static/js/examples.js

/* Generated by build_examples.py from the paper_examples bundles. Do not edit by hand. */
window.OVH_EXAMPLES = [{"id":"ex01","num":1,"inPaper":true,"dataset":"FSC-147","model":"GPT-6 Sol","domain":"count","tags":["code","grounding"],"composition":true,"slug":"count-pills","question":"How many pill are there in the image? Your final answer should be a single integer.","pred":"139","gt":"139","score":1.0,"correct":true,"overview":{"task":"Count the pills in a photograph of about 140 pills of many colours and two shapes, oblong capsules and small round tablets, scattered on a dark surface.","why it is hard":"There are too many to count reliably by eye, and a single detector query does not reliably find both shapes.","how the agent solves it":"It runs two grounding queries, one general and one for round pills, then asks the coding agent to merge the two sets of detections without counting any pill twice. The first merge fails silently; the agent notices the impossible result and rewrites the merge, which then finds the five pills the first query missed."},"notool":"137","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex01/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"pill","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex01/tool_1.jpg"}],"out":"Visual_Grounding_Tool grounded 134 objects for the prompt 'pill'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 901, 26, 999], \"label\": \"pill\"},\n\t{\"bbox_2d\": [0, 52, 29, 204], \"label\": \"pill\"},\n\t{\"bbox_2d\": [0, 825, 78, 913], \"label\": \"pill\"},\n\t{\"bbox_2d\": [0, 278, 25, 417], \"label\": \"pill\"},\n\t{\"bbox_2d\": [0, 674, 49, 818], \"label\": \"pill\"},\n\t{\"bbox_2d\": [0, 473, 34, 622], \"label\": \"pill\"},\n\t{\"bbox_2d\": [1, 0, 66, 84], \"label\": \"pill\"},\n\t{\"bbox_2d\": [7, 418, 89, 485], \"label\": \"pill\"},\n\t{\"bbox_2d\": [29, 179, 107, 242], \"label\": \"pill\"},\n\t{\"bbox_2d\": [58, 554, 116, 663], \"label\": \"pill\"},\n\t{\"bbox_2d\": [59, 915, 109, 1000], \"label\": \"pill\"},\n\t{\"bbox_2d\": [65, 0, 106, 53], \"label\": \"pill\"},\n\t{\"bbox_2d\": [90, 307, 135, 375], \"label\": \"pill\"},\n\t{\"bbox_2d\": [97, 202, 156, 287], \"label\": \"pill\"},\n\t{\"bbox_2d\": [102, 4, 149, 75], \"label\": \"pill\"},\n\t{\"bbox_2d\": [103, 603, 159, 688], \"label\": \"pill\"},\n\t{\"bbox_2d\": [106, 380, 189, 520], \"label\": \"pill\"},\n\t{\"bbox_2d\": [107, 762, 157, 889], \"label\": \"pill\"},\n\t{\"bbox_2d\": [122, 59, 198, 204], \"label\": \"pill\"},\n\t{\"bbox_2d\": [128, 960, 184, 1000], \"label\": \"pill\"},\n\t{\"bbox_2d\": [130, 513, 214, 559], \"label\": \"pill\"},\n\t{\"bbox_2d\": [161, 153, 260, 264], \"label\": \"pill\"},\n\t{\"bbox_2d\": [163, 0, 249, 23], \"label\": \"pill\"},\n\t{\"bbox_2d\": [180, 658, 212, 799], \"label\": \"pill\"},\n\t{\"bbox_2d\": [181, 421, 273, 515], \"label\": \"pill\"},\n\t{\"bbox_2d\": [182, 237, 267, 348], \"label\": \"pill\"},\n\t{\"bbox_2d\": [194, 877, 242, 999], \"label\": \"pill\"},\n\t{\"bbox_2d\": [200, 10, 283, 92], \"label\": \"pill\"},\n\t{\"bbox_2d\": [221, 545, 308, 604], \"label\": \"pill\"},\n\t{\"bbox_2d\": [223, 754, 319, 845], \"label\": \"pill\"},\n\t{\"bbox_2d\": [250, 467, 303, 547], \"label\": \"pill\"},\n\t{\"bbox_2d\": [253, 306, 301, 377], \"label\": \"pill\"},\n\t{\"bbox_2d\": [276, 0, 361, 25], \"label\": \"pill\"},\n\t{\"bbox_2d\": [277, 83, 315, 229], \"label\": \"pill\"},\n\t{\"bbox_2d\": [284, 691, 317, 744], \"label\": \"pill\"},\n\t{\"bbox_2d\": [293, 10, 378, 77], \"label\": \"pill\"},\n\t{\"bbox_2d\": [310, 189, 358, 261], \"label\": \"pill\"},\n\t{\"bbox_2d\": [313, 235, 399, 346], \"label\": \"pill\"},\n\t{\"bbox_2d\": [316, 370, 368, 446], \"label\": \"pill\"},\n\t{\"bbox_2d\": [318, 683, 385, 783], \"label\": \"pill\"},\n\t{\"bbox_2d\": [320, 446, 364, 590], \"label\": \"pill\"},\n\t{\"bbox_2d\": [354, 594, 407, 722], \"label\": \"pill\"},\n\t{\"bbox_2d\": [365, 428, 396, 555], \"label\": \"pill\"},\n\t{\"bbox_2d\": [368, 839, 404, 968], \"label\": \"pill\"},\n\t{\"bbox_2d\": [373, 129, 460, 194], \"label\": \"pill\"},\n\t{\"bbox_2d\" …","boxes":[[0,901,26,999],[0,52,29,204],[0,825,78,913],[0,278,25,417],[0,674,49,818],[0,473,34,622],[1,0,66,84],[7,418,89,485],[29,179,107,242],[58,554,116,663],[59,915,109,1000],[65,0,106,53],[90,307,135,375],[97,202,156,287],[102,4,149,75],[103,603,159,688],[106,380,189,520],[107,762,157,889],[122,59,198,204],[128,960,184,1000],[130,513,214,559],[161,153,260,264],[163,0,249,23],[180,658,212,799],[181,421,273,515],[182,237,267,348],[194,877,242,999],[200,10,283,92],[221,545,308,604],[223,754,319,845],[250,467,303,547],[253,306,301,377],[276,0,361,25],[277,83,315,229],[284,691,317,744],[293,10,378,77],[310,189,358,261],[313,235,399,346],[316,370,368,446],[318,683,385,783],[320,446,364,590],[354,594,407,722],[365,428,396,555],[368,839,404,968],[373,129,460,194],[383,22,430,137],[397,910,454,998],[414,490,450,538],[417,348,473,431],[417,741,447,786],[434,192,476,256],[446,0,542,41],[451,255,494,319],[453,482,497,550],[456,893,480,956],[458,814,478,845],[466,626,553,761],[472,551,529,637],[477,342,533,427],[484,179,532,250],[492,801,534,837],[502,490,541,546],[534,136,618,219],[546,477,585,534],[546,112,566,139],[552,274,609,360],[555,530,612,614],[564,858,596,906],[565,107,594,149],[565,0,609,16],[567,766,616,842],[583,33,663,120],[587,342,682,429],[596,108,645,178],[605,633,654,707],[615,494,665,619],[623,878,662,935],[630,86,716,214],[633,830,654,862],[643,73,678,127],[649,823,711,885],[652,718,701,790],[657,635,737,708],[692,275,713,305],[692,502,727,554],[712,234,769,318],[712,830,770,917],[713,46,770,131],[718,963,751,1000],[726,0,769,26],[727,490,821,555],[731,659,793,750],[739,182,781,245],[754,975,788,996],[756,338,791,389],[758,108,813,191],[761,766,848,862],[772,238,867,323],[778,0,816,66],[781,939,862,1000],[784,210,819,260],[784,395,805,425],[795,428,816,458],[800,388,820,416],[815,403,916,461],[816,0,868,83],[826,843,869,908],[829,672,895,831],[833,590,854,621],[837,516,859,545],[841,289,927,360],[841,138,891,209],[842,77,942,131],[845,566,866,596],[850,466,896,531],[850,346,938,414],[855,533,883,574],[861,623,916,749],[885,822,957,915],[890,868,968,964],[895,0,955,23],[903,91,979,231],[904,232,999,316],[927,419,956,461],[930,925,984,1000],[935,677,983,748],[939,385,959,415],[942,584,985,673],[953,298,1000,384],[954,386,1000,464],[965,632,999,676],[966,795,998,903],[975,34,1000,155],[977,667,998,711]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"round pill","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex01/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 72 objects for the prompt 'round pill'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [91, 307, 136, 374], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [98, 202, 156, 288], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [102, 3, 150, 76], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [104, 603, 160, 688], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [128, 959, 185, 1000], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [250, 467, 303, 547], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [253, 310, 302, 384], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [284, 691, 317, 743], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [311, 190, 358, 262], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [316, 370, 368, 446], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [398, 911, 455, 998], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [415, 491, 450, 538], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [418, 742, 448, 787], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [418, 348, 474, 431], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [434, 192, 476, 258], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [451, 256, 494, 320], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [453, 482, 497, 551], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [456, 893, 480, 956], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [458, 815, 479, 845], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [472, 552, 529, 637], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [476, 342, 533, 427], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [484, 179, 532, 251], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [493, 802, 533, 837], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [499, 426, 520, 456], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [502, 490, 541, 547], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [511, 294, 540, 338], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [530, 369, 586, 454], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [546, 112, 566, 140], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [547, 477, 586, 534], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [553, 274, 610, 359], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [555, 530, 612, 615], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [564, 858, 596, 907], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [565, 108, 594, 149], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [567, 767, 617, 843], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [597, 109, 646, 179], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [604, 633, 654, 706], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [623, 878, 661, 936], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [633, 830, 654, 863], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [644, 74, 679, 127], \"label\": \"round pill\"},\n\t{\"bbox_2d\": [652, 717, 702, 790], \"label\": \" …","boxes":[[91,307,136,374],[98,202,156,288],[102,3,150,76],[104,603,160,688],[128,959,185,1000],[250,467,303,547],[253,310,302,384],[284,691,317,743],[311,190,358,262],[316,370,368,446],[398,911,455,998],[415,491,450,538],[418,742,448,787],[418,348,474,431],[434,192,476,258],[451,256,494,320],[453,482,497,551],[456,893,480,956],[458,815,479,845],[472,552,529,637],[476,342,533,427],[484,179,532,251],[493,802,533,837],[499,426,520,456],[502,490,541,547],[511,294,540,338],[530,369,586,454],[546,112,566,140],[547,477,586,534],[553,274,610,359],[555,530,612,615],[564,858,596,907],[565,108,594,149],[567,767,617,843],[597,109,646,179],[604,633,654,706],[623,878,661,936],[633,830,654,863],[644,74,679,127],[652,717,702,790],[661,382,712,453],[672,0,709,10],[692,276,713,306],[692,503,727,554],[712,233,770,318],[712,830,770,917],[713,46,770,131],[725,0,769,27],[731,659,793,750],[738,182,781,245],[751,978,791,1000],[756,338,791,389],[758,107,814,192],[783,395,804,425],[785,211,820,260],[795,427,816,458],[801,387,823,417],[825,843,869,908],[832,589,854,620],[837,516,859,546],[842,138,892,210],[845,566,866,597],[851,466,896,532],[855,534,883,574],[927,417,956,460],[930,924,984,1000],[935,678,984,748],[939,385,959,416],[953,299,1000,384],[954,386,1000,465],[965,632,1000,676],[979,668,1000,714]]},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Match the 134 detected pills against round-pill detections using mask overlap or bounding box intersection to count round pills absent from original pill detections, listing locations of missing ones and total union count."},"imgs":[],"code":"import numpy as np\nfrom PIL import Image, ImageDraw\nimport base64\nimport io\nimport json\n\n# Decode masks to numpy arrays for intersection computation\ndef decode_mask(mask_str):\n    # Each mask is a base64-encoded string representing a segmentation mask\n    # We'll decode it to a numpy array of shape (H, W) with 0/1 values\n    # The format is assumed to be a run-length encoded or compressed binary mask\n    # For simplicity, we'll treat it as a base64-encoded PNG-like mask\n    try:\n        # Decode base64 string to bytes\n        mask_bytes = base64.b64decode(mask_str)\n        # Create a PIL Image from the bytes\n        img = Image.open(io.BytesIO(mask_bytes))\n        # Convert to numpy array\n        mask_array = np.array(img)\n        # Ensure binary mask (0 or 1)\n        mask_array = (mask_array > 0).astype(np.uint8)\n        return mask_array\n    except Exception:\n        # If decoding fails, return None\n        return None\n\n# Compute IoU (Intersection over Union) between two masks\ndef compute_iou(mask1, mask2):\n    if mask1 is None or mask2 is None:\n        return 0.0\n    # Ensure masks are binary and same shape\n    mask1 = (mask1 > 0).astype(np.uint8)\n    mask2 = (mask2 > 0).astype(np.uint8)\n    # Compute intersection and union\n    intersection = np.logical_and(mask1, mask2).sum()\n    union = np.logical_or(mask1, mask2).sum()\n    if union == 0:\n        return 0.0\n    return intersection / union\n\n# Compute IoU between two bounding boxes\ndef compute_bbox_iou(box1, box2):\n    # box format: [x1, y1, x2, y2] (top-left, bottom-right)\n    x1_1, y1_1, x2_1, y2_1 = box1\n    x1_2, y1_2, x2_2, y2_2 = box2\n    \n    # Compute intersection\n    x_left = max(x1_1, x1_2)\n    y_top = max(y1_1, y1_2)\n    x_right = min(x2_1, x2_2)\n    y_bottom = min(y2_1, y2_2)\n    \n    if x_right <= x_left or y_bottom <= y_top:\n        return 0.0\n    \n    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n    # Compute union\n    area1 = (x2_1 - x1_1) * (y2_1 - y1_1)\n    area2 = (x2_2 - x1_2) * (y2_2 - y1_2)\n    union_area = area1 + area2 - intersection_area\n    \n    return intersection_area / union_area if union_area > 0 else 0.0\n\n# Get mask dimensions from original image\nH, W = segment_pill[\"orig_img_h\"], segment_pill[\"orig_img_w\"]\n\n# Decode all masks for both datasets\ndecoded_masks_pill = [decode_mask(mask) for mask in segment_pill[\"pred_masks\"]]\ndecoded_masks_round_pill = [decode_mask(mask) for mask in segment_round_pill[\"pred_masks\"]]\n\n# Initialize lists to store matched and unmatched round pills\nmatched_round_pills = []\nunmatched_round_pills = []\n\n# For each round pill, find the best match among original pills using IoU\nfor i, round_mask in enumerate(decoded_masks_round_pill):\n    best_iou = 0.0\n    best_match_idx = -1\n    best_bbox_iou = 0.0\n    best_bbox_idx = -1\n    \n    for j, pill_mask in enumerate(decoded_masks_pill):\n        if pill_mask is not None and round_mask is not None:\n            mask_iou = compute_iou(pill_mask, round_mask)\n            bbox_iou = compute_bbox_iou(segment_round_pill[\"pred_boxes\"][i], segment_pill[\"pred_boxes\"][j])\n            # Use a combined score or choose the higher one\n            combined_score = (mask_iou + bbox_iou) / 2.0\n            if combined_score > best_iou:\n                best_iou = combined_score\n                best_match_idx = j\n                best_bbox_iou = bbox_iou\n                best_bbox_idx = j\n    \n    if best_iou > 0.5:  # Threshold for matching\n        matched_round_pills.append((i, best_match_idx, best_iou, best_bbox_iou))\n    else:\n        unmatched_round_pills.append((i, segment_round_pill[\"pred_boxes\"][i], segment_round_pill[\"pred_scores\"][i]))\n\n# Count total union of detected pills (unique pills from both sets)\nall_masks = decoded_masks_pill + decoded_masks_round_pill\nunique_masks = []\nfor mask in all_masks:\n    if mask is not None:\n        # Check if mask is already in unique_masks\n        if not any(np.array_equal(mask, existing_mask) for existing_mask in unique_masks):\n            unique_masks.append(mask)\n\ntotal_union_count = len(unique_masks)\n\n# Output results\nprint(f\"Total round pills detected: {len(segment_round_pill['pred_masks'])}\")\nprint(f\"Total original pills detected: {len(segment_pill['pred_masks'])}\")\nprint(f\"Matched round pills: {len(matched_round_pills)}\")\nprint(f\"Unmatched round pills: {len(unmatched_round_pills)}\")\nprint(f\"Total union count: {total_union_count}\")\n\n# List locations of unmatched round pills\nprint(\"\\nUnmatched round pills (location and score):\")\nfor idx, box, score in unmatched_round_pills:\n    print(f\"  Index: {idx}, BBox: {box}, Score: {score}\")\n\n# Optional: Print matched pairs\nprint(\"\\nMatched round pills (round_idx, original_idx, IoU, bbox_iou):\")\nfor round_idx, orig_idx, iou, bbox_iou in matched_round_pills:\n    print(f\"  Round {round_idx} -> Original {orig_idx}, IoU: {iou:.4f}, BBox IoU: {bbox_iou:.4f}\")","out":"Total round pills detected: 72\nTotal original pills detected: 134\nMatched round pills: 0\nUnmatched round pills: 72\nTotal union count: 0\n\nUnmatched round pills (location and score):\n  Index: 0, BBox: [52.31292724609375, 117.94264221191406, 78.2960433959961, 143.74490356445312], Score: 0.921875\n  Index: 1, BBox: [56.460731506347656, 77.62562561035156, 90.11420440673828, 110.55953979492188], Score: 0.91015625\n  Index: 2, BBox: [58.77238082885742, 1.2981762886047363, 86.19441986083984, 29.0463809967041], Score: 0.9140625\n  Index: 3, BBox: [59.73981857299805, 231.58026123046875, 91.96317291259766, 264.26812744140625], Score: 0.921875\n  Index: 4, BBox: [73.73127746582031, 368.23492431640625, 106.33198547363281, 384.2291259765625], Score: 0.85546875\n  Index: 5, BBox: [144.1370849609375, 179.3444061279297, 174.76834106445312, 209.95547485351562], Score: 0.9296875\n  Index: 6, BBox: [145.93304443359375, 119.18267822265625, 173.69546508789062, 147.4763641357422], Score: 0.9140625\n  Index: 7, BBox: [163.64910888671875, 265.3359680175781, 182.84002685546875, 285.3944396972656], Score: 0.875\n  Index: 8, BBox: [178.857666015625, 72.8042221069336, 206.23037719726562, 100.41905212402344], Score: 0.93359375\n  Index: 9, BBox: [182.19520568847656, 142.0789031982422, 212.25015258789062, 171.2662811279297], Score: 0.9375\n  Index: 10, BBox: [229.33837890625, 349.857177734375, 262.0947265625, 383.17645263671875], Score: 0.89453125\n  Index: 11, BBox: [238.81997680664062, 188.43115234375, 259.37640380 …"},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Use ONLY the pred_boxes pixel coordinate arrays for the original 134 pill detections and the 72 round pill detections. Match round boxes to original boxes by intersection-over-union >=0.45 or center proximity; report numbered round detections that are missing from original and original detections with major overlaps (duplicates). No mask decoding."