extension = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}.get(preview.format)
-        if not extension:
-            return {"status": "unsupported_image_format"}
-        path = output / ("preview" + extension)
-        path.write_bytes(data)
-        return {"status": "downloaded", "path": str(path), "visual_review": "PENDING"}
-    except Exception as exc:
-        return {"status": "failed", "error": safe_error(exc)}
-
-
-def run(plan: dict, args):
-    from operators.gen_3d_object.operator import Gen3DObjectOperator
-
-    class SharedStoreOperator(Gen3DObjectOperator):
-        def _resolve_out_path(self, inp, task_id):
-            output, _ = task_directories(plan, provider, inp)
-            output.mkdir(parents=True, exist_ok=True)
-            return inp["game_id"], output, output / "model.glb"
-
-    if not args.allow_paid:
-        raise ValueError("Paid generation requires --allow-paid and explicit provider credit caps")
-    validate_inputs(plan)
-    selected = list(dict.fromkeys(args.provider or list(plan["config"]["providers"])))
-    caps = {p: getattr(args, p + "_credit_cap") for p in selected}
-    for provider in selected:
-        if not caps[provider] or caps[provider] < reserved_credits(Path(plan["directory"]) / "submissions" / provider):
-            raise ValueError(f"Supply a positive --{provider}-credit-cap covering existing reservations")
-        cloud_api.require_api_key(None, provider.upper() + "_API_KEY", "See scripts/asset/README_3d_comparison.md")
-    selection = set(args.task_id or [])
-    known = {item["task"]["task_id"] for item in plan["tasks"]}
-    if selection - known:
-        raise ValueError(f"Unknown task IDs: {sorted(selection - known)}")
-    jobs = [item for item in plan["tasks"] if not selection or item["task"]["task_id"] in selection]
-    if args.limit is not None:
-        if args.limit < 1:
-            raise ValueError("--limit must be positive")
-        jobs = jobs[:args.limit]
-    write_json(Path(plan["directory"]) / "spending_authorization.json", {
-        "plan_sha256": plan["plan_sha256"], "credit_caps": caps, "recorded_at": now(),
-        "selected_task_ids": [item["task"]["task_id"] for item in jobs],
-        "note": "Caps reserve estimated credits; account-side limits remain authoritative"})
-    interrupted = False
-    for provider in selected:
-        failures = 0
-        for item in jobs:
-            task = item["task"]
-            output, evaluation = task_directories(plan, provider, task)
-            previous = read_json(output / "meta.json", {})
-            if previous and previous.get("plan_sha256") != plan["plan_sha256"]:
-                raise ValueError(f"Another batch owns this provider directory; do not overwrite: {output}")
-            if not previous and output.is_dir() and any(p.name != ".gitkeep" for p in output.iterdir()):
-                raise ValueError(f"Untracked provider artifacts must not be overwritten: {output}")
-            if previous.get("status") == "delivered" and (output / "model.glb").is_file():
-                imported = output / "import/model.glb"
-                if previous.get("artifact_sha256") == digest(output / "model.glb") and imported.is_file() and previous.get("import_sha256") == digest(imported):
-                    continue
-            if previous.get("status") == "failed":
-                continue  # one generation attempt, no cherry-picking on rerun
-            output.mkdir(parents=True, exist_ok=True)
-            meta = {"game_id": task["game_id"], "task_id": task["task_id"], "asset_name": task["asset_name"],
-                "task_kind": "3d_object", "run_id": plan["batch_id"], "provider": provider,
-                "plan_sha256": plan["plan_sha256"], "context_condition": item["context_condition"],
-                "full_context": plan["documents"][task["game_id"]], "source_task": task,
-                "shared_prompt": item["shared_prompt"], "reference": item["reference"],
-                "status": "running", "updated_at": now(), "visual_status": "PENDING"}
-            write_json(output / "meta.json", meta)
-            model = None
-            stop = False
-            try:
-                model = make_model(plan, provider, task, caps[provider])
-                # Preserve native output for a fair comparison. No automatic mesh cleanup/import.
-                inp = {**task, "strip_ground_plate": False}
-                inp.pop("asset_id", None)
-                if item["reference"]:
-                    inp["image_path"] = item["reference"]["path"]
-                else:
-                    inp["prompt"] = item["shared_prompt"]
-                result = SharedStoreOperator(model, run_id=plan["batch_id"]).run(inp)
-                raw_qa = basic_qa(Path(result["glb_path"]), task)
-                meta.update(native_delivered=raw_qa["checks"]["parseable_glb"], result=result,
-                            native_structural_qa=raw_qa["status"], model_call=model.last_call_info,
-                            artifact_sha256=digest(output / "model.glb"))
-                write_json(evaluation / "qa.json", {"native": raw_qa, "import": {"status": "PENDING"},
-                                                     "visual_status": "PENDING"})
-                imported, postprocess = process_import_mesh(Path(result["glb_path"]), task)
-                import_qa = basic_qa(imported, task)
-                import_qa["texture_budget_verified"] = all(max(t["after"]) <= task["texture_size"] for t in postprocess["textures"])
-                import_qa["checks"]["texture_budget"] = import_qa["texture_budget_verified"]
-                import_qa["status"] = "PASS" if all(import_qa["checks"].values()) else "FAIL"
-                write_json(evaluation / "qa.json", {"native": raw_qa, "import": import_qa,
-                    "postprocess": postprocess, "visual_status": "PENDING"})
-                meta.update(status="delivered", result=result, model_call=model.last_call_info,
-                            import_delivered=import_qa["checks"]["parseable_glb"],
-                            artifact_sha256=digest(output / "model.glb"), import_sha256=digest(imported),
-                            native_structural_qa=raw_qa["status"], import_structural_qa=import_qa["status"],
-                            postprocess=postprocess,
-                            preview=download_preview(model, output))
-                failures = 0
-            except Exception as exc:
-                status = "failed"
-                if isinstance(exc, cloud_api.CloudTaskTimeout):
-                    status = "remote_pending"
-                elif isinstance(exc, AmbiguousSubmission):
-                    status, stop = "submission_uncertain", True
-                elif isinstance(exc, (BudgetExceeded, cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError)):
-                    status, stop = "blocked", True
-                elif isinstance(exc, cloud_api.CloudAPIServerError):
-                    status = "resume_required"  # known receipts survive poll/download failures
-                elif not isinstance(exc, (cloud_api.CloudTaskFailed, cloud_api.CloudAPIRequestError, ValueError)):
-                    status = "resume_required"  # retain a downloaded asset after local/tool failures
-                meta.update(status=status, error=safe_error(exc), error_type=type(exc).__name__)
-                failures += 1
-                stop = stop or failures >= 3
-            finally:
-                if model is not None:
-                    model.unload()
-                meta["updated_at"] = now()
-                journal = read_json(Path(plan["directory"]) / "submissions" / provider /
-                    (task["game_id"] + "__" + task["task_id"] + ".json"), {})
-                meta["generation_cost"] = [{"endpoint": record["endpoint"], "remote_id": record.get("remote_id"),
-                    "reservation": record["reserved_credits"], "reported_credits": record.get("reported_credits"),
-                    "status": record["status"]} for record in journal.get("submissions", {}).values()]
-                write_json(output / "meta.json", meta)
-                refresh_reports(plan)
-            print(json.dumps({"provider": provider, "task_id": task["task_id"], "status": meta["status"],
-                              "error": meta.get("error")}, ensure_ascii=False), flush=True)
-            if stop:
-                interrupted = True
-                break
-        if interrupted:
-            break
-    incomplete = any(read_json(task_directories(plan, p, item["task"])[0] / "meta.json", {}).get("status") != "delivered"
-                     for p in selected for item in jobs)
-    return 2 if interrupted or incomplete else 0
-
-
-def recover(plan: dict, args):
-    """Attach an inspected remote ID to an ambiguous receipt; never submit here."""
