{"run":{"job_id":"86267","host":"GPU-830","model_id":"/mnt/ceph3/ec/jacky/sam3.1","propose_model":"gemini-3.8-flash","select_model":"gemini-3.8-flash","sample_fps":4.0,"choice_frames":8,"clips_requested":20,"output_dir":"/home/zzk/tmp/rmot_smoke_real_86267","wall_seconds":729.57,"measured":{"mean_clip_duration_seconds":17.8658425,"frames_per_clip":69.4,"tracks_per_clip":3.4,"noun_phrase_gate_survival":1.0,"verification_yield":0.6481481481481481},"rejections":{"no_candidates_retrieved":7,"count_mismatch":8,"empty_interval":2,"implausible_box":1,"stationary_motion_expression":1}},"source":{"bucket":"mv-ytun-external1","prefix":"silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/","category":"test_vehicles","filters":"extension .mp4; storage class not GLACIER or DEEP_ARCHIVE","shuffle_seed":0,"clips":["s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_003_scene_023.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_044.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_002.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_008_scene_014.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_001_scene_010.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_006_scene_028.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_013.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--Dx9HVcQWs_chunk_002_scene_013.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--Dx9HVcQWs_chunk_001_scene_034.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_015_scene_004.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_012_scene_010.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_012_scene_017.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_042.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_019.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_001.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_010_scene_016.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--X6fL8DOMM_chunk_000_scene_003.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_010.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_012_scene_020.mp4","s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_018.mp4"]},"clips":[{"index":1,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_003_scene_023.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_003_scene_023.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":32.0,"frames_kept":124,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68,17.94,18.2,18.46,18.72,18.98,19.240000000000002,19.5,19.76,20.02,20.28,20.54,20.8,21.06,21.32,21.580000000000002,21.84,22.1,22.36,22.62,22.88,23.14,23.400000000000002,23.66,23.92,24.18,24.44,24.7,24.96,25.22,25.48,25.740000000000002,26.0,26.26,26.52,26.78,27.04,27.3,27.560000000000002,27.82,28.080000000000002,28.34,28.6,28.86,29.12,29.38,29.64,29.900000000000002,30.16,30.42,30.68,30.94,31.2,31.46,31.720000000000002,31.98],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":54298085,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the black dog walking across the cobblestone pathway\",\n \"noun_phrase\": \"black dog\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the brown dog ascending the stone stairs in the background\",\n \"noun_phrase\": \"brown dog\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the brown dog sniffing the stone basin\",\n \"noun_phrase\": \"brown dog\",\n \"kind\": \"interaction\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the black dog walking across the cobblestone pathway","noun_phrase":"black dog","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the brown dog ascending the stone stairs in the background","noun_phrase":"brown dog","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the brown dog sniffing the stone basin","noun_phrase":"brown dog","kind":"interaction","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the black dog walking across the cobblestone pathway","noun_phrase":"black dog","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the brown dog ascending the stone stairs in the background","noun_phrase":"brown dog","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the brown dog sniffing the stone basin","noun_phrase":"brown dog","kind":"interaction","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["black dog","brown dog"],"expressions_in":3,"frames":124,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68,17.94,18.2,18.46,18.72,18.98,19.240000000000002,19.5,19.76,20.02,20.28,20.54,20.8,21.06,21.32,21.580000000000002,21.84,22.1,22.36,22.62,22.88,23.14,23.400000000000002,23.66,23.92,24.18,24.44,24.7,24.96,25.22,25.48,25.740000000000002,26.0,26.26,26.52,26.78,27.04,27.3,27.560000000000002,27.82,28.080000000000002,28.34,28.6,28.86,29.12,29.38,29.64,29.900000000000002,30.16,30.42,30.68,30.94,31.2,31.46,31.720000000000002,31.98],"session":"one session over 124 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":2},"outputs":{"raw_tracks":[{"noun_phrase":"black dog","object_id":0,"observations":[{"timestamp":0.0,"box":[0.3723958432674408,0.4851851761341095,0.6661458611488342,0.8083333075046539],"occluded":false},{"timestamp":0.26,"box":[0.33385419845581055,0.470370352268219,0.6234375536441803,0.8083333075046539],"occluded":false},{"timestamp":0.52,"box":[0.2953125238418579,0.45185184478759766,0.5906250476837158,0.8620370328426361],"occluded":false},{"timestamp":0.78,"box":[0.2526041865348816,0.4407407343387604,0.572916716337204,0.852777749300003],"occluded":false},{"timestamp":1.04,"box":[0.21562501788139343,0.45370370149612427,0.5661458671092987,0.8629629611968994],"occluded":false},{"timestamp":1.3,"box":[0.1953125149011612,0.44722220301628113,0.567708358168602,0.8481481075286865],"occluded":false},{"timestamp":1.56,"box":[0.1848958432674408,0.43703702092170715,0.5489583611488342,0.8175925612449646],"occluded":false},{"timestamp":1.82,"box":[0.1666666716337204,0.4120370149612427,0.7614583820104599,0.8324073851108551],"occluded":false},{"timestamp":2.08,"box":[0.13593751192092896,0.4259259104728699,0.5302083790302277,0.9990740418434143],"occluded":false},{"timestamp":2.34,"box":[0.12031250447034836,0.4157407283782959,0.5250000283122063,0.9990740418434143],"occluded":false},{"timestamp":2.6,"box":[0.16354167461395264,0.40833333134651184,0.5614583492279053,0.8148148059844971],"occluded":false},{"timestamp":2.86,"box":[0.24947917461395264,0.3305555582046509,0.9078125357627869,0.7370370328426361],"occluded":false},{"timestamp":3.12,"box":[0.32864585518836975,0.3472222089767456,0.744791716337204,0.7435185015201569],"occluded":false},{"timestamp":3.38,"box":[0.3921875059604645,0.43611109256744385,0.7968750298023224,0.8120370209217072],"occluded":false},{"timestamp":3.64,"box":[0.40677085518836975,0.4851851761341095,0.7708333730697632,0.852777749300003],"occluded":false},{"timestamp":3.9,"box":[0.39531251788139343,0.5175926089286804,0.7072916924953461,0.8666666746139526],"occluded":false},{"timestamp":4.16,"box":[0.39635419845581055,0.5046296119689941,0.6640625596046448,0.8425925672054291],"occluded":false},{"timestamp":4.42,"box":[0.4203125238418579,0.4888888895511627,0.6510417014360428,0.8638888895511627],"occluded":false},{"timestamp":4.68,"box":[0.4348958432674408,0.4749999940395355,0.6354166865348816,0.875],"occluded":false},{"timestamp":4.94,"box":[0.4166666865348816,0.46759259700775146,0.5932291895151138,0.8935185074806213],"occluded":false},{"timestamp":5.2,"box":[0.40000003576278687,0.44814813137054443,0.5354167073965073,0.912962943315506],"occluded":false},{"timestamp":5.46,"box":[0.3348958492279053,0.5055555701255798,0.44739585369825363,0.8666666746139526],"occluded":false},{"timestamp":5.72,"box":[0.27760419249534607,0.5898147821426392,0.3776042014360428,0.9361110627651215],"occluded":false},{"timestamp":5.98,"box":[0.20468750596046448,0.720370352268219,0.3088541775941849,0.9722222089767456],"occluded":false},{"timestamp":6.24,"box":[0.12916667759418488,0.854629635810852,0.2187500149011612,0.9722222238779068],"occluded":false}],"scores":[0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562,0.9609756469726562]},{"noun_phrase":"black 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brown dog sniffing the stone basin","kind":"interaction","is_negative":true,"tracks":[]}],"rmot_expression_count":3,"rmot_negative_count":1,"rmot_motion_count":1}}}}},{"index":2,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_044.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_044.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":10.32,"frames_kept":40,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":12411656,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the white car parked along the street\",\n \"noun_phrase\": \"white car\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the dark car parked beside the railing by the white building\",\n \"noun_phrase\": \"dark car\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a moving car driving up the road\",\n \"noun_phrase\": \"car\",\n \"kind\": \"motion\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the white car parked along the street","noun_phrase":"white car","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the dark car parked beside the railing by the white building","noun_phrase":"dark 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Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 11 frames, numbered 1 to 11. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the white car parked along the street\n2. the dark car parked beside the railing by the white building\n3. a moving car driving up the road","questions":["the white car parked along the street","the dark car parked beside the railing by the white building","a moving car driving up the road"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [3],\n \"start_frame\": 6,\n \"end_frame\": 11,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [6],\n \"start_frame\": 3,\n \"end_frame\": 8,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 11,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[3],"start_frame":6,"end_frame":11,"confidence":1.0},{"description_index":2,"matching_ids":[6],"start_frame":3,"end_frame":8,"confidence":1.0},{"description_index":3,"matching_ids":[],"start_frame":1,"end_frame":11,"confidence":1.0}],"answers":[{"expression":"the white car parked along the street","matching_presentation_ids":[3],"start_choice_frame":6,"end_choice_frame":11,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"the dark car parked beside the railing by the white building","matching_presentation_ids":[6],"start_choice_frame":3,"end_choice_frame":8,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"a moving car driving up the road","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":11,"confidence":1.0,"self_reported_confidence":1.0}]}},"verify":{"inputs":{"expressions":3,"tracklets":7,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry 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moving car driving up the road","kind":"motion","is_negative":true,"tracks":[]}],"rmot_expression_count":3,"rmot_negative_count":1,"rmot_motion_count":1}}}}},{"index":3,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_002.mp4","status":"complete_no_labels","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_002.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":15.4,"frames_kept":60,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":18605944,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the stone doorway leading into the ruined interior\",\n \"noun_phrase\": \"stone doorway\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the collapsed wooden roof beams overhead inside the ruin\",\n \"noun_phrase\": \"collapsed wooden roof beams\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a closed wooden door set into the stone entryway\",\n \"noun_phrase\": \"wooden door\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the stone doorway leading into the ruined interior","noun_phrase":"stone doorway","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the collapsed wooden roof beams overhead inside the ruin","noun_phrase":"collapsed wooden roof beams","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a closed wooden door set into the stone entryway","noun_phrase":"wooden door","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the stone doorway leading into the ruined interior","noun_phrase":"stone doorway","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the collapsed wooden roof beams overhead inside the ruin","noun_phrase":"collapsed wooden roof beams","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a closed wooden door set into the stone entryway","noun_phrase":"wooden door","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["collapsed wooden roof beams","stone doorway","wooden door"],"expressions_in":3,"frames":60,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34],"session":"one session over 60 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":3},"outputs":{"raw_tracks":[{"noun_phrase":"collapsed wooden roof beams","object_id":0,"observations":[{"timestamp":2.34,"box":[0.3005208373069763,0.031481482088565826,0.6453125178813934,0.14074073731899261],"occluded":false},{"timestamp":2.6,"box":[0.3057291805744171,0.03055555559694767,0.7406250238418579,0.11666666157543659],"occluded":false},{"timestamp":2.86,"box":[0.2953125238418579,0.03055555559694767,0.8057292103767395,0.1185185182839632],"occluded":false},{"timestamp":3.12,"box":[0.15260417759418488,0.031481482088565826,0.6859375387430191,0.15648148208856583],"occluded":false},{"timestamp":3.38,"box":[0.4385416805744171,0.03055555559694767,0.710416704416275,0.15648148395121098],"occluded":false}],"scores":[0.6640419960021973,0.6640419960021973,0.6640419960021973,0.6640419960021973,0.6640419960021973]}],"tracklets":[{"noun_phrase":"collapsed