},"imgs":[],"code":"import numpy as np\n\n# Extract pred_boxes for original pills and round pills\noriginal_boxes = segment_pill[\"pred_boxes\"]\nround_boxes = segment_round_pill[\"pred_boxes\"]\n\n# Convert to numpy arrays for easier computation\noriginal_boxes = np.array(original_boxes)\nround_boxes = np.array(round_boxes)\n\n# Helper function to compute IoU\ndef iou(box1, box2):\n    # box format: [x1, y1, x2, y2] (assuming top-left and bottom-right)\n    x1_1, y1_1, x2_1, y2_1 = box1\n    x1_2, y1_2, x2_2, y2_2 = box2\n    \n    # Compute intersection\n    x_int = max(0, min(x2_1, x2_2) - max(x1_1, x1_2))\n    y_int = max(0, min(y2_1, y2_2) - max(y1_1, y1_2))\n    intersection = x_int * y_int\n    \n    # Compute union\n    area1 = (x2_1 - x1_1) * (y2_1 - y1_1)\n    area2 = (x2_2 - x1_2) * (y2_2 - y1_2)\n    union = area1 + area2 - intersection\n    \n    if union == 0:\n        return 0.0\n    return intersection / union\n\n# Helper function to compute center proximity (Euclidean distance between centers)\ndef center_proximity(box1, box2):\n    x1_1, y1_1, x2_1, y2_1 = box1\n    x1_2, y1_2, x2_2, y2_2 = box2\n    center1_x, center1_y = (x1_1 + x2_1) / 2, (y1_1 + y2_1) / 2\n    center2_x, center2_y = (x1_2 + x2_2) / 2, (y1_2 + y2_2) / 2\n    return np.sqrt((center1_x - center2_x)**2 + (center1_y - center2_y)**2)\n\n# Threshold for matching: IoU >= 0.45 OR center proximity <= 10 pixels (arbitrary, but reasonable for visual matching)\nMATCH_THRESHOLD_IOU = 0.45\nMATCH_THRESHOLD_CENTER = 10.0\n\n# Initialize lists to track matches and unmatched detections\nmatched_round_indices = set()\nmatched_original_indices = set()\nunmatched_round_indices = []\nunmatched_original_indices = []\n\n# For each round pill detection, try to match with original pill detections\nfor round_idx, round_box in enumerate(round_boxes):\n    matched = False\n    for orig_idx, orig_box in enumerate(original_boxes):\n        iou_val = iou(round_box, orig_box)\n        center_dist = center_proximity(round_box, orig_box)\n        \n        if iou_val >= MATCH_THRESHOLD_IOU or center_dist <= MATCH_THRESHOLD_CENTER:\n            # Found a match\n            matched = True\n            matched_round_indices.add(round_idx)\n            matched_original_indices.add(orig_idx)\n            break\n    \n    if not matched:\n        unmatched_round_indices.append(round_idx)\n\n# Find original detections that have major overlaps (duplicates) - i.e., matched with multiple round pills\nduplicate_original_indices = []\nfor orig_idx in matched_original_indices:\n    count = 0\n    for round_idx in range(len(round_boxes)):\n        if round_idx not in matched_round_indices:\n            continue\n        iou_val = iou(original_boxes[orig_idx], round_boxes[round_idx])\n        center_dist = center_proximity(original_boxes[orig_idx], round_boxes[round_idx])\n        if iou_val >= MATCH_THRESHOLD_IOU or center_dist <= MATCH_THRESHOLD_CENTER:\n            count += 1\n    if count > 1:\n        duplicate_original_indices.append(orig_idx)\n\n# Output results\nprint(\"Round detections missing from original:\", unmatched_round_indices)\nprint(\"Original detections with major overlaps (duplicates):\", duplicate_original_indices)","out":"Round detections missing from original: [23, 25, 26, 40, 41]\nOriginal detections with major overlaps (duplicates): []"}]},{"id":"ex02","num":2,"inPaper":true,"dataset":"OdinW-13-All","model":"GPT-6 Sol","domain":"count","tags":["grounding","zoom"],"composition":true,"slug":"detect-pascalvoc-objects","question":"Locate every instance that belongs to the following categories: 'aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor'. Report the bounding boxes in JSON format as a list of objects, one object per detected instance, where each object has a \"bbox_2d\" key with [x1, y1, x2, y2] and a \"label\" key with the category name exactly as written above. Coordinates must be normalised on a 0-1000 scale, with (0,0) as the top-left corner and (1000,1000) as the bottom-right corner of the image. For example: [{\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"aeroplane\"}, {\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"bicycle\"}]. If none of the listed categories is present, output an empty list [].","pred":"23 boxes","gt":"23 boxes","score":null,"correct":null,"overview":{"task":"Detect every object of the twenty PascalVOC categories in a photograph of fourteen men posing around a dinner table, and return labelled boxes.","why it is hard":"The people overlap heavily, some are visible only as a face between two others, and the table is covered with bottles and plates.","how the agent solves it":"After a first pass over the four categories present, it zooms on the crowded table and issues narrower queries, ending with a referring expression for one partly hidden man. The final answer matches 22 of the 23 ground-truth objects."},"notool":"21 boxes","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex02/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"person","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex02/tool_1.jpg"}],"out":"Visual_Grounding_Tool grounded 13 objects for the prompt 'person'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 422, 191, 871], \"label\": \"person\"},\n\t{\"bbox_2d\": [29, 208, 234, 533], \"label\": \"person\"},\n\t{\"bbox_2d\": [165, 414, 260, 667], \"label\": \"person\"},\n\t{\"bbox_2d\": [211, 402, 324, 636], \"label\": \"person\"},\n\t{\"bbox_2d\": [251, 272, 426, 480], \"label\": \"person\"},\n\t{\"bbox_2d\": [313, 389, 462, 589], \"label\": \"person\"},\n\t{\"bbox_2d\": [381, 236, 478, 505], \"label\": \"person\"},\n\t{\"bbox_2d\": [428, 219, 695, 512], \"label\": \"person\"},\n\t{\"bbox_2d\": [470, 410, 545, 574], \"label\": \"person\"},\n\t{\"bbox_2d\": [521, 384, 635, 642], \"label\": \"person\"},\n\t{\"bbox_2d\": [536, 382, 888, 1000], \"label\": \"person\"},\n\t{\"bbox_2d\": [543, 434, 727, 846], \"label\": \"person\"},\n\t{\"bbox_2d\": [569, 240, 717, 470], \"label\": \"person\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[0,422,191,871],[29,208,234,533],[165,414,260,667],[211,402,324,636],[251,272,426,480],[313,389,462,589],[381,236,478,505],[428,219,695,512],[470,410,545,574],[521,384,635,642],[536,382,888,1000],[543,434,727,846],[569,240,717,470]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"dining table","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex02/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'dining table'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [108, 722, 677, 942], \"label\": \"dining table\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[108,722,677,942]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"bottle","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex02/tool_3.jpg"}],"out":"Visual_Grounding_Tool grounded 5 objects for the prompt 'bottle'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [271, 495, 327, 642], \"label\": \"bottle\"},\n\t{\"bbox_2d\": [318, 533, 364, 645], \"label\": \"bottle\"},\n\t{\"bbox_2d\": [319, 640, 354, 794], \"label\": \"bottle\"},\n\t{\"bbox_2d\": [369, 576, 409, 705], \"label\": \"bottle\"},\n\t{\"bbox_2d\": [431, 504, 483, 681], \"label\": \"bottle\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_3.","boxes":[[271,495,327,642],[318,533,364,645],[319,640,354,794],[369,576,409,705],[431,504,483,681]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"chair","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex02/tool_4.jpg"}],"out":"Visual_Grounding_Tool grounded 3 objects for the prompt 'chair'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 745, 260, 999], \"label\": \"chair\"},\n\t{\"bbox_2d\": [0, 485, 61, 635], \"label\": \"chair\"},\n\t{\"bbox_2d\": [785, 612, 939, 999], \"label\": \"chair\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_4.","boxes":[[0,745,260,999],[0,485,61,635],[785,612,939,999]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[450,350,900,810],"padding":50},"imgs":[{"h":"tool_generated_image_5","src":"static/examples/ex02/tool_5.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_5."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"table","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex02/tool_6.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'table'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [109, 741, 677, 940], \"label\": \"table\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_6.","boxes":[[109,741,677,940]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"man","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_7","src":"static/examples/ex02/tool_7.jpg"}],"out":"Visual_Grounding_Tool grounded 13 objects for the prompt 'man'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 422, 190, 870], \"label\": \"man\"},\n\t{\"bbox_2d\": [29, 209, 234, 533], \"label\": \"man\"},\n\t{\"bbox_2d\": [166, 415, 259, 667], \"label\": \"man\"},\n\t{\"bbox_2d\": [212, 402, 324, 636], \"label\": \"man\"},\n\t{\"bbox_2d\": [251, 272, 426, 478], \"label\": \"man\"},\n\t{\"bbox_2d\": [313, 389, 462, 589], \"label\": \"man\"},\n\t{\"bbox_2d\": [381, 236, 478, 504], \"label\": \"man\"},\n\t{\"bbox_2d\": [430, 219, 694, 512], \"label\": \"man\"},\n\t{\"bbox_2d\": [470, 410, 545, 573], \"label\": \"man\"},\n\t{\"bbox_2d\": [521, 384, 636, 642], \"label\": \"man\"},\n\t{\"bbox_2d\": [537, 382, 887, 1000], \"label\": \"man\"},\n\t{\"bbox_2d\": [543, 432, 726, 845], \"label\": \"man\"},\n\t{\"bbox_2d\": [570, 240, 716, 470], \"label\": \"man\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_7.","boxes":[[0,422,190,870],[29,209,234,533],[166,415,259,667],[212,402,324,636],[251,272,426,478],[313,389,462,589],[381,236,478,504],[430,219,694,512],[470,410,545,573],[521,384,636,642],[537,382,887,1000],[543,432,726,845],[570,240,716,470]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"person in dark shirt","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_8","src":"static/examples/ex02/tool_8.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'person in dark shirt'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [251, 273, 426, 483], \"label\": \"person in dark shirt\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_8.","boxes":[[251,273,426,483]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[630,400,785,730],"padding":50},"imgs":[{"h":"tool_generated_image_9","src":"static/examples/ex02/tool_9.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_9."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"man behind the man in white shirt","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_10","src":"static/examples/ex02/tool_10.jpg"}],"out":"Visual_Grounding_Tool grounded 7 objects for the prompt 'man behind the man in white shirt'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [165, 414, 259, 667], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [211, 402, 324, 637], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [381, 235, 478, 506], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [430, 219, 694, 513], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [470, 409, 545, 573], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [522, 384, 635, 642], \"label\": \"man behind the man in white shirt\"},\n\t{\"bbox_2d\": [537, 382, 887, 1000], \"label\": \"man behind the man in white shirt\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_10.","boxes":[[165,414,259,667],[211,402,324,637],[381,235,478,506],[430,219,694,513],[470,409,545,573],[522,384,635,642],[537,382,887,1000]]}]},{"id":"ex03","num":3,"inPaper":true,"dataset":"FSCD-147","model":"GPT-6 Sol","domain":"count","tags":["grounding","zoom"],"composition":true,"slug":"detect-fruit-packages","question":"Locate all instances of 'package of fresh cut fruit' in this image. For each instance, output a bounding box as [x1, y1, x2, y2] where coordinates are normalised on a 0-1000 scale (with (0,0) as the top-left corner and (1000,1000) as the bottom-right corner). Your answer should be a list of lists. If there are no instances of 'package of fresh cut fruit' in the image, output an empty list [] as your answer.","pred":"53 boxes","gt":"53 boxes","score":null,"correct":null,"overview":{"task":"Box every “package of fresh cut fruit” on a supermarket shelf that holds 53 plastic fruit cups in rows.","why it is hard":"The category name as given is unusual; a detector queried with it finds only part of the shelf.","how the agent solves it":"It rephrases the category as “fruit cup”, which finds every cup, checks two regions with zooms, and returns 53 boxes that all match. Note that the no-tool answer scores zero only because it used pixel coordinates instead of the requested 0–1000 scale (see the note under the results)."},"notool":"53 boxes","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex03/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"package of fresh cut fruit","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex03/tool_1.jpg"}],"out":"Visual_Grounding_Tool grounded 25 objects for the prompt 'package of fresh cut fruit'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [337, 673, 434, 873], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [343, 859, 437, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [431, 226, 527, 383], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [445, 855, 538, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [445, 674, 541, 869], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [532, 225, 626, 381], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [535, 381, 625, 526], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [546, 663, 637, 863], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [547, 850, 638, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [631, 225, 729, 372], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [637, 371, 733, 527], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [640, 669, 731, 859], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [648, 855, 735, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [738, 208, 829, 372], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [739, 367, 824, 526], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [740, 674, 837, 871], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [745, 864, 838, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [834, 205, 927, 372], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [836, 367, 924, 526], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [841, 863, 934, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [842, 676, 936, 872], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [936, 866, 1000, 1000], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [937, 336, 1000, 525], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [939, 676, 1000, 870], \"label\": \"package of fresh cut fruit\"},\n\t{\"bbox_2d\": [941, 237, 1000, 339], \"label\": \"package of fresh cut fruit\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[337,673,434,873],[343,859,437,1000],[431,226,527,383],[445,855,538,1000],[445,674,541,869],[532,225,626,381],[535,381,625,526],[546,663,637,863],[547,850,638,1000],[631,225,729,372],[637,371,733,527],[640,669,731,859],[648,855,735,1000],[738,208,829,372],[739,367,824,526],[740,674,837,871],[745,864,838,1000],[834,205,927,372],[836,367,924,526],[841,863,934,1000],[842,676,936,872],[936,866,1000,1000],[937,336,1000,525],[939,676,1000,870],[941,237,1000,339]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"fruit cup","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex03/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 57 objects for the prompt 'fruit cup'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 234, 28, 387], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [0, 0, 26, 59], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [0, 390, 25, 527], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [1, 668, 105, 871], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [6, 867, 102, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [26, 385, 130, 527], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [29, 229, 132, 385], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [35, 0, 124, 60], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [83, 664, 120, 861], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [106, 866, 128, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [114, 676, 212, 867], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [120, 865, 212, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [135, 0, 234, 59], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [138, 234, 234, 386], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [140, 386, 233, 527], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [166, 663, 234, 856], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [207, 858, 230, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [227, 676, 335, 873], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [234, 866, 336, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [235, 0, 333, 57], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [236, 230, 331, 387], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [236, 386, 331, 526], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [332, 385, 432, 526], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [334, 220, 428, 384], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [336, 0, 425, 58], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [337, 673, 434, 873], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [343, 859, 437, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [432, 226, 527, 381], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [432, 382, 528, 526], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [440, 0, 521, 57], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [445, 674, 541, 868], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [445, 855, 538, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [533, 225, 626, 380], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [534, 381, 625, 525], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [539, 0, 619, 56], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [546, 666, 637, 862], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [547, 851, 639, 1000], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [631, 225, 729, 372], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [637, 370, 733, 526], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [640, 0, 723, 59], \"label\": \"fruit cup\"},\n\t{\"bbox_2d\": [640, 670, 731, 859], \"label\": \"fruit cup\" …","boxes":[[0,234,28,387],[0,0,26,59],[0,390,25,527],[1,668,105,871],[6,867,102,1000],[26,385,130,527],[29,229,132,385],[35,0,124,60],[83,664,120,861],[106,866,128,1000],[114,676,212,867],[120,865,212,1000],[135,0,234,59],[138,234,234,386],[140,386,233,527],[166,663,234,856],[207,858,230,1000],[227,676,335,873],[234,866,336,1000],[235,0,333,57],[236,230,331,387],[236,386,331,526],[332,385,432,526],[334,220,428,384],[336,0,425,58],[337,673,434,873],[343,859,437,1000],[432,226,527,381],[432,382,528,526],[440,0,521,57],[445,674,541,868],[445,855,538,1000],[533,225,626,380],[534,381,625,525],[539,0,619,56],[546,666,637,862],[547,851,639,1000],[631,225,729,372],[637,370,733,526],[640,0,723,59],[640,670,731,859],[647,856,735,999],[738,209,830,371],[738,367,823,525],[740,0,829,68],[740,675,836,871],[745,866,838,1000],[834,0,921,68],[834,204,927,371],[836,368,924,526],[841,866,934,1000],[842,676,936,872],[936,867,1000,1000],[937,336,1000,525],[939,677,1000,870],[940,237,1000,339],[976,8,1000,67]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[0,640,343,1000],"padding":50},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex03/tool_3.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_3."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[0,0,1000,85],"padding":50},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex03/tool_4.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_4."}]},{"id":"ex04","num":4,"inPaper":true,"dataset":"MMSearch-Plus","model":"GPT-6 Luna","domain":"search","tags":["image-search","ocr","text-search","webpage-visit","zoom"],"composition":true,"slug":"paris-fashion-week-brand","question":"In Paris Fashion Week,  what brand did the person in the image represent? In which year did the event occur, and what is the event's name?","pred":"The person was attending the Hermès show. The event was Paris Fashion Week Fall/Winter 2025.","gt":"StreetStyle Winter 2025 Hermes","score":null,"correct":null,"overview":{"task":"A street photograph from Paris Fashion Week shows a woman in a beige jacket and an orange scarf (faces are blurred in the benchmark image). Which brand's show was she attending, and which event and year was it?","why it is hard":"Nothing in the image names the show; without tools the model guesses a Miu Miu show in 2024.","how the agent solves it":"A reverse image search finds no match, so the agent turns the photo into a text description and runs an image search with it. The top result is a street-style gallery titled “What they wore to Hermès” at Paris Fashion Week Fall/Winter 2025, and a page visit confirms the event and season.","caveat":"The perception step contributes nothing: the agent zooms on what it takes for a shopping bag, but the OCR reads a Paris litter-bag slogan (“Ensemble rendons Paris propre”). The answer comes from search alone."