-    checked_id(args.remote_task_id)
-    matches = [item["task"] for item in plan["tasks"] if item["task"]["task_id"] == args.task_id]
-    if len(matches) != 1:
-        raise ValueError("Recover requires exactly one known task")
-    task = matches[0]
-    journal = Path(plan["directory"]) / "submissions" / args.provider / (task["game_id"] + "__" + task["task_id"] + ".json")
-    state = read_json(journal, {})
-    record = state.get("submissions", {}).get(args.request_hash)
-    if not record or record["status"] not in ("submitting", "uncertain") or record["endpoint"] == "/files":
-        raise ValueError("Receipt is not an ambiguous generation submission")
-    record.update(status="created", remote_id=args.remote_task_id, reconciled_at=now(),
-                  response={"result": args.remote_task_id} if args.provider == "meshy" else
-                  {"code": 0, "data": {"task_id": args.remote_task_id}})
-    write_json(journal, state)
-    return {"recovered_task_id": args.remote_task_id, "network_requests": 0}
-
-
-def main(argv=None):
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--config", type=Path, default=DEFAULT_CONFIG)
-    sub = parser.add_subparsers(dest="command", required=True)
-    for name in ("prepare", "status", "run", "recover"):
-        command = sub.add_parser(name)
-        command.add_argument("--batch-id", required=True)
-        if name == "run":
-            command.add_argument("--allow-paid", action="store_true")
-            command.add_argument("--meshy-credit-cap", type=int)
-            command.add_argument("--tripo-credit-cap", type=int)
-            command.add_argument("--provider", action="append", choices=("meshy", "tripo"))
-            command.add_argument("--task-id", action="append")
-            command.add_argument("--limit", type=int, help="Limit tasks per selected provider; full inventory is retained")
-        elif name == "recover":
-            command.add_argument("--provider", required=True, choices=("meshy", "tripo"))
-            command.add_argument("--task-id", required=True)
-            command.add_argument("--request-hash", required=True)
-            command.add_argument("--remote-task-id", required=True)
-    args = parser.parse_args(argv)
-    config = read_json(args.config)
-    with exclusive_batch(batch_directory(config, args.batch_id)):
-        if args.command == "prepare":
-            plan = prepare(config, args.batch_id)
-            print(json.dumps(portable_value({"plan": str(Path(plan["directory"]) / "plan.json"),
-                "tasks_per_provider": len(plan["tasks"]), "estimated_credits": plan["estimated_credits"],
-                "recommended_credit_caps": plan["recommended_credit_caps"]}), indent=2))
-        else:
-            plan = load_plan(config, args.batch_id)
-            if args.command == "run":
-                return run(plan, args)
-            value = recover(plan, args) if args.command == "recover" else refresh_reports(plan)
-            print(json.dumps(portable_value(value), indent=2, ensure_ascii=False))
-    return 0
-
-
-if __name__ == "__main__":
-    try:
-        raise SystemExit(main())
-    except (ValueError, OSError, cloud_api.CloudAPIError) as exc:
-        print(safe_error(exc), file=sys.stderr)
-        raise SystemExit(2)
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/package-lock.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/package-lock.json
deleted file mode 100644
index 070ddb648ed455c400b216bf2bc6e6ac1df75912..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/package-lock.json
+++ /dev/null
@@ -1,763 +0,0 @@
-{
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-  "requires": true,
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-      "name": "bench-asset-comparison-mesh-tools",
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-        "meshoptimizer": "0.24.0",
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-      "license": "MIT",
-      "optional": true,
-      "dependencies": {
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-      "license": "MIT",
-      "dependencies": {
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-      "funding": {
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-      }
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-        "@img/sharp-linuxmusl-arm64": "0.35.5",
-        "@img/sharp-linuxmusl-x64": "0.35.5",
-        "@img/sharp-webcontainers-wasm32": "0.35.5",
-        "@img/sharp-win32-arm64": "0.35.5",
-        "@img/sharp-win32-ia32": "0.35.5",
-        "@img/sharp-win32-x64": "0.35.5"
-      },
-      "peerDependenciesMeta": {
-        "@types/node": {
-          "optional": true
-        }
-      }
-    },
-    "node_modules/tslib": {
-      "version": "2.8.1",
-      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
-      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
-      "license": "0BSD",
-      "optional": true
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-      "version": "1.0.1",
-      "resolved": "https://registry.npmjs.org/uniq/-/uniq-1.0.1.tgz",
-      "integrity": "sha512-Gw+zz50YNKPDKXs+9d+aKAjVwpjNwqzvNpLigIruT4HA9lMZNdMqs9x07kKHB/L9WRzqp4+DlTU5s4wG2esdoA==",
-      "license": "MIT"
-    }
-  }
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/process_mesh.mjs b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/process_mesh.mjs
deleted file mode 100644
index 557990005845f673eb56da1e4dbf7f4309f7f7df..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/process_mesh.mjs
+++ /dev/null
@@ -1,77 +0,0 @@
-// One fixed pipeline for both services; keep the original GLB separately.
-import {readFileSync, mkdirSync} from 'node:fs';
-import {dirname} from 'node:path';
-import {performance} from 'node:perf_hooks';
-import {NodeIO, Logger} from '@gltf-transform/core';
-import {ALL_EXTENSIONS} from '@gltf-transform/extensions';
-import {simplify, weld} from '@gltf-transform/functions';
-import {MeshoptDecoder, MeshoptSimplifier} from 'meshoptimizer';
-import draco from 'draco3dgltf';
-import sharp from 'sharp';
-
-const settings = JSON.parse(readFileSync(new URL('./package.json', import.meta.url)));
-await Promise.all([MeshoptDecoder.ready, MeshoptSimplifier.ready]);
-const runtime = {node: process.versions.node, dependencies: settings.dependencies};
-if (process.argv[2] === '--check') {
-  console.log(JSON.stringify({status: 'ready', ...runtime}));
-} else {
-  const [source, destination, triangleArg, textureArg] = process.argv.slice(2);
-  const triangleTarget = Number(triangleArg), textureTarget = Number(textureArg);
-  if (!source || !destination || !Number.isInteger(triangleTarget) || triangleTarget <= 0
-      || !Number.isInteger(textureTarget) || textureTarget <= 0) {
-    throw new Error('Expected: source.glb destination.glb triangle_target texture_size');
-  }
-  const started = performance.now();
-  const io = new NodeIO().registerExtensions(ALL_EXTENSIONS).registerDependencies({
-    'draco3d.decoder': await draco.createDecoderModule(),
-    'meshopt.decoder': MeshoptDecoder,
-  });
-  const document = await io.read(source);
-  document.setLogger(new Logger(Logger.Verbosity.SILENT));
-  // The decoded import GLB is not dependent on the provider's mesh codec.
-  for (const extension of document.getRoot().listExtensionsUsed()) {
-    if (['KHR_draco_mesh_compression', 'EXT_meshopt_compression'].includes(extension.extensionName)) {
-      extension.dispose();
-    }
-  }
-  function triangleCount() {
-    let count = 0;
-    for (const mesh of document.getRoot().listMeshes()) {
-      for (const primitive of mesh.listPrimitives()) {
-        if (primitive.getMode() !== 4) throw new Error('Only triangle primitives are supported');
-        const n = (primitive.getIndices() || primitive.getAttribute('POSITION')).getCount();
-        count += n / 3;
-      }
-    }
-    return count;
-  }
-  const before = triangleCount();
-  if (before > triangleTarget) {
-    await document.transform(weld(), simplify({simplifier: MeshoptSimplifier,
-      ratio: triangleTarget / before, error: 0.01, lockBorder: false}));
-  }
-  const textures = [];
-  for (const texture of document.getRoot().listTextures()) {
-    const image = texture.getImage();
-    if (!image) throw new Error('Texture has no decoded image');
-    const input = Buffer.from(image);
-    const info = await sharp(input).metadata();
-    let sizeAfter = [info.width, info.height];
-    if (Math.max(...sizeAfter) > textureTarget) {
-      const {data, info: resized} = await sharp(input).resize({width: textureTarget, height: textureTarget,
-        fit: 'inside', withoutEnlargement: true, kernel: 'lanczos3'}).png({compressionLevel: 9}).toBuffer({resolveWithObject: true});
-      texture.setImage(new Uint8Array(data)).setMimeType('image/png').setURI('');
-      sizeAfter = [resized.width, resized.height];
-    }
-    textures.push({name: texture.getName(), before: [info.width, info.height], after: sizeAfter});
-  }
-  const after = triangleCount();
-  mkdirSync(dirname(destination), {recursive: true});
-  await io.write(destination, document);
-  console.log(JSON.stringify({status: after <= triangleTarget ? 'PASS' : 'FAIL',
-    triangles_before: before, triangles_after: after, triangle_target: triangleTarget,
-    texture_target: textureTarget, textures, runtime,
-    policy: 'weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data',
-    elapsed_sec: Math.round((performance.now() - started) / 10) / 100,
-    visual_review: 'PENDING'}));
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources_index.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources_index.json
deleted file mode 100644
index 1969a51d352f38355ae44a66bd3e4af7e116e0ce..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources_index.json
+++ /dev/null
@@ -1,413 +0,0 @@
-{
-  "files": [
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt",
-      "sha256": "e2dc641cbc543ce792639ce040b548502195a86bb90c370a660616de2382c63b",
-      "bytes": 41243
-    },
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt",
-      "sha256": "b8c92ef5d0bda2175e0438b237489d1149af37a03b7d3af7bdb884055ccacd00",
-      "bytes": 3579
-    },
-    {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png",
-      "sha256": "4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817",
-      "bytes": 909706
-    },
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-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png",
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-      "bytes": 72213
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-  "parent_plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d"
-}
diff --git a/outputs/G045_daily_street_lightlane/.generation/3d_object/tripo/obj200_meshy_tripo_20261015_v2/asset_task_index.json b/outputs/G045_daily_street_lightlane/.generation/3d_object/tripo/obj200_meshy_tripo_20261015_v2/asset_task_index.json
deleted file mode 100644
index fe7d3dd13e6b56dc789caa4d479e6dae7f5e2bcb..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/.generation/3d_object/tripo/obj200_meshy_tripo_20261015_v2/asset_task_index.json
+++ /dev/null
@@ -1,378 +0,0 @@
-{
-  "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "game_id": "G045_daily_street_lightlane",
-  "provider": "tripo",
-  "scope": "3d_object_only_not_complete_stage2",
-  "tasks": [
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-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_001",
-        "asset_name": "cat_resident_player",
-        "name_zh": "猫兽人主角",
-        "role": "character",
-        "input_mode": "image_to_3d",
-        "prompt": "Cat-beastfolk resident player, light-beige fur, teal-green short jacket, deep-blue shorts and orange crossbody satchel, clean symmetrical full-body front-facing T-pose, semi-realistic stylized game character, arms extended, feet visible, plain studio background, one character only, humanoid rig ready, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1045,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "P01,WD03",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png",
-        "sha256": "4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/4837bd0a0beda70815de3feb3249d03527ca68c8014aa2e08e50abad2ac83817.png"
-      },
-      "subject_group": "character",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_002",
-        "asset_name": "street_bicycle",
-        "name_zh": "街区自行车",
-        "role": "vehicle",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood bicycle per reference image: orange low-step frame, two dark wheels with clear axle hierarchy, beige front basket and dark saddle, frame-and-wheels relationship readable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1046,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "vehicle",
-        "spec_ref": "P02,V01",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png",
-        "sha256": "518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/518bbca739b6ac8509ec0e78cc777f61a92437377649f9ab58f4e88b2089432b.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_003",
-        "asset_name": "apartment_building",
-        "name_zh": "公寓建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Two-story apartment building per reference image: grey-blue main block with exterior corridor and stairs, orange front door, laundry lines and shared garbage spot, collision-friendly blocky silhouette, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1047,
-        "decimation_target": 8000,
-        "texture_size": 4096,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P03,W01",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png",
-        "sha256": "068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/068e8e84a1662109d16cf755489779df9a7020ce5dc958a208c0e14546800351.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_004",
-        "asset_name": "convenience_store",
-        "name_zh": "便利店建筑",
-        "role": "building",
-        "input_mode": "image_to_3d",
-        "prompt": "Neighborhood convenience store per reference image: teal-green awning sign, warm white doors and windows, visible shelves through glass, side parking spot, original branding-free facade, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1048,