wooden roof beams","object_id":0,"presentation_id":1,"observations":[{"timestamp":2.34,"box":[0.3005208373069763,0.031481482088565826,0.6453125178813934,0.14074073731899261],"occluded":false},{"timestamp":2.6,"box":[0.3057291805744171,0.03055555559694767,0.7406250238418579,0.11666666157543659],"occluded":false},{"timestamp":2.86,"box":[0.2953125238418579,0.03055555559694767,0.8057292103767395,0.1185185182839632],"occluded":false},{"timestamp":3.12,"box":[0.15260417759418488,0.031481482088565826,0.6859375387430191,0.15648148208856583],"occluded":false},{"timestamp":3.38,"box":[0.4385416805744171,0.03055555559694767,0.710416704416275,0.15648148395121098],"occluded":false}],"mean_score":0.6640419960021973}],"dropped":{},"fates":[{"noun_phrase":"collapsed wooden roof beams","object_id":0,"frames":5,"mean_score":0.6640419960021973,"mean_area":0.04418431862962811,"outcome":"kept","presentation_id":1,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null}],"gates":{"min_track_frames":3,"min_mean_score":0.5,"merge_iou":0.8,"max_tracklets":12}}},"render":{"inputs":{"tracklets":1,"as_video":true,"fps":1.0,"max_frames":30},"outputs":{"media":"/home/zzk/tmp/rmot_smoke_real_86267/renders/--cif7Nv6BI_chunk_014_scene_002_choices.mp4","media_bytes":4054783,"frames":[{"index":1,"timestamp":0.0,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":2,"timestamp":1.04,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":3,"timestamp":2.08,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":4,"timestamp":3.12,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":5,"timestamp":4.16,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":6,"timestamp":5.2,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":7,"timestamp":6.24,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":8,"timestamp":7.28,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":9,"timestamp":8.06,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":10,"timestamp":9.1,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":11,"timestamp":10.14,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":12,"timestamp":11.18,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":13,"timestamp":12.22,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":14,"timestamp":13.26,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":15,"timestamp":14.3,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"},{"index":16,"timestamp":15.34,"file":"--cif7Nv6BI_chunk_014_scene_002_choices.mp4"}]}},"select":{"inputs":{"model":"gemini-3.8-flash","temperature":0.0,"as_video":true,"media_bytes":4054783,"media":["--cif7Nv6BI_chunk_014_scene_002_choices.mp4"],"choice_frames":16,"system_prompt":"You are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 16 frames, numbered 1 to 16. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the stone doorway leading into the ruined interior\n2. the collapsed wooden roof beams overhead inside the ruin\n3. a closed wooden door set into the stone entryway","questions":["the stone doorway leading into the ruined interior","the collapsed wooden roof beams overhead inside the ruin","a closed wooden door set into the stone entryway"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 16,\n \"confidence\": 0.95\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [1],\n \"start_frame\": 4,\n \"end_frame\": 4,\n \"confidence\": 0.85\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 16,\n \"confidence\": 0.99\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[],"start_frame":1,"end_frame":16,"confidence":0.95},{"description_index":2,"matching_ids":[1],"start_frame":4,"end_frame":4,"confidence":0.85},{"description_index":3,"matching_ids":[],"start_frame":1,"end_frame":16,"confidence":0.99}],"answers":[{"expression":"the stone doorway leading into the ruined interior","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":16,"confidence":0.95,"self_reported_confidence":0.95},{"expression":"the collapsed wooden roof beams overhead inside the ruin","matching_presentation_ids":[1],"start_choice_frame":4,"end_choice_frame":4,"confidence":0.85,"self_reported_confidence":0.85},{"expression":"a closed wooden door set into the stone entryway","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":16,"confidence":0.99,"self_reported_confidence":0.99}]}},"verify":{"inputs":{"expressions":3,"tracklets":1,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[],"rejected":{"a closed wooden door set into the stone entryway":"no_candidates_retrieved","the stone doorway leading into the ruined interior":"count_mismatch","the collapsed wooden roof beams overhead inside the ruin":"empty_interval"},"rejection_counts":{"no_candidates_retrieved":1,"count_mismatch":1,"empty_interval":1},"row":null}}}},{"index":4,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_008_scene_014.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_008_scene_014.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":11.88,"frames_kept":46,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":15512964,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the dog walking up the stone path\",\n \"noun_phrase\": \"dog\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the man wearing a black jacket standing on the path\",\n \"noun_phrase\": \"man in black jacket\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the dog walking up the stone path","noun_phrase":"dog","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the man wearing a black jacket standing on the path","noun_phrase":"man in black jacket","kind":"appearance","expected_count":1,"is_negative":false}],"accepted":[{"expression":"the dog walking up the stone path","noun_phrase":"dog","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the man wearing a black jacket standing on the path","noun_phrase":"man in black jacket","kind":"appearance","expected_count":1,"is_negative":false}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["dog","man in black jacket"],"expressions_in":2,"frames":46,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001],"session":"one session over 46 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, 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path","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88],"boxes":[[0.5973958373069763,0.3638888895511627,0.6526041738688946,0.6407407224178314],[0.5838541984558105,0.385185182094574,0.6380208693444729,0.6462962925434113],[0.5619791746139526,0.40648147463798523,0.6067708432674408,0.6611110866069794],[0.5640625357627869,0.4138888716697693,0.6114583723247051,0.67037034034729],[0.5281250476837158,0.4268518388271332,0.5786458849906921,0.6925925612449646],[0.5083333849906921,0.4120370149612427,0.5744792222976685,0.680555522441864],[0.4932291805744171,0.4203703701496124,0.5473958514630795,0.6972222030162811],[0.4567708671092987,0.4435185194015503,0.511979203671217,0.7287037074565887],[0.3723958432674408,0.4287036955356598,0.42239584773778915,0.7249999940395355],[0.24062500894069672,0.3712962865829468,0.32083334773778915,0.9990740418434143],[0.4062500298023224,0.4148148000240326,0.500520870089531,0.835185170173645],[0.47968751192092896,0.4314814805984497,0.5546875149011612,0.9277777671813965],[0.5078125,0.46481481194496155,0.5958333387970924,0.972222238779068],[0.5281250476837158,0.432407408952713,0.6140625551342964,0.972222238779068],[0.5338541865348816,0.4148148000240326,0.6312500238418579,0.9712962806224823],[0.5869792103767395,0.4157407283782959,0.697395883500576,0.9722222089767456],[0.6291667222976685,0.39814814925193787,0.7557292282581329,0.972222238779068],[0.6500000357627869,0.39629629254341125,0.8026042133569717,0.9712962806224823],[0.6328125596046448,0.42222222685813904,0.8234375715255737,0.9749999940395355],[0.6067708730697632,0.3731481432914734,0.8317708820104599,0.9740740656852722],[0.5802083611488342,0.3740740716457367,0.8166667073965073,0.9759258925914764],[0.24635417759418488,0.2935185134410858,0.46822918951511383,0.9731481373310089]]}]}],"rmot_expression_count":1,"rmot_negative_count":0,"rmot_motion_count":0}}}}},{"index":5,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_001_scene_010.mp4","status":"complete_no_labels","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_001_scene_010.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":14.72,"frames_kept":57,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":14433278,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the bell hanging inside the belfry\",\n \"noun_phrase\": \"bell\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the rooster sculpture atop the roof\",\n \"noun_phrase\": \"rooster sculpture\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the small statue standing in the wall niche above the doorway\",\n \"noun_phrase\": \"statue\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the bell hanging inside the belfry","noun_phrase":"bell","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the rooster sculpture atop the roof","noun_phrase":"rooster sculpture","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the small statue standing in the wall niche above the doorway","noun_phrase":"statue","kind":"spatial","expected_count":1,"is_negative":false}],"accepted":[{"expression":"the bell hanging inside the belfry","noun_phrase":"bell","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the rooster sculpture atop the roof","noun_phrase":"rooster sculpture","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the small statue standing in the wall niche above the doorway","noun_phrase":"statue","kind":"spatial","expected_count":1,"is_negative":false}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["bell","rooster 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are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 16 frames, numbered 1 to 16. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the bell hanging inside the belfry\n2. the rooster sculpture atop the roof\n3. the small statue standing in the wall niche above the doorway","questions":["the bell hanging inside the belfry","the rooster sculpture atop the roof","the small statue standing in the wall niche above the doorway"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [2],\n \"start_frame\": 1,\n \"end_frame\": 16,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 16,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [1],\n \"start_frame\": 14,\n \"end_frame\": 14,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[2],"start_frame":1,"end_frame":16,"confidence":1.0},{"description_index":2,"matching_ids":[],"start_frame":1,"end_frame":16,"confidence":1.0},{"description_index":3,"matching_ids":[1],"start_frame":14,"end_frame":14,"confidence":1.0}],"answers":[{"expression":"the bell hanging inside the belfry","matching_presentation_ids":[2],"start_choice_frame":1,"end_choice_frame":16,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"the rooster sculpture atop the roof","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":16,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"the small statue standing in the wall niche above the doorway","matching_presentation_ids":[1],"start_choice_frame":14,"end_choice_frame":14,"confidence":1.0,"self_reported_confidence":1.0}]}},"verify":{"inputs":{"expressions":3,"tracklets":2,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[],"rejected":{"the bell hanging inside the belfry":"implausible_box","the rooster sculpture atop the roof":"count_mismatch","the small statue standing in the wall niche above the doorway":"empty_interval"},"rejection_counts":{"implausible_box":1,"count_mismatch":1,"empty_interval":1},"row":null}}}},{"index":6,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_006_scene_028.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_006_scene_028.