},"notool":"The brand is **Miu Miu**. The event took place in **2024** and was the **Miu Miu Spring/Summer 2025 show** during Paris Fashion Week.","final":"The person was attending the Hermès show. The event was Paris Fashion Week Fall/Winter 2025.","inputs":[{"h":"input_image_1","src":"static/examples/ex04/input_1.jpg"}],"steps":[{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_1"},"imgs":[],"say":"I’ll use reverse-image search to identify the street-style photo or event source, which may reveal the represented brand and event details.","out":"[Image Search — Visual Matches]\nNO RELEVANT MATCHES\n\nWhy each raw match was rejected:\nAll 10 candidates show studio product photos of jackets, which visually mismatch the QUERY image's street scene. Their text metadata correctly describes the products but is irrelevant to the QUERY image's context. No candidate image matches the QUERY image's subject or environment.\n\nSuggested next actions:\n- Image_Search_Tool(image_id=input_image_1)\n- Text_Search_Tool(query='Paris street scene with woman in beige jacket', search_type=images)"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Paris Fashion Week street style woman beige jacket orange scarf paper shopping bag Paris black boots","search_type":"images"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex04/tool_1.jpg"},{"h":"tool_generated_image_2","src":"static/examples/ex04/tool_2.jpg"},{"h":"tool_generated_image_3","src":"static/examples/ex04/tool_3.jpg"}],"out":"Image refs: tool_generated_image_1, tool_generated_image_2, tool_generated_image_3\n[Search Results — Type: images]\n1. Paris Fashion Week FW 2025 - What they Wore to Hermes - FunkyForty\n   Image: [tool_generated_image_1]\n   Source: FunkyForty   [webpage_url_1]\n   Size: 1707x2560\n2. Showgoers Embraced the Colors of Fall for Day 5 of Paris Fashion Week -  Fashionista\n   Image: [tool_generated_image_2]\n   Source: Fashionista   [webpage_url_2]\n   Size: 2560x3840\n3. 6 Outfit Ideas From Paris Fashion Week Street Style | Vogue\n   Image: [tool_generated_image_3]\n   Source: Vogue   [webpage_url_3]\n   Size: 1920x1080\n\n[Summary]\nCandidate 1 (FunkyForty) shows a woman in a beige jacket, orange scarf, and black boots, holding a paper shopping bag, matching the query closely. Candidate 7 (Fashionista) features a woman in a beige jacket and black boots, with a blue scarf and a blue bag, aligning with the query’s core elements. Candidate 4 (Vogue) displays a woman in a beige jacket and black boots, with an orange bag, reinforcing the street style theme. Other candidates show varied outfits, including mismatched colors (e.g., tan boots with beige jackets), different bag types (e.g., quilted bags, leopard-print scarves), or group shots without the specific orange scarf or paper bag. Sources include Vogue, FunkyForty, and Fashionista, covering multiple seasons and years (2024–2025).\ntool_generated_image_1\ntool_generated_image_2\ntool_generated_image_3"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[468,477,551,772],"padding":70},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex04/tool_4.jpg"}],"say":"I’ll inspect the lettering on the woman’s shopping bag; it could provide a clue to the brand associated with her.","out":"Zoomed in image rendered below; refer to it as tool_generated_image_4."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_4"},"imgs":[],"out":"OCR_Tool transcribed the following text from the image:\n\nENSMLI\nRENOIS\nPARI\nPROPIE\nPARIS"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_1"],"goal":"Find whether the matching woman is shown and identify her outfit brand/represented brand, the Paris Fashion Week year and event name/season."},"imgs":[],"say":"The person was attending the Hermès show. The event was Paris Fashion Week Fall/Winter 2025.","out":"The useful information in https://funkyforty.com/paris-fashion-week-fw-2025-what-they-wore-to-hermes/ for user goal Find whether the matching woman is shown and identify her outfit brand/represented brand, the Paris Fashion Week year and event name/season. as follows: \n\nEvidence in page: \nParis Fashion Week FW 2025 - What they Wore to Hermes. The article discusses street style at Hermes during Paris Fashion Week FW 2025, including styling ideas and bag trends, with photos taken by Yvonne Aeberhard Stutz. The event is clearly identified as Paris Fashion Week Fall/Winter 2025.\n\nSummary: \nThe webpage documents street style at Paris Fashion Week Fall/Winter 2025, specifically focusing on what attendees wore to the Hermes show. While it does not explicitly name a specific woman, it references the event, season, and brand (Hermes) as requested in the goal."}]},{"id":"ex05","num":5,"inPaper":true,"dataset":"MMSearch-Plus","model":"Qwen3-VL-32B","domain":"search","tags":["image-search","text-search","webpage-visit"],"composition":true,"slug":"great-depression-years-apart","question":"图1中发生在大萧条时代的著名历史事件，和图2中的事件相隔多少年？","pred":"9年","gt":"9","score":null,"correct":null,"overview":{"task":"The question, in Chinese, asks: how many years apart are the famous Great-Depression-era event commemorated in the first image and the event shown in the second? The first image is a rusted steel monument in a park; the second is a black-and-white photograph of a fight.","how the agent solves it":"It identifies each image with reverse image search and dates each event from the web: the Ford Hunger March of 7 March 1932, and the Ford Strikers Riot of 3 April 1941. One page visit fails with a server error, and the agent recovers with a text search. It answers 9 years. The model reasons in Chinese throughout."},"notool":"","final":"现在我已经知道：\n\n- 图1中的事件是**福特饥饿游行（Ford Hunger March）**，发生在 **1932年3月7日**。\n- 图2中的事件是**福特罢工者骚乱（Ford Strikers Riot）**，发生在 **1941年4月3日**。\n\n因此，这两个事件之间的年份差可以通过简单的减法计算得出：1941 - 1932 = 9年。\n\n所以，图1和图2中的事件相隔9年。","inputs":[{"h":"input_image_1","src":"static/examples/ex05/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex05/input_2.jpg"}],"steps":[{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex05/tool_1.jpg"},{"h":"tool_generated_image_2","src":"static/examples/ex05/tool_2.jpg"},{"h":"tool_generated_image_3","src":"static/examples/ex05/tool_3.jpg"}],"say":"我需要先识别图1中的地点和相关的历史事件。从图1的墙上的文字“FORT STREET BRIDGE PARK”可以推断出这是位于美国密歇根州底特律市的一个公园。这个公园与底特律在工业时代的历史有关。然后，我需要确定图2中的历史事件是什么，并计算两个事件之间的时间间隔。\n\n首先，我会使用图像搜索工具来确认图1中的雕塑或地标是否与某个特定的历史事件相关联。","out":"Image refs: tool_generated_image_1, tool_generated_image_2, tool_generated_image_3\n[Image Search — Visual Matches]\n1. Fort Street Bridge Park chronicles labor and environmental history\n   Source: planetdetroit.org/2024/03/fort-street-bridge-park   [webpage_url_1]\n   Image: [tool_generated_image_1]\n2. MotorCities - Fort Street Bridge Interpretive Park\n   Source: motorcities.org/fortstreet   [webpage_url_2]\n   Image: [tool_generated_image_2]\n3. COMMUNITY BENEFITS\n   Source: gordiehoweinternationalbridge.com/wp-content/uploads/2024/11/Community-Benefi...   [webpage_url_3]\n   Image: [tool_generated_image_3]\n\n[Summary]\nThe QUERY image is identified as the Fort Street Bridge Park sculpture, a rust-colored steel monument featuring a large gear and lattice structure, situated in a park with a visible 'FORT STREET BRIDGE PARK' sign. Multiple sources, including planetdetroit.org and motorcities.org, confirm this is the interpretive park sculpture. Additional images from the Gordie Howe International Bridge website (candidate 7) show the same sculpture from different angles, reinforcing its identity as part of the bridge's community art program. The sculpture is also documented in installation photos (candidate 3) and public viewing shots (candidate 6), all consistent with the QUERY image's visual details.\ntool_generated_image_1\ntool_generated_image_2\ntool_generated_image_3"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_1"],"goal":"Find out which historical event during the Great Depression is commemorated at Fort Street Bridge Park."},"imgs":[],"say":"根据图像搜索结果，图1显示的是位于底特律的Fort Street Bridge Park的雕塑。该公园纪念了底特律的劳工和环境历史。为了找到与大萧条时期相关的具体历史事件，我将访问其中一个来源网页以获取更多信息。","out":"The useful information in https://planetdetroit.org/2024/03/fort-street-bridge-park/ for user goal Find out which historical event during the Great Depression is commemorated at Fort Street Bridge Park. as follows: \n\nEvidence in page: \nSigns explain why this sculpture and park are here: to honor the memory of protesters who met on this very spot on March 7, 1932, before marching up Miller Road to the massive Ford Rouge River Complex located in the adjacent city of Dearborn. ... This crowd of several thousand marched up the road on one of the coldest days of winter. They were greeted at the Dearborn border with clouds of tear gas, jets of cold water and a shower of bullets. It was then that the Ford Hunger March became the Ford Massacre.\n\nSummary: \nFort Street Bridge Park commemorates the Ford Hunger March of March 7, 1932, a pivotal labor protest during the Great Depression in which unemployed Ford workers marched to demand better working conditions and an end to discrimination, only to be violently suppressed by Ford security and Dearborn police — an event that became known as the Ford Massacre."},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_2"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex05/tool_4.jpg"},{"h":"tool_generated_image_5","src":"static/examples/ex05/tool_5.jpg"},{"h":"tool_generated_image_6","src":"static/examples/ex05/tool_6.jpg"}],"say":"现在我知道了图1中的事件是1932年的福特饥饿游行（Ford Hunger March），也被称为福特大屠杀（Ford Massacre）。接下来，我需要分析图2中的事件并确定其发生时间。图2是一张黑白照片，描绘了一群人正在打斗，其中一人举着棍子。这看起来像一个历史性的暴力事件。我将使用图像搜索工具来识别这张图片所代表的具体事件。","out":"Image refs: tool_generated_image_4, tool_generated_image_5, tool_generated_image_6\n[Image Search — Visual Matches]\n1. Ford Strikers Riot - A black and white photo of a group of men fighting - PICRYL - Public Domain Media Search Engine Public Domain Search\n   Source: picryl.com/media/ford-strikers-riot-a4a7fb   [webpage_url_4]\n   Image: [tool_generated_image_4]\n2. Moments The Pulitzer Prize Photographs 1942-1977 Sheryle John Leekley Vtg Book - very-good · Whatnot: Shop, Sell, Connect\n   Source: whatnot.com/listing/TGlzdGluZ05vZGU6NzAzNzkzMTEy   [webpage_url_5]\n   Image: [tool_generated_image_5]\n3. 75 Years Ago Workers Successfully Defeated Henry Ford — History News Network\n   Source: historynewsnetwork.org/.../75-years-ago-workers-successfully-defeated-henry-f   [webpage_url_6]\n   Image: [tool_generated_image_6]\n\n[Summary]\nMultiple sources, including PICRYL and a History News Network article, identify the image as depicting the Ford Strikers Riot, a significant labor conflict. The image is also referenced in a vintage book of Pulitzer Prize photographs, confirming its historical importance. A separate source, a blog post by Robert Paul Wolff, references the image in a discussion about labor and tolerance, further contextualizing its significance. The image is visually consistent across these sources, showing a group of men in work attire engaged in a physical altercation outside a factory building. One candidate (rank 5) incorrectly attributes the image to Glasgow, while another (rank 6) shows a completely different scene from Finland, indicating a mismatch in both visual and textual content.\ntool_generated_image_4\ntool_generated_image_5\ntool_generated_image_6"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_4"],"goal":"Determine the exact date of the Ford Strikers Riot shown in the image."},"imgs":[],"say":"图像搜索结果显示，图2中的事件被标识为“福特罢工者骚乱”（Ford Strikers Riot）。为了准确确定这一事件的发生时间，我需要查阅更详细的信息。我将访问PICRYL网站上提供的链接，因为它是直接描述该事件的可靠来源。","out":"The useful information in https://picryl.com/media/ford-strikers-riot-a4a7fb for user goal Determine the exact date of the Ford Strikers Riot shown in the image. as follows: \n\nEvidence in page: \nFailed to fetch https://picryl.com/media/ford-strikers-riot-a4a7fb (HTTP 500, transient) after 3 attempts Detail: 500 Server Error: Internal Server Error for url: https://scrape.serper.dev/. Suggestion: source temporarily unavailable; try again later or pick a different URL.\n\nSummary: \nFETCH FAILED.\n\nReason: Failed to fetch https://picryl.com/media/ford-strikers-riot-a4a7fb (HTTP 500, transient) after 3 attempts Detail: 500 Server Error: Internal Server Error for url: https://scrape.serper.dev/. Suggestion: source temporarily unavailable; try again later or pick a different URL.\nDiagnosis: Transient failure (5xx / 429 / timeout) after 3 retries. The source MAY be back online in a minute; retrying this URL once more after a short pause is reasonable. If it fails again, switch to a different source.\n\nSuggested next actions for the agent:\n\n  - Tool switches (the agent may also re-call this same Webpage_Visit_Tool on a DIFFERENT URL — nothing is excluded just because the current URL failed):\n    - Webpage_Visit_Tool(urls=[<a different webpage_url_N>], goal=...)\n        Try a different URL from the same search results. If the prior search had multiple results and only this one fetched, the answer may be on another one of them.\n    - Text_Search_Tool(query=<rephrased>, search_type=<search|news|scholar|...>)\n        Re-search to surface fresher / non-blocked sources for the same fact. If the failing URL was the only result, the search query may have been too narrow.\n    - Image_Search_Tool(image_id=<input_image_N | tool_generated_image_N>)\n        If your question is fundamentally about identifying an image, switch to reverse image search — bypasses the failing webpage entirely."},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Ford Strikers Riot date","search_type":"search"},"imgs":[],"say":"由于无法访问PICRYL网站，我需要尝试另一个来源来确定福特罢工者骚乱的确切日期。我将使用文本搜索工具来查找关于“福特罢工者骚乱”的信息，特别是它的发生时间。","out":"[Search Results — Type: search]\n1. File:Ford Strikers Riot.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/wiki/File:Ford_Strikers_Riot.jpg   [tool_generated_image_7]\n   Date, Taken on 3 April 1941. Source, The Detroit News, The legacy of Henry ... File:Ford Strikers Riot.png · Category:1941-04-03. File usage on other wikis.\n2. Unemployed Detroit auto workers conduct Hunger March to protest ...\n   Source: nvdatabase.swarthmore.edu/content/unemployed-detroit-auto-workers-conduct-hun...   [webpage_url_7]\n   On 1 April 1941, Ford workers conducted a walkout in order to protest the firing of eight union members. Because of the strike, the River Rouge plant was ...\n3. Ford Strikers Riot - Wikipedia\n   Source: en.wikipedia.org/wiki/Ford_Strikers_Riot   [webpage_url_8]\n   Ford Strikers Riot is a 1941 photograph which shows a strikebreaker getting beaten by United Auto Workers (UAW) strikers.\n\n[Summary]\nThe Ford Strikers Riot occurred on April 3, 1941, as documented in a photograph taken by Milton Brooks and published by The Detroit News, capturing a strikebreaker being beaten by United Auto Workers strikers. A strike began on April 1, 1941, after Ford fired eight union members, leading to a walkout and escalating tensions. The strike lasted about ten days, culminating in violence at the Ford River Rouge Plant, with strikers using around 2,000 vehicles to block access to the plant. The event is also referenced in Wikipedia and Wikimedia Commons as a pivotal moment in American labor history, with the photograph later winning a Pulitzer Prize for Photography."}]},{"id":"ex06","num":6,"inPaper":true,"dataset":"RealX-Bench","model":"GPT-6 Luna","domain":"search","tags":["grounding","image-search","ocr","zoom"],"composition":true,"slug":"spray-brand","question":"Which brand does the item on the far right in the top row belong to?","pred":"MYONS (弥玥泉)","gt":"Miyue Spring","score":1.0,"correct":true,"overview":{"task":"A photograph shows a crowded skincare shelf. Which brand does the item at the far right of the top row belong to? The item is a tall white spray can whose label is far too small to read.","why it is hard":"Nothing legible identifies the product; without tools the model guesses AHC.","how the agent solves it":"Zooms and OCR on the top shelf return only book spines and dates, and a reverse search of the whole shelf returns generic “shelfie” boards. The agent goes back to the image: it zooms on the bottles again, grounds them to see where each one sits, crops tightly, and reverse-searches the crop, which matches Miyuequan (Miyue Spring) hydrating spray."},"notool":"The item on the far right in the top row appears to be from AHC.","final":"MYONS (弥玥泉)","inputs":[{"h":"input_image_1","src":"static/examples/ex06/input_1.jpg"}],"steps":[{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[812,0,872,178],"padding":70},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex06/tool_1.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_1."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_1"},"imgs":[],"out":"OCR_Tool transcribed the following text from the image:\n\n1982年10月1日"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[962,18,999,176],"padding":70},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex06/tool_2.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_2."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_2"},"imgs":[],"out":"OCR_Tool transcribed the following text from the image:\n\n3 中國公物業\n2 符合的直文例語\n9 製作例語\n1 毛優的風趣人生"},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex06/tool_3.jpg"},{"h":"tool_generated_image_4","src":"static/examples/ex06/tool_4.jpg"},{"h":"tool_generated_image_5","src":"static/examples/ex06/tool_5.jpg"}],"out":"Image refs: tool_generated_image_3, tool_generated_image_4, tool_generated_image_5\n[Image Search — Visual Matches]\n1. Discover 130 Beauty Shelf and shelf full of beauty products ideas | beauty products on a shelf, skincare shelfie ideas, korean skincare products on shelf and more\n   Source: pinterest.com/honeymaesh/beauty-shelf   [webpage_url_1]\n   Image: [tool_generated_image_3]\n2. I'm a Recent Dermstore Convert—Here Are the 21 Items I Would Buy in Bulk\n   Source: es.pinterest.com/pin/266064290476051096   [webpage_url_2]\n   Image: [tool_generated_image_4]\n3. Aesthetic Skincare Cabinet\n   Source: pinterest.com/ideas/aesthetic-skincare-cabinet/946064078656   [webpage_url_3]\n   Image: [tool_generated_image_5]\n\n[Summary]\nMultiple candidates (2, 3, and 5) show well-organized shelves filled with a wide variety of beauty and skincare products, visually matching the QUERY image's subject. Candidate 2 from Pinterest highlights 'beauty shelf' and 'skincare shelfie ideas', while candidate 3 from Pinterest features '21 Items I Would Buy in Bulk' and shows a similar densely packed arrangement. Candidate 5, titled 'Aesthetic Skincare Cabinet', also displays a curated collection of skincare items on wooden shelves. The QUERY image's dark wood shelving and specific product brands like Pantene, La Mer, and Laneige are not explicitly matched in any candidate, but the overall visual theme of a personal beauty product collection is consistently represented across the top three matches.\ntool_generated_image_3\ntool_generated_image_4\ntool_generated_image_5"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[814,26,870,178],"padding":100},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex06/tool_6.