-        "decimation_target": 8000,
-        "texture_size": 4096,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P04,W02",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png",
-        "sha256": "ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/ae12b5d5611464b119a5736de576872ed7314851516d345eecb592ce462d13dd.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_005",
-        "asset_name": "maintenance_toolkit_set",
-        "name_zh": "维修工具组",
-        "role": "prop",
-        "input_mode": "image_to_3d",
-        "prompt": "Street maintenance and odd-job toolkit set per reference image: toolbox, broom, trash picker, folded crate and small lost-item pouch props, everyday readable silhouettes, single kit, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1049,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W03,E04",
-        "image_path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": null,
-      "reference": {
-        "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png",
-        "sha256": "594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360",
-        "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/594b534029b26974f1215cc5c34f2fb270ac124fbfee2ab057a7a9e52bafc360.png"
-      },
-      "subject_group": "rigid",
-      "context_condition": "reference_only"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_006",
-        "asset_name": "npc_housekeeper_neighbor",
-        "name_zh": "N01 家务熟手邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1050,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Friendly middle-aged neighbor with housework apron, cloth headwrap and small cleaning caddy in hand, sturdy everyday silhouette distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_007",
-        "asset_name": "npc_curious_neighbor",
-        "name_zh": "N02 好奇邻居",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1051,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young curious neighbor leaning slightly forward with round glasses and a shoulder satchel, energetic posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_008",
-        "asset_name": "npc_stroll_partner",
-        "name_zh": "N03 散步伙伴",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1052,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retired strolling elder with light windbreaker and walking cane, relaxed friendly posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_009",
-        "asset_name": "npc_illustrator",
-        "name_zh": "N04 插画师",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1053,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q05",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Young illustrator with beret, sketchbook under one arm and ink-stained fingerless gloves, thoughtful posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_010",
-        "asset_name": "npc_landlord",
-        "name_zh": "N05 房东",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1054,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q07",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Stern but kind landlord with buttoned vest, ledger book in hand and key ring at belt, composed posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_011",
-        "asset_name": "npc_store_clerk",
-        "name_zh": "N06 店员",
-        "role": "character",
-        "input_mode": "text_to_3d",
-        "prompt": "Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1055,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "humanoid",
-        "animation_strategy": "retarget",
-        "spec_ref": "N01,N03,Q02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Convenience store clerk with striped apron, small name badge and handheld scanner pouch, welcoming posture distinguishable from player, stylized low-poly, single character, Y-up, pivot at base, humanoid rig ready, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "character",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_012",
-        "asset_name": "environment_delivery_car",
-        "name_zh": "环境车辆",
-        "role": "vehicle",
-        "input_mode": "text_to_3d",
-        "prompt": "Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1056,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "vehicle",
-        "spec_ref": "V05,F04",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Small old-town box delivery van with rounded cab, two visible axles and blank side panels, environment traffic only not player drivable, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_013",
-        "asset_name": "street_lamp_post",
-        "name_zh": "街区路灯",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1057,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "P06,WD01,VC02",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Old-style street lamp post with warm lantern head and blank runtime-lit panel, curved arm and cast base, night lane landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_014",
-        "asset_name": "riverside_park_bench",
-        "name_zh": "河边公园长椅",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1058,
-        "decimation_target": 8000,
-        "texture_size": 1024,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W04,L06",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Wooden riverside park bench with slat backrest and armrests on small stone pads, rest interaction landmark, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    },
-    {
-      "task": {
-        "game_id": "G045_daily_street_lightlane",
-        "task_id": "G045_daily_street_lightlane_obj_015",
-        "asset_name": "street_vending_machine",
-        "name_zh": "商业街售货机",
-        "role": "prop",
-        "input_mode": "text_to_3d",
-        "prompt": "Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-        "seed": 1059,
-        "decimation_target": 8000,
-        "texture_size": 2048,
-        "rig": "none",
-        "animation_strategy": "procedural",
-        "spec_ref": "W03",
-        "poly_policy": "low_poly_priority",
-        "l_tier_repair_target": true
-      },
-      "shared_prompt": "Game context (excerpt): 日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。\nAsset: Retro neighborhood vending machine with lit front panel, blank branding and coin slot, shopping street fixture, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail",
-      "reference": null,
-      "subject_group": "rigid",
-      "context_condition": "shared_short_context"
-    }
-  ]
-}
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/pos_z.png
deleted file mode 100644
index eb136dd92376fe6e7574442ce1339a2e8389dabe..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/pos_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/render_meta.json
deleted file mode 100644
index e78fad92fa99f444af0ba408376114c917217f00..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/render_meta.json
+++ /dev/null
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_001_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/model.glb",
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-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_001",
-  "variant": "native",
-  "source_sha256": "96ab755df7df7a75198c5f2e13447aa29ab440acad58200a206a737c5c248531",
-  "views": [
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-    },
-    {
-      "axis": "-z",
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-    },
-    {
-      "axis": "+x",
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-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/native/pos_y.png"
-    },
-    {
-      "axis": "iso",
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-    "three": "180",
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-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/qa.json
deleted file mode 100644
index 0d7368927eff95f6728feac534d39c1568bf4d8a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": false,
-      "materials_present": true,
-      "textures_present": true,
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-      "meshes": 1,
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-    "visual_status": "PENDING",
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-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
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-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
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-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
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-  "postprocess": {
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-    "triangles_after": 7999,
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-          2048
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-        "after": [
-          2048,
-          2048
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-      {
-        "name": "normal",
-        "before": [
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-        "after": [
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-      {
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-        "meshoptimizer": "0.24.0",
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-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.08,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/contact_sheet.jpg
deleted file mode 100644
index 0ba47f7f0dad8ab0262370d1e41b2cbce58ba38a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/iso.png
deleted file mode 100644
index 4dec23017428cf53ad1e8063e62dfad155da109c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_x.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_x.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_x.png
deleted file mode 100644
index 5ec4b21a248f43ae4e77f7519d896fdc24e2a428..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_x.png
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_y.png
deleted file mode 100644
index b979662ecd883eeb636c4a569fa5cfc8f4ad8bcf..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_z.png
deleted file mode 100644
index cc03670bfa439127f40e3635d882c3839f254559..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/render_meta.json
deleted file mode 100644
index 4a28132b83a3172363e4e9c579831526f221c290..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_001_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_001",
-  "variant": "import",
-  "source_sha256": "e8ccee2a818b8b26e2a91eaa7b252644ffe248ebbfdb18e376431b42c5f1618f",
-  "views": [
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-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_z.png"
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-      "axis": "+x",
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-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/import/pos_y.png"
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-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/iso.png
deleted file mode 100644
index 62a666c62ea59d7dc916b9826179a7a4776d4f44..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/iso.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/neg_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/neg_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/neg_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/pos_x.png
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+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/render_meta.json
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/previews/native/render_meta.json
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-{
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/qa.json
deleted file mode 100644
index 1af6d1ec7219a71b5e42c4643ffb9c69139e4b41..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/import/pos_y.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/import/pos_z.png
+++ /dev/null
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_002_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/import/model.glb",
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-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_002",
-  "variant": "import",
-  "source_sha256": "5a52df46d92b0b64acd22a6f6a58aa263f883a8958271c45e9a24be933a2eeac",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/contact_sheet.jpg
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/contact_sheet.jpg
+++ /dev/null
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/iso.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/render_meta.json
deleted file mode 100644
index 51d4f5d98db1cc50af61973065ba59a3d0fb2735..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_002_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_002",
-  "variant": "native",
-  "source_sha256": "cd30123a73d2889682004e66c6c433fc47b5f03070fbb2578d2298395b589aba",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/pos_y.png"
-    },
-    {
-      "axis": "iso",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/iso.png"
-    }
-  ],
-  "geometry": {
-    "bounds": [
-      1.89807790517807,
-      0.8895009756088257,
-      0.27727995812892914
-    ],
-    "bounds_min": [
-      -0.9501849412918091,
-      -0.4455380141735077,
-      -0.13854998350143433
-    ],
-    "bounds_max": [
-      0.947892963886261,
-      0.443962961435318,
-      0.1387299746274948
-    ],
-    "meshes": 1,
-    "triangles": 7562,
-    "skinned": 0,
-    "force_front_side": false,
-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/qa.json
deleted file mode 100644
index adeb4dda77692e6934139728d12300ab5cea5645..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true
-    },
-    "summary": {
-      "bytes": 11651744,
-      "triangles": 7562,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 4,
-      "images": 4,
-      "nodes": 1,
-      "generator": "Khronos glTF Blender I/O v4.4.55"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true,
-      "texture_budget": true
-    },
-    "summary": {
-      "bytes": 11651724,
-      "triangles": 7562,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 4,
-      "images": 4,
-      "nodes": 1,
-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 7562,
-    "triangles_after": 7562,
-    "triangle_target": 8000,