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":16.14,"frames_kept":63,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":16693150,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\"expression\": \"the rectangular white information sign mounted near the base of the boulders\", \"noun_phrase\": \"white sign\", \"kind\": \"appearance\", \"expected_count\": 1, \"is_negative\": false},\n {\"expression\": \"the wooden pallet resting on the ground by the rocks on the right\", \"noun_phrase\": \"wooden pallet\", \"kind\": \"spatial\", \"expected_count\": 1, \"is_negative\": false},\n {\"expression\": \"a person walking along the cobblestone path\", \"noun_phrase\": \"person\", \"kind\": \"appearance\", \"expected_count\": 0, \"is_negative\": true}\n ]\n}","raw_items":[{"expression":"the rectangular white information sign mounted near the base of the boulders","noun_phrase":"white sign","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the wooden pallet resting on the ground by the rocks on the right","noun_phrase":"wooden pallet","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a person walking along the cobblestone path","noun_phrase":"person","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the rectangular white information sign mounted near the base of the boulders","noun_phrase":"white sign","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the wooden pallet resting on the ground by the rocks on the right","noun_phrase":"wooden pallet","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a person walking along the cobblestone path","noun_phrase":"person","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["person","white sign","wooden 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are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the woman talking in front of the shop window who steps out of view\",\n \"noun_phrase\": \"woman\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a woman wearing a hat\",\n \"noun_phrase\": \"woman\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the woman talking in front of the shop window who steps out of view","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a woman wearing a hat","noun_phrase":"woman","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the woman talking in front of the shop window who steps out of view","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a woman wearing a hat","noun_phrase":"woman","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["woman"],"expressions_in":2,"frames":69,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68],"session":"one session over 69 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":1},"outputs":{"raw_tracks":[{"noun_phrase":"woman","object_id":0,"observations":[{"timestamp":0.0,"box":[0.2994791865348816,0.17129629850387573,0.7786458730697632,0.9712963104248047],"occluded":false},{"timestamp":0.26,"box":[0.30937501788139343,0.1787036955356598,0.7463541924953461,0.9722221791744232],"occluded":false},{"timestamp":0.52,"box":[0.2942708432674408,0.182407408952713,0.7125000357627869,0.9731481373310089],"occluded":false},{"timestamp":0.78,"box":[0.3135416805744171,0.19814814627170563,0.7161458730697632,0.9740740805864334],"occluded":false},{"timestamp":1.04,"box":[0.11562500894069672,0.23148147761821747,0.7416667193174362,0.974074050784111],"occluded":false},{"timestamp":1.3,"box":[0.37916669249534607,0.260185182094574,0.7546875476837158,0.9731481075286865],"occluded":false},{"timestamp":1.56,"box":[0.19947917759418488,0.287962943315506,0.7682292014360428,0.9731481373310089],"occluded":false},{"timestamp":1.82,"box":[0.22708334028720856,0.2657407224178314,0.7718750387430191,0.9722221791744232],"occluded":false},{"timestamp":2.08,"box":[0.40781253576278687,0.2657407224178314,0.7843750417232513,0.9722221791744232],"occluded":false},{"timestamp":2.34,"box":[0.4140625298023224,0.2481481432914734,0.8000000417232513,0.9722222089767456],"occluded":false},{"timestamp":2.6,"box":[0.17135417461395264,0.22962962090969086,0.8088542222976685,0.9712962955236435],"occluded":false},{"timestamp":2.86,"box":[0.41822919249534607,0.21944443881511688,0.8083333671092987,0.9722221940755844],"occluded":false},{"timestamp":3.12,"box":[0.3916666805744171,0.20370370149612427,0.7895833551883698,0.9722222089767456],"occluded":false},{"timestamp":3.38,"box":[0.37291669845581055,0.18703703582286835,0.7848958969116211,0.9722221940755844],"occluded":false},{"timestamp":3.64,"box":[0.37135419249534607,0.16018518805503845,0.7901042103767395,0.9712962806224823],"occluded":false},{"timestamp":3.9,"box":[0.36250001192092896,0.15648147463798523,0.7791666984558105,0.9731481373310089],"occluded":false},{"timestamp":4.16,"box":[0.3265625238418579,0.17222221195697784,0.7406250536441803,0.9740740209817886],"occluded":false},{"timestamp":4.42,"box":[0.31197917461395264,0.1935185194015503,0.7145833671092987,0.9740740656852722],"occluded":false},{"timestamp":4.68,"box":[0.2984375059604645,0.19907407462596893,0.6927083730697632,0.9731481522321701],"occluded":false},{"timestamp":4.94,"box":[0.2848958373069763,0.18518517911434174,0.679166704416275,0.9731481522321701],"occluded":false},{"timestamp":5.2,"box":[0.28177085518836975,0.1814814805984497,0.6770833730697632,0.9740740656852722],"occluded":false},{"timestamp":5.46,"box":[0.2760416865348816,0.18703703582286835,0.6755208671092987,0.9750000089406967],"occluded":false},{"timestamp":5.72,"box":[0.2578125,0.19074073433876038,0.6744791865348816,0.9740740358829498],"occluded":false},{"timestamp":5.98,"box":[0.26979169249534607,0.19629628956317902,0.6817708909511566,0.9759258776903152],"occluded":false},{"timestamp":6.24,"box":[0.2828125059604645,0.192592591047287,0.694791704416275,0.9759258925914764],"occluded":false},{"timestamp":6.5,"box":[0.2968750298023224,0.17685185372829437,0.7161458730697632,0.9722221940755844],"occluded":false},{"timestamp":6.76,"box":[0.30156251788139343,0.1657407432794571,0.7281250357627869,0.9731481224298477],"occluded":false},{"timestamp":7.0200000000000005,"box":[0.29479166865348816,0.14999999105930328,0.7270833551883698,0.9722222238779068],"occluded":false},{"timestamp":7.28,"box":[0.2942708432674408,0.14351852238178253,0.7364583611488342,0.9722222238779068],"occluded":false},{"timestamp":7.54,"box":[0.30156251788139343,0.13518518209457397,0.7473958730697632,0.9731481075286865],"occluded":false},{"timestamp":7.8,"box":[0.3083333373069763,0.14999999105930328,0.7562500238418579,0.9731481224298477],"occluded":false},{"timestamp":8.06,"box":[0.30781251192092896,0.1509259194135666,0.7520833611488342,0.9722221940755844],"occluded":false},{"timestamp":8.32,"box":[0.30781251192092896,0.1509259194135666,0.7468750476837158,0.9731481522321701],"occluded":false},{"timestamp":8.58,"box":[0.29635417461395264,0.13611111044883728,0.7437500357627869,0.9722221791744232],"occluded":false},{"timestamp":8.84,"box":[0.2942708432674408,0.138888880610466,0.7385416924953461,0.9722221940755844],"occluded":false},{"timestamp":9.1,"box":[0.29114586114883423,0.14351852238178253,0.7312500476837158,0.9731481224298477],"occluded":false},{"timestamp":9.36,"box":[0.2786458432674408,0.14537036418914795,0.7151041924953461,0.9731481075286865],"occluded":false},{"timestamp":9.620000000000001,"box":[0.2734375,0.15555554628372192,0.7026041746139526,0.9722222089767456],"occluded":false},{"timestamp":9.88,"box":[0.2614583373069763,0.1546296328306198,0.6895833611488342,0.9731481522321701],"occluded":false},{"timestamp":10.14,"box":[0.2526041865348816,0.1537037044763565,0.6786458790302277,0.9731481224298477],"occluded":false},{"timestamp":10.4,"box":[0.25052085518836975,0.1453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Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the woman talking in front of the shop window who steps out of view\n2. a woman wearing a hat","questions":["the woman talking in front of the shop window who steps out of view","a woman wearing a hat"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [1],\n \"start_frame\": 1,\n \"end_frame\": 16,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 19,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[1],"start_frame":1,"end_frame":16,"confidence":1.0},{"description_index":2,"matching_ids":[],"start_frame":1,"end_frame":19,"confidence":1.0}],"answers":[{"expression":"the woman talking in front of the shop window who steps out of 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"a woman wearing a hat","kind":"appearance","is_negative":true,"tracks":[]}],"rejected":{},"rejection_counts":{},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_013.mp4","scene_key":"--cif7Nv6BI_chunk_014_scene_013","category":"test_vehicles","height":1080,"width":1920,"fps":50.0,"num_frames":893,"duration":17.86,"rmot_expressions":[{"expression":"the woman talking in front of the shop window who steps out of view","kind":"motion","is_negative":false,"tracks":[{"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82],"boxes":[[0.2994791865348816,0.17129629850387573,0.7786458730697632,0.9712963104248047],[0.30937501788139343,0.1787036955356598,0.7463541924953461,0.9722221791744232],[0.2942708432674408,0.182407408952713,0.7125000357627869,0.9731481373310089],[0.3135416805744171,0.19814814627170563,0.7161458730697632,0.9740740805864334],[0.11562500894069672,0.23148147761821747,0.7416667193174362,0.974074050784111],[0.37916669249534607,0.260185182094574,0.7546875476837158,0.9731481075286865],[0.19947917759418488,0.287962943315506,0.7682292014360428,0.9731481373310089],[0.22708334028720856,0.2657407224178314,0.7718750387430191,0.9722221791744232],[0.40781253576278687,0.2657407224178314,0.7843750417232513,0.9722221791744232],[0.4140625298023224,0.2481481432914734,0.8000000417232513,0.9722222089767456],[0.17135417461395264,0.22962962090969086,0.8088542222976685,0.9712962955236435],[0.41822919249534607,0.21944443881511688,0.8083333671092987,0.9722221940755844],[0.3916666805744171,0.20370370149612427,0.7895833551883698,0.9722222089767456],[0.37291669845581055,0.18703703582286835,0.7848958969116211,0.9722221940755844],[0.37135419249534607,0.16018518805503845,0.7901042103767395,0.9712962806224823],[0.36250001192092896,0.15648147463798523,0.7791666984558105,0.9731481373310089],[0.3265625238418579,0.17222221195697784,0.7406250536441803,0.9740740209817886],[0.31197917461395264,0.1935185194015503,0.7145833671092987,0.9740740656852722],[0.2984375059604645,0.19907407462596893,0.6927083730697632,0.9731481522321701],[0.2848958373069763,0.18518517911434174,0.679166704416275,0.9731481522321701],[0.28177085518836975,0.1814814805984497,0.6770833730697632,0.9740740656852722],[0.2760416865348816,0.18703703582286835,0.6755208671092987,0.9750000089406967],[0.2578125,0.19074073433876038,0.6744791865348816,0.9740740358829498],[0.26979169249534607,0.19629628956317902,0.6817708909511566,0.9759258776903152],[0.2828125059604645,0.192592591047287,0.694791704416275,0.9759258925914764],[0.2968750298023224,0.17685185372829437,0.7161458730697632,0.9722221940755844],[0.30156251788139343,0.1657407432794571,0.7281250357627869,0.9731481224298477],[0.29479166865348816,0.14999999105930328,0.7270833551883698,0.9722222238779068],[0.2942708432674408,0.14351852238178253,0.7364583611488342,0.9722222238779068],[0.30156251788139343,0.13518518209457397,0.7473958730697632,0.9731481075286865],[0.3083333373069763,0.14999999105930328,0.7562500238418579,0.9731481224298477],[0.30781251192092896,0.1509259194135666,0.7520833611488342,0.9722221940755844],[0.30781251192092896,0.1509259194135666,0.7468750476837158,0.9731481522321701],[0.29635417461395264,0.13611111044883728,0.7437500357627869,0.9722221791744232],[0.2942708432674408,0.138888880610466,0.7385416924953461,0.9722221940755844],[0.29114586114883423,0.14351852238178253,0.7312500476837158,0.9731481224298477],[0.2786458432674408,0.14537036418914795,0.7151041924953461,0.9731481075286865],[0.2734375,0.15555554628372192,0.7026041746139526,0.9722222089767456],[0.2614583373069763,0.1546296328306198,0.6895833611488342,0.9731481522321701],[0.2526041865348816,0.1537037044763565,0.6786458790302277,0.9731481224298477],[0.25052085518836975,0.14537036418914795,0.6812500357627869,0.9722222089767456],[0.24687501788139343,0.16111110150814056,0.6760416924953461,0.9777777642011642],[0.2473958432674408,0.18333333730697632,0.6802083551883698,0.9740740656852722],[0.24635417759418488,0.16296295821666718,0.6833333522081375,0.974074050784111],[0.2343750149011612,0.14814814925193787,0.680208370089531,0.9749999940395355],[0.26875001192092896,0.16388888657093048,0.7161458730697632,0.9731481224298477],[0.2671875059604645,0.1787036955356598,0.7140625417232513,0.9740740358829498],[0.2723958492279053,0.17685185372829437,0.7161458730697632,0.9731481522321701],[0.2848958373069763,0.182407408952713,0.7265625298023224,0.9740740358829498],[0.3187500238418579,0.18981480598449707,0.7656250596046448,0.9740740656852722],[0.3609375059604645,0.1527777761220932,0.8343750238418579,0.9712962955236435],[0.41979169845581055,0.13333332538604736,0.8947917222976685,0.9722222089767456],[0.4208333492279053,0.1379629671573639,0.8656250536441803,0.9731481373310089],[0.35572919249534607,0.17592592537403107,0.7588542103767395,0.9722222238779068],[0.2526041865348816,0.182407408952713,0.6328125298023224,0.972222238779068],[0.1302083432674408,0.1527777761220932,0.5536458790302277,0.9722221940755844],[0.09270834177732468,0.10462962836027145,0.5416667088866234,0.972222201526165],[0.0,0.028703702613711357,0.4427083432674408,0.971296263858676]]}]},{"expression":"a woman wearing a hat","kind":"appearance","is_negative":true,"tracks":[]}],"rmot_expression_count":2,"rmot_negative_count":1,"rmot_motion_count":1}}}}},{"index":8,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--Dx9HVcQWs_chunk_002_scene_013.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--Dx9HVcQWs_chunk_002_scene_013.