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_6."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"shampoo bottle","image_id":"tool_generated_image_6"},"imgs":[{"h":"tool_generated_image_7","src":"static/examples/ex06/tool_7.jpg"}],"out":"Visual_Grounding_Tool grounded 4 objects for the prompt 'shampoo bottle'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 144, 18, 615], \"label\": \"shampoo bottle\"},\n\t{\"bbox_2d\": [16, 50, 197, 621], \"label\": \"shampoo bottle\"},\n\t{\"bbox_2d\": [198, 52, 376, 623], \"label\": \"shampoo bottle\"},\n\t{\"bbox_2d\": [376, 54, 554, 624], \"label\": \"shampoo bottle\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_7.","boxes":[[0,144,18,615],[16,50,197,621],[198,52,376,623],[376,54,554,624]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[804,37,872,176],"padding":120},"imgs":[{"h":"tool_generated_image_8","src":"static/examples/ex06/tool_8.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_8."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[805,25,875,175],"padding":100},"imgs":[{"h":"tool_generated_image_9","src":"static/examples/ex06/tool_9.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_9."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_9"},"imgs":[],"out":"OCR_Tool transcribed the following text from the image:\n\n2015年1月1日"},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_9"},"imgs":[{"h":"tool_generated_image_10","src":"static/examples/ex06/tool_10.jpg"},{"h":"tool_generated_image_11","src":"static/examples/ex06/tool_11.jpg"},{"h":"tool_generated_image_12","src":"static/examples/ex06/tool_12.jpg"}],"say":"MYONS (弥玥泉)","out":"Image refs: tool_generated_image_10, tool_generated_image_11, tool_generated_image_12\n[Image Search — Visual Matches]\n1. Amazon.com: YILIAN Face Mist Natural Facial Spray with HA, Soothing and Calming Sensitive Skin, 10.14 fl. oz.\n   Source: amazon.com/clp/B0DTT9SV1Y   [webpage_url_4]\n   Image: [tool_generated_image_10]\n2. Fast delivery][Hot selling in China] Miyuequan Hydrating Spray Moisturizing for Women Soothing Skin Pre-Makeup Glacier Hot Spring Small Molecule Toner Miyuequan | Daraz.com.np\n   Source: daraz.com.np/products/fast-deliveryhot-selling-in-china-miyuequan-hydrating-s...   [webpage_url_5]\n   Image: [tool_generated_image_11]\n3. Buy 小米补水神器Online at a Better Price | Lazada Malaysia\n   Source: lazada.com.my/tag/小米补水神器   [webpage_url_6]\n   Image: [tool_generated_image_12]\n\n[Summary]\nMultiple sources identify the product as '弥玥泉' (MYONS) brand facial spray, with consistent branding including a mountain graphic and '小分子' (small molecule) text. Candidate 1 (Amazon) and Candidate 5 (Daraz) show the same product bottle design and volume (300ml), confirming the brand and product type. Candidate 7 (Lazada) provides additional marketing context, highlighting '敏感肌修护' (sensitive skin repair) and '源自新疆冰川温泉水' (from Xinjiang glacier hot spring water), which aligns with the product's visual design. The product is consistently described as a moisturizing spray for sensitive skin across all matches, with variations in packaging context (e.g., shelf display, hand-held, promotional ad).\ntool_generated_image_10\ntool_generated_image_11\ntool_generated_image_12"}]},{"id":"ex07","num":7,"inPaper":true,"dataset":"RealX-Bench","model":"GPT-6 Sol","domain":"search","tags":["image-search","ocr","text-search","zoom"],"composition":true,"slug":"foundation-brand","question":"Which brand does the liquid foundation on the far right on the storage shelf in the picture belong to?","pred":"KATO-KATO","gt":"KATO","score":1.0,"correct":true,"overview":{"task":"A makeup organizer holds brushes and cosmetics. Which brand is the liquid foundation at the far right? Its label is unreadable; its most distinctive feature is a crystal-faceted cap.","why it is hard":"Without tools the model guesses Dior.","how the agent solves it":"OCR reads nothing, and reverse-searching a crop of the bottle finds no match. The agent then searches by description (“faceted crystal cap”), tries a Jill Stuart hypothesis and drops it, and reverse-searches a tighter crop. That search's summary names the bottle as KATOKATO, and a targeted image search returns a KATO-KATO foundation whose cap matches the crop.","caveat":"The brand name first appears in the reverse-search tool's own summary of the crop, so the decisive step is partly inside the tool."},"notool":"The liquid foundation on the far right appears to be **Dior**.","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex07/input_1.jpg"}],"steps":[{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"input_image_1"},"imgs":[],"say":"I’ll check the label on the rightmost foundation bottle on the shelf.","out":"OCR_Tool did not transcribe any readable text."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[423,495,485,585],"padding":70},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex07/tool_1.jpg"}],"say":"The label wasn’t readable at full size, so I’ll inspect the rightmost bottle more closely.","out":"Zoomed in image rendered below; refer to it as tool_generated_image_1."},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_1"},"imgs":[],"say":"I’ll compare the bottle’s distinctive cap and shape with product images to confirm the brand.","out":"[Image Search — Visual Matches]\nNO RELEVANT MATCHES\n\nWhy each raw match was rejected:\nAll candidates show single cosmetic products (nail polish or foundation) in isolation, while the QUERY image shows a multi-tiered makeup organizer with brushes and cosmetics. The image-text alignment is correct for each candidate but irrelevant to the QUERY image's subject. The text metadata is domain-relevant (beauty products) but fails to match the QUERY image's specific visual subject.\n\nSuggested next actions:\n- Image_Search_Tool(image_id=input_image_1)"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[432,511,474,570],"padding":50},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex07/tool_2.jpg"}],"say":"I’ll look closer at the bottle’s lettering and cap.","out":"Zoomed in image rendered below; refer to it as tool_generated_image_2."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_2"},"imgs":[],"out":"OCR_Tool did not transcribe any readable text."},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"foundation bottle ornate faceted silver cap rounded glass beige foundation black base luxury brand","search_type":"images"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex07/tool_3.jpg"},{"h":"tool_generated_image_4","src":"static/examples/ex07/tool_4.jpg"},{"h":"tool_generated_image_5","src":"static/examples/ex07/tool_5.jpg"}],"out":"Image refs: tool_generated_image_3, tool_generated_image_4, tool_generated_image_5\n[Search Results — Type: images]\n1. Top 7 Luxury Foundations: Flawless, Long-Lasting & Easy to Apply\n   Image: [tool_generated_image_3]\n   Source: Lemon8   [webpage_url_1]\n   Size: 1440x1920\n2. Top 7 Luxury Foundations: Flawless, Long-Lasting & Easy to Apply\n   Image: [tool_generated_image_4]\n   Source: Lemon8   [webpage_url_1]\n   Size: 1440x1920\n3. Top 7 Luxury Foundations: Flawless, Long-Lasting & Easy to Apply\n   Image: [tool_generated_image_5]\n   Source: Lemon8   [webpage_url_1]\n   Size: 1278x1704\n\n[Summary]\nSeveral candidates depict luxury foundation bottles matching the query's key attributes: ornate, faceted, silver caps, rounded glass, beige foundation, and black bases. Candidate 5 (Charlotte Tilbury Airbrush Flawless Foundation) shows a faceted, gold-toned cap on a rounded glass bottle. Candidate 3 (Dior Forever Skin Glow) features a black cap and clear glass bottle with beige foundation. Candidate 8 (Haus Labs) displays a silver, faceted cap on a rounded glass bottle. Other candidates include non-matching caps (e.g., gold, black, dropper), non-luxury brands (e.g., Glam Air), or non-foundation products (e.g., Sisley-Paris anti-aging serum).\ntool_generated_image_3\ntool_generated_image_4\ntool_generated_image_5"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"foundation bottle clear faceted jewel crystal cap silver beige liquid round short bottle black base","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\nNO RELEVANT RESULTS\n\nWhy each raw result was rejected:\nAll candidates show foundation bottles that are either square, cylindrical, or lack the specified faceted crystal design, silver cap, or black base. Image 4 is the closest but lacks the cap color and base detail. Image 9 has beige liquid but a green cap and is not clear or faceted. Image 5 and 8 show frosted bottles, not clear or faceted. Image 1, 2, 3, 6, 7 show square bottles with black caps, not round or faceted. Image 10 shows a tube, not a bottle. All candidates fail on at least one key attribute (shape, cap color, base color, faceted crystal design).\n\nSuggested query rewrites:\n- clear faceted glass foundation bottle with black cap and beige liquid\n- jewel crystal foundation bottle round short black base\n- refillable clear glass foundation bottle with silver cap\n\nSuggested next actions:\n- Text_Search_Tool(query=\"clear faceted glass foundation bottle with black cap and beige liquid\", search_type=images)\n- Text_Search_Tool(query=\"jewel crystal foundation bottle round short black base\", search_type=images)\n- Text_Search_Tool(query=\"refillable clear glass foundation bottle with silver cap\", search_type=images)"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Jill Stuart foundation bottle crystal ornate silver cap round beige","search_type":"images"},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex07/tool_6.jpg"},{"h":"tool_generated_image_7","src":"static/examples/ex07/tool_7.jpg"},{"h":"tool_generated_image_8","src":"static/examples/ex07/tool_8.jpg"}],"out":"Image refs: tool_generated_image_6, tool_generated_image_7, tool_generated_image_8\n[Search Results — Type: images]\n1. Diamond Tips Concealer | PRODUCTS | JILL STUART Beauty Official Site\n   Image: [tool_generated_image_6]\n   Source: www.jillstuart-beauty.com   [webpage_url_2]\n   Size: 640x640\n2. JILL STUART Base Makeup New Items | JILL STUART Beauty Official Site\n   Image: [tool_generated_image_7]\n   Source: www.jillstuart-beauty.com   [webpage_url_3]\n   Size: 750x1090\n3. Basemake | PRODUCTS | JILL STUART Beauty Official Site\n   Image: [tool_generated_image_8]\n   Source: www.jillstuart-beauty.com   [webpage_url_4]\n   Size: 640x640\n\n[Summary]\nMultiple candidates from JILL STUART Beauty's official site showcase foundation bottles with crystal caps and ornate silver detailing, matching the query's key descriptors. Candidate 2 (Diamond Tips Concealer) and Candidate 4 (Gem Drop serum primer) both feature round, beige-toned bottles topped with faceted crystal caps and intricate silver embellishments. Candidate 5 (Illuminating Serum Primer) also matches the description with a crystal cap and silver band. eBay listings (7, 8, 10) show similar products but are less aligned with the 'foundation' specification, while other official site images (1, 3, 6, 9) show related products like powders or mists, reducing their direct relevance to the query.\ntool_generated_image_6\ntool_generated_image_7\ntool_generated_image_8"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Jill Stuart liquid foundation glass bottle ornate crystal cap black base foundation","search_type":"images"},"imgs":[{"h":"tool_generated_image_9","src":"static/examples/ex07/tool_9.jpg"},{"h":"tool_generated_image_10","src":"static/examples/ex07/tool_10.jpg"},{"h":"tool_generated_image_11","src":"static/examples/ex07/tool_11.jpg"}],"out":"Image refs: tool_generated_image_9, tool_generated_image_10, tool_generated_image_11\n[Search Results — Type: images]\n1. JILL STUART Diamond Tips Concealer Cover type 5.3mL Japan\n   Image: [tool_generated_image_9]\n   Source: eBay   [webpage_url_5]\n   Size: 923x1200\n2. JILL STUART Base Makeup New Items & Limited Items | NEW ITEM | JILL STUART  Beauty Official Site\n   Image: [tool_generated_image_10]\n   Source: JILL STUART   [webpage_url_6]\n   Size: 650x618\n3. JILL STUART Base Makeup New Item | NEW ITEM | JILL STUART Beauty Official  Site\n   Image: [tool_generated_image_11]\n   Source: www.jillstuart-beauty.com   [webpage_url_7]\n   Size: 750x1090\n\n[Summary]\nMultiple official JILL STUART sources (2024, 2025, 2017, 2016, 2015) showcase liquid foundation bottles with ornate crystal caps and silver embellishments, matching the query's key visual descriptors. Candidate 4 (eBay) and Candidate 2 (2024) show the signature crystal caps and liquid foundation format. Candidate 7 (2025) features the 'Gem Drop' line with glass bottles and crystal caps, though it is labeled as a 'serum primer' rather than foundation. Candidate 8 (Vecteezy) shows a generic glass bottle with a black cap, lacking the ornate crystal cap and brand-specific design. Candidate 3 (SKS Bottle) is a plain glass bottle with a black pump, unrelated to Jill Stuart. Candidate 6 (2016) shows a 'loose in liquid' product, not a liquid foundation. Candidate 1 and 5 show powder products alongside liquid foundations, not matching the query's focus on liquid foundation.\ntool_generated_image_9\ntool_generated_image_10\ntool_generated_image_11"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"foundation bottle \"black\" \"crystal\" cap \"silver\" round liquid foundation","search_type":"images"},"imgs":[{"h":"tool_generated_image_12","src":"static/examples/ex07/tool_12.jpg"},{"h":"tool_generated_image_13","src":"static/examples/ex07/tool_13.jpg"},{"h":"tool_generated_image_14","src":"static/examples/ex07/tool_14.jpg"}],"out":"Image refs: tool_generated_image_12, tool_generated_image_13, tool_generated_image_14\n[Search Results — Type: images]\n1. 30ml Custom Logo Oem Plastic Empty Cosmetic Liquid Foundation Bottle Round  Silver Clear Foundation Stick With Huge Brush Wand - Buy Product on  Alibaba.com\n   Image: [tool_generated_image_12]\n   Source: Alibaba.com   [webpage_url_8]\n   Size: 2268x2268\n2. 30ml Custom Logo Oem Plastic Empty Cosmetic Liquid Foundation Bottle Round  Silver Clear Foundation Stick With Huge Brush Wand - Buy Product on  Alibaba.com\n   Image: [tool_generated_image_13]\n   Source: Alibaba.com   [webpage_url_8]\n   Size: 800x800\n3. 30ml Clear Round Glass Foundation Bottle with Pump and Full Over Cap\n   Image: [tool_generated_image_14]\n   Source: Made-in-China.com   [webpage_url_9]\n   Size: 550x550\n\n[Summary]\nMultiple candidates (2, 3, and 1) depict round, clear glass foundation bottles with black pump dispensers and silver caps, matching the query's key descriptors. Candidate 2 and 3, sourced from Alibaba, show identical product images with silver caps and clear glass bodies, explicitly labeled as 'Custom Logo Oem Plastic Empty Cosmetic Liquid Foundation Bottle Round'. Candidate 1, from Made-in-China.com, shows a clear round glass bottle with a black pump and full-over cap, aligning with the 'black' and 'crystal' descriptors. Other candidates (4-9) show branded foundation bottles with black caps but lack the 'silver cap' or 'crystal' glass attributes, or are not round or empty bottles as specified.\ntool_generated_image_12\ntool_generated_image_13\ntool_generated_image_14"},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_2"},"imgs":[{"h":"tool_generated_image_15","src":"static/examples/ex07/tool_15.jpg"},{"h":"tool_generated_image_16","src":"static/examples/ex07/tool_16.jpg"},{"h":"tool_generated_image_17","src":"static/examples/ex07/tool_17.jpg"}],"out":"Image refs: tool_generated_image_15, tool_generated_image_16, tool_generated_image_17\n[Image Search — Visual Matches]\n1. Zoya Bridal Bliss Collection (Part II) Swatch and Review - LacquerExpression\n   Source: lacquerexpression.com/2018/06/mainstream-swatch-and-review-zoya_28.html   [webpage_url_10]\n   Image: [tool_generated_image_15]\n2. Amazon.com: ZOYA Nail Polish, Maxine, 0.5 fl. oz. : Beauty & Personal Care\n   Source: amazon.com/ZOYA-Nail-Polish-Maxine-0-5/dp/B07HHN2N6C   [webpage_url_11]\n   Image: [tool_generated_image_16]\n3. Zoya CALM Nail Polish Collection Swatches | Spring 2020\n   Source: pinterest.com/pin/zoya-calm-nail-polish-collection-swatches--676173331545648814   [webpage_url_12]\n   Image: [tool_generated_image_17]\n\n[Summary]\nThe QUERY image shows a makeup organizer with various beauty products, including a ZOYA nail polish bottle and a KATOKATO foundation bottle. Multiple candidates (5, 1, and 10) correctly identify ZOYA products, with candidate 5 showing Zoya Avril polish swatches, candidate 1 showing Zoya Maxine polish, and candidate 10 showing the ZOYA CALM collection from Spring 2020. Candidate 3 and 7 also show foundation products, but their images are not visually similar to the QUERY image's foundation bottle. Candidate 2, 6, and 9 show nail polishes from other brands (Priti, Adrianne K, Karma Naturals) that are not visually similar to the QUERY image. Candidate 4 and 8 show nail polish swatches or products that are not from ZOYA and do not match the QUERY image's specific product arrangement.\ntool_generated_image_15\ntool_generated_image_16\ntool_generated_image_17"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"\"KATOKATO\" liquid foundation crystal cap bottle","search_type":"images"},"imgs":[{"h":"tool_generated_image_18","src":"static/examples/ex07/tool_18.jpg"}],"out":"Image refs: tool_generated_image_18\n[Search Results — Type: images]\n1. KATO-KATO – Chic Decent\n   Image: [tool_generated_image_18]\n   Source: Chic Decent   [webpage_url_13]\n   Size: 645x645\n\n[Summary]\nThe primary candidate (rank 1) from Chic Decent clearly shows the KATO-KATO liquid foundation with its distinctive crystal cap, matching the query exactly. The second candidate (rank 2) displays a KATO-KATO product but it is a 'Misty Project' spray with a black cap, not a liquid foundation. The third candidate (rank 3) is a 'Cloudy to Misty Lightweight Setting Spray' also with a black cap, confirming it is not the requested foundation. All three sources are from Chic Decent or The Hype Cosmetics Outlet, both retailing KATO-KATO products, but only the first image correctly depicts the liquid foundation with the crystal cap as specified in the query.\ntool_generated_image_18"}]},{"id":"ex08","num":8,"inPaper":true,"dataset":"FVQA","model":"Qwen3-VL-32B","domain":"search","tags":["grounding","image-search","ocr","text-search","webpage-visit","zoom"],"composition":true,"slug":"backpack-awards","question":"What awards has this product won?","pred":"The Deuter Speed Lite Pro backpack has won the ISPO Award 2024 for Lightweight Speed on the Trail.","gt":"ISPO Award 2024","score":null,"correct":null,"overview":{"task":"A photograph shows two hikers resting with their backpacks. What awards has this product won?","why it is hard":"The product is not named anywhere, and the logos on the backpacks are too small to read.","how the agent solves it":"It grounds the two backpacks, tries to read each with zoom and OCR, and, when that fails, reverse-searches the crop of the green backpack, which identifies it as a Deuter Speed Lite Pro. A product page mentions no award, so it searches for awards and verifies the result on the manufacturer's press release: the ISPO Award 2024. Nine calls use six different tools."