-    "texture_target": 2048,
-    "textures": [
-      {
-        "name": "Image_3",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "normal",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "Image_0",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "Image_1",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      }
-    ],
-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.05,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/contact_sheet.jpg
deleted file mode 100644
index 2fc7c72bdec62fb1e6f25e3f99e11c47182063e2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b5061064635ccae52e7622d3ef1ebde1872ac7ed258254efc77af06e23c77da1
-size 65458
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/iso.png
deleted file mode 100644
index 593d1e1fc8e294e198dfae0820adc9e43d04f26c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fc39c8b1de9e695f458d4a4fbb74fc894fdc52b022ba6c7729aa50b195bde0d2
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_x.png
deleted file mode 100644
index 78f5f80386b3d6f7b0c9aa832a07289c313a37e2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ca0d9cc28878f97d230e81f711bb8545032da86190c98cbcb9c82fa9e9495561
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_z.png
deleted file mode 100644
index 3decf7694b7625d28f82c2b6579402fdf63c618b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_x.png
deleted file mode 100644
index 9012e2d1af43e5851e603a9a0dc7ba212b28dc29..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ce7ceeb6192b516a5b6ad550262bdea10ddf62ea2e333f1e3c86e4588baaaf9e
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_y.png
deleted file mode 100644
index 8f3187fc59525a44cae37c1008128756cac81c1f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eef7ed997fc190956a1fc2dbbd9b57dd89c13554184f91e9dbf7aa5a08ad81f8
-size 28766
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_z.png
deleted file mode 100644
index 59fb6e5f719cb479eb1c62a23c255ccdbc4db0a8..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7e68dd4da2ca105b2b30d765eb78e98862f4e7f83a3aee25d4e7606703303d00
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/render_meta.json
deleted file mode 100644
index e4847ab4eb4554ebb0bc71645797c3fa0f234f99..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_002_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_002",
-  "variant": "import",
-  "source_sha256": "c040af636a1fd5faa8546c12fb9ac11e89ea2831162de6d5d3773c270a656b80",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/pos_y.png"
-    },
-    {
-      "axis": "iso",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/import/iso.png"
-    }
-  ],
-  "geometry": {
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-      1
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-    "bounds_min": [
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-      -0.5
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-    "bounds_max": [
-      0.09686887264251709,
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-      0.5
-    ],
-    "meshes": 1,
-    "triangles": 7998,
-    "skinned": 0,
-    "force_front_side": false,
-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
index 6c9690de0cdbdbf4d2f105d1f9d689ea703ab740..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:061de45a93cf3c267d6beee04cb71aa10f6cb122e10e4dad9039a271f7005b2c
-size 65434
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/iso.png
deleted file mode 100644
index 5db3859a9bacd4d59f15847ff04b263104d81dc6..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_x.png
deleted file mode 100644
index 40395f8d255295d75d83dfd9bf9f69273a99e08f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_z.png
deleted file mode 100644
index 08149136e17f06348c6b67b27ae541176b059306..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_x.png
deleted file mode 100644
index 39d7d12e8a9f4037f51bd2625fec46040af62e77..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_y.png
deleted file mode 100644
index b6b53700751f782fc5e2883b7ca07dda3bc0df8a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_z.png
deleted file mode 100644
index 01ff6af43308eb457aa25a5f65131876dfdea72b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/render_meta.json
deleted file mode 100644
index ec70f9bd38cd5e0a97e785ef4bd0993c33324c70..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_002_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_002",
-  "variant": "native",
-  "source_sha256": "f0953b65f7ee57715820f5ddb878df61057998c57028650d1585097693cbc275",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/previews/native/neg_x.png"
-    },
-    {
-      "axis": "+y",
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-    },
-    {
-      "axis": "iso",
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-      0.5
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-    "force_front_side": false,
-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/qa.json
deleted file mode 100644
index 14a2d5ed6276fa5bee7803eac64db3fc6bb6f19b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": false,
-      "materials_present": true,
-      "textures_present": true,
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-      "meshes": 1,
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-      "textures": 3,
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-      "nodes": 1,
-      "generator": "https://tripo3d.ai"
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-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
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-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 10385,
-    "triangles_after": 7998,
-    "triangle_target": 8000,
-    "texture_target": 2048,
-    "textures": [
-      {
-        "name": "Color_f250d2da-3f5c-44e4-bf37-77841bac3d81",
-        "before": [
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-          2048
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-        "after": [
-          2048,
-          2048
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-      {
-        "name": "ORM_f250d2da-3f5c-44e4-bf37-77841bac3d81",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      },
-      {
-        "name": "NormalGL_f250d2da-3f5c-44e4-bf37-77841bac3d81",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
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-      }
-    ],
-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.07,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/contact_sheet.jpg
deleted file mode 100644
index 5239eed7ccf4890a3331597fd47c2dd21e0c5453..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/iso.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/neg_x.png
+++ /dev/null
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/neg_z.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/pos_x.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/pos_y.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 905b56771544b37f6372427e1334e70317dc78ed..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/render_meta.json
deleted file mode 100644
index 99a40501146bc6255f0ffb81ef06a2a38713bd9f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_003_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_003",
-  "variant": "import",
-  "source_sha256": "b5b471ce19f4786212cb6a7da53e3824876e67f4cd490a7e2d58c5f8d6eb741b",
-  "views": [
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-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/neg_z.png"
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-      "axis": "+x",
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-    },
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-      "axis": "-x",
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-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/pos_y.png"
-    },
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/contact_sheet.jpg
deleted file mode 100644
index 5d9d9c018464405fe360c0dbc8f3a89b19a665f1..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native/render_meta.json
deleted file mode 100644
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+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_003_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/native",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_003",
-  "variant": "native",
-  "source_sha256": "186083550124e52f912c66ed7a9f61da159f6fb2cc57d85ed4abc89b9f9786c7",
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-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/qa.json
deleted file mode 100644
index ec01f015c239ebcccb4f7a52bcec29f5b8f5fde0..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-    "requested_texture_size": 4096,
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-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
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-  "import": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true,
-      "texture_budget": true
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-      "triangles": 5598,
-      "meshes": 1,
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-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 4096,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 5598,
-    "triangles_after": 5598,
-    "triangle_target": 8000,
-    "texture_target": 4096,
-    "textures": [
-      {
-        "name": "Image_3",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "normal",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
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-      {
-        "name": "Image_0",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      {
-        "name": "Image_1",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      }
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-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.04,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/contact_sheet.jpg
deleted file mode 100644
index 96f44bcd0890668b33d3524cc795c7896142408c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/iso.png
deleted file mode 100644
index 0effd276dd028165e5066b03ed48f45f3bb590e2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/iso.png
+++ /dev/null
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deleted file mode 100644
index 6aba4f21b827511020630ba83faf4efba4a8f737..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/neg_x.png
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@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/neg_z.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/neg_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_x.png
deleted file mode 100644
index 0b666d7efbbccf66532e0fd7e81ca8fbcf972547..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_x.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_y.png
deleted file mode 100644
index 5784a7104823eda7f98a8dbb572cddeb0d74b8bf..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_y.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_z.png
deleted file mode 100644
index 98c25e93474bb3eb56f02c2d1208654d9ee58708..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/render_meta.json
deleted file mode 100644
index 3d8d61184c007ffefdb0426253c8e73805d4c190..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_003_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/import/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_003",
-  "variant": "import",
-  "source_sha256": "f356037b374fa996d89b3dc97a843ab1ad679c7baf3ccec06290004def14a03c",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/import/pos_x.png"
-    },
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-      "axis": "-x",
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-      "axis": "+y",
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-      "axis": "iso",
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
index e84008e8b0709267a84f26e07752386d14259f9c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/iso.png
deleted file mode 100644
index 7556a90c8aa7ec14660029a8b6db879998062f12..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/iso.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/neg_x.png
deleted file mode 100644
index 1cb2014739d232c030cc94da5b806769e55e9bda..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/neg_x.png
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deleted file mode 100644
index 961bda6f7f2911fbee138b60fbace6082196aab1..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/neg_z.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_x.png
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deleted file mode 100644
index 07cae7223a48e4974de18db078a44b7255caa81c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_y.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_z.png
deleted file mode 100644
index a829466bfd097d7348c7688ea3a269f1973e9c07..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/render_meta.json
deleted file mode 100644
index bebc43b9ecb402003a30a4b2203265f5f2e02eac..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_003_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_003",
-  "variant": "native",
-  "source_sha256": "b35669f257e7f5096c7c53d25834ae5ec0c58520b68c26d2e7dfbf1c41856a51",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/previews/native/pos_x.png"
-    },
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-      "axis": "-x",
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-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/qa.json
deleted file mode 100644
index 632df8866f3850cb0e9facaa731efad560090a8a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-    "requested_texture_size": 4096,
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-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true,
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-      "meshes": 1,
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-      "images": 3,
-      "nodes": 1,
-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 4096,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 8534,
-    "triangles_after": 7999,
-    "triangle_target": 8000,
-    "texture_target": 4096,
-    "textures": [
-      {
-        "name": "Color_abcf3e83-c2da-4bec-9423-36a607dfab5c",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      },