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":23.976023976023978,"duration_seconds":16.016,"frames_kept":65,"blank_frames_dropped":0,"timestamps":[0.0,0.25025,0.5005,0.75075,1.001,1.25125,1.5015,1.75175,2.002,2.25225,2.5025,2.75275,3.003,3.25325,3.5035,3.7537499999999997,4.004,4.25425,4.5045,4.75475,5.005,5.25525,5.5055,5.75575,6.006,6.25625,6.5065,6.756749999999999,7.007,7.25725,7.507499999999999,7.75775,8.008,8.25825,8.5085,8.75875,9.009,9.25925,9.5095,9.75975,10.01,10.26025,10.5105,10.76075,11.011,11.26125,11.5115,11.76175,12.012,12.26225,12.5125,12.762749999999999,13.013,13.26325,13.513499999999999,13.76375,14.014,14.264249999999999,14.5145,14.76475,15.014999999999999,15.26525,15.5155,15.765749999999999,16.016],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":6122951,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the man gesturing with his hands while speaking\",\n \"noun_phrase\": \"man in green hoodie\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the black and neon green baseball cap\",\n \"noun_phrase\": \"baseball cap\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a person wearing glasses\",\n \"noun_phrase\": \"person\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the man gesturing with his hands while speaking","noun_phrase":"man in green hoodie","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the black and neon green baseball cap","noun_phrase":"baseball 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person wearing glasses":"no_candidates_retrieved","the man gesturing with his hands while speaking":"stationary_motion_expression"},"rejection_counts":{"no_candidates_retrieved":1,"stationary_motion_expression":1},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--Dx9HVcQWs_chunk_002_scene_013.mp4","scene_key":"--Dx9HVcQWs_chunk_002_scene_013","category":"test_vehicles","height":1080,"width":1920,"fps":23.976023976023978,"num_frames":384,"duration":16.016,"rmot_expressions":[{"expression":"the black and neon green baseball 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are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the cyclist leading the group in the center\",\n \"noun_phrase\": \"cyclist\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the cyclist on the right who falls behind the others\",\n \"noun_phrase\": \"cyclist\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a parked car on the road shoulder\",\n \"noun_phrase\": \"car\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the cyclist leading the group in the center","noun_phrase":"cyclist","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the cyclist on the right who falls behind the others","noun_phrase":"cyclist","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a parked car on the road shoulder","noun_phrase":"car","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the cyclist leading the group in the center","noun_phrase":"cyclist","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the cyclist on the right who falls behind the others","noun_phrase":"cyclist","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a parked car on the road 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others","kind":"motion","is_negative":false,"tracks":[{"timestamps":[0.0,0.25025,0.5005,0.75075,1.001,1.25125,1.5015,1.75175,2.002,2.25225,2.5025,2.75275,3.003,3.25325,3.5035,3.7537499999999997,4.004,4.25425,4.5045,4.75475,5.005,5.25525,5.5055,5.75575,6.25625,7.25725,7.507499999999999,7.75775,8.008,8.25825,8.75875,9.009,9.25925,9.5095,9.75975,10.01,10.26025,10.5105,10.76075,11.011,11.26125,11.5115,11.76175,12.012,12.26225,12.5125,12.762749999999999,13.013,13.26325,13.513499999999999,13.76375,14.014],"boxes":[[0.5869792103767395,0.3722222149372101,0.6781250461935997,0.6888888776302338],[0.5979167222976685,0.36574074625968933,0.6802083924412727,0.6833333373069763],[0.5953125357627869,0.37037035822868347,0.6786458715796471,0.6666666567325592],[0.5958333611488342,0.3759259283542633,0.6750000342726707,0.6583333313465118],[0.5932291746139526,0.3768518567085266,0.6687500104308128,0.6666666567325592],[0.5864583849906921,0.3861111104488373,0.6583333909511566,0.5499999970197678],[0.5843750238418579,0.39351850748062134,0.6546875238418579,0.6481481194496155],[0.5671875476837158,0.39537036418914795,0.635416716337204,0.6648148000240326],[0.5645833611488342,0.39537036418914795,0.6307291984558105,0.6712962985038757],[0.5630208849906921,0.39907407760620117,0.6260417252779007,0.6509259343147278],[0.5593750476837158,0.4037036895751953,0.6218750476837158,0.6499999761581421],[0.5598958730697632,0.4046296179294586,0.6171875409781933,0.6601851582527161],[0.5588542222976685,0.4055555462837219,0.6093750596046448,0.6499999910593033],[0.5505208373069763,0.412962943315506,0.6072916723787785,0.6398147940635681],[0.5640625357627869,0.4435185194015503,0.6036458723247051,0.6129629611968994],[0.5640625357627869,0.5425925850868225,0.5750000365078449,0.6527777686715126],[0.5666667222976685,0.4722222089767456,0.5822917222976685,0.5879629477858543],[0.5588542222976685,0.4722222089767456,0.5848958902060986,0.6249999850988388],[0.5453125238418579,0.49166664481163025,0.58645835891366,0.6509259045124054],[0.5390625,0.44814813137054443,0.5864583365619183,0.6518518328666687],[0.5432292222976685,0.4314814805984497,0.5864583924412727,0.635185182094574],[0.5489583611488342,0.4277777671813965,0.5703125298023224,0.571296289563179],[0.5604166984558105,0.42499998211860657,0.5781250335276127,0.5925925672054291],[0.5770833492279053,0.45092591643333435,0.5968750175088644,0.5537036955356598],[0.5901042222976685,0.5046296119689941,0.604166722856462,0.5416666492819786],[0.6015625596046448,0.43888887763023376,0.6260417271405458,0.527777761220932],[0.5843750238418579,0.4166666567325592,0.60729169100523,0.5268518403172493],[0.5671875476837158,0.4175925850868225,0.59218754991889,0.6444444358348846],[0.5552083849906921,0.4194444417953491,0.5750000532716513,0.6240740716457367],[0.542187511920929,0.4203703701496124,0.5921875163912773,0.6537037044763565],[0.35208335518836975,0.4287036955356598,0.5718750357627869,0.6370370239019394],[0.5057291984558105,0.4259259104728699,0.5552083663642406,0.6518518328666687],[0.5062500238418579,0.4268518388271332,0.5572916939854622,0.6537036895751953],[0.4911458492279053,0.4296296238899231,0.5536458492279053,0.635185182094574],[0.5,0.4296296238899231,0.5484375022351742,0.6398148089647293],[0.5005208849906921,0.4296296238899231,0.5489583872258663,0.64907406270504],[0.483854204416275,0.43611109256744385,0.5317708738148212,0.6416666507720947],[0.4843750298023224,0.43888887763023376,0.5307292006909847,0.6342592388391495],[0.5,0.43611109256744385,0.5401041693985462,0.6407407224178314],[0.5036458373069763,0.43981480598449707,0.5401041731238365,0.6370370239019394],[0.5041667222976685,0.43888887763023376,0.5395833924412727,0.6212962865829468],[0.5140625238418579,0.4435185194015503,0.5375000257045031,0.635185182094574],[0.5083333849906921,0.4453703761100769,0.5401042215526104,0.6296296268701553],[0.5083333849906921,0.44814813137054443,0.5406250543892384,0.6222222000360489],[0.5026041865348816,0.44999998807907104,0.5364583544433117,0.6231481283903122],[0.499479204416275,0.44814813137054443,0.5333333723247051,0.6277777552604675],[0.4906250238418579,0.4277777671813965,0.5234375260770321,0.6185185015201569],[0.4911458492279053,0.45185184478759766,0.5213541835546494,0.6166666597127914],[0.4973958730697632,0.45277777314186096,0.5296875424683094,0.6240740716457367],[0.4911458492279053,0.4546296298503876,0.5255208499729633,0.6175925880670547],[0.49635419249534607,0.46388888359069824,0.5239583607763052,0.6064814776182175],[0.49635419249534607,0.460185170173645,0.5151041932404041,0.5870370268821716]]}]}],"rmot_expression_count":2,"rmot_negative_count":0,"rmot_motion_count":1}}}}},{"index":10,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_015_scene_004.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_015_scene_004.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":20.48,"frames_kept":79,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68,17.94,18.2,18.46,18.72,18.98,19.240000000000002,19.5,19.76,20.02,20.28],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":32060538,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the woman who shields her eyes with her hand\",\n \"noun_phrase\": \"woman\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the bearded man with long dreadlocks\",\n \"noun_phrase\": \"bearded man with dreadlocks\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the bearded man wearing a white hoodie\",\n \"noun_phrase\": \"bearded man\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the woman who shields her eyes with her hand","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the bearded man with long dreadlocks","noun_phrase":"bearded man with dreadlocks","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the bearded man wearing a white hoodie","noun_phrase":"bearded man","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the woman who shields her eyes with her hand","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the bearded man with long dreadlocks","noun_phrase":"bearded man with dreadlocks","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the bearded man wearing a white hoodie","noun_phrase":"bearded man","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["bearded man","bearded man with 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If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 21 frames, numbered 1 to 21. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the woman who shields her eyes with her hand\n2. the bearded man with long dreadlocks\n3. the bearded man wearing a white hoodie","questions":["the woman who shields her eyes with her hand","the bearded man with long dreadlocks","the bearded man wearing a white hoodie"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [1],\n \"start_frame\": 9,\n \"end_frame\": 21,\n \"confidence\": 0.75\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [2],\n \"start_frame\": 1,\n \"end_frame\": 21,\n \"confidence\": 0.75\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 21,\n \"confidence\": 0.7\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[1],"start_frame":9,"end_frame":21,"confidence":0.75},{"description_index":2,"matching_ids":[2],"start_frame":1,"end_frame":21,"confidence":0.75},{"description_index":3,"matching_ids":[],"start_frame":1,"end_frame":21,"confidence":0.7}],"answers":[{"expression":"the woman who shields her eyes with her hand","matching_presentation_ids":[1],"start_choice_frame":9,"end_choice_frame":21,"confidence":0.75,"self_reported_confidence":0.75},{"expression":"the bearded man with long dreadlocks","matching_presentation_ids":[2],"start_choice_frame":1,"end_choice_frame":21,"confidence":0.75,"self_reported_confidence":0.75},{"expression":"the bearded man wearing a white hoodie","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":21,"confidence":0.7,"self_reported_confidence":0.7}]}},"verify":{"inputs":{"expressions":3,"tracklets":3,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[{"expression":"the woman who shields her eyes with her 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bearded man wearing a white hoodie":"no_candidates_retrieved"},"rejection_counts":{"no_candidates_retrieved":1},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_015_scene_004.mp4","scene_key":"--cif7Nv6BI_chunk_015_scene_004","category":"test_vehicles","height":1080,"width":1920,"fps":50.0,"num_frames":1024,"duration":20.48,"rmot_expressions":[{"expression":"the woman who shields her eyes with her 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are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the giant boulder resting on the roof of the stone house\",\n \"noun_phrase\": \"giant boulder\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the red double doors of the house beneath the boulder\",\n \"noun_phrase\": \"red door\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the stone bell tower rising above the village rooftops\",\n \"noun_phrase\": \"stone bell tower\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the giant boulder resting on the roof of the stone house","noun_phrase":"giant boulder","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the red double doors of the house beneath 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"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":44088561,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the bearded man walking along the stone street at night\",\n \"noun_phrase\": \"bearded man\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the silver car parked beside the illuminated building\",\n \"noun_phrase\": \"silver car\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a moving car driving through the street\",\n \"noun_phrase\": \"car\",\n \"kind\": \"motion\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the bearded man walking along the stone street at night","noun_phrase":"bearded man","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the silver car parked beside the illuminated building","noun_phrase":"silver 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Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the bearded man walking along the stone street at night\n2. the silver car parked beside the illuminated building\n3. a moving car driving through the street","questions":["the bearded man walking along the stone street at night","the silver car parked beside the illuminated building","a moving car driving through the street"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [4],\n \"start_frame\": 2,\n \"end_frame\": 4,\n \"confidence\": 0.72\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [3],\n \"start_frame\": 13,\n \"end_frame\": 22,\n \"confidence\": 0.7\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 30,\n \"confidence\": 0.75\n }\n 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moving car driving through the street","kind":"motion","is_negative":true,"tracks":[]}],"rmot_expression_count":3,"rmot_negative_count":1,"rmot_motion_count":2}}}}},{"index":13,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_042.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_000_scene_042.