},"notool":"","final":"The verified information confirms that the Deuter Speed Lite Pro backpack won the ISPO Award 2024 for Lightweight Speed on the Trail. This is the only award mentioned in the reliable sources consulted. Therefore, I can confidently state the award won by the product shown in the image.","inputs":[{"h":"input_image_1","src":"static/examples/ex08/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"backpack","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex08/tool_1.jpg"}],"say":"The image shows two people sitting outdoors with backpacks, likely hiking or camping. There is no clear indication of a specific product being advertised or labeled in the image. Without identifying the exact product, it’s impossible to determine any awards it may have won. I need to identify the product first.","out":"Visual_Grounding_Tool grounded 2 objects for the prompt 'backpack'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [199.49, 423.01, 407.82, 875.1], 'object 2': [552.17, 550.46, 684.45, 951.58]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[199,423,408,875],[552,550,684,952]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[199,423,408,875],"padding":50},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex08/tool_2.jpg"}],"say":"I have identified two backpacks in the image. To determine which product might have won awards, I need to examine them more closely. I'll start by zooming in on the purple backpack (labeled as object 1) to see if there are any visible brand names, logos, or labels that could help identify the product.","out":"Zoomed in image rendered below; refer to it as tool_generated_image_2."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_2"},"imgs":[],"say":"I've zoomed in on the purple backpack, but the details are still not clear enough to read any text or logos. I'll now attempt to use OCR to extract any text from this zoomed-in image to identify the brand or model of the backpack.","out":"OCR_Tool did not transcribe any readable text."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[552,550,684,951],"padding":50},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex08/tool_3.jpg"}],"say":"The OCR tool failed to extract any text from the zoomed-in image of the purple backpack. I will now zoom in on the green backpack (labeled as object 2) to check for any visible branding or labels.","out":"Zoomed in image rendered below; refer to it as tool_generated_image_3."},{"tool":"OCR_Tool","cap":"ocr","args":{"image_id":"tool_generated_image_3"},"imgs":[],"say":"I've zoomed in on the green backpack, and it appears to have some text or a logo on it. I will now use OCR to extract any readable text from this zoomed-in image to identify the brand or model.","out":"OCR_Tool transcribed the following text from the image:\n\n-"},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_3"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex08/tool_4.jpg"},{"h":"tool_generated_image_5","src":"static/examples/ex08/tool_5.jpg"},{"h":"tool_generated_image_6","src":"static/examples/ex08/tool_6.jpg"}],"say":"Despite zooming in and attempting OCR, no readable text was extracted from either backpack. Since the visual inspection hasn't yielded any identifiable brand or product name, I will now use an image search to identify the backpacks based on their appearance.","out":"Image refs: tool_generated_image_4, tool_generated_image_5, tool_generated_image_6\n[Image Search — Visual Matches]\n1. DEUTER Speed Lite Pro 25 Hiking Backpack in Mineral Grove | eBay Australia\n   Source: ebay.com.au/itm/388231206979   [webpage_url_1]\n   Image: [tool_generated_image_4]\n2. deuter Men's Speed Lite Pro 19 Hiking Backpack : Amazon.ca: Sports & Outdoors\n   Source: amazon.ca/deuter-Mens-Speed-Hiking-Backpack/dp/B0F79T5CY9   [webpage_url_2]\n   Image: [tool_generated_image_5]\n3. Terravia Pack 14L - Weathered Stone | Patagonia | Peggs & son.\n   Source: peggsandson.com/products/products-terravia-pack-14l-weathered-stone-24639   [webpage_url_3]\n   Image: [tool_generated_image_6]\n\n[Summary]\nThe QUERY image shows a light green hiking backpack with a visible brand logo and a person wearing it outdoors. Candidate 1 (eBay Australia) and Candidate 2 (Amazon.ca) both show the same Deuter Speed Lite Pro model, with Candidate 1 matching the color 'Mineral Grove' and Candidate 2 showing the 'Speed Lite Pro 19' variant — both visually consistent with the QUERY image's design and color. Candidate 6 (Peggs & Son) shows a Patagonia Terravia Pack 14L in 'Weathered Stone' color, which is visually similar in style and color tone, though the brand and model differ. Other candidates (3, 4, 5, 7) show different brands (Deuter Attack, Osprey, Deuter AC Lite, Vaude) and distinct designs, making them less relevant to the specific visual identity of the QUERY image.\ntool_generated_image_4\ntool_generated_image_5\ntool_generated_image_6"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_2"],"goal":"Find information about awards won by the Deuter Speed Lite Pro 19 hiking backpack."},"imgs":[],"say":"The image search results strongly indicate that the green backpack is a Deuter Speed Lite Pro model. Now, I will visit the Amazon.ca product page for the Deuter Speed Lite Pro 19 to gather information about any awards it may have won.","out":"The useful information in https://www.amazon.ca/deuter-Mens-Speed-Hiking-Backpack/dp/B0F79T5CY9 for user goal Find information about awards won by the Deuter Speed Lite Pro 19 hiking backpack. as follows: \n\nEvidence in page: \ndeuter Men's Speed Lite Pro 19 Hiking Backpack : Amazon.ca: Sports & Outdoors\n\nBrand: deuter\n\n4.7 out of 5 stars (18)\n\n\ndeuter Men's Speed Lite Pro 19 Hiking Backpack\n\n\n 1. \n\n 2. \n 3. \n 4. \n 5. \n 6. \n 7. \n 8. \n\nLoading Images...\n\nDouble-tap to zoom\n\nBrief content visible, double tap to read full content.\n\nFull content visible, double tap to read brief content.\n\nSize: 19 L\n\nColour Name: Mineral-grove\n\n{\"mobile_buybox_group_1\":[{\"displayPrice\":\"$243.61\",\"priceAmount\":243.61,\"currencySymbol\":\"$\",\"integerValue\":\"243\",\"decimalSeparator\":\".\",\"fractionalValue\":\"61\",\"symbolPosition\":\"left\",\"hasSpace\":false,\"showFractionalPartIfEmpty\":true,\"offerListingId\":\"6sWeWXGQzh2cpPC17FYwenyQPoo7llN1pV7pe4%2BwTvQ5FpoT51MwX7j4SSrR%2Fs2SWOjBZNZ3PkP0bR5B%2Bugzx%2FySTXozbx65fDQnTINYrDHi1puuxbk5pDm4yABe7Xfa4VWjn%2FiosnmLRBrvBGvwwMG0jb%2BoBShmKaFpe1wsbiLPOC6iQTniamo%2BZ2R%2BMgtZ\",\"locale\":\"en-CA\",\"buyingOptionType\":\"NEW\",\"aapiBuyingOptionIndex\":0}]}\n\nBrief content visible, double tap to read full content.\n\nFull content visible, double tap to read brief content.\n\n\nPurchase options and add-ons\n\n\n$243.61\n\n$22.74 delivery June 9 - 18. Details\n\nOr fastest delivery June 2 - 4. Details\n\nUsually ships within 4 to 5 days\n\nQuantity:\n\n$$243.61 () Includes selected options. Includes initial monthly payment and selected options. Details\n\nPrice\n\nSubtotal\n\n$$243.61\n\nSubtotal\n\nInitial payment breakdown\n\nShipping cost, delivery date and order total (including tax) shown at checkout.\n\nThe enhancements that you chose aren't available for this seller. Details\n\nTo add the following enhancements to your purchase, choose a different seller.\n\n%cardName%\n\n${cardName} not available for the seller that you chose\n\n${cardName} unavailable for quantities greater than ${maxQuantity}.\n\nShipper / Seller\n\nBixemy\n\nBixemy\n\nShipper / Seller\n\nBixemy\n\nReturns\n\nEligible for Return or Refund within 30 days of receipt\n\nEligible for Return or Refund within 30 days of receipt\n\nThis item can be returned in its original condition for a full refund within 30 days of receipt. You may receive a partial or no refund on used, damaged or materially different returns.\n\nRead full return policy\n\nPayment\n\nSecure transaction\n\nYour transaction is secure\n\nWe work hard to protect your security and privacy. Our payment …"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"awards won by Deuter Speed Lite Pro backpack","search_type":"search"},"imgs":[],"say":"The Amazon.ca product page for the Deuter Speed Lite Pro 19 hiking backpack does not mention any awards won by the product. I will now search for information about awards won by the Deuter Speed Lite Pro series or Deuter backpacks in general.","out":"[Search Results — Type: search]\n1. The New deuter Speed Lite Pro Wins ISPO Award for Lightweight ...\n   Source: outdoorindustry.org/press-release/the-new-deuter-speed-lite-pro-wins-ispo-awa...   [webpage_url_4]\n   The New deuter Speed Lite Pro Wins ISPO Award for Lightweight Speed on the Trail · Unmatched Comfort and Stability · Quick Access on the Move ...\n2. New Deuter Speed Lite Pro 2025 Backpacks - Gear Tips Club Blog\n   Source: geartips.club/blog/en/new-deuter-speed-lite-pro-2025-backpacks   [webpage_url_5]\n   ... earned the ISPO 2024 Award for the Speed Lite Pro. The new Deuter Speed Lite Pro is even lighter. The new Deuter Speed Lite Pro surpasses ...\n3. Speed Lite Pro 28 SL | black | M | 4046051170237 - deuter\n   Source: deuter.com/us-en/speed-lite-pro-28-sl/4046051170237   [webpage_url_6]\n   This featherweight backpack weighs in at just 680 grams (Speed Lite Pro 25) und 740 grams (Speed Lite Pro 28 SL). Which means you can put more energy into ...\n\n[Summary]\nThe Deuter Speed Lite Pro backpack won the ISPO Award in 2024 for Lightweight Speed on the Trail, as confirmed by multiple sources including outdoorindustry.org and geartips.club. The backpack is noted for being lighter than previous models, with the Speed Lite Pro 25 weighing 680 grams and the 28 SL at 740 grams. It features sustainable materials, improved breathability, and enhanced comfort for fast-paced mountain adventures, with quick-access pockets and vest-style shoulder straps. Additional reviews from CleverHiker and Gearjunkie highlight its pocket-rich design and performance on trails, while Reddit users discuss its utility for day hikes and overnight treks."},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_4"],"goal":"Verify the award won by the Deuter Speed Lite Pro backpack as mentioned in the press release."},"imgs":[],"say":"The search results confirm that the Deuter Speed Lite Pro backpack won the ISPO Award in 2024 for Lightweight Speed on the Trail. To verify this information, I will visit the outdoorindustry.org press release page.","out":"The useful information in https://outdoorindustry.org/press-release/the-new-deuter-speed-lite-pro-wins-ispo-award-for-lightweight-speed-on-the-trail/ for user goal Verify the award won by the Deuter Speed Lite Pro backpack as mentioned in the press release. as follows: \n\nEvidence in page: \nThe New deuter Speed Lite Pro Wins ISPO Award for Lightweight Speed on the Trail... deuter, a global leader in backpacks and outdoor equipment, celebrates the ISPO Award 2024 win for its new Speed Lite Pro backpack series. ... 'We’re honored to receive the ISPO award for our new Speed Lite Pro series.' ... 'The new Speed Lite Pro impresses with its great fit, good ventilation and low weight,' says the IPSO Award Jury.\n\nSummary: \nThe Deuter Speed Lite Pro backpack won the ISPO Award 2024 for Lightweight Speed on the Trail, as confirmed by deuter’s press release and the award jury’s statement, highlighting its lightweight design, comfort, and performance for speed hiking."}]},{"id":"ex09","num":9,"inPaper":true,"dataset":"BrowseComp-VL","model":"Qwen3-VL-32B","domain":"search","tags":["image-search","text-search","webpage-visit"],"composition":true,"slug":"flowering-plant-distribution","question":"In the image, this flowering plant species, often referred to by its common name which resembles a distinctive animal feature, belongs to a family that also includes a genus commonly found in temperate regions of the Northern Hemisphere. This plant is endemic to several provinces in southwestern China, where it thrives in a variety of habitats. Which regions of China is this plant native to?","pred":"Yunnan, Guizhou, Sichuan, and Guangxi provinces in southwestern China","gt":"Sichuan, eastern Yunnan, northern Guangxi, and Guizhou.","score":null,"correct":null,"overview":{"task":"A photograph shows the leaves and catkins of a flowering tree. From a riddle-like description (a common name that resembles an animal feature, a family that also contains a common Northern-Hemisphere genus, endemic to several provinces of southwestern China), name the regions of China where it is native.","how the agent approaches it":"A reverse image search fails, so the agent hypothesizes a species, <i>Castanopsis argentea</i>, tests it with a text search and a Wikipedia visit, finds that the species is native to Java and Sumatra rather than China, rejects it, and searches again at the level of the genus.","caveat":"The species is never identified. The four provinces in the final answer first appear in the model's own reasoning, before any search supports them, and the searches only confirm that the genus is diverse in Yunnan. The answer matches the ground truth, but it is not fully grounded in the retrieved evidence."},"notool":"","final":"The search results confirm that the genus *Castanopsis* includes many species endemic to southwestern China, particularly Yunnan province, which has 38 species of *Castanopsis*, including 11 that are endemic. The common name \"chinquapin\" or \"chinkapin\" resembles an animal feature (possibly \"chipmunk\" or \"chinchilla\"), fitting the description in the question. Additionally, the plant belongs to the Fagaceae family, which includes oaks and beeches—common in temperate regions of the Northern Hemisphere.\n\nTherefore, the plant in the image is likely a species of *Castanopsis*, and it is native to southwestern China, primarily Yunnan, but also potentially extending to neighboring provinces like Guizhou, Sichuan, and Guangxi, as suggested by the earlier search results.\n\nI am now confident in providing the answer.","inputs":[{"h":"input_image_1","src":"static/examples/ex09/input_1.jpg"}],"steps":[{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex09/tool_1.jpg"},{"h":"tool_generated_image_2","src":"static/examples/ex09/tool_2.jpg"},{"h":"tool_generated_image_3","src":"static/examples/ex09/tool_3.jpg"}],"say":"The image shows a plant with green leaves and catkin-like structures, which resemble the common name \"hazel\" or \"beech,\" possibly referring to a plant like *Corylus* (hazelnut) or another member of the Betulaceae family. However, the question specifically mentions a plant endemic to southwestern China, with a common name resembling a distinctive animal feature—this strongly suggests *Castanopsis*, commonly called \"chestnut\" or sometimes \"horse chestnut\" (though horse chestnut is typically *Aesculus*). But more likely, given the habitat and region, it could refer to *Castanopsis argentea* or similar species, which are part of the Fagaceae family (which also includes oaks and beeches).\n\nTo identify the exact species and confirm its native regions in China, I need to perform a reverse image search to determine the precise plant species.","out":"Image refs: tool_generated_image_1, tool_generated_image_2, tool_generated_image_3\n[Image Search — Visual Matches]\n1. Wondering what this plant is in my new yard? : r/whatsthisplant\n   Source: reddit.com/.../wondering_what_this_plant_is_in_my_new_yard   [webpage_url_1]\n   Image: [tool_generated_image_1]\n2. Is this Weeping Elm? in the Plant ID forum - Garden.org\n   Source: garden.org/thread/view/164184/Is-this-Weeping-Elm   [webpage_url_2]\n   Image: [tool_generated_image_2]\n3. How can I tell what type of cherry tree I have? : r/PlantIdentification\n   Source: reddit.com/.../how_can_i_tell_what_type_of_cherry_tree_i_have   [webpage_url_3]\n   Image: [tool_generated_image_3]\n\n[Summary]\nThe QUERY image shows a tree with distinctive hanging catkins and broad, serrated leaves, consistent with a species in the Betulaceae family. Candidate 3 (Reddit r/whatsthisplant) visually matches the QUERY image closely, showing similar catkins and leaf structure. Candidate 1 (Garden.org) also shows a weeping tree with similar leaf shape and drooping branches, supporting the Weeping Elm identification. Candidate 4 (Reddit r/PlantIdentification) shows a cherry tree with similar leaf shape but lacks the catkins, making it less relevant. Candidate 2 (Dave's Garden) shows a different tree with different leaf shape and no catkins, making it visually mismatched to the QUERY image.\ntool_generated_image_1\ntool_generated_image_2\ntool_generated_image_3"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"flowering plant endemic to southwestern China common name resembles animal feature Fagaceae family","search_type":"search"},"imgs":[],"say":"The initial image search did not yield a clear match for a plant endemic to southwestern China with a common name resembling an animal feature. The closest matches were for trees like Weeping Elm or cherry trees, which are not consistent with the description in the question. Therefore, I need to refine the search by focusing on plants native to southwestern China that fit the description—specifically, a flowering plant with a common name resembling an animal feature and belonging to a family including genera common in temperate Northern Hemisphere regions (like Fagaceae, which includes oaks, beeches, and chestnuts).\n\nI will now conduct a targeted text search for plants endemic to southwestern China with a common name resembling an animal feature and belonging to the Fagaceae family.","out":"[Search Results — Type: search]\nNO RELEVANT RESULTS\n\nWhy each raw result was rejected:\nAll results describe the Fagaceae family broadly but do not identify any species endemic to southwestern China with a common name resembling an animal feature. None of the results mention such a plant, even though they detail genera, species counts, and geographic distributions (e.g., Europe, not China).\n\nSuggested query rewrites:\n- endemic flowering plant in southwestern China with animal-like common name Fagaceae (rationale: none of the results address this specific combination)\n- Chinese endemic plant with animal name in Fagaceae family (rationale: results focus on general family traits, not regional endemics with common names)\n\nSuggested next actions:\n- Text_Search_Tool(query='endemic flowering plant southwestern China animal common name Fagaceae', search_type=plants)\n- Text_Search_Tool(query='Chinese endemic Fagaceae plant with animal name', search_type=plants)"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Castanopsis argentea endemic regions China","search_type":"search"},"imgs":[],"say":"The initial text search did not yield relevant results. I need to refine the query further to focus on specific plant species endemic to southwestern China that belong to the Fagaceae family and have a common name resembling an animal feature. A strong candidate is *Castanopsis argentea*, commonly known as \"silver chestnut\" or sometimes colloquially associated with \"horse chestnut\" due to its fruit resemblance, although \"horse chestnut\" usually refers to *Aesculus*. Another possibility is *Quercus* spp., but these are generally not described with animal-feature common names.\n\nGiven the description in the question, the plant is likely *Castanopsis argentea*, which is endemic to Yunnan, Guizhou, Sichuan, and Guangxi provinces in southwestern China. Let me verify this by searching for \"Castanopsis argentea endemic regions China\".","out":"[Search Results — Type: search]\n1. Castanopsis argentea - Wikipedia\n   Source: en.wikipedia.org/wiki/Castanopsis_argentea   [webpage_url_4]\n   It is mostly found in undisturbed forests of West Java around Mount Gede and Mount Halimun, with a density of ca. 12 trees/hectare. It also occurs in national ...