-      {
-        "name": "ORM_abcf3e83-c2da-4bec-9423-36a607dfab5c",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
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-      {
-        "name": "NormalGL_abcf3e83-c2da-4bec-9423-36a607dfab5c",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      }
-    ],
-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.05,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/contact_sheet.jpg
deleted file mode 100644
index 1a770d73fdc2e5e7ea5ae81fcb573ff4354bdd1b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/iso.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/neg_x.png
+++ /dev/null
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/neg_z.png
+++ /dev/null
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deleted file mode 100644
index 08275754259ea4e772737f4c1a9588f7b40da244..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 324fd5780baa155a51efdebdf6c6b83727bf1014..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_004_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/import/model.glb",
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-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_004",
-  "variant": "import",
-  "source_sha256": "ff2d9930a6e1aab79beb8a1f2add8ca4d412728a911dd912a421ae2dee20da66",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_x.png"
-    },
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-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/pos_y.png"
-    },
-    {
-      "axis": "iso",
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 81019
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/iso.png
deleted file mode 100644
index b5a529bdd4a4dd17f2b11e2e63f11295b7790eb0..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/iso.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/neg_x.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/neg_z.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/pos_x.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/pos_y.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/pos_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/render_meta.json
deleted file mode 100644
index f3fdf0898bd7c83153d152fc3a04788c18d19ce2..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_004_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_004",
-  "variant": "native",
-  "source_sha256": "e404898990d7a94bfd17e918751f6c592262d2dc0f4bc3638ed8c04990c64f7c",
-  "views": [
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-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/neg_z.png"
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-    {
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-    },
-    {
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/qa.json
deleted file mode 100644
index 100878874343108e2a2b0b15e99106c2ee521071..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-    "requested_texture_size": 4096,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true,
-      "texture_budget": true
-    },
-    "summary": {
-      "bytes": 9283712,
-      "triangles": 4948,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 4,
-      "images": 4,
-      "nodes": 1,
-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 4096,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 4948,
-    "triangles_after": 4948,
-    "triangle_target": 8000,
-    "texture_target": 4096,
-    "textures": [
-      {
-        "name": "Image_3",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "normal",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "Image_0",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "Image_1",
-        "before": [
-          2048,
-          2048
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-        "after": [
-          2048,
-          2048
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-      }
-    ],
-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.03,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/contact_sheet.jpg
deleted file mode 100644
index ddee69a99031a231e844a2a57976830ae0b5699c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:59c23e8f533046ed0d71d9e4f1cbeb959f8e176f0397c6ef7f8c21d6088e1fdf
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/iso.png
deleted file mode 100644
index 92e567e479f6c0e7467a6c152f81aecd73c7229d..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_x.png
deleted file mode 100644
index e2de4bc2b1a8a64f50964abb44de8887785ca433..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 2a42e13ba68282bf8b75b2c45b214c2358f87b93..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_x.png
deleted file mode 100644
index c8f01d27e39835f2b3e8168080757e2db1b8540c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_y.png
deleted file mode 100644
index d6916f01e8b456dc9ea55d8dd4ae7f192c802d5e..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_z.png
deleted file mode 100644
index 44f96e7efb73ef9cc8e6953c9760e25ecf1453df..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/render_meta.json
deleted file mode 100644
index 9c6e35c8d7b857080e30f5899fd68475a004db17..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_004_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_004",
-  "variant": "import",
-  "source_sha256": "bc87b428eeb1b0ffb066e6c055e41c4c9f720315462e0fb3762b5d7c50101c7b",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/pos_y.png"
-    },
-    {
-      "axis": "iso",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/import/iso.png"
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
index 0e128b6abd40c4ff8576192dbb7f5d1adce7d4e9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/iso.png
deleted file mode 100644
index 92e567e479f6c0e7467a6c152f81aecd73c7229d..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_x.png
deleted file mode 100644
index e2de4bc2b1a8a64f50964abb44de8887785ca433..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_z.png
deleted file mode 100644
index 2a42e13ba68282bf8b75b2c45b214c2358f87b93..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_x.png
deleted file mode 100644
index c8f01d27e39835f2b3e8168080757e2db1b8540c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_x.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_y.png
deleted file mode 100644
index d6916f01e8b456dc9ea55d8dd4ae7f192c802d5e..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_z.png
deleted file mode 100644
index 44f96e7efb73ef9cc8e6953c9760e25ecf1453df..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/render_meta.json
deleted file mode 100644
index fc84f3db7e80633686ecbc62ef2135c82a86d63f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_004_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_004",
-  "variant": "native",
-  "source_sha256": "2fa02058099f003cff644b635ee287b96ceef077dfafff35f971effd8bbe8147",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/previews/native/pos_y.png"
-    },
-    {
-      "axis": "iso",
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-    "meshes": 1,
-    "triangles": 7013,
-    "skinned": 0,
-    "force_front_side": false,
-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/qa.json
deleted file mode 100644
index 8469e78f09752361d6af5d9e950b52070f3b85de..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true
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-      "triangles": 7013,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 3,
-      "images": 3,
-      "nodes": 1,
-      "generator": "https://tripo3d.ai"
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-    "requested_texture_size": 4096,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true,
-      "texture_budget": true
-    },
-    "summary": {
-      "bytes": 2089760,
-      "triangles": 7013,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 3,
-      "images": 3,
-      "nodes": 1,
-      "generator": "glTF-Transform v4.2.1"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 4096,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "postprocess": {
-    "status": "PASS",
-    "triangles_before": 7013,
-    "triangles_after": 7013,
-    "triangle_target": 8000,
-    "texture_target": 4096,
-    "textures": [
-      {
-        "name": "Color_62f31bb7-0e64-4931-912c-244de1c4e503",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "ORM_62f31bb7-0e64-4931-912c-244de1c4e503",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      },
-      {
-        "name": "NormalGL_62f31bb7-0e64-4931-912c-244de1c4e503",
-        "before": [
-          2048,
-          2048
-        ],
-        "after": [
-          2048,
-          2048
-        ]
-      }
-    ],
-    "runtime": {
-      "node": "20.17.0",
-      "dependencies": {
-        "@gltf-transform/core": "4.2.1",
-        "@gltf-transform/extensions": "4.2.1",
-        "@gltf-transform/functions": "4.2.1",
-        "draco3dgltf": "1.5.7",
-        "meshoptimizer": "0.24.0",
-        "sharp": "0.35.5"
-      }
-    },
-    "policy": "weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data",
-    "elapsed_sec": 0.03,
-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/contact_sheet.jpg
deleted file mode 100644
index f9074aee8e7c6630bcc5f79291604ea0c43d7655..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:24850f35dbfe6a6842df55d851190901aada038e25a047aceadfa329f4bb9ac4
-size 75190
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/iso.png
deleted file mode 100644
index b890ad57dba33f277b8dcd244acc53d1536e90c8..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/iso.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_x.png
deleted file mode 100644
index e7f7ea811234b32bfabbdcfe824751af4cab0f6e..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 14b0d23fc0efd32ec3aec9502ec327c94e4808df..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:be66e4bacdc5d8dae806f46c0527f4412df57b66e2f097dedccdb458063d70bf
-size 100107
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_x.png
deleted file mode 100644
index 0784e630343deeb7f1ae3bff7f14e94012ae560a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_x.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:55e9f01c835a86801089193adb346313023184d1dbe41397c2cb801b136d0258
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_y.png
deleted file mode 100644
index ae1e1ed214d3af94175d0a1136ef31a2701e5825..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:37eed55ed274474f5c5cda91152e0e7167fdc0ede603cf10ed57d263bcf222a6
-size 82313
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_z.png
deleted file mode 100644
index c2a6a982ab813b9a7a8381a547a2b41bb8c23501..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:95359351298c8e10737976bc73dac68288ffa57b65cb29335873bffa8c614d6c
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/render_meta.json
deleted file mode 100644
index 63caea726baa48074f2360e5c5f93132bf2fd712..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_005_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_005",
-  "variant": "import",
-  "source_sha256": "26c0862df0df281dcca220e06b773d3f7acc143366a04e6f12b92ea54d1397e9",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/neg_x.png"
-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/pos_y.png"
-    },
-    {
-      "axis": "iso",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/import/iso.png"
-    }
-  ],
-  "geometry": {
-    "bounds": [
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-      1.899190902709961,
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-    "meshes": 1,
-    "triangles": 7670,
-    "skinned": 0,
-    "force_front_side": false,
-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/contact_sheet.jpg
deleted file mode 100644
index ab2bd5f8e5bb836fc6b99edc3fb547fe79f49b05..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c0dd643a39458285ffdce6b59be0b5c0851c1cd90d2d1f9242ff5248d4e54bb8
-size 75044
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/iso.png
deleted file mode 100644
index b890ad57dba33f277b8dcd244acc53d1536e90c8..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/iso.png
+++ /dev/null
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_x.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_z.png
deleted file mode 100644
index 14b0d23fc0efd32ec3aec9502ec327c94e4808df..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 100107
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_x.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_x.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_y.png
deleted file mode 100644
index ae1e1ed214d3af94175d0a1136ef31a2701e5825..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_y.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_z.png
deleted file mode 100644
index c2a6a982ab813b9a7a8381a547a2b41bb8c23501..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_z.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:95359351298c8e10737976bc73dac68288ffa57b65cb29335873bffa8c614d6c
-size 106885
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/render_meta.json
deleted file mode 100644
index a71e82a2b7c63b63dd8135d4a65fb2885117568e..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_005_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_005",
-  "variant": "native",
-  "source_sha256": "ebd120910642f2690772b740ec1c59e417c2acb7cc6478cec7b0622744f4cb79",
-  "views": [
-    {
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-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/pos_x.png"
-    },
-    {
-      "axis": "-x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/previews/native/neg_x.png"
-    },
-    {
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-    {
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-    "renderer": "WebKit WebGL",
-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/qa.json
deleted file mode 100644
index b81c34ebe60a974bc90ba2138129d1c6af2e66e7..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/contact_sheet.jpg
deleted file mode 100644