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":10.56,"frames_kept":41,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":13357762,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the white ceramic teapot sitting on a saucer\",\n \"noun_phrase\": \"white teapot\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the glass carafe containing orange juice next to the bread\",\n \"noun_phrase\": \"glass carafe\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the white ceramic teapot sitting on a saucer","noun_phrase":"white teapot","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the glass carafe containing orange juice next to the bread","noun_phrase":"glass carafe","kind":"spatial","expected_count":1,"is_negative":false}],"accepted":[{"expression":"the white ceramic teapot sitting on a saucer","noun_phrase":"white teapot","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"the glass carafe containing orange juice next to the bread","noun_phrase":"glass carafe","kind":"spatial","expected_count":1,"is_negative":false}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["glass carafe","white teapot"],"expressions_in":2,"frames":41,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4],"session":"one session over 41 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":2},"outputs":{"raw_tracks":[{"noun_phrase":"glass 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saucer","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16],"boxes":[[0.4442708492279053,0.1953703612089157,0.7598958611488342,0.6703703552484512],[0.38072919845581055,0.1675925850868225,0.8979167342185974,0.6379629373550415],[0.3203125298023224,0.13148148357868195,0.6312500536441803,0.6055555492639542],[0.25416669249534607,0.08703703433275223,0.5713542103767395,0.5712962821125984],[0.19583334028720856,0.060185182839632034,0.5192708522081375,0.5555555410683155],[0.125,0.06388888508081436,0.46041667461395264,0.5749999657273293],[0.04739583656191826,0.1111111119389534,0.6479166857898235,0.6472222283482552],[0.0,0.19907407462596893,0.3526041805744171,0.7759259194135666],[0.0,0.29629629850387573,0.31302085518836975,0.9592592716217041],[0.0,0.49444442987442017,0.27552086114883423,0.9712962806224823],[0.0,0.6472222208976746,0.21562501788139343,0.9712962806224823],[0.0,0.7453703284263611,0.17343750596046448,0.9712962508201599],[0.0,0.824999988079071,0.13125000894069672,0.9712962806224823],[0.0,0.825925886631012,0.13333334028720856,0.9712962508201599],[0.0,0.7944444417953491,0.17395834624767303,0.9712962955236435],[0.0,0.7555555701255798,0.27031251788139343,0.972222238779068],[0.0,0.6092592477798462,0.3828125298023224,0.9722222089767456]]}]},{"expression":"the glass carafe containing orange juice next to the bread","kind":"spatial","is_negative":false,"tracks":[{"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16],"boxes":[[0.9093750715255737,0.032407406717538834,0.999479241669178,0.4305555559694767],[0.8494791984558105,0.031481482088565826,0.999479204416275,0.5537037029862404],[0.7963542342185974,0.03055555559694767,0.9234375804662704,0.3416666481643915],[0.7437500357627869,0.03055555559694767,0.8458333760499954,0.28888888098299503],[0.6989583969116211,0.03055555559694767,0.7906250655651093,0.24999999441206455],[0.6484375596046448,0.03055555559694767,0.7322917282581329,0.2574074063450098],[0.6020833849906921,0.03055555559694767,0.6864583864808083,0.2833333406597376],[0.5697916746139526,0.031481482088565826,0.6854166835546494,0.33796296268701553],[0.5473958849906921,0.03055555559694767,0.6812500655651093,0.4768518302589655],[0.5276042222976685,0.03055555559694767,0.6781250685453415,0.7129629347473383],[0.510937511920929,0.06851851940155029,0.6703125238418579,0.6990740895271301],[0.5052083730697632,0.10277777910232544,0.6734375506639481,0.7824074029922485],[0.5057291984558105,0.13703703880310059,0.679166704416275,0.6953703761100769],[0.5208333730697632,0.14629629254341125,0.697916716337204,0.7101851999759674],[0.5479167103767395,0.14999999105930328,0.7250000536441803,0.7138888984918594],[0.6109375357627869,0.14999999105930328,0.7963542193174362,0.7101851850748062],[0.6916667222976685,0.14259259402751923,0.8817708939313889,0.6962962597608566]]}]}],"rmot_expression_count":2,"rmot_negative_count":0,"rmot_motion_count":0}}}}},{"index":14,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_019.mp4","status":"complete_no_labels","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_019.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":11.540000000000001,"frames_kept":45,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":16631425,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the white car parked further up the street\",\n \"noun_phrase\": \"white car\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the decorative green metal balcony railing on the upper floor\",\n \"noun_phrase\": \"green metal balcony railing\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a red car parked on the street\",\n \"noun_phrase\": \"red car\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the white car parked further up the street","noun_phrase":"white car","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the decorative green metal balcony railing on the upper floor","noun_phrase":"green metal balcony railing","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"a red car parked on the street","noun_phrase":"red car","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the white car parked further up the street","noun_phrase":"white car","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the decorative green metal balcony railing on the upper floor","noun_phrase":"green metal balcony railing","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"a red car parked on the street","noun_phrase":"red car","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["green metal balcony railing","red car","white car"],"expressions_in":3,"frames":45,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44],"session":"one session over 45 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":3},"outputs":{"raw_tracks":[{"noun_phrase":"green metal balcony 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metal balcony railing","object_id":0,"frames":18,"mean_score":0.9649122953414917,"mean_area":0.07926727044948621,"outcome":"kept","presentation_id":3,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null},{"noun_phrase":"green metal balcony railing","object_id":3,"frames":15,"mean_score":0.9532163739204407,"mean_area":0.256874492173056,"outcome":"kept","presentation_id":4,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null},{"noun_phrase":"white car","object_id":0,"frames":5,"mean_score":0.7460317611694336,"mean_area":0.003710455494633888,"outcome":"kept","presentation_id":2,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null},{"noun_phrase":"white car","object_id":1,"frames":3,"mean_score":0.7142857313156128,"mean_area":0.005257362604990157,"outcome":"kept","presentation_id":1,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null}],"gates":{"min_track_frames":3,"min_mean_score":0.5,"merge_iou":0.8,"max_tracklets":12}}},"render":{"inputs":{"tracklets":4,"as_video":true,"fps":1.0,"max_frames":30},"outputs":{"media":"/home/zzk/tmp/rmot_smoke_real_86267/renders/--cif7Nv6BI_chunk_011_scene_019_choices.mp4","media_bytes":3809091,"frames":[{"index":1,"timestamp":0.0,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":2,"timestamp":1.04,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":3,"timestamp":2.08,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":4,"timestamp":3.12,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":5,"timestamp":4.16,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":6,"timestamp":5.2,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":7,"timestamp":6.24,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":8,"timestamp":7.28,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":9,"timestamp":8.32,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":10,"timestamp":9.36,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":11,"timestamp":10.4,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"},{"index":12,"timestamp":11.44,"file":"--cif7Nv6BI_chunk_011_scene_019_choices.mp4"}]}},"select":{"inputs":{"model":"gemini-3.8-flash","temperature":0.0,"as_video":true,"media_bytes":3809091,"media":["--cif7Nv6BI_chunk_011_scene_019_choices.mp4"],"choice_frames":12,"system_prompt":"You are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 12 frames, numbered 1 to 12. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the white car parked further up the street\n2. the decorative green metal balcony railing on the upper floor\n3. a red car parked on the street","questions":["the white car parked further up the street","the decorative green metal balcony railing on the upper floor","a red car parked on the street"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 12,\n \"confidence\": 0.95\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [3, 4],\n \"start_frame\": 1,\n \"end_frame\": 9,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [2],\n \"start_frame\": 5,\n \"end_frame\": 5,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[],"start_frame":1,"end_frame":12,"confidence":0.95},{"description_index":2,"matching_ids":[3,4],"start_frame":1,"end_frame":9,"confidence":1.0},{"description_index":3,"matching_ids":[2],"start_frame":5,"end_frame":5,"confidence":1.0}],"answers":[{"expression":"the white car parked further up the street","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":12,"confidence":0.95,"self_reported_confidence":0.95},{"expression":"the decorative green metal balcony railing on the upper floor","matching_presentation_ids":[3,4],"start_choice_frame":1,"end_choice_frame":9,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"a red car parked on the street","matching_presentation_ids":[2],"start_choice_frame":5,"end_choice_frame":5,"confidence":1.0,"self_reported_confidence":1.0}]}},"verify":{"inputs":{"expressions":3,"tracklets":4,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[],"rejected":{"a red car parked on the street":"no_candidates_retrieved","the white car parked further up the street":"count_mismatch","the decorative green metal balcony railing on the upper floor":"count_mismatch"},"rejection_counts":{"no_candidates_retrieved":1,"count_mismatch":2},"row":null}}}},{"index":15,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_001.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_001.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":29.560000000000002,"frames_kept":114,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68,17.94,18.2,18.46,18.72,18.98,19.240000000000002,19.5,19.76,20.02,20.28,20.54,20.8,21.06,21.32,21.580000000000002,21.84,22.1,22.36,22.62,22.88,23.14,23.400000000000002,23.66,23.92,24.18,24.44,24.7,24.96,25.22,25.48,25.740000000000002,26.0,26.26,26.52,26.78,27.04,27.3,27.560000000000002,27.82,28.080000000000002,28.34,28.6,28.86,29.12,29.38],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":48078773,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the person with dreadlocks who walks towards the stone structure\",\n \"noun_phrase\": \"person with dreadlocks\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the stone trough containing water\",\n \"noun_phrase\": \"stone trough\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the person with dreadlocks who walks towards the stone structure","noun_phrase":"person with dreadlocks","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the stone trough containing water","noun_phrase":"stone trough","kind":"appearance","expected_count":1,"is_negative":false}],"accepted":[{"expression":"the person with dreadlocks who walks towards the stone structure","noun_phrase":"person with dreadlocks","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the stone trough containing water","noun_phrase":"stone trough","kind":"appearance","expected_count":1,"is_negative":false}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["person with dreadlocks","stone 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are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the bearded man wearing a black hoodie\",\n \"noun_phrase\": \"bearded man\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a man wearing a white shirt\",\n \"noun_phrase\": \"man\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the bearded man wearing a black hoodie","noun_phrase":"bearded man","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"a man wearing a white shirt","noun_phrase":"man","kind":"appearance","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the bearded man wearing a black hoodie","noun_phrase":"bearded man","kind":"appearance","expected_count":1,"is_negative":false},{"expression":"a man wearing a white shirt","noun_phrase":"man","kind":"appearance","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["bearded man","man"],"expressions_in":2,"frames":90,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38,16.64,16.9,17.16,17.42,17.68,17.94,18.2,18.46,18.72,18.98,19.240000000000002,19.5,19.76,20.02,20.28,20.54,20.8,21.06,21.32,21.580000000000002,21.84,22.1,22.36,22.62,22.88,23.14],"session":"one session over 90 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":2},"outputs":{"raw_tracks":[{"noun_phrase":"bearded 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man","object_id":0,"frames":5,"mean_score":0.7419355511665344,"mean_area":0.17895872753352648,"outcome":"merged_duplicate","presentation_id":null,"merged_into_phrase":"man","merged_into_object_id":0,"merge_iou":0.9934598171185364},{"noun_phrase":"man","object_id":0,"frames":6,"mean_score":0.9532163739204407,"mean_area":0.16542728190701203,"outcome":"kept","presentation_id":1,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null}],"gates":{"min_track_frames":3,"min_mean_score":0.5,"merge_iou":0.8,"max_tracklets":12}}},"render":{"inputs":{"tracklets":1,"as_video":true,"fps":1.0,"max_frames":30},"outputs":{"media":"/home/zzk/tmp/rmot_smoke_real_86267/renders/--cif7Nv6BI_chunk_010_scene_016_choices.mp4","media_bytes":7898341,"frames":[{"index":1,"timestamp":0.0,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":2,"timestamp":1.04,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":3,"timestamp":2.08,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":4,"timestamp":3.12,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":5,"timestamp":3.9,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":6,"timestamp":4.94,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":7,"timestamp":5.98,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":8,"timestamp":7.0200000000000005,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":9,"timestamp":8.06,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":10,"timestamp":9.1,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":11,"timestamp":10.14,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":12,"timestamp":11.18,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":13,"timestamp":11.96,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":14,"timestamp":13.0,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":15,"timestamp":14.040000000000001,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":16,"timestamp":15.08,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":17,"timestamp":16.12,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":18,"timestamp":17.16,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":19,"timestamp":18.2,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":20,"timestamp":19.240000000000002,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":21,"timestamp":20.02,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":22,"timestamp":21.06,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":23,"timestamp":22.1,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"},{"index":24,"timestamp":23.14,"file":"--cif7Nv6BI_chunk_010_scene_016_choices.mp4"}]}},"select":{"inputs":{"model":"gemini-3.8-flash","temperature":0.0,"as_video":true,"media_bytes":7898341,"media":["--cif7Nv6BI_chunk_010_scene_016_choices.mp4"],"choice_frames":24,"system_prompt":"You are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 24 frames, numbered 1 to 24. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the bearded man wearing a black hoodie\n2. a man wearing a white shirt","questions":["the bearded man wearing a black hoodie","a man wearing a white shirt"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [1],\n \"start_frame\": 23,\n \"end_frame\": 24,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 24,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[1],"start_frame":23,"end_frame":24,"confidence":1.0},{"description_index":2,"matching_ids":[],"start_frame":1,"end_frame":24,"confidence":1.0}],"answers":[{"expression":"the bearded man wearing a black hoodie","matching_presentation_ids":[1],"start_choice_frame":23,"end_choice_frame":24,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"a man wearing a white shirt","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":24,"confidence":1.0,"self_reported_confidence":1.0}]}},"verify":{"inputs":{"expressions":2,"tracklets":1,"selections":2,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[{"expression":"the bearded man wearing a black hoodie","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[22.1,22.36,22.62,22.88,23.14],"boxes":[[0.6536458730697632,0.43518516421318054,0.8911458849906921,0.9740740358829498],[0.5666667222976685,0.37962961196899414,0.8322917222976685,0.9759259223937988],[0.483854204416275,0.3583333194255829,0.7734375596046448,0.9759258925914764],[0.42343753576278687,0.3166666626930237,0.7401042282581329,0.9787036776542664],[0.3916666805744171,0.2537037134170532,0.7036458551883698,0.9759259223937988]]}]},{"expression":"a man wearing a white shirt","kind":"appearance","is_negative":true,"tracks":[]}],"rejected":{},"rejection_counts":{},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_010_scene_016.mp4","scene_key":"--cif7Nv6BI_chunk_010_scene_016","category":"test_vehicles","height":1080,"width":1920,"fps":50.0,"num_frames":1161,"duration":23.22,"rmot_expressions":[{"expression":"the bearded man wearing a black hoodie","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[22.1,22.36,22.62,22.88,23.14],"boxes":[[0.6536458730697632,0.43518516421318054,0.8911458849906921,0.9740740358829498],[0.5666667222976685,0.37962961196899414,0.8322917222976685,0.9759259223937988],[0.483854204416275,0.3583333194255829,0.7734375596046448,0.9759258925914764],[0.42343753576278687,0.3166666626930237,0.7401042282581329,0.9787036776542664],[0.3916666805744171,0.2537037134170532,0.7036458551883698,0.9759259223937988]]}]},{"expression":"a man wearing a white shirt","kind":"appearance","is_negative":true,"tracks":[]}],"rmot_expression_count":2,"rmot_negative_count":1,"rmot_motion_count":0}}}}},{"index":17,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--X6fL8DOMM_chunk_000_scene_003.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--X6fL8DOMM_chunk_000_scene_003.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":29.97002997002997,"duration_seconds":16.783433333333335,"frames_kept":63,"blank_frames_dropped":0,"timestamps":[0.0,0.26693333333333336,0.5338666666666667,0.8008000000000001,1.0677333333333334,1.3346666666666667,1.6016000000000001,1.8685333333333334,2.135466666666667,2.4024,2.6693333333333333,2.936266666666667,3.2032000000000003,3.4701333333333335,3.7370666666666668,4.0040000000000004,4.270933333333334,4.537866666666667,4.8048,5.071733333333333,5.338666666666667,5.6056,5.872533333333334,6.139466666666667,6.4064000000000005,6.673333333333334,6.940266666666667,7.2072,7.4741333333333335,7.741066666666667,8.008000000000001,8.274933333333333,8.541866666666667,8.8088,9.075733333333334,9.342666666666666,9.6096,9.876533333333334,10.143466666666667,10.410400000000001,10.677333333333333,10.944266666666667,11.2112,11.478133333333334,11.745066666666668,12.012,12.278933333333335,12.545866666666667,12.812800000000001,13.079733333333333,13.346666666666668,13.6136,13.880533333333334,14.147466666666668,14.4144,14.681333333333335,14.948266666666667,15.215200000000001,15.482133333333334,15.749066666666668,16.016000000000002,16.282933333333332,16.549866666666667],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":21829724,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the man sitting in the back seat of the car\",\n \"noun_phrase\": \"man in backseat\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the man driving the car\",\n \"noun_phrase\": \"car driver\",\n \"kind\": \"interaction\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the passenger closest to the camera wearing a black cap\",\n \"noun_phrase\": \"man in black cap\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n }\n ]\n}","raw_items":[{"expression":"the man sitting in the back seat of the car","noun_phrase":"man in backseat","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the man driving the car","noun_phrase":"car driver","kind":"interaction","expected_count":1,"is_negative":false},{"expression":"the passenger closest to the camera wearing a black cap","noun_phrase":"man in black cap","kind":"appearance","expected_count":1,"is_negative":false}],"accepted":[{"expression":"the man sitting in the back seat of the car","noun_phrase":"man in backseat","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the man driving the car","noun_phrase":"car driver","kind":"interaction","expected_count":1,"is_negative":false},{"expression":"the passenger closest to the camera wearing a black cap","noun_phrase":"man in black cap","kind":"appearance","expected_count":1,"is_negative":false}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["car driver","man in backseat","man in black 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cap","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[0.0,0.26693333333333336,0.5338666666666667,0.8008000000000001,1.0677333333333334,1.3346666666666667,1.6016000000000001,1.8685333333333334,2.135466666666667,2.4024,2.6693333333333333,2.936266666666667,3.2032000000000003,3.4701333333333335,3.7370666666666668,4.0040000000000004,4.270933333333334,4.537866666666667,4.8048,5.071733333333333,5.338666666666667,5.6056,5.872533333333334,6.139466666666667,6.4064000000000005,6.673333333333334,6.940266666666667,7.2072,7.4741333333333335,7.741066666666667,8.008000000000001,8.274933333333333,8.541866666666667,8.8088,9.075733333333334,9.342666666666666,9.6096,9.876533333333334,10.143466666666667,10.410400000000001,10.677333333333333,10.944266666666667,11.2112,11.478133333333334,11.745066666666668,12.012,12.278933333333335,12.545866666666667,12.812800000000001,13.079733333333333,13.346666666666668,13.6136,13.880533333333334,14.147466666666668,14.4144,14.681333333333335,14.948266666666667,15.215200000000001,15.482133333333334,15.749066666666668,16.016000000000002,16.282933333333332,16.549866666666667],"boxes":[[0.0,0.0,0.4802083671092987,0.9990740418434143],[0.0,0.0,0.4776041805744171,0.9990740418434143],[0.0,0.0,0.44583335518836975,0.9990740418434143],[0.0,0.0,0.4583333432674408,0.9990740418434143],[0.0,0.0,0.476041704416275,0.9990740418434143],[0.0,0.0,0.4833333492279053,0.9990740418434143],[0.0,0.0,0.5010417103767395,0.9990740418434143],[0.0,0.0,0.510937511920929,0.9990740418434143],[0.0,0.0,0.5223958492279053,0.9990740418434143],[0.0,0.0,0.4755208492279053,0.9990740418434143],[0.0,0.0,0.5213541984558105,0.9990740418434143],[0.0,0.0,0.542187511920929,0.9990740418434143],[0.0,0.0,0.5557292103767395,0.9990740418434143],[0.0,0.0,0.5614583492279053,0.9990740418434143],[0.0,0.0,0.5687500238418579,0.9990740418434143],[0.0,0.0,0.5765625238418579,0.9990740418434143],[0.0,0.0,0.5687500238418579,0.9990740418434143],[0.0,0.0,0.5864583849906921,0.9990740418434143],[0.0,0.0,0.5744792222976685,0.9990740418434143],[0.0,0.0,0.5895833373069763,0.9990740418434143],[0.0,0.0,0.6270833611488342,0.9990740418434143],[0.0,0.0,0.6062500476837158,0.9990740418434143],[0.0,0.0,0.5911458730697632,0.9990740418434143],[0.0,0.0,0.5963541865348816,0.9990740418434143],[0.0,0.0,0.5661458373069763,0.9990740418434143],[0.0,0.0,0.5703125,0.9990740418434143],[0.0,0.0,0.5807291865348816,0.9990740418434143],[0.0,0.0,0.5963541865348816,0.9990740418434143],[0.0,0.0,0.9078125357627869,0.9990740418434143],[0.0,0.0,0.9218750596046448,0.9990740418434143],[0.0,0.0,0.5645833611488342,0.9990740418434143],[0.0,0.0,0.5875000357627869,0.9990740418434143],[0.0,0.0,0.5510417222976685,0.9990740418434143],[0.0,0.0,0.5729166865348816,0.9990740418434143],[0.0,0.0,0.5526041984558105,0.9990740418434143],[0.02031250111758709,0.0,0.5739583503454924,0.9990740418434143],[0.0,0.0,0.5781250596046448,0.9990740418434143],[0.0,0.0,0.5843750238418579,0.9990740418434143],[0.0,0.0,0.5723958611488342,0.9990740418434143],[0.0,0.0,0.5744792222976685,0.9990740418434143],[0.0,0.0,0.5666667222976685,0.9990740418434143],[0.0,0.0,0.5625,0.9990740418434143],[0.0,0.0,0.589062511920929,0.9990740418434143],[0.0,0.0,0.6151041984558105,0.9990740418434143],[0.0,0.0,0.6369792222976685,0.9990740418434143],[0.0,0.0,0.6421875357627869,0.9990740418434143],[0.0,0.0,0.6312500238418579,0.9990740418434143],[0.0,0.0,0.6322916746139526,0.9990740418434143],[0.0,0.0,0.628125011920929,0.9990740418434143],[0.0,0.0,0.6229166984558105,0.9990740418434143],[0.0,0.0,0.6312500238418579,0.9990740418434143],[0.0,0.0,0.6348958611488342,0.9990740418434143],[0.0,0.0,0.6317708492279053,0.9990740418434143],[0.0,0.0,0.6296875476837158,0.9990740418434143],[0.0,0.0,0.6072916984558105,0.9990740418434143],[0.0,0.0,0.5859375596046448,0.9990740418434143],[0.0,0.0,0.5854166746139526,0.9990740418434143],[0.0,0.0,0.5921875238418579,0.9990740418434143],[0.0,0.0,0.5869792103767395,0.9990740418434143],[0.0,0.0,0.5833333730697632,0.9990740418434143],[0.0,0.0,0.5744792222976685,0.9990740418434143],[0.0,0.0,0.5708333849906921,0.9990740418434143],[0.0020833334419876337,0.0,0.5442708453629166,0.9990740418434143]]}]}],"rmot_expression_count":3,"rmot_negative_count":0,"rmot_motion_count":0}}}}},{"index":18,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_010.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_010.