\n2. Ecological Dynamics of Forest Stands with Castanopsis argentea ...\n   Source: mdpi.com/2073-445X/14/6/1187   [webpage_url_5]\n   This species can be found in primary or old secondary forests, typically on dry, fertile soils with stoniness at altitudes ranging from 150 to 1750 m above sea ...\n3. [PDF] Vegetation community and species association of Castanopsis spp ...\n   Source: smujo.id/biodiv/article/download/9223/5257/51208   [webpage_url_6]\n   Previous studies revealed that. Castanopsis species were distributed abundantly, especially in South China and the Indo-China region (Chen et al. 2013; Cheuk ...\n\n[Summary]\nCastanopsis argentea is mostly found in undisturbed forests of West Java around Mount Gede and Mount Halimun, with a density of ca. 12 trees/hectare, as noted in its Wikipedia entry. It can also be found in primary or old secondary forests in China, typically on dry, fertile soils at altitudes from 150 to 1750 m above sea level. Previous studies indicate that Castanopsis species are distributed abundantly in South China and the Indo-China region, with China hosting 30 endemic species of Castanopsis (Li et al., 2015)."},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_4"],"goal":"Confirm whether Castanopsis argentea is native to any regions in China or if there are other Castanopsis species endemic to southwestern China."},"imgs":[],"say":"The search results suggest that while *Castanopsis argentea* is mentioned, its primary distribution is in West Java, Indonesia, not China. However, the third result indicates that Castanopsis species are abundant in South China and the Indo-China region, and China hosts 30 endemic species of Castanopsis. This implies that the plant in question might be a different species within the *Castanopsis* genus endemic to southwestern China.\n\nLet me visit the Wikipedia page for *Castanopsis argentea* to check for any mention of Chinese distribution, and then search for a specific *Castanopsis* species endemic to southwestern China.","out":"The useful information in https://en.wikipedia.org/wiki/Castanopsis_argentea for user goal Confirm whether Castanopsis argentea is native to any regions in China or if there are other Castanopsis species endemic to southwestern China. as follows: \n\nEvidence in page: \nCastanopsis argentea is native to the islands of Java and Sumatra. Plants of the World Online describes a broader range for this species, from Assam in eastern India through Myanmar and Thailand, as well as on Sumatra, Java, and Borneo. There is no mention of China in the text.\n\nSummary: \nCastanopsis argentea is native to Java and Sumatra, with a broader range including Assam, Myanmar, Thailand, and Borneo, but not China. The webpage does not mention any Castanopsis species endemic to southwestern China."},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Castanopsis species endemic to southwestern China common name animal feature","search_type":"search"},"imgs":[],"say":"Since *Castanopsis argentea* is not native to China, I need to search for a different species within the *Castanopsis* genus that is endemic to southwestern China. Based on the earlier search result mentioning \"South China and the Indo-China region\" and \"30 endemic species of Castanopsis in China,\" I will now search for a specific *Castanopsis* species native to southwestern China, particularly one whose common name resembles an animal feature.","out":"[Search Results — Type: search]\n1. Castanopsis\n   Source: en.wikipedia.org/wiki/Castanopsis   [webpage_url_7]\n   Castanopsis, commonly called chinquapin or chinkapin, is a genus of evergreen trees belonging to the beech family, Fagaceae.\n2. The first report of an 'evergreen Castanopsis type' wood ...\n   Source: onlinelibrary.wiley.com/doi/full/10.1002/gj.3919   [webpage_url_8]\n   by D Mantzouka · 2021 · Cited by 6 — Yunnan (38 spp.) has the highest Castanopsis species diversity in China, with 11 species endemic to Yunnan, and Indochina has 82 species.\n3. Castanopsis - AsianFagaceae.com\n   Source: asianfagaceae.com/castanopsis   [webpage_url_9]\n   About half of the species are found in China, and 30 spp. are endemic (Li et al., 2015), in a wide range of habitat types. They are often keystone species in ...\n\n[Summary]\nCastanopsis, commonly called chinquapin or chinkapin, is a genus of evergreen trees in the beech family Fagaceae. Yunnan province in southwestern China hosts the highest diversity of Castanopsis species (38 spp.), with 11 species endemic to the region. About half of all Castanopsis species are found in China, with 30 species endemic to the country, and they often serve as keystone species in subtropical forests. The genus includes species like Castanopsis tibetana and Castanopsis chinensis, which are native to China and play important ecological roles in mountain ecosystems."}]},{"id":"ex10","num":10,"inPaper":true,"dataset":"RealWorldQA","model":"Qwen3-VL-32B","domain":"vqa","tags":["code","depth","grounding"],"composition":true,"slug":"cars-before-crosswalk","question":"Are there any cars closer to us than the nearest crosswalk?\nOptions:\nA. Yes\nB. No\nPlease select the correct answer from the options above.","pred":"B. No","gt":"B","score":1.0,"correct":true,"overview":{"task":"In a photograph taken from a car at a city intersection, are there any cars closer to the camera than the nearest crosswalk?","why it is hard":"Judging relative distance along a street from one image is error-prone; without tools the same model answers “yes”, pointing at a silver minivan.","how the agent solves it":"It estimates metric depth for the whole image, grounds the crosswalk and the cars, and has the coding agent compare the crosswalk's depth with each car's. The crosswalk is 17.4 m away and no car is closer."},"notool":"","final":"Based on the depth analysis, the crosswalk is located at an average depth of 17.40 meters from the camera, while the average depth of the cars is 54.14 meters. This indicates that all cars are farther away from the camera than the crosswalk. Therefore, there are no cars closer to us than the nearest crosswalk.","inputs":[{"h":"input_image_1","src":"static/examples/ex10/input_1.jpg"}],"steps":[{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_1","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex10/tool_1.jpg"}],"say":"First, I need to locate the nearest crosswalk in the image. Then, I will check if there are any cars positioned closer to the camera than that crosswalk. To do this accurately, I'll use the Depth_Estimation_Tool to estimate relative distances and identify which objects (cars and crosswalk) are closer to the viewer.","out":"Depth estimation complete. Shape: [938, 1442], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_1."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"crosswalk","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex10/tool_2.jpg"}],"say":"Now I will locate the crosswalk in the image. The crosswalk is visible on the right side of the road, near the bottom of the image. From the depth map (tool_generated_image_1), the crosswalk appears in a region with relatively low depth values (dark purple/blue), indicating it is close to the camera. Next, I need to identify any cars that are even closer than this crosswalk. In the original image, there is a silver car on the left side of the road, just past the stop sign, which appears to be very close to the camera. Its position in the depth map also shows a low depth value, suggesting it is closer than the crosswalk. To confirm this, I will use the Visual_Grounding_Tool to locate both the crosswalk and the cars.","out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'crosswalk'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [788.82, 646.79, 1000, 744.88]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[789,647,1000,745]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"cars","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex10/tool_3.jpg"}],"say":"Now I will locate the cars in the image using the Visual_Grounding_Tool. I need to identify any cars that are closer to the camera than the crosswalk, which is located on the right side of the road near the bottom. The car on the left side of the road appears to be very close to the camera and may be closer than the crosswalk.","out":"Visual_Grounding_Tool grounded 15 objects for the prompt 'cars'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [176.53, 513.87, 214.9, 620.67], 'object 2': [196.14, 505.4, 330.53, 637.25], 'object 3': [325.42, 525.2, 345.27, 592.08], 'object 4': [335.01, 525.61, 362.31, 590.18], 'object 5': [361.83, 532.05, 391.95, 577.99], 'object 6': [389.67, 539.5, 403.25, 573.49], 'object 7': [401.02, 535.78, 414.74, 568.96], 'object 8': [415.52, 537.59, 429.71, 565.0], 'object 9': [466.41, 534.55, 494.33, 571.55], 'object 10': [515.88, 533.44, 529.58, 567.8], 'object 11': [525.95, 530.37, 551.8, 575.87], 'object 12': [541.83, 534.66, 565.86, 594.67], 'object 13': [558.22, 512.4, 620.77, 606.17], 'object 14': [582.0, 524.63, 671.94, 624.84], 'object 15': [658.99, 527.86, 679.94, 580.83]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_3.","boxes":[[177,514,215,621],[196,505,331,637],[325,525,345,592],[335,526,362,590],[362,532,392,578],[390,540,403,573],[401,536,415,569],[416,538,430,565],[466,535,494,572],[516,533,530,568],[526,530,552,576],[542,535,566,595],[558,512,621,606],[582,525,672,625],[659,528,680,581]]},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Compute the average depth at the center of each detected car bounding box and the average depth at the center of the crosswalk bounding box. Compare these depths to determine if any cars are closer to the camera than the crosswalk."},"imgs":[],"say":"Now I will use the Python_Coding_Agent_Tool to compute the average depth at the center of each detected car and compare it with the average depth of the crosswalk. This will allow me to determine if any cars are closer to the camera than the crosswalk.","code":"import numpy as np\n\n# Extract depth map and its dimensions\ndepth_map = depth_result[\"depth_meters\"]\nheight, width = depth_result[\"depth_shape\"]\n\n# Extract crosswalk bounding box\ncrosswalk_box = segment_crosswalk[\"pred_boxes\"][0]\ncrosswalk_center_x = (crosswalk_box[0] + crosswalk_box[2]) / 2\ncrosswalk_center_y = (crosswalk_box[1] + crosswalk_box[3]) / 2\n\n# Convert center coordinates to integer (pixel indices)\ncrosswalk_center_x_int = int(crosswalk_center_x)\ncrosswalk_center_y_int = int(crosswalk_center_y)\n\n# Get depth at crosswalk center\ncrosswalk_depth = depth_map[crosswalk_center_y_int, crosswalk_center_x_int]\n\n# Extract car bounding boxes\ncar_boxes = segment_cars[\"pred_boxes\"]\n\n# Compute average depth at center of each car bounding box\ncar_centers_depths = []\nfor box in car_boxes:\n    center_x = (box[0] + box[2]) / 2\n    center_y = (box[1] + box[3]) / 2\n    center_x_int = int(center_x)\n    center_y_int = int(center_y)\n    car_depth = depth_map[center_y_int, center_x_int]\n    car_centers_depths.append(car_depth)\n\n# Compute average depth for all cars\navg_car_depth = np.mean(car_centers_depths)\n\n# Compare: any car closer than crosswalk?\ncars_closer_than_crosswalk = any(car_depth < crosswalk_depth for car_depth in car_centers_depths)\n\n# Output results\nprint(f\"Crosswalk center depth: {crosswalk_depth:.2f} meters\")\nprint(f\"Average car center depth: {avg_car_depth:.2f} meters\")\nprint(f\"Any car closer to camera than crosswalk? {'Yes' if cars_closer_than_crosswalk else 'No'}\")\n\n# Assign results to variables for result namespace\ncrosswalk_center_depth = crosswalk_depth\naverage_car_center_depth = avg_car_depth\nany_car_closer_than_crosswalk = cars_closer_than_crosswalk","out":"Crosswalk center depth: 17.40 meters\nAverage car center depth: 54.14 meters\nAny car closer to camera than crosswalk? No"}]},{"id":"ex11","num":11,"inPaper":true,"dataset":"SimpleVQA","model":"GPT-6 Sol","domain":"vqa","tags":["image-search","text-search","webpage-visit","zoom"],"composition":true,"slug":"identify-church","question":"What church is depicted in this picture?","pred":"St. Augustine’s Parish Church in Perchtoldsdorf, Austria.","gt":"Pfarrkirche hl. Augustinus, Perchtoldsdorf","score":1.0,"correct":true,"overview":{"task":"Name the church in a photograph of a dark Gothic interior with a large baroque altar.","why it is hard":"Many Gothic churches look alike; without tools the model guesses Antwerp Cathedral.","how the agent solves it":"Searching the whole photograph and describing it in text return only look-alike churches for eleven calls. The breakthrough is to crop the altarpiece alone and reverse-search the crop, which finds an archive photo of this exact painting; a final text search confirms the church."},"notool":"This appears to be the Cathedral of Our Lady in Antwerp, Belgium.","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex11/input_1.jpg"}],"steps":[{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"input_image_1"},"imgs":[],"out":"[Image Search — Visual Matches]\n1. St Hilda's: “It was like a Cathedral!” – Shrine of the Relic Image of Our Lady of Guadalupe\n   Source: relicourladyofguadalupe.wordpress.com/.../st-hildas-it-was-like-a-cathedral   [webpage_url_1]\n   Image: [tool_generated_image_1]\n2. Oh Mary conceived without sin, pray for us who have recourse to Thee!\n   Source: instagram.com/p/DRkWXrTj0OL   [webpage_url_2]\n   Image: [tool_generated_image_2]\n3. File:Tomb of Saint Rufinus - Main altar - San Rufino - Assisi 2016.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/wiki/File:Tomb_of_Saint_Rufinus_-_Main_altar_-_San_Rufi...   [tool_generated_image_3]\n   Image: [tool_generated_image_4]\n4. File:Saint Thomas More Cathedral altar (Arlington, Virginia).JPG - Wikimedia Commons\n   Source: commons.wikimedia.org/.../File:Saint_Thomas_More_Cathedral_altar_(Arlington,_Virginia).JPG   [tool_generated_image_5]\n   Image: [tool_generated_image_6]\n5. The Vatican has released the first official image of Pope Francis in repose, following his passing on Monday at the age of 88. The photo shows the late pontiff in an open\n   Source: facebook.com/.../1109098747902513   [webpage_url_3]\n   Image: [tool_generated_image_7]\n6. Antependium with White Flowers and Gold Trimmings | Green altar frontal, Church altar frontal, White lace\n   Source: pinterest.com/pin/antependium--131167407884070646   [webpage_url_4]\n   Image: [tool_generated_image_8]\n7. Museums and Churches heating systems | ThermalTT\n   Source: thermaltt.com/en/heating-for-churches-and-museums-floors   [webpage_url_5]\n   Image: [tool_generated_image_9]\n8. Hayato Okamura on X / X\n   Source: x.com/HayatoOkamura/status/2089714218945692075   [webpage_url_6]\n   Image: [tool_generated_image_10]\n9. Saint Joseph Maronite Catholic Church prepares for children's holy eucharist\n   Source: facebook.com/.../849633540540529   [webpage_url_7]\n   Image: [tool_generated_image_11]\n10. New Church Architecture: St. Mary of Sorrows in Fairfax, Virginia ~ Liturgical Arts Journal\n   Source: liturgicalartsjournal.com/2025/11/new-church-architecture-st-mary-of.html   [webpage_url_8]\n   Image: [tool_generated_image_12]"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_1"],"goal":"Determine whether the photograph of dark Gothic church with ornate dark wood and gold baroque high altar is shown and identify church location."},"imgs":[],"out":"The useful information in https://relicourladyofguadalupe.wordpress.com/2017/10/14/st-hildas-it-was-like-a-cathedral/ for user goal Determine whether the photograph of dark Gothic church with ornate dark wood and gold baroque high altar is shown and identify church location. as follows: \n\nEvidence in page: \nSt Hilda’s: “It was like a Cathedral!” – Shrine of the Relic Image of Our Lady of Guadalupe\n\nFather Michael Doherty, Parish Priest of St Hilda’s Church, Stevenage, was clearly delighted with the Pilgrimage Visit of the Relic Image. “It was like being a cathedral for the weekend!” he exclaimed. “St Hilda’s has become the National Shrine of Our Lady of Guadalupe!”\n\nHe was standing in his beautiful church with its cathedral-sized sanctuary, which was lavishly decorated with flowers – mainly roses for Our Lady, but also a number of cheerful sunflowers on the back wall and, of course, with the Relic Image taking pride of place in the sanctuary.\n\n\nThis came at the end of five months of planning and four very busy and well-attended days of Masses, talks, devotions, reflections, veneration and other events from Friday 6th to Monday 9th October 2017. What was evident throughout was the warmth of the welcome and the hospitality shown by Father Michael and his parishioners, their love for Our Lady and the great effort they had put in to make sure that this was a visit to remember for everybody.\n\nThe Pilgrimage Visit started on the Friday evening with the Service of Reception and Guadalupe Mass. It was a privilege to have with us The Right Rev Paul McAleenan, Auxiliary Bishop of Westminster, as our Principal Celebrant.\n\nBishop Paul received into his care, on behalf of Father Michael and the Parish of St Hilda’s, the Relic Image for the duration of the Pilgrimage Visit. The Guadalupe Mass following straight on, including Bishop Paul’s homily focusing on Our Lady as Protectress of the Unborn and the Mother of us all.\n\nEach day, following morning and evening Mass, The Guardians, supported by Father Michael, gave a series of themed reflections, using the Guadalupe Miracles as the starting point to examine our place within the Church and the call for a New Evangelisation. The Guardians were ably supported by the Knights of St Columba (KSC) Brothers led by Grand Knight Leo Williams. The KSC Brothers were in place from church opening until closing, controlling the management of pilgrim numbers and assisting with the visitors’ spiritual experience.\n\nSaturday brought long l …"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"church interior gothic stone pointed arch dark carved wooden baroque altar painting virgin mary angels chandeliers","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\n1. Cathedral of Our Lady in Antwerp: Rubens, Gothic architecture and UNESCO\n   Image: [tool_generated_image_13]\n   Source: FromPlaceToPlace.travel   [webpage_url_9]\n   Size: 768x1024\n2. Baroque Altar\n   Image: [tool_generated_image_14]\n   Source: Instagram   [webpage_url_10]\n   Size: 640x1136\n3. Interior of Gothic Church of the Assumption of the Blessed Virgin Mary,  Krakow, Poland Editorial Stock Photo - Image of windows, ribbed: 373006693\n   Image: [tool_generated_image_15]\n   Source: Dreamstime.com   [webpage_url_11]\n   Size: 675x900\n4. 1,923 Tall Altar Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_16]\n   Source: Dreamstime   [webpage_url_12]\n   Size: 600x900\n5. 2,771 Altar Features Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_17]\n   Source: Dreamstime   [webpage_url_13]\n   Size: 600x900\n6. 3,437 Altar Dark Church Stock Photos - Free & Royalty-Free Stock Photos  from Dreamstime\n   Image: [tool_generated_image_18]\n   Source: Dreamstime.com   [webpage_url_14]\n   Size: 800x533\n7. 5,704 Church Ceiling Background Stock Photos - Free & Royalty-Free Stock  Photos from Dreamstime\n   Image: [tool_generated_image_19]\n   Source: Dreamstime.com   [webpage_url_15]\n   Size: 800x621\n8. Ultimate Guide to Visiting Pompeii | 14 Best Things You Need to Know, See,  and Do at Pompeii, Italy — Shirshendu Sengupta Photography and Travel |  Photographer and Travel Blogger based in\n   Image: [tool_generated_image_20]\n   Source: Shirshendu Sengupta   [webpage_url_16]\n   Size: 1333x1000\n9. 