index 470b4954a398ecb023f754939ad9ca21419b769d..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/iso.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/neg_x.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/pos_x.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/pos_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/pos_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/pos_y.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/pos_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/render_meta.json
deleted file mode 100644
index d656d9b7151ac8911928d93bb87c145ca9e50ece..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_005_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/import/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_005",
-  "variant": "import",
-  "source_sha256": "209536ee58565de37789e3a7b548d407060b311f10a5dab6c4631e0cde39d1df",
-  "views": [
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-      "axis": "+z",
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-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/import/neg_z.png"
-    },
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-      "axis": "+x",
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-  "status": "rendered"
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/iso.png
deleted file mode 100644
index 252f47c5b3be61f5258c34251d7dedf3538d4657..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/iso.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/neg_x.png
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/neg_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/neg_z.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/neg_z.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_x.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_y.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_y.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_y.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_z.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_z.png
deleted file mode 100644
index 80e1fd0be91d0986eea709fa61cc70149c617176..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_z.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/render_meta.json
deleted file mode 100644
index a498f672867bb54bcca03b1920b38cfbf431a2f6..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "tripo_G045_daily_street_lightlane_obj_005_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_005",
-  "variant": "native",
-  "source_sha256": "08390de9da53ab52fae8bbb0b4a987682e04b05085e8dc9efaff982dac39ce74",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/neg_z.png"
-    },
-    {
-      "axis": "+x",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_x.png"
-    },
-    {
-      "axis": "-x",
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-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/previews/native/pos_y.png"
-    },
-    {
-      "axis": "iso",
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-    "three": "180",
-    "animation_clips": 0
-  },
-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/qa.json
deleted file mode 100644
index 90c75cf810470510f8c8c8ed668c2eed2dd86291..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
-    "status": "FAIL",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": false,
-      "materials_present": true,
-      "textures_present": true,
-      "uv_present": true,
-      "self_contained": true
-    },
-    "summary": {
-      "bytes": 1603676,
-      "triangles": 9037,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 3,
-      "images": 3,
-      "nodes": 1,
-      "generator": "https://tripo3d.ai"
-    },
-    "triangle_target": 8000,
-    "requested_texture_size": 2048,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
-    "limitations": [
-      "No visual, orientation, scale, rigging or engine acceptance yet",
-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_006_import",
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+++ /dev/null
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deleted file mode 100644
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deleted file mode 100644
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_006_native",
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-    },
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/meshy/qa.json
deleted file mode 100644
index 19a5198b63c9765cc8d0e007f9217e5162adfedc..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/meshy/qa.json
+++ /dev/null
@@ -1,135 +0,0 @@
-{
-  "native": {
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-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
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-  "import": {
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_006_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/import/model.glb",
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-{
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/tripo/qa.json
deleted file mode 100644
index 05ef370979429b52809fd8fdc0c111d3d0e09fcd..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/tripo/qa.json
+++ /dev/null
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-{
-  "native": {
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-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
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-  "import": {
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-{
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-    "visual_review": "PENDING"
-  },
-  "visual_status": "PENDING"
-}
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/contact_sheet.jpg
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/neg_z.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/pos_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/pos_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_007_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/import/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_007",
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-  "views": [
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/contact_sheet.jpg
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/iso.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/pos_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_007_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_007",
-  "variant": "native",
-  "source_sha256": "b1375267d470832a9644f12f3a92a3df4b384a32631006354cd7a27cec8797ab",
-  "views": [
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-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/previews/native/neg_z.png"
-    },
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-    },
-    {
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-    {
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/qa.json
deleted file mode 100644
index 4ddba68c32db14a7b242a44df7318ab078eac012..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": false,
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-      "textures_present": true,
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-      "self_contained": true
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-      "triangles": 10440,
-      "meshes": 1,
-      "materials": 1,
-      "textures": 3,
-      "images": 3,
-      "nodes": 1,
-      "generator": "https://tripo3d.ai"
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-    "requested_texture_size": 2048,
-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
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-  "import": {
-    "status": "PASS",
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-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/meshy/previews/import/contact_sheet.jpg
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-{
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-{
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/import/pos_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/import/render_meta.json
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_008_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/import/model.glb",
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-  "task_id": "G045_daily_street_lightlane_obj_008",
-  "variant": "import",
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-  "views": [
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/contact_sheet.jpg
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/iso.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/previews/native/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_008_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_008",
-  "variant": "native",
-  "source_sha256": "8b77dd1a67a34627e7aaf1cbf2d652216d2fbf689aeed22117fc4c0766785085",
-  "views": [
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-    },
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-      "axis": "-z",
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-      "axis": "+x",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/qa.json
deleted file mode 100644
index c2c87a44fc2365bd093809d9297d93221fba317a..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-    "texture_budget_verified": false,
-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/meshy/previews/import/contact_sheet.jpg
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/meshy/previews/import/contact_sheet.jpg
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-{
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-{
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-{
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/previews/native/render_meta.json
deleted file mode 100644
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+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_009_native",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/qa.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/qa.json
+++ /dev/null
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-{
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-  "import": {
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/meshy/previews/import/contact_sheet.jpg
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-{
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-{
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_010_import",
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-      "axis": "-z",
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+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/previews/native/render_meta.json
deleted file mode 100644
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+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_010_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_010",
-  "variant": "native",
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-    },
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-      "axis": "+y",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/qa.json
deleted file mode 100644
index 43b1f6e7886124084aa046c8972b835ec57b5558..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-    "texture_budget_verified": false,
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
-  },
-  "import": {
-    "status": "PASS",
-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/meshy/previews/import/contact_sheet.jpg
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-{
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-{
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_011_import",
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/previews/native/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/previews/native/render_meta.json
deleted file mode 100644
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+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_011_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_011",
-  "variant": "native",
-  "source_sha256": "4ee76f28b4842e5f9bf37605684bc4c45d6e9855b5a0b059b6d160ce7fda67f2",
-  "views": [
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-    },
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-    },
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-    },
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/qa.json
deleted file mode 100644
index f78dade09d11149837e01a903bd683edb8ebc5c4..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
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-  "import": {
-    "status": "PASS",
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-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/meshy/previews/import/contact_sheet.jpg
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_012_native",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/meshy/qa.json
deleted file mode 100644
index 51ddbe7c3becce8b765d1e9cc034bbe4d855a8de..0000000000000000000000000000000000000000
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-{
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-{
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/pos_x.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/render_meta.json
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+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_012_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_012",
-  "variant": "native",
-  "source_sha256": "4d4bd560d70eb3ce0985a0305d6c57d3cb5eb7b1d8889ec5814e12af01cb7092",
-  "views": [
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-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/previews/native/neg_z.png"
-    },
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-    "three": "180",
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-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/qa.json
deleted file mode 100644
index ca2ef8422ab46b41a73ac7a02dfb671c05ffdaaa..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "meshes": 1,
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-      "textures": 3,
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-      "nodes": 1,
-      "generator": "https://tripo3d.ai"
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-      "Native texture resolution retained; task texture_size is not enforced",
-      "Basic checks do not validate every accessor, topology defect or PBR channel"
-    ]
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-  "import": {
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-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
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-    "requested_texture_size": 2048,