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":16.5,"frames_kept":64,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":30727427,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the white pickup truck that drives across the intersection\",\n \"noun_phrase\": \"white pickup truck\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a parked white pickup truck\",\n \"noun_phrase\": \"white pickup truck\",\n \"kind\": \"motion\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the white pickup truck that drives across the intersection","noun_phrase":"white pickup truck","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a parked white pickup truck","noun_phrase":"white pickup truck","kind":"motion","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the white pickup truck that drives across the intersection","noun_phrase":"white pickup truck","kind":"motion","expected_count":1,"is_negative":false},{"expression":"a parked white pickup truck","noun_phrase":"white pickup truck","kind":"motion","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["white pickup truck"],"expressions_in":2,"frames":64,"sample_fps":4.0,"frame_size":"1920x1080","timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56,14.82,15.08,15.34,15.6,15.860000000000001,16.12,16.38],"session":"one session over 64 JPEG frames; then per phrase: reset_session, add_prompt(text) on frame 0, propagate_in_video","passes":1},"outputs":{"raw_tracks":[{"noun_phrase":"white pickup 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pickup truck","object_id":0,"frames":13,"mean_score":0.7575758099555969,"mean_area":0.20784945087048315,"outcome":"kept","presentation_id":1,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null}],"gates":{"min_track_frames":3,"min_mean_score":0.5,"merge_iou":0.8,"max_tracklets":12}}},"render":{"inputs":{"tracklets":1,"as_video":true,"fps":1.0,"max_frames":30},"outputs":{"media":"/home/zzk/tmp/rmot_smoke_real_86267/renders/--cif7Nv6BI_chunk_011_scene_010_choices.mp4","media_bytes":5217427,"frames":[{"index":1,"timestamp":0.0,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":2,"timestamp":1.04,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":3,"timestamp":2.08,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":4,"timestamp":3.12,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":5,"timestamp":4.16,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":6,"timestamp":5.2,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":7,"timestamp":6.24,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":8,"timestamp":7.28,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":9,"timestamp":8.32,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":10,"timestamp":9.1,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":11,"timestamp":10.14,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":12,"timestamp":11.18,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":13,"timestamp":12.22,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":14,"timestamp":13.26,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":15,"timestamp":14.3,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":16,"timestamp":15.34,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"},{"index":17,"timestamp":16.38,"file":"--cif7Nv6BI_chunk_011_scene_010_choices.mp4"}]}},"select":{"inputs":{"model":"gemini-3.8-flash","temperature":0.0,"as_video":true,"media_bytes":5217427,"media":["--cif7Nv6BI_chunk_011_scene_010_choices.mp4"],"choice_frames":17,"system_prompt":"You are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 17 frames, numbered 1 to 17. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the white pickup truck that drives across the intersection\n2. a parked white pickup truck","questions":["the white pickup truck that drives across the intersection","a parked white pickup truck"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [1],\n \"start_frame\": 10,\n \"end_frame\": 13,\n \"confidence\": 0.72\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 17,\n \"confidence\": 0.72\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[1],"start_frame":10,"end_frame":13,"confidence":0.72},{"description_index":2,"matching_ids":[],"start_frame":1,"end_frame":17,"confidence":0.72}],"answers":[{"expression":"the white pickup truck that drives across the intersection","matching_presentation_ids":[1],"start_choice_frame":10,"end_choice_frame":13,"confidence":0.72,"self_reported_confidence":0.72},{"expression":"a parked white pickup truck","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":17,"confidence":0.72,"self_reported_confidence":0.72}]}},"verify":{"inputs":{"expressions":2,"tracklets":1,"selections":2,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[{"expression":"the white pickup truck that drives across the intersection","kind":"motion","is_negative":false,"tracks":[{"timestamps":[9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22],"boxes":[[0.16302084922790527,0.44722220301628113,0.6968750357627869,0.7833333015441895],[0.21302084624767303,0.46574074029922485,0.7713541835546494,0.8361110985279083],[0.2880208492279053,0.44814813137054443,0.8776041865348816,0.8537036776542664],[0.4333333671092987,0.5185185074806213,0.999479204416275,0.9722222089767456],[0.47083336114883423,0.5694444179534912,0.9994792342185974,0.9990740418434143],[0.4776041805744171,0.5046296119689941,0.999479204416275,0.9722222089767456],[0.32239586114883423,0.5018518567085266,0.9994792342185974,0.970370352268219],[0.05312500149011612,0.470370352268219,0.7010416761040688,0.9638888537883759],[0.0,0.520370364189148,0.5286458730697632,0.9944444298744202],[0.0,0.47870370745658875,0.4046875238418579,0.9740740656852722],[0.0,0.34074074029922485,0.23906251788139343,0.9731481075286865],[0.0,0.46481481194496155,0.1510416716337204,0.9759258925914764],[0.0,0.5462962985038757,0.07968750596046448,0.9509259164333344]]}]},{"expression":"a parked white pickup truck","kind":"motion","is_negative":true,"tracks":[]}],"rejected":{},"rejection_counts":{},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_011_scene_010.mp4","scene_key":"--cif7Nv6BI_chunk_011_scene_010","category":"test_vehicles","height":1080,"width":1920,"fps":50.0,"num_frames":825,"duration":16.5,"rmot_expressions":[{"expression":"the white pickup truck that drives across the intersection","kind":"motion","is_negative":false,"tracks":[{"timestamps":[9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22],"boxes":[[0.16302084922790527,0.44722220301628113,0.6968750357627869,0.7833333015441895],[0.21302084624767303,0.46574074029922485,0.7713541835546494,0.8361110985279083],[0.2880208492279053,0.44814813137054443,0.8776041865348816,0.8537036776542664],[0.4333333671092987,0.5185185074806213,0.999479204416275,0.9722222089767456],[0.47083336114883423,0.5694444179534912,0.9994792342185974,0.9990740418434143],[0.4776041805744171,0.5046296119689941,0.999479204416275,0.9722222089767456],[0.32239586114883423,0.5018518567085266,0.9994792342185974,0.970370352268219],[0.05312500149011612,0.470370352268219,0.7010416761040688,0.9638888537883759],[0.0,0.520370364189148,0.5286458730697632,0.9944444298744202],[0.0,0.47870370745658875,0.4046875238418579,0.9740740656852722],[0.0,0.34074074029922485,0.23906251788139343,0.9731481075286865],[0.0,0.46481481194496155,0.1510416716337204,0.9759258925914764],[0.0,0.5462962985038757,0.07968750596046448,0.9509259164333344]]}]},{"expression":"a parked white pickup truck","kind":"motion","is_negative":true,"tracks":[]}],"rmot_expression_count":2,"rmot_negative_count":1,"rmot_motion_count":2}}}}},{"index":19,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_012_scene_020.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_012_scene_020.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":14.56,"frames_kept":57,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48,12.74,13.0,13.26,13.52,13.780000000000001,14.040000000000001,14.3,14.56],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":13891853,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the lit street lamp mounted on the wall along the path\",\n \"noun_phrase\": \"street lamp\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the stone crest carving above the entrance portal\",\n \"noun_phrase\": \"stone carving\",\n \"kind\": \"appearance\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a pedestrian walking along the narrow street\",\n \"noun_phrase\": \"pedestrian\",\n \"kind\": \"appearance\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the lit street lamp mounted on the wall along the path","noun_phrase":"street lamp","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"the stone crest carving above the entrance 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portal","kind":"appearance","is_negative":false,"tracks":[{"timestamps":[1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86],"boxes":[[0.3708333671092987,0.19629628956317902,0.484375037252903,0.5277777761220932],[0.42135417461395264,0.28518518805503845,0.5437500178813934,0.6324073970317841],[0.4270833432674408,0.25648146867752075,0.575520858168602,0.6601851582527161],[0.42604169249534607,0.24351851642131805,0.5984375327825546,0.7037036865949631],[0.4192708432674408,0.21574074029922485,0.6083333492279053,0.7037037014961243],[0.29635417461395264,0.18518517911434174,0.6140625178813934,0.7129629701375961],[0.2968750298023224,0.19907407462596893,0.6187500357627869,0.7194444388151169],[0.29635417461395264,0.19907407462596893,0.616666704416275,0.724074050784111]]}]}],"rmot_expression_count":1,"rmot_negative_count":0,"rmot_motion_count":0}}}}},{"index":20,"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_018.mp4","status":"complete","steps":{"decode":{"inputs":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_018.mp4","sample_fps":4.0},"outputs":{"width":1920,"height":1080,"source_fps":50.0,"duration_seconds":12.56,"frames_kept":49,"blank_frames_dropped":0,"timestamps":[0.0,0.26,0.52,0.78,1.04,1.3,1.56,1.82,2.08,2.34,2.6,2.86,3.12,3.38,3.64,3.9,4.16,4.42,4.68,4.94,5.2,5.46,5.72,5.98,6.24,6.5,6.76,7.0200000000000005,7.28,7.54,7.8,8.06,8.32,8.58,8.84,9.1,9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.700000000000001,11.96,12.22,12.48],"error":""}},"propose":{"inputs":{"model":"gemini-3.8-flash","temperature":0.4,"video_bytes":14915301,"system_prompt":"You are annotating video clips to build training data for referring multi-object tracking: given a video and a natural-language description, a model must output the boxes of every object matching that description, over time.\n\nYour job is to write the descriptions. You do NOT output coordinates, boxes, pixel positions, or any numbers about where things are. A separate detector finds the objects.\n\nFor every clip you produce a list of items. Each item has two texts that must NOT be the same:\n\nEXPRESSION — what a person would say to single out the object or objects. It may describe motion, temporal order, interaction with other objects, and spatial relations. It should read like something a person would actually say.\n Good: \"the black car that's turning left\"\n Good: \"the cyclist who overtakes the bus\"\n Good: \"the two people who walk out of frame on the right\"\n\nNOUN_PHRASE — the same object as a plain, timeless visual category with its visible attributes, and NOTHING else. The detector is prompted with this text and looks at ONE frame at a time, so anything about time, order, or action is useless to it and makes it fail.\n Allowed: category nouns, colours, materials, clothing, size, type\n Forbidden: verbs and participles of action (\"turning\", \"walks\", \"is moving\"), temporal words (\"then\", \"starts\", \"after\", \"first\", \"last\"), relative clauses (\"that ...\", \"which ...\", \"who ...\"), ordinals and rank words (\"first\", \"leftmost\", \"nearest\"), and anything over six words.\n \"the black car that's turning left\" -> \"black car\"\n \"the cyclist who overtakes the bus\" -> \"cyclist\"\n \"the two people who walk out of frame on the right\" -> \"person\"\n\nKIND — what makes the expression hard: \"motion\", \"appearance\", \"spatial\", or \"interaction\". Aim for about 35% \"motion\" items: motion is the part a single-frame model cannot already solve, so it is the most valuable, but a list of only motion items is not usable training data either.\n\nEXPECTED_COUNT — how many distinct objects in the clip match the EXPRESSION. Count carefully; this is checked against the detector's answer, and a wrong count discards the item.\n\nIS_NEGATIVE — at most ONE item may be a negative, and only when you also write at least one non-negative item. A negative is an expression that recombines attributes that ARE present in the clip into a whole that is NOT. If the only black car is moving, \"the parked black car\" is a good negative. Its NOUN_PHRASE must still be the category that IS present (\"black car\"), because that is what makes the absence checkable rather than merely unverified. A negative's EXPECTED_COUNT is 0.\n\nRules that discard an item, so follow them exactly:\n- Never write coordinates, box values, pixel positions, or percentages of the frame.\n- Never let a NOUN_PHRASE contain a verb of action, a temporal word, a relative clause, or an ordinal.\n- Never write an EXPRESSION whose object is not visible in the clip, unless it is marked IS_NEGATIVE.\n- Prefer objects that are clearly visible and identifiable over background clutter.\n- Do not describe the camera or the shot; describe objects in the scene.\n","user_prompt":"Watch this clip and write between 1 and 3 items following the rules. Cover different objects rather than restating one object several ways, and include any negative in the same list.