1,923 Tall Altar Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_21]\n   Source: Dreamstime   [webpage_url_12]\n   Size: 600x900\n10. 6,710 Altar Angel Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_22]\n   Source: Dreamstime   [webpage_url_17]\n   Size: 800x533"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[125,155,830,905],"padding":50},"imgs":[{"h":"tool_generated_image_23","src":"static/examples/ex11/tool_23.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_23."},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_23"},"imgs":[],"out":"[Image Search — Visual Matches]\n1. So beautiful Gothic Architecture!\n   Source: facebook.com/.../10241119283933448   [webpage_url_18]\n   Image: [tool_generated_image_24]\n2. This is St. Salvator Church, a Roman Catholic Church in the Klementinum, in Old Town, Prague, Czech Republic. As early as 1578 - 1581, the Society of Jesus, led by Rector Giovanni\n   Source: reddit.com/.../this_is_st_salvator_church_a_roman_catholic   [webpage_url_19]\n   Image: [tool_generated_image_25]\n3. Sight seeing 😁 #architecture #church #vienna #traveladdict #sightseeing\n   Source: instagram.com/p/DYGPee4CJ4W   [webpage_url_20]\n   Image: [tool_generated_image_26]\n4. File:Kostol Nanebovzatia Panny Márie (Ľubica), hlavný oltár.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/.../File:Kostol_Nanebovzatia_Panny_Márie_(Ľubica),_hlavný_oltár.jpg   [tool_generated_image_27]\n   Image: [tool_generated_image_28]\n5. The Church of St. Alexius and Former Hospital in Opole, Poland Editorial Stock Image - Image of history, conversion: 408665269\n   Source: dreamstime.com/opole-poland-june-church-st-alexius-former-hospital-built-fund...   [webpage_url_21]\n   Image: [tool_generated_image_29]\n6. Bad Zurzach CH, Katholische Kirche, welche Vielfalt\n   Source: facebook.com/groups/116360931708113/posts/9789385474405562   [webpage_url_22]\n   Image: [tool_generated_image_30]\n7. some recent memories from Europe 🇪🇺\n   Source: instagram.com/p/DcgaMryCsOY   [webpage_url_23]\n   Image: [tool_generated_image_31]\n8. 30+ Perchtoldsdorf Stock Photos, Pictures & Royalty-Free Images - iStock\n   Source: istockphoto.com/photos/perchtoldsdorf   [webpage_url_24]\n   Image: [tool_generated_image_32]\n9. Bologna - Madonna with the Saints in Oratorio De Batutti Editorial Photo - Image of indoor, paint: 39406946\n   Source: dreamstime.com/royalty-free-stock-image-bologna-madonna-saints-oratorio-de-ba...   [webpage_url_25]\n   Image: [tool_generated_image_33]\n10. Vienna Cathedral inside | TikTok\n   Source: tiktok.com/discover/vienna-cathedral-inside   [webpage_url_26]\n   Image: [tool_generated_image_34]"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"\"Stephansdom\" \"altar\" \"Maria\" \"barock\" \"Seitenaltar\" schwarze Säulen","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\n1. wdf - wupper digitale fotografie - Münster, St. Paulus-Dom\n   Image: [tool_generated_image_35]\n   Source: wupper digitale fotografie   [webpage_url_27]\n   Size: 1000x1500\n2. wdf - wupper digitale fotografie - □ Sakralkunst\n   Image: [tool_generated_image_36]\n   Source: wupper digitale fotografie   [webpage_url_28]\n   Size: 764x1021\n3. Dom zu Gurk | AustriaWiki im Austria-Forum\n   Image: [tool_generated_image_37]\n   Source: Austria-Forum   [webpage_url_29]\n   Size: 250x369\n4. LIDAL\n   Image: [tool_generated_image_38]\n   Source: GAMS - Uni Graz   [webpage_url_30]\n   Size: 3024x4032\n5. wdf - wupper digitale fotografie - Münster, St. Paulus-Dom\n   Image: [tool_generated_image_39]\n   Source: wupper digitale fotografie   [webpage_url_27]\n   Size: 764x509\n6. wdf - wupper digitale fotografie - □ Sakralkunst\n   Image: [tool_generated_image_40]\n   Source: wupper digitale fotografie   [webpage_url_28]\n   Size: 764x1021\n7. wdf - wupper digitale fotografie - □ Sakralkunst\n   Image: [tool_generated_image_41]\n   Source: wupper digitale fotografie   [webpage_url_28]\n   Size: 764x1021\n8. wdf - wupper digitale fotografie - Münster, St. Paulus-Dom\n   Image: [tool_generated_image_42]\n   Source: wupper digitale fotografie   [webpage_url_27]\n   Size: 743x214\n9. Kaiser Karl VI. - Schirmherr des Glaubens am Anfang der Frühaufklärung im  Barockzeitalter | Lanz Ernst | User im Austria-Forum\n   Image: [tool_generated_image_43]\n   Source: Austria-Forum   [webpage_url_31]\n   Size: 2520x1680\n10. wdf - wupper digitale fotografie - □ Sakralkunst\n   Image: [tool_generated_image_44]\n   Source: wupper digitale fotografie   [webpage_url_28]\n   Size: 764x1021"},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_35"},"imgs":[],"out":"[Image Search — Visual Matches]\n1. wdf - wupper digitale fotografie - Münster, St. Paulus-Dom\n   Source: kdwupper.de/paulusdom-in-münster.html   [webpage_url_32]\n   Image: [tool_generated_image_35]\n2. Rochester NY Chapter | American Guild of Organists\n   Source: rocago.org   [webpage_url_33]\n   Image: [tool_generated_image_45]\n3. Pipedreams 1806: The Eastman Kids at Sacred Heart Cathedral\n   Source: pipedreams.publicradio.org/listings/2018/1806   [webpage_url_34]\n   Image: [tool_generated_image_46]\n4. Trier Cathedral - Wikipedia\n   Source: en.wikipedia.org/wiki/Trier_Cathedral   [webpage_url_35]\n   Image: [tool_generated_image_47]\n5. File:Orléans - église Saint-Marceau, intérieur 22.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/wiki/File:Orléans_-_église_Saint-Marceau,_intérieur_22.jpg   [tool_generated_image_48]\n   Image: [tool_generated_image_49]\n6. Malmesbury Abbey - Wikipedia\n   Source: en.wikipedia.org/wiki/Malmesbury_Abbey   [webpage_url_36]\n   Image: [tool_generated_image_50]\n7. Poitiers, Cathédrale Saint-Pierre (Orgue de Chœur) | Pipe Organ Map\n   Source: pipeorganmap.com/organ/poitiers-cathédrale-saint-pierre-orgue-de-chœur   [webpage_url_37]\n   Image: [tool_generated_image_51]\n8. Aristide Cavaille-Coll's famous organ at Notre-Dame-des-Champs\n   Source: facebook.com/groups/660405240701596/posts/27939862135662538   [webpage_url_38]\n   Image: [tool_generated_image_52]\n9. The 15 most beautiful churches in Paris - Culture blog\n   Source: culturezvous.com/en/15-most-beautiful-churches-in-paris   [webpage_url_39]\n   Image: [tool_generated_image_53]\n10. Zurich — where Switzerland is so much more than watches, banks and perfect order. 🇨🇭✨ A city where medieval streets meet a modern lifestyle, the Limmat flows straight into the lake, and\n   Source: instagram.com/p/Db_nl1CjIwJ   [webpage_url_40]\n   Image: [tool_generated_image_54]"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"\"St. Paulus Dom\" \"altar\" \"Himmelfahrt\" \"barock\"","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\n1. Dom zu Eichstätt – Wikipedia\n   Image: [tool_generated_image_55]\n   Source: Wikipedia   [webpage_url_41]\n   Size: 250x333\n2. Monaco di Baviera - Chiesa dello Spirito Santo (1724-1730), affreschi della  navata dipinti da Cosmas Damian Asam tra il 1724 e il 1730. Rappresentano  Re Davide con l'arpa e la fondazione dell'Ospedale\n   Image: [tool_generated_image_56]\n   Source: Instagram   [webpage_url_42]\n   Size: 1440x1920\n3. Regensburger Dom - Wikiwand\n   Image: [tool_generated_image_57]\n   Source: Wikiwand   [webpage_url_43]\n   Size: 500x622\n4. Freisinger Dom – Wikipedia\n   Image: [tool_generated_image_58]\n   Source: Wikipedia   [webpage_url_44]\n   Size: 250x424\n5. wdf - wupper digitale fotografie - Boldixum, Föhr, St. Nicolai\n   Image: [tool_generated_image_59]\n   Source: wupper digitale fotografie   [webpage_url_45]\n   Size: 764x508\n6. Kölner Dommusik | Die Domkantorei Köln unter der Leitung von Joachim Geibel  begleitet in dieser Woche die Internationale Wallfahrt des @erzbistumkoeln_  nach... | Instagram\n   Image: [tool_generated_image_60]\n   Source: Instagram   [webpage_url_46]\n   Size: 1170x2080\n7. Aachener Dom – Wikipedia\n   Image: [tool_generated_image_61]\n   Source: Wikipedia   [webpage_url_47]\n   Size: 250x375\n8. Hildesheimer Dom - Wikiwand\n   Image: [tool_generated_image_62]\n   Source: Wikiwand   [webpage_url_48]\n   Size: 500x334\n9. Dom zu Eichstätt – Wikipedia\n   Image: [tool_generated_image_63]\n   Source: Wikipedia   [webpage_url_41]\n   Size: 250x333\n10. wdf - wupper digitale fotografie - St. Viktor, Xanten\n   Image: [tool_generated_image_64]\n   Source: wupper digitale fotografie   [webpage_url_49]\n   Size: 764x1146"},{"tool":"Webpage_Visit_Tool","cap":"web","args":{"urls":["webpage_url_32"],"goal":"Identify whether this Münster St Paulus Dom contains a tall dark wooden Baroque altarpiece with an image of Mary dressed blue above saint kneeling and gold tabernacle, and which altar it is."},"imgs":[],"out":"The useful information in https://www.kdwupper.de/paulusdom-in-münster.html for user goal Identify whether this Münster St Paulus Dom contains a tall dark wooden Baroque altarpiece with an image of Mary dressed blue above saint kneeling and gold tabernacle, and which altar it is. as follows: \n\nEvidence in page: \n{\n  \"rationale\": \"The user is seeking to identify a specific Baroque altarpiece in the St. Paulus-Dom, Münster, described as tall, dark wooden, with an image of Mary dressed blue above a kneeling saint and a gold tabernacle. The webpage content contains detailed descriptions of altars and their features, including the Hauptaltar and the Laurentiusaltar, which match this description.\",\n  \"evidence\": \"The Hauptaltar is described as being located in the Ostvierung and featuring statues of the Gottesmutter and the Apostles from the second half of the 14th century. The text also mentions that the original Baroque Hochaltar, which was a reliquary shrine with painted and carved wings, was located in the Chorhaupt and was later moved to the Westchor. The Laurentiusaltar is described as being in the south aisle and featuring a kneeling saint (St. Laurentius) and a tabernacle (Zelt) made of silver, which is always adorned with candles and flowers. The text also notes that the Laurentiusaltar was created in 1555 and is located in the Dom, where Bernhard von Münster was buried in 1557. The description of the Laurentiusaltar includes a depiction of a kneeling saint (St. Laurentius) and a tabernacle (Zelt) made of silver, which is always adorned with candles and flowers. The text also notes that the Laurentiusaltar was created in 1555 and is located in the Dom, where Bernhard von Münster was buried in 1557. The description of the Laurentiusaltar includes a depiction of a kneeling saint (St. Laurentius) and a tabernacle (Zelt) made of silver, which is always adorned with candles and flowers. The text also notes that the Laurentiusaltar was created in 1555 and is located in the Dom, where Bernhard von Münster was buried in 1557. The description of the Laurentiusaltar includes a depiction of a kneeling saint (St. Laurentius) and a tabernacle (Zelt) made of silver, which is always adorned with candles and flowers. The text also notes that the Laurentiusaltar was created in 1555 and is located in the Dom, where Bernhard von Münster was buried in 1557. The description of the Laurentiusaltar includes a depiction of a kneeling saint (St. Laurentius) and a tabernacle (Ze …"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"gothic cathedral baroque altar \"Madonna\" \"kneeling\" dark wood gold tabernacle","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\n1. 676 Gold Tabernacle Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_65]\n   Source: Dreamstime   [webpage_url_50]\n   Size: 600x900\n2. 676 Gold Tabernacle Stock Photos - Free & Royalty-Free Stock Photos from  Dreamstime\n   Image: [tool_generated_image_66]\n   Source: Dreamstime   [webpage_url_50]\n   Size: 600x900\n3. Renaissance altar in a gothic church hi-res stock photography and images -  Alamy\n   Image: [tool_generated_image_67]\n   Source: Alamy   [webpage_url_51]\n   Size: 1300x1065\n4. Carved Tabernacle - Etsy\n   Image: [tool_generated_image_68]\n   Source: Etsy   [webpage_url_52]\n   Size: 1170x1822\n5. 1,937 Christian Tabernacle Stock Photos - Free & Royalty-Free Stock Photos  from Dreamstime\n   Image: [tool_generated_image_69]\n   Source: Dreamstime   [webpage_url_53]\n   Size: 600x900\n6. Renaissance altar in a gothic church hi-res stock photography and images -  Alamy\n   Image: [tool_generated_image_70]\n   Source: Alamy   [webpage_url_51]\n   Size: 866x1390\n7. 2,845 Catholic Tabernacle Royalty-Free Images, Stock Photos & Pictures |  Shutterstock\n   Image: [tool_generated_image_71]\n   Source: Shutterstock   [webpage_url_54]\n   Size: 389x280\n8. Christian altar decoration hi-res stock photography and images - Page 16 -  Alamy\n   Image: [tool_generated_image_72]\n   Source: Alamy   [webpage_url_55]\n   Size: 866x1390\n9. Side chapel of the Cathedral of Ravenna, housing a miraculous image of the  Madonna and Child.\n   Image: [tool_generated_image_73]\n   Source: Facebook   [webpage_url_56]\n   Size: 985x815\n10. 115 Decorated Tabernacle Stock Photos - Free & Royalty-Free Stock Photos  from Dreamstime\n   Image: [tool_generated_image_74]\n   Source: Dreamstime.com   [webpage_url_57]\n   Size: 600x900"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"\"gotische\" \"Kirche\" \"barocker\" \"Hochaltar\" \"Rosenkranz\" \"Tabernakel\"","search_type":"images"},"imgs":[],"out":"[Search Results — Type: images]\n1. verlag-st-peter.at - Die Kirchen von Hopfgarten im Brixental - Pfarrkirche:  Rundgang Altäre\n   Image: [tool_generated_image_75]\n   Source: Kirchenführer aus dem Verlag St. Peter   [webpage_url_58]\n   Size: 400x600\n2. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_76]\n   Source: Alamy   [webpage_url_59]\n   Size: 1300x956\n3. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_77]\n   Source: Alamy   [webpage_url_59]\n   Size: 1300x958\n4. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_78]\n   Source: Alamy   [webpage_url_59]\n   Size: 1300x1073\n5. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_79]\n   Source: Alamy   [webpage_url_59]\n   Size: 866x1390\n6. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_80]\n   Source: Alamy   [webpage_url_59]\n   Size: 1300x991\n7. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_81]\n   Source: Alamy   [webpage_url_59]\n   Size: 867x1390\n8. Barockheiligtum mit hochaltar -Fotos und -Bildmaterial in hoher Auflösung –  Alamy\n   Image: [tool_generated_image_82]\n   Source: Alamy   [webpage_url_59]\n   Size: 866x1390\n9. verlag-st-peter.at - Die Kirchen von Hopfgarten im Brixental - Pfarrkirche:  Rundgang Altäre\n   Image: [tool_generated_image_83]\n   Source: Kirchenführer aus dem Verlag St. Peter   [webpage_url_58]\n   Size: 400x600\n10. verlag-st-peter.at - Die Kirchen von Hopfgarten im Brixental - Pfarrkirche:  Rundgang Altäre\n   Image: [tool_generated_image_84]\n   Source: Kirchenführer aus dem Verlag St. Peter   [webpage_url_58]\n   Size: 400x600"},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[360,440,590,713],"padding":50},"imgs":[{"h":"tool_generated_image_85","src":"static/examples/ex11/tool_85.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_85."},{"tool":"Image_Search_Tool","cap":"isearch","args":{"image_id":"tool_generated_image_85"},"imgs":[],"out":"[Image Search — Visual Matches]\n1. File:Perchtoldsdorf Pfarrkirche Hochaltarbild 01.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/wiki/File:Perchtoldsdorf_Pfarrkirche_Hochaltarbild_01.jpg   [tool_generated_image_86]\n   Image: [tool_generated_image_87]\n2. Solum Church - Wikipedia\n   Source: en.wikipedia.org/wiki/Solum_Church   [webpage_url_60]\n   Image: [tool_generated_image_88]\n3. found in the bonn münster- what is it & who is it by? : r/WhatIsThisPainting\n   Source: reddit.com/.../found_in_the_bonn_münster_what_is_it_who_is_it_by   [webpage_url_61]\n   Image: [tool_generated_image_89]\n4. Dominikanerkirche in Vienna's UNESCO Heritage Innere Stadt, Austria (17th cen.) [OC] : r/ArchitecturalRevival\n   Source: reddit.com/.../dominikanerkirche_in_viennas_unesco_heritage   [webpage_url_62]\n   Image: [tool_generated_image_90]\n5. File:Juan de Toledo Inmaculada.jpg - Wikimedia Commons\n   Source: commons.wikimedia.org/wiki/File:Juan_de_Toledo_Inmaculada.jpg   [tool_generated_image_91]\n   Image: [tool_generated_image_92]\n6. Liturgia Latina: 12th February, The Seven Holy Founders of the Servite Order, Confessors\n   Source: liturgialatina.blogspot.com/2011/02/12th-february-seven-holy-founders-of.html   [webpage_url_63]\n   Image: [tool_generated_image_93]\n7. Pictures do not do justice here… just spectacular in every sense ❤️ another corner church!\n   Source: facebook.com/georgia.korelis.2025/photos/797298986317681   [webpage_url_64]\n   Image: [tool_generated_image_94]\n8. Glory be to God forever. A great joy to document through photo even a fraction of the Christian beauty in Poland. The first couple of slides are from a church that is\n   Source: instagram.com/p/DcEa8LGm_LN   [webpage_url_65]\n   Image: [tool_generated_image_95]\n9. Ascension, Fresco By Niccolo Di Pietro Gerini, Sacristy In Basilica Di Santa Croce (Basilica Of The Holy Cross) - Famous Franciscan Church In Florence, Italy Stock Photo, Picture and Royalty Free Image.\n   Source: 123rf.com/photo_120194404_ascension-fresco-by-niccolo-di-pietro-gerini-sacris...   [webpage_url_66]\n   Image: [tool_generated_image_96]\n10. Jan Lukáš Kracker – Wikipedie\n   Source: cs.wikipedia.org/wiki/Jan_Lukáš_Kracker   [webpage_url_67]\n   Image: [tool_generated_image_97]"},{"tool":"Text_Search_Tool","cap":"tsearch","args":{"query":"Perchtoldsdorf Pfarrkirche Hochaltar Kirche Mariä Himmelfahrt altar","search_type":"search"},"imgs":[],"out":"{\"ok\": true, \"result\": \"[Search Results — Type: search]\\n1. Pfarrkirche St. Augustinus und Pfarrhof\\n   Source: perchtoldsdorf.at/Pfarrkirche_St_Augustinus_und_Pfarrhof   [webpage_url_68]\\n   von Perchtoldsdorf im Jahre 1217 zur Pfarrkirche. Der Hochaltar mit tordierten Säulen. Hochaltar mit dem Hauptbild „Mariä Himmelfahrt“ und dem hl. Augustinus ...\\n2. Pfarrkirche | röm.-kath. Pfarre Perchtoldsdorf\\n   Source: pfarre-perchtoldsdorf.at/unsere-pfarre/pfarrkirche   [webpage_url_69]\\n   Neuigkeiten, bevorstehende Veranstaltungen und Informationen zu Taufen, Trauungen und weiteren kirchlichen Anlässen der röm. kath. Pfarre Perchtoldsdorf.\\n3. File:Perchtoldsdorf Pfarrkirche Hochaltarbild 01.jpg\\n   Source: commons.wikimedia.org/wiki/File:Perchtoldsdorf_Pfarrkirche_Hochaltarbild_01.jpg   [tool_generated_image_86]\\n   High altar of the parish church Perchtoldsdorf, Lower Austria. Altarpiece Assumption of Mary by the circle of Martino Altomonte, around 1700.\\n4. Mariä Himmelfahrt am 1️⃣5️⃣. August ist ein Hochfest ...\\n   Source: facebook.com/.../1497708092160325   [webpage_url_70]\\n   Mariä Himmelfahrt am 1️⃣5️⃣. August ist ein Hochfest der katholischen Kirche und findet sich am anonymen Blatt des barocken Hochaltars ...\\n5. Pfarrkirche Perchtoldsdorf\\n   Source: de.wikipedia.org/wiki/Pfarrkirche_Perchtoldsdorf   [webpage_url_71]\\n   Dieser Altar wurde 1951 wieder gegen den barocken Altar getauscht und neuerlich in der Spitalkirche aufgestellt. Nach den Zerstörungen in den letzten ...\\n6. Modell eines Mariae-Himmelfahrt-Altars Franz Stadler\\n   Source: liebieghaus.de/.../modell-eines-mariae-himmelfahrt-altars   [webpage_url_72]\\n   Der Hochaltar, den das Modell vorbereitet, steht in der Kirche Mariae Himmelfahrt der Gemeinde Pfarrkirchen im oberösterreichischen Mühlviertel.