-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
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-        "after": [
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-      {
-        "name": "ORM_ad320eaa-c03c-4fad-8540-80ef962dcb3f",
-        "before": [
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-          2048
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-        "after": [
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-        "name": "NormalGL_ad320eaa-c03c-4fad-8540-80ef962dcb3f",
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-  "visual_status": "PENDING"
-}
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/contact_sheet.jpg
+++ /dev/null
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/render_meta.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "label": "meshy_G045_daily_street_lightlane_obj_013_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_013",
-  "variant": "import",
-  "source_sha256": "ba493187324190e3dc06d76fb3de7db56eef3a444ffb421704ecb94044870d46",
-  "views": [
-    {
-      "axis": "+z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/pos_z.png"
-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/previews/import/neg_z.png"
-    },
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_013_native",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/qa.json
deleted file mode 100644
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-{
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deleted file mode 100644
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_013_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/import/model.glb",
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-  "provider": "tripo",
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-  "variant": "import",
-  "source_sha256": "cfec04a1edafe9efc53081c38cb40d1decf95231f16bbed5030f7e8579bb6917",
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/pos_x.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_013_native",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_013",
-  "variant": "native",
-  "source_sha256": "bd16b477f3ee508518548a8c1c4350a4a65ceee389002fc50eeb22f539584ced",
-  "views": [
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-    },
-    {
-      "axis": "-z",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/previews/native/neg_z.png"
-    },
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-  "status": "rendered"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/qa.json
deleted file mode 100644
index d0b6bdedea9683804c8d2e7db5722ea7addda8e9..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/qa.json
+++ /dev/null
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-{
-  "native": {
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-    "checks": {
-      "parseable_glb": true,
-      "glb_v2_exact_length": true,
-      "positive_triangle_count": true,
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-      "generator": "https://tripo3d.ai"
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-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
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-    ]
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-  "import": {
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-      "triangle_budget": true,
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-    "texture_budget_verified": true,
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-  "visual_status": "PENDING"
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/contact_sheet.jpg
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/pos_z.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_014_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import",
-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_014",
-  "variant": "import",
-  "source_sha256": "5991c59efaab193455397fc4e366a04ae346d800d1dbde972dc54d7d5b5bb29d",
-  "views": [
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-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/import/pos_z.png"
-    },
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-      "axis": "-z",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/previews/native/render_meta.json
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_014_native",
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/qa.json
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index da2d239982037f3a8635bc63e554646923b3ca4f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/qa.json
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-{
-  "native": {
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-      "positive_triangle_count": true,
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-  "import": {
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-      "positive_triangle_count": true,
-      "triangle_budget": true,
-      "materials_present": true,
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-    "texture_budget_verified": true,
-    "visual_status": "PENDING",
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-      "Native texture resolution retained; task texture_size is not enforced",
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-  "postprocess": {
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/contact_sheet.jpg
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/pos_z.png
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-version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/render_meta.json
deleted file mode 100644
index 33ed26cc5c0fb030402e301e1c8721040ef50d28..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_014_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/import/model.glb",
-  "output": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/import",
-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_014",
-  "variant": "import",
-  "source_sha256": "2ac7d79f41f3b6051e68b6b1efed788ca3e2ac5f263bd4e8c00e2bc9ae970edf",
-  "views": [
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-    },
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-  "status": "rendered"
-}
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/contact_sheet.jpg
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/contact_sheet.jpg
+++ /dev/null
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/pos_x.png
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/pos_y.png
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/previews/native/render_meta.json
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-{
-  "label": "tripo_G045_daily_street_lightlane_obj_014_native",
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-    },
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-    },
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-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/qa.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "native": {
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-  "import": {
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-  "visual_status": "PENDING"
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/contact_sheet.jpg
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deleted file mode 100644
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/pos_z.png
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/render_meta.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/render_meta.json
+++ /dev/null
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-{
-  "label": "meshy_G045_daily_street_lightlane_obj_015_import",
-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/import/model.glb",
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-  "provider": "meshy",
-  "task_id": "G045_daily_street_lightlane_obj_015",
-  "variant": "import",
-  "source_sha256": "1cc27ba3a0e0c49548b65b04d4568cb03609a83c00399d505d1cc54d9bc0f0d5",
-  "views": [
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-    },
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-      "axis": "-z",
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-    },
-    {
-      "axis": "+y",
-      "file": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/import/pos_y.png"
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/contact_sheet.jpg b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/contact_sheet.jpg
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/contact_sheet.jpg
+++ /dev/null
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/iso.png b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/iso.png
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/previews/native/iso.png
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-{
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/qa.json
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-{
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/import/pos_z.png
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-{
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-  "source_sha256": "ed8525ebca72623fc55d0b4a19ff28e114dee68a4cc7d038dc0e502c3ec82c6d",
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/native/neg_z.png
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deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/native/pos_x.png
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deleted file mode 100644
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/native/render_meta.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/native/render_meta.json
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/previews/native/render_meta.json
+++ /dev/null
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-{
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-  "source": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/model.glb",
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-  "provider": "tripo",
-  "task_id": "G045_daily_street_lightlane_obj_015",
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-    },
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-    },
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\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/qa.json b/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/qa.json
deleted file mode 100644
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--- a/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/qa.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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-      "generator": "https://tripo3d.ai"
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-    ]
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-  "import": {
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diff --git a/outputs/G045_daily_street_lightlane/eval/3d_object/obj200_meshy_tripo_20261015_v2/catalog/catalog.json b/outputs/G045_daily_street_lightlane/eval/3d_object/obj200_meshy_tripo_20261015_v2/catalog/catalog.json
deleted file mode 100644
index 57fa6dfd192e799812352deb76cb3c2233b9f90f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/eval/3d_object/obj200_meshy_tripo_20261015_v2/catalog/catalog.json
+++ /dev/null
@@ -1,495 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
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-  "created_at": "2026-10-16T11:10:47+00:00",
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-      "task_ids": [
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-        "G045_daily_street_lightlane_obj_002",
-        "G045_daily_street_lightlane_obj_003",
-        "G045_daily_street_lightlane_obj_004",
-        "G045_daily_street_lightlane_obj_005",
-        "G045_daily_street_lightlane_obj_006"
-      ],
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-          "variant": "native",
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-          "variant": "import",
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diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_index.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_index.json
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--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_index.json
+++ /dev/null
@@ -1,638 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
-  "batch_id": "obj200_meshy_tripo_20261015_v2",
-  "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "scope": "3d_object_only_not_complete_stage2",
-  "providers": {
-    "meshy": {
-      "delivered": 15
-    },
-    "tripo": {
-      "delivered": 15
-    }
-  },
-  "frozen_game_inputs": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/frozen_game_inputs.json",
-  "sources_index": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources_index.json",
-  "integrity_record": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json",
-  "assets": [
-    {
-      "provider": "meshy",
-      "task_id": "G045_daily_street_lightlane_obj_001",
-      "status": "delivered",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/model.glb",
-      "import_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/import/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/meta.json",
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/qa.json",
-      "receipt": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_001.json",
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-        "import": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/previews/import/contact_sheet.jpg"
-      },
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-      ],
-      "visual_status": "PENDING",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS"
-    },
-    {
-      "provider": "meshy",
-      "task_id": "G045_daily_street_lightlane_obj_002",
-      "status": "delivered",
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-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/qa.json",
-      "receipt": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_002.json",
-      "previews": {
-        "native": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/native/contact_sheet.jpg",
-        "import": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/previews/import/contact_sheet.jpg"
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-        "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/1d0421ee6a9344421a99a540325ae990a6721287353c895e0e91ee5adf07c503.meta.json"