\n"},"outputs":{"raw_response":"{\n \"items\": [\n {\n \"expression\": \"the woman who enters the frame on the left\",\n \"noun_phrase\": \"woman\",\n \"kind\": \"motion\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"the postcard display stand outside the cafe entrance\",\n \"noun_phrase\": \"display stand\",\n \"kind\": \"spatial\",\n \"expected_count\": 1,\n \"is_negative\": false\n },\n {\n \"expression\": \"a person sitting on the stone bench outside the cafe\",\n \"noun_phrase\": \"person\",\n \"kind\": \"spatial\",\n \"expected_count\": 0,\n \"is_negative\": true\n }\n ]\n}","raw_items":[{"expression":"the woman who enters the frame on the left","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the postcard display stand outside the cafe entrance","noun_phrase":"display stand","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a person sitting on the stone bench outside the cafe","noun_phrase":"person","kind":"spatial","expected_count":0,"is_negative":true}],"accepted":[{"expression":"the woman who enters the frame on the left","noun_phrase":"woman","kind":"motion","expected_count":1,"is_negative":false},{"expression":"the postcard display stand outside the cafe entrance","noun_phrase":"display stand","kind":"spatial","expected_count":1,"is_negative":false},{"expression":"a person sitting on the stone bench outside the cafe","noun_phrase":"person","kind":"spatial","expected_count":0,"is_negative":true}],"gated":[]}},"track":{"inputs":{"tracker":"Sam31MultiplexTracker","model_id":"/mnt/ceph3/ec/jacky/sam3.1","noun_phrases":["display 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propagate_in_video","passes":3},"outputs":{"raw_tracks":[{"noun_phrase":"person","object_id":0,"observations":[{"timestamp":8.84,"box":[0.0,0.12222222238779068,0.16875000298023224,0.9740740433335304],"occluded":false},{"timestamp":9.1,"box":[0.0,0.049074072390794754,0.28854167461395264,0.9777777381241322],"occluded":false},{"timestamp":9.36,"box":[0.0,0.06666666269302368,0.3812500238418579,0.9944444298744202],"occluded":false},{"timestamp":9.620000000000001,"box":[0.0,0.05833333358168602,0.40416669845581055,0.9814814887940884],"occluded":false},{"timestamp":9.88,"box":[0.0,0.029629629105329514,0.3218750059604645,0.9833333306014538],"occluded":false},{"timestamp":10.14,"box":[0.0,0.028703702613711357,0.2718750238418579,0.9759259354323149],"occluded":false},{"timestamp":10.4,"box":[0.0,0.026851851493120193,0.2916666865348816,0.9768518395721912],"occluded":false},{"timestamp":10.66,"box":[0.0,0.03055555559694767,0.2666666805744171,0.9777777884155512],"occluded":false},{"timestamp":10.92,"box":[0.0,0.028703702613711357,0.2526041865348816,0.9759259354323149],"occluded":false},{"timestamp":11.18,"box":[0.0,0.025925925001502037,0.17500001192092896,0.9787036683410406],"occluded":false},{"timestamp":11.44,"box":[0.0,0.17407406866550446,0.1015625074505806,0.9759258776903152],"occluded":false},{"timestamp":11.96,"box":[0.0,0.8305555582046509,0.06927083432674408,0.9768518507480621],"occluded":false},{"timestamp":12.22,"box":[0.0,0.9018518328666687,0.01770833507180214,0.9749999791383743],"occluded":false},{"timestamp":12.48,"box":[0.0,0.654629647731781,0.0937500074505806,0.9814814925193787],"occluded":false}],"scores":[0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789,0.9336099624633789]},{"noun_phrase":"woman","object_id":0,"observations":[{"timestamp":8.84,"box":[0.0,0.12314814329147339,0.16822917759418488,0.9740740656852722],"occluded":false},{"timestamp":9.1,"box":[0.0,0.05000000074505806,0.2880208492279053,0.9777777679264545],"occluded":false},{"timestamp":9.36,"box":[0.0,0.06759259104728699,0.3812500238418579,0.9944444000720978],"occluded":false},{"timestamp":9.620000000000001,"box":[0.0,0.05833333358168602,0.40416669845581055,0.9814814887940884],"occluded":false},{"timestamp":9.88,"box":[0.0,0.029629629105329514,0.3218750059604645,0.9833333306014538],"occluded":false},{"timestamp":10.14,"box":[0.0,0.028703702613711357,0.2718750238418579,0.9759259354323149],"occluded":false},{"timestamp":10.4,"box":[0.0,0.02777777798473835,0.2916666865348816,0.9768518079072237],"occluded":false},{"timestamp":10.66,"box":[0.0,0.03055555559694767,0.2666666805744171,0.9768518302589655],"occluded":false},{"timestamp":10.92,"box":[0.0,0.028703702613711357,0.2526041865348816,0.9759259354323149],"occluded":false},{"timestamp":11.18,"box":[0.0,0.025925925001502037,0.17500001192092896,0.9787036683410406],"occluded":false},{"timestamp":11.44,"box":[0.0,0.17407406866550446,0.1015625074505806,0.9759258776903152],"occluded":false},{"timestamp":11.96,"box":[0.0,0.8305555582046509,0.06927083432674408,0.9768518507480621],"occluded":false},{"timestamp":12.22,"box":[0.0,0.9018518328666687,0.01770833507180214,0.9749999791383743],"occluded":false},{"timestamp":12.48,"box":[0.0,0.654629647731781,0.0937500074505806,0.9814814925193787],"occluded":false}],"scores":[0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448,0.9375000596046448]}],"tracklets":[{"noun_phrase":"person","object_id":0,"presentation_id":1,"observations":[{"timestamp":8.84,"box":[0.0,0.12222222238779068,0.16875000298023224,0.9740740433335304],"occluded":false},{"timestamp":9.1,"box":[0.0,0.049074072390794754,0.28854167461395264,0.9777777381241322],"occluded":false},{"timestamp":9.36,"box":[0.0,0.06666666269302368,0.3812500238418579,0.9944444298744202],"occluded":false},{"timestamp":9.620000000000001,"box":[0.0,0.05833333358168602,0.40416669845581055,0.9814814887940884],"occluded":false},{"timestamp":9.88,"box":[0.0,0.029629629105329514,0.3218750059604645,0.9833333306014538],"occluded":false},{"timestamp":10.14,"box":[0.0,0.028703702613711357,0.2718750238418579,0.9759259354323149],"occluded":false},{"timestamp":10.4,"box":[0.0,0.026851851493120193,0.2916666865348816,0.9768518395721912],"occluded":false},{"timestamp":10.66,"box":[0.0,0.03055555559694767,0.2666666805744171,0.9777777884155512],"occluded":false},{"timestamp":10.92,"box":[0.0,0.028703702613711357,0.2526041865348816,0.9759259354323149],"occluded":false},{"timestamp":11.18,"box":[0.0,0.025925925001502037,0.17500001192092896,0.9787036683410406],"occluded":false},{"timestamp":11.44,"box":[0.0,0.17407406866550446,0.1015625074505806,0.9759258776903152],"occluded":false},{"timestamp":11.96,"box":[0.0,0.8305555582046509,0.06927083432674408,0.9768518507480621],"occluded":false},{"timestamp":12.22,"box":[0.0,0.9018518328666687,0.01770833507180214,0.9749999791383743],"occluded":false},{"timestamp":12.48,"box":[0.0,0.654629647731781,0.0937500074505806,0.9814814925193787],"occluded":false}],"mean_score":0.9336099624633789}],"dropped":{"merged_duplicate":1},"fates":[{"noun_phrase":"person","object_id":0,"frames":14,"mean_score":0.9336099624633789,"mean_area":0.19730245818705924,"outcome":"kept","presentation_id":1,"merged_into_phrase":null,"merged_into_object_id":null,"merge_iou":null},{"noun_phrase":"woman","object_id":0,"frames":14,"mean_score":0.9375000596046448,"mean_area":0.19714389999830711,"outcome":"merged_duplicate","presentation_id":null,"merged_into_phrase":"person","merged_into_object_id":0,"merge_iou":0.999291389446089}],"gates":{"min_track_frames":3,"min_mean_score":0.5,"merge_iou":0.8,"max_tracklets":12}}},"render":{"inputs":{"tracklets":1,"as_video":true,"fps":1.0,"max_frames":30},"outputs":{"media":"/home/zzk/tmp/rmot_smoke_real_86267/renders/--cif7Nv6BI_chunk_014_scene_018_choices.mp4","media_bytes":3180404,"frames":[{"index":1,"timestamp":0.0,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":2,"timestamp":1.04,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":3,"timestamp":2.08,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":4,"timestamp":3.12,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":5,"timestamp":4.16,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":6,"timestamp":5.2,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":7,"timestamp":6.24,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":8,"timestamp":7.28,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":9,"timestamp":8.32,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":10,"timestamp":9.36,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":11,"timestamp":10.4,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":12,"timestamp":11.44,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"},{"index":13,"timestamp":12.48,"file":"--cif7Nv6BI_chunk_014_scene_018_choices.mp4"}]}},"select":{"inputs":{"model":"gemini-3.8-flash","temperature":0.0,"as_video":true,"media_bytes":3180404,"media":["--cif7Nv6BI_chunk_014_scene_018_choices.mp4"],"choice_frames":13,"system_prompt":"You are labelling video data for referring multi-object tracking.\n\nYou will see a numbered sequence of frames from one clip, in time order. Each frame has its own number and timestamp printed in the top-left corner. Candidate objects have been detected for you and drawn on the frames as coloured boxes, each labelled with a number. The same object keeps the same number and the same colour on every frame. Below each frame is a strip of enlarged crops of those same numbered boxes, so you can identify a small or distant object.\n\nFor each description you are given, answer two questions:\n\n1. MATCHING_IDS — which of the numbered objects the description refers to. List every number that matches, or an EMPTY LIST if none of them do. An empty list is a correct and expected answer: some descriptions are deliberately about something that is not in this clip. Do not pick a number just to have picked one, and do not pick a number for an object that merely resembles the description.\n\n2. START_FRAME and END_FRAME — the frame numbers between which the described state actually holds. If the description is \"the car that's turning left\", the interval is the frames in which that car is turning, NOT every frame in which the car is visible. Use the frame numbers printed on the frames. START_FRAME must not be greater than END_FRAME. When MATCHING_IDS is empty, give the full range (the first and last frame numbers); it is ignored.\n\n3. CONFIDENCE — how sure you are of the MATCHING_IDS answer, from 0.0 to 1.0. Be honest: a low number costs the example nothing, and an overconfident wrong answer becomes a wrong label.\n\nRules:\n- Only use numbers that are actually drawn on the frames.\n- Never output coordinates, box values, or pixel positions.\n- Judge motion by comparing the object's position across frames, not by guessing from one frame.\n","user_prompt":"The video has 13 frames, numbered 1 to 13. Each frame is one moment of the clip and has its number burned into the top left corner.\n\nAnswer for each of the following descriptions, in the order given:\n1. the woman who enters the frame on the left\n2. the postcard display stand outside the cafe entrance\n3. a person sitting on the stone bench outside the cafe","questions":["the woman who enters the frame on the left","the postcard display stand outside the cafe entrance","a person sitting on the stone bench outside the cafe"]},"outputs":{"raw_response":"{\n \"answers\": [\n {\n \"description_index\": 1,\n \"matching_ids\": [\n 1\n ],\n \"start_frame\": 10,\n \"end_frame\": 13,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 2,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 13,\n \"confidence\": 1.0\n },\n {\n \"description_index\": 3,\n \"matching_ids\": [],\n \"start_frame\": 1,\n \"end_frame\": 13,\n \"confidence\": 1.0\n }\n ]\n}","raw_answers":[{"description_index":1,"matching_ids":[1],"start_frame":10,"end_frame":13,"confidence":1.0},{"description_index":2,"matching_ids":[],"start_frame":1,"end_frame":13,"confidence":1.0},{"description_index":3,"matching_ids":[],"start_frame":1,"end_frame":13,"confidence":1.0}],"answers":[{"expression":"the woman who enters the frame on the left","matching_presentation_ids":[1],"start_choice_frame":10,"end_choice_frame":13,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"the postcard display stand outside the cafe entrance","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":13,"confidence":1.0,"self_reported_confidence":1.0},{"expression":"a person sitting on the stone bench outside the cafe","matching_presentation_ids":[],"start_choice_frame":1,"end_choice_frame":13,"confidence":1.0,"self_reported_confidence":1.0}]}},"verify":{"inputs":{"expressions":3,"tracklets":1,"selections":3,"gates":"interval, count, unknown ids, confidence, then geometry (motion)"},"outputs":{"kept":[{"expression":"the woman who enters the frame on the left","kind":"motion","is_negative":false,"tracks":[{"timestamps":[9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.96,12.22,12.48],"boxes":[[0.0,0.06666666269302368,0.3812500238418579,0.9944444298744202],[0.0,0.05833333358168602,0.40416669845581055,0.9814814887940884],[0.0,0.029629629105329514,0.3218750059604645,0.9833333306014538],[0.0,0.028703702613711357,0.2718750238418579,0.9759259354323149],[0.0,0.026851851493120193,0.2916666865348816,0.9768518395721912],[0.0,0.03055555559694767,0.2666666805744171,0.9777777884155512],[0.0,0.028703702613711357,0.2526041865348816,0.9759259354323149],[0.0,0.025925925001502037,0.17500001192092896,0.9787036683410406],[0.0,0.17407406866550446,0.1015625074505806,0.9759258776903152],[0.0,0.8305555582046509,0.06927083432674408,0.9768518507480621],[0.0,0.9018518328666687,0.01770833507180214,0.9749999791383743],[0.0,0.654629647731781,0.0937500074505806,0.9814814925193787]]}]},{"expression":"a person sitting on the stone bench outside the cafe","kind":"spatial","is_negative":true,"tracks":[]}],"rejected":{"the postcard display stand outside the cafe entrance":"count_mismatch"},"rejection_counts":{"count_mismatch":1},"row":{"storage_key":"s3://mv-ytun-external1/silver/test_vehicles/deliveries/videos/delivery1/scene_cutting/v1.0/payload/scenes/--cif7Nv6BI_chunk_014_scene_018.mp4","scene_key":"--cif7Nv6BI_chunk_014_scene_018","category":"test_vehicles","height":1080,"width":1920,"fps":50.0,"num_frames":628,"duration":12.56,"rmot_expressions":[{"expression":"the woman who enters the frame on the left","kind":"motion","is_negative":false,"tracks":[{"timestamps":[9.36,9.620000000000001,9.88,10.14,10.4,10.66,10.92,11.18,11.44,11.96,12.22,12.48],"boxes":[[0.0,0.06666666269302368,0.3812500238418579,0.9944444298744202],[0.0,0.05833333358168602,0.40416669845581055,0.9814814887940884],[0.0,0.029629629105329514,0.3218750059604645,0.9833333306014538],[0.0,0.028703702613711357,0.2718750238418579,0.9759259354323149],[0.0,0.026851851493120193,0.2916666865348816,0.9768518395721912],[0.0,0.03055555559694767,0.2666666805744171,0.9777777884155512],[0.0,0.028703702613711357,0.2526041865348816,0.9759259354323149],[0.0,0.025925925001502037,0.17500001192092896,0.9787036683410406],[0.0,0.17407406866550446,0.1015625074505806,0.9759258776903152],[0.0,0.8305555582046509,0.06927083432674408,0.9768518507480621],[0.0,0.9018518328666687,0.01770833507180214,0.9749999791383743],[0.0,0.654629647731781,0.0937500074505806,0.9814814925193787]]}]},{"expression":"a person sitting on the stone bench outside the cafe","kind":"spatial","is_negative":true,"tracks":[]}],"rmot_expression_count":2,"rmot_negative_count":1,"rmot_motion_count":1}}}}}]}