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-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS"
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-    {
-      "provider": "meshy",
-      "task_id": "G045_daily_street_lightlane_obj_003",
-      "status": "delivered",
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-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/qa.json",
-      "receipt": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_003.json",
-      "previews": {
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-        "import": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/previews/import/contact_sheet.jpg"
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-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS"
-    },
-    {
-      "provider": "meshy",
-      "task_id": "G045_daily_street_lightlane_obj_004",
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-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/qa.json",
-      "receipt": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/receipts/meshy/G045_daily_street_lightlane__G045_daily_street_lightlane_obj_004.json",
-      "previews": {
-        "native": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/native/contact_sheet.jpg",
-        "import": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/previews/import/contact_sheet.jpg"
-      },
-      "cache_metadata": [
-        "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/26a93022400fe0ef24f99d483f9a0340da570705cce3afa74bcb7447dcafd06f.meta.json"
-      ],
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-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS"
-    },
-    {
-      "provider": "meshy",
-      "task_id": "G045_daily_street_lightlane_obj_005",
-      "status": "delivered",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/model.glb",
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-      "import_structural_qa": "PASS"
-    },
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-      "import_structural_qa": "PASS"
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-  "preview_catalog": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/obj200_meshy_tripo_20261015_v2/catalog/catalog.json",
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-}
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_records.zip b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_records.zip
deleted file mode 100644
index 9116c64ffc9e5f548dcc95524f619891c7a7763b..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/delivery_records.zip
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:efc8b9d0613a0beb8ce59c36c0e673407a1312e67b326a81cffd7127f0e84dc1
-size 29867181
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_generation_report.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_generation_report.json
deleted file mode 100644
index 970901bfe5ef3fdfcedb5c37efca75b2dd9e9db5..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_generation_report.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "task_count": 15,
-  "status_counts": {
-    "delivered": 15
-  },
-  "failures": [],
-  "visual_accepted": 0
-}
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_manifest.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_manifest.json
deleted file mode 100644
index 76bb08aed2cbe13f655cc5a339c2b34ed0c9724f..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/asset_manifest.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
-  "provider": "meshy",
-  "batch_id": "obj200_meshy_tripo_20261015_v2",
-  "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "scope": "3d_object_only_not_complete_stage2",
-  "assets": [
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-      "task_id": "G045_daily_street_lightlane_obj_001",
-      "asset_name": "cat_resident_player",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "character",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_002",
-      "asset_name": "street_bicycle",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/meshy/meta.json",
-      "native_structural_qa": "PASS",
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-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_003",
-      "asset_name": "apartment_building",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_004",
-      "asset_name": "convenience_store",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/import/model.glb",
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-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_005",
-      "asset_name": "maintenance_toolkit_set",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_006",
-      "asset_name": "npc_housekeeper_neighbor",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_007",
-      "asset_name": "npc_curious_neighbor",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_008",
-      "asset_name": "npc_stroll_partner",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_009",
-      "asset_name": "npc_illustrator",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_010",
-      "asset_name": "npc_landlord",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_011",
-      "asset_name": "npc_store_clerk",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/meshy/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_012",
-      "asset_name": "environment_delivery_car",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_013",
-      "asset_name": "street_lamp_post",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_014",
-      "asset_name": "riverside_park_bench",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_015",
-      "asset_name": "street_vending_machine",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/meshy/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/meshy/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    }
-  ],
-  "full_context": {
-    "general_requirement": {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt",
-      "sha256": "e2dc641cbc543ce792639ce040b548502195a86bb90c370a660616de2382c63b",
-      "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt"
-    },
-    "object_requirement": {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt",
-      "sha256": "b8c92ef5d0bda2175e0438b237489d1149af37a03b7d3af7bdb884055ccacd00",
-      "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt"
-    },
-    "context_excerpt": "日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。",
-    "context_method": "deterministic title/world/core-loop excerpts; full brief retained locally"
-  },
-  "status": "pending_visual_review"
-}
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/meta.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/meta.json
deleted file mode 100644
index 1046a3ad62356b007ca80d50ab0e0c93d7f66324..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/meshy/meta.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
-  "task_id": "G045_daily_street_lightlane_pipeline",
-  "task_kind": "pipeline",
-  "run_id": "obj200_meshy_tripo_20261015_v2",
-  "status": "comparison_only",
-  "provider": "meshy",
-  "scope": "3d_object_only_not_complete_stage2"
-}
\ No newline at end of file
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_generation_report.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_generation_report.json
deleted file mode 100644
index 970901bfe5ef3fdfcedb5c37efca75b2dd9e9db5..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_generation_report.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "task_count": 15,
-  "status_counts": {
-    "delivered": 15
-  },
-  "failures": [],
-  "visual_accepted": 0
-}
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_manifest.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_manifest.json
deleted file mode 100644
index 968c9220990ff65cc1c38a3ee08d4da2a166c46c..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/asset_manifest.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
-  "provider": "tripo",
-  "batch_id": "obj200_meshy_tripo_20261015_v2",
-  "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d",
-  "scope": "3d_object_only_not_complete_stage2",
-  "assets": [
-    {
-      "task_id": "G045_daily_street_lightlane_obj_001",
-      "asset_name": "cat_resident_player",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_001/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_001/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "character",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_002",
-      "asset_name": "street_bicycle",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_002/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_002/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_003",
-      "asset_name": "apartment_building",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_003/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_003/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_004",
-      "asset_name": "convenience_store",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_004/tripo/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_004/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_005",
-      "asset_name": "maintenance_toolkit_set",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_005/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_005/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "reference_only",
-      "subject_group": "rigid",
-      "input_mode": "image_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_006",
-      "asset_name": "npc_housekeeper_neighbor",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_006/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_006/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_007",
-      "asset_name": "npc_curious_neighbor",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_007/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_007/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_008",
-      "asset_name": "npc_stroll_partner",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_008/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_008/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_009",
-      "asset_name": "npc_illustrator",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_009/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_009/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_010",
-      "asset_name": "npc_landlord",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_010/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_010/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_011",
-      "asset_name": "npc_store_clerk",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_011/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_011/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "character",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_012",
-      "asset_name": "environment_delivery_car",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_012/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_012/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_013",
-      "asset_name": "street_lamp_post",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_013/tripo/meta.json",
-      "native_structural_qa": "FAIL",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_013/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_014",
-      "asset_name": "riverside_park_bench",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_014/tripo/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_014/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    },
-    {
-      "task_id": "G045_daily_street_lightlane_obj_015",
-      "asset_name": "street_vending_machine",
-      "task_kind": "3d_object",
-      "status": "delivered",
-      "artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/import/model.glb",
-      "native_artifact": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/model.glb",
-      "meta": "bench/test_data/outputs/G045_daily_street_lightlane/assets/3d_object/G045_daily_street_lightlane_obj_015/tripo/meta.json",
-      "native_structural_qa": "PASS",
-      "import_structural_qa": "PASS",
-      "native_delivered": true,
-      "import_delivered": true,
-      "qa": "bench/test_data/outputs/G045_daily_street_lightlane/eval/3d_object/G045_daily_street_lightlane_obj_015/tripo/qa.json",
-      "visual_status": "PENDING",
-      "context_condition": "shared_short_context",
-      "subject_group": "rigid",
-      "input_mode": "text_to_3d",
-      "error": null
-    }
-  ],
-  "full_context": {
-    "general_requirement": {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt",
-      "sha256": "e2dc641cbc543ce792639ce040b548502195a86bb90c370a660616de2382c63b",
-      "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt"
-    },
-    "object_requirement": {
-      "path": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt",
-      "sha256": "b8c92ef5d0bda2175e0438b237489d1149af37a03b7d3af7bdb884055ccacd00",
-      "source": "bench/test_data/outputs/G045_daily_street_lightlane/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt"
-    },
-    "context_excerpt": "日常街区的光轨穿行 | - W01 公寓：玩家房间、走廊、邻居门口、房东交互点、公共垃圾区。玩家房间可进入，具休息、洗澡与整理入口。 | - G03 核心循环：起床查看状况 → 选机会和路线 → 行动 → 获得现金/人情/后果 → 处理生活状态 → 回家结算与睡觉 → 次日。玩家可暂不接任务，自由探索。",
-    "context_method": "deterministic title/world/core-loop excerpts; full brief retained locally"
-  },
-  "status": "pending_visual_review"
-}
diff --git a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/meta.json b/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/meta.json
deleted file mode 100644
index 6e6377cb0682e2d7968184808232844a238262dd..0000000000000000000000000000000000000000
--- a/outputs/G045_daily_street_lightlane/pipeline/G045_daily_street_lightlane_pipeline/obj200_meshy_tripo_20261015_v2/tripo/meta.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "game_id": "G045_daily_street_lightlane",
-  "task_id": "G045_daily_street_lightlane_pipeline",
-  "task_kind": "pipeline",
-  "run_id": "obj200_meshy_tripo_20261015_v2",
-  "status": "comparison_only",
-  "provider": "tripo",
-  "scope": "3d_object_only_not_complete_stage2"
-}
\ No newline at end of file