\\n7. Pfarre Perchtoldsdorf\\n   Source: erzdioezese-wien.at/pfarre/14424125   [webpage_url_73]\\n   Pfarre Perchtoldsdorf. Perchtoldsdorf T +43 (1) 869 02 26. Pfarrkirche Perchtoldsdorf Marktplatz 2380. Marktplatz 14 2380 - Perchtoldsdorf E: pfarre. ...\\n8. Perchtoldsdorf PFK TV\\n   Source: youtube.com/watch   [webpage_url_74]\\n   Hl. Messe - 30.08.2026, 10:30 Uhr - Mt 16, 21–27. Pfarrkirche Perchtoldsdorf Live ; 11-Jährige stellt sich der JURY.. dann holt sie ihre OBOE ...\\n9. Pfarrkirche Maria Himmelfahrt\\n   Source: naturwanderpark.eu/en/pois/pfarrkirche-maria-himmelfahrt   [webpage_url_75]\\n   In the new part of the church, there is the late Gothic high altar from 1540, which shows the lif …"}]},{"id":"ex12","num":12,"inPaper":true,"dataset":"V*","model":"GPT-6 Sol","domain":"vqa","tags":["grounding","zoom"],"composition":true,"slug":"backpack-left-or-right","question":"Is the woman with a backpack on the left or right side of the person with a beanie?\nOptions:\nA. right\nB. left\nPlease select the correct answer from the options above.","pred":"B. left","gt":"B","score":1.0,"correct":true,"overview":{"task":"In a large landscape photograph of a hilltop town, say whether a woman with a backpack is to the left or right of a person wearing a beanie.","why it is hard":"The two people stand near a bus on a road far below the town and are only a few pixels tall; without tools the model answers “right”.","how the agent solves it":"Grounding on the full image cannot separate the two people, so the agent zooms in three times, each time closer, and grounds both people again inside the final crop, where they are large enough to tell apart."},"notool":"A. right","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex12/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"person wearing beanie","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex12/tool_1.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'person wearing beanie'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [369, 830, 381, 853], \"label\": \"person wearing beanie\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[369,830,381,853]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"woman with backpack","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex12/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'woman with backpack'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [368, 831, 381, 853], \"label\": \"woman with backpack\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[368,831,381,853]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[350,810,410,875],"padding":100},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex12/tool_3.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_3."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[500,810,605,920],"padding":100},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex12/tool_4.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_4."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_1","coords":[508,862,538,895],"padding":50},"imgs":[{"h":"tool_generated_image_5","src":"static/examples/ex12/tool_5.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_5."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"woman with backpack","image_id":"tool_generated_image_5"},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex12/tool_6.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'woman with backpack'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [419, 447, 511, 569], \"label\": \"woman with backpack\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_6.","boxes":[[419,447,511,569]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"person with beanie","image_id":"tool_generated_image_5"},"imgs":[{"h":"tool_generated_image_7","src":"static/examples/ex12/tool_7.jpg"}],"out":"Visual_Grounding_Tool grounded 2 objects for the prompt 'person with beanie'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [419, 446, 511, 569], \"label\": \"person with beanie\"},\n\t{\"bbox_2d\": [472, 435, 527, 560], \"label\": \"person with beanie\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_7.","boxes":[[419,446,511,569],[472,435,527,560]]}]},{"id":"ex13","num":13,"inPaper":true,"dataset":"MMSI-Bench","model":"GPT-6 Sol","domain":"spatial","tags":["grounding","zoom"],"composition":true,"slug":"kitchen-sofa-directions","question":"The kitchen is located to the north relative to the sofa. Which of the following statements is correct?\nOptions: A: The mirror is to the southwest of the refrigerator, B: The radius of the vase in Figure 2 is larger than the radius of the white lampshade of the table lamp in Figure 5, C: There are a total of 16 throw pillows in the picture (including those partially covered by other objects), D: There are a total of 12 throw pillows in the picture (including those partially covered by other objects), \nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``.","pred":"C","gt":"C","score":1.0,"correct":true,"overview":{"task":"Seven photographs show one living room from different positions. Of four statements about the room, choose the correct one; two of them give the number of throw pillows, 16 or 12, counting partly covered ones.","why it is hard":"The pillows are spread across several views, some appear in more than one view, and some are partly hidden; without tools the model picks 12.","how the agent solves it":"It grounds “throw pillows” in each of the four views that show seating, zooms on both halves of the main sofa, and chooses the statement with 16 pillows."},"notool":"D","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex13/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex13/input_2.jpg"},{"h":"input_image_3","src":"static/examples/ex13/input_3.jpg"},{"h":"input_image_4","src":"static/examples/ex13/input_4.jpg"},{"h":"input_image_5","src":"static/examples/ex13/input_5.jpg"},{"h":"input_image_6","src":"static/examples/ex13/input_6.jpg"},{"h":"input_image_7","src":"static/examples/ex13/input_7.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"throw pillows","image_id":"input_image_5"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex13/tool_1.jpg"}],"out":"Visual_Grounding_Tool grounded 11 objects for the prompt 'throw pillows'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 684, 113, 866], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [55, 717, 159, 861], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [180, 677, 308, 825], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [326, 709, 428, 830], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [352, 664, 457, 817], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [592, 787, 715, 908], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [631, 707, 772, 841], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [690, 819, 831, 1000], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [755, 738, 898, 993], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [841, 905, 937, 1000], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [930, 816, 1000, 1000], \"label\": \"throw pillows\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[0,684,113,866],[55,717,159,861],[180,677,308,825],[326,709,428,830],[352,664,457,817],[592,787,715,908],[631,707,772,841],[690,819,831,1000],[755,738,898,993],[841,905,937,1000],[930,816,1000,1000]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"throw pillows","image_id":"input_image_3"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex13/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 3 objects for the prompt 'throw pillows'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [814, 671, 883, 806], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [939, 663, 974, 738], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [963, 669, 1000, 748], \"label\": \"throw pillows\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[814,671,883,806],[939,663,974,738],[963,669,1000,748]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"throw pillows","image_id":"input_image_4"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex13/tool_3.jpg"}],"out":"Visual_Grounding_Tool grounded 3 objects for the prompt 'throw pillows'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 658, 46, 729], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [144, 881, 277, 1000], \"label\": \"throw pillows\"},\n\t{\"bbox_2d\": [963, 724, 1000, 852], \"label\": \"throw pillows\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_3.","boxes":[[0,658,46,729],[144,881,277,1000],[963,724,1000,852]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"throw pillows","image_id":"input_image_6"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex13/tool_4.jpg"}],"out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'throw pillows'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [0, 799, 102, 1000], \"label\": \"throw pillows\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_4.","boxes":[[0,799,102,1000]]},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_5","coords":[530,660,1000,1000],"padding":50},"imgs":[{"h":"tool_generated_image_5","src":"static/examples/ex13/tool_5.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_5."},{"tool":"Zoom_In_Tool","cap":"zoom","args":{"image_id":"input_image_5","coords":[0,650,530,1000],"padding":50},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex13/tool_6.jpg"}],"out":"Zoomed in image rendered below; refer to it as tool_generated_image_6."}]},{"id":"ex14","num":14,"inPaper":true,"dataset":"MMSI-Bench","model":"Qwen3-VL-32B","domain":"spatial","tags":["code","depth","grounding"],"composition":true,"slug":"piano-vs-table-length","question":"Which is longer, the piano or the table under the mirror?\nOptions: A: The table under the mirror is longer, B: Sometimes the table under the mirror is longer, sometimes the piano is longer, C: They are the same length, D: The piano is longer, \nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``.","pred":"A","gt":"A","score":1.0,"correct":true,"overview":{"task":"Two photographs show two rooms of a historic house: a parlour with a piano, and a dining room. Which is longer, the piano or the table under the mirror? Without tools the same model answers D, that the piano is longer.","how the agent approaches it":"It grounds both objects, estimates metric depth in both photos, and has the coding agent convert each box to a real-world length.","caveat":"The answer is correct, but the evidence does not support it. The mirror hangs above the fireplace, so the “table under the mirror” is the mantel shelf. Grounding returns it as object 3, but the agent chooses the round dining table (object 2) instead, and concludes that the table, at 2.77 m, is longer than the piano, at 2.21 m. This example shows the harness's tool chain, not a sound comparison."},"notool":"","final":"The Python_Coding_Agent_Tool computed the real-world lengths of the piano and the dining table. The piano is approximately 2.21 meters long, while the dining table (the round table under the mirror) is approximately 2.77 meters long. Therefore, the table under the mirror is longer than the piano.","inputs":[{"h":"input_image_1","src":"static/examples/ex14/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex14/input_2.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"piano","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex14/tool_1.jpg"}],"say":"To determine which is longer—the piano or the table under the mirror—I need to compare their lengths. The piano is visible in input_image_1, and the table under the mirror is visible in input_image_2. Since they are in different images, I need to use a tool to estimate their lengths relative to each other. I will first ground both objects in their respective images to get bounding boxes, then use a depth estimation tool to get relative scales, and finally compare them using Python_Coding_Agent_Tool.","out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'piano'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [503.72, 477.32, 787.18, 999.63]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[504,477,787,1000]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"table under the mirror","image_id":"input_image_2"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex14/tool_2.jpg"}],"out":"Visual_Grounding_Tool grounded 3 objects for the prompt 'table under the mirror'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [2.6, 762.44, 205.84, 901.39], 'object 2': [38.62, 823.36, 556.79, 1000], 'object 3': [294.14, 573.73, 587.94, 865.76]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[3,762,206,901],[39,823,557,1000],[294,574,588,866]]},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_1","colormap":"grayscale"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex14/tool_3.jpg"}],"say":"From the grounding results, I have identified the piano in input_image_1 with bounding box [503.72, 477.32, 787.18, 999.63]. In input_image_2, there are three objects grounded under the prompt 'table under the mirror': a small side table (object 1), a round dining table (object 2), and a fireplace mantel (object 3). The most relevant object for comparison is the round dining table (object 2) with bounding box [38.62, 823.36, 556.79, 1000].\n\nTo compare their lengths accurately, I need to account for perspective and depth. I will estimate the depth map for both images to get relative depth values at the centers of the piano and the dining table, then compute their projected lengths using the bounding box dimensions and depth information.","out":"Depth estimation complete. Shape: [1022, 1274], colormap: grayscale.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_3."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_2","colormap":"grayscale"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex14/tool_4.jpg"}],"out":"Depth estimation complete. Shape: [1022, 1274], colormap: grayscale.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_4."},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Compute the real-world length of the piano from input_image_1 and the dining table (object 2) from input_image_2 using their bounding boxes and depth values at their centers. The piano's bounding box is [503.72, 477.32, 787.18, 999.63] and the dining table's bounding box is [38.62, 823.36, 556.79, 1000]. Use the depth maps from the Depth_Estimation_Tool to get the depth at the center of each object. Then, calculate the projected length using the formula: real_length = (bbox_width / image_width) * depth * 2 (assuming a rough perspective correction factor). Compare the two lengths and determine which is longer."},"imgs":[],"say":"Now I have depth maps for both images. I will use the Python_Coding_Agent_Tool to compute the actual lengths of the piano and the dining table (object 2) by combining their bounding box dimensions with the depth information at their centers. This will allow me to compare their real-world lengths accurately.","code":"import numpy as np\n\n# Given bounding boxes (from the objective)\npiano_bbox = [503.72, 477.32, 787.18, 999.63]  # [x1, y1, x2, y2]\ntable_bbox = [38.62, 823.36, 556.79, 1000]     # [x1, y1, x2, y2]\n\n# Image dimensions (from segment_piano and depth_result)\nimage_width = segment_piano[\"orig_img_w\"]  # 1280\nimage_height = segment_piano[\"orig_img_h\"]  # 1024\n\n# Depth maps\ndepth_map_1 = depth_result[\"depth_meters\"]  # for piano (input_image_1)\ndepth_map_2 = depth_result_2[\"depth_meters\"]  # for table (input_image_2)\n\n# Compute center coordinates for each object\npiano_center_x = (piano_bbox[0] + piano_bbox[2]) / 2\npiano_center_y = (piano_bbox[1] + piano_bbox[3]) / 2\ntable_center_x = (table_bbox[0] + table_bbox[2]) / 2\ntable_center_y = (table_bbox[1] + table_bbox[3]) / 2\n\n# Convert center coordinates to integer for depth map indexing\npiano_center_x_int = int(piano_center_x)\npiano_center_y_int = int(piano_center_y)\ntable_center_x_int = int(table_center_x)\ntable_center_y_int = int(table_center_y)\n\n# Get depth at center points (clip to bounds if needed)\n# Ensure coordinates are within depth map bounds\npiano_center_x_int = max(0, min(piano_center_x_int, depth_map_1.shape[1] - 1))\npiano_center_y_int = max(0, min(piano_center_y_int, depth_map_1.shape[0] - 1))\ntable_center_x_int = max(0, min(table_center_x_int, depth_map_2.shape[1] - 1))\ntable_center_y_int = max(0, min(table_center_y_int, depth_map_2.shape[0] - 1))\n\npiano_depth = depth_map_1[piano_center_y_int, piano_center_x_int]\ntable_depth = depth_map_2[table_center_y_int, table_center_x_int]\n\n# Compute projected real-world lengths\n# Formula: real_length = (bbox_width / image_width) * depth * 2\npiano_width = piano_bbox[2] - piano_bbox[0]\ntable_width = table_bbox[2] - table_bbox[0]\n\npiano_real_length = (piano_width / image_width) * piano_depth * 2\ntable_real_length = (table_width / image_width) * table_depth * 2\n\n# Compare and output results\nprint(f\"Piano real-world length: {piano_real_length:.4f} meters\")\nprint(f\"Dining table real-world length: {table_real_length:.4f} meters\")\n\nif piano_real_length > table_real_length:\n    longer_object = \"piano\"\nelse:\n    longer_object = \"dining table\"\n\nprint(f\"The {longer_object} is longer.\")\n\n# Assign results to variables for result namespace\npiano_real_length = piano_real_length\ntable_real_length = table_real_length\nlonger_object = longer_object","out":"Piano real-world length: 2.2109 meters\nDining table real-world length: 2.7731 meters\nThe dining table is longer."}]},{"id":"ex15","num":15,"inPaper":true,"dataset":"SparBench","model":"GPT-6 Sol","domain":"spatial","tags":["camera-trajectory","code","depth"],"composition":true,"slug":"bookshelf-distance","question":"State the Euclidean distance in meters from the center of bookshelf (red point) to the center of bookshelf (blue point). Calculate or judge based on the 3D center points of these objects. Provide only one number for your response.\nPlease answer the question using a single word or phrase.","pred":"2.0","gt":"1.9","score":0.9,"correct":true,"overview":{"task":"Three photographs of a library are taken from nearby positions. A red point marks a bookshelf in the first photo and a blue point marks another bookshelf in the third. Give the 3D distance between them in metres.","why it is hard":"The two points never appear in the same photo, so the distance cannot be judged from any single view; without tools the model answers 4.0 m.","how the agent solves it":"It recovers where each photo was taken from, measures metric depth at each point, and has the coding agent place both points in one 3D frame and measure between them: 2.0 m, against a ground truth of 1.9 m."},"notool":"4.0","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex15/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex15/input_2.jpg"},{"h":"input_image_3","src":"static/examples/ex15/input_3.jpg"}],"steps":[{"tool":"Camera_Trajectory_Tool","cap":"camera","args":{"image_ids":["input_image_1","input_image_2","input_image_3"]},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex15/tool_1.jpg"}],"out":"Camera trajectory summary:\n- input_image_1: reference camera and reference view.\n- input_image_2:\n    camera movement: salient direction: none (all movements < 1 meter); detailed: right (+0.01), down (+0.16), backward (-0.08)\n    camera rotation: salient direction: left (yaw -27.4°), down (pitch +19.8°); detailed: left (yaw -27.4°), down (pitch +19.8°)\n- input_image_3:\n    camera movement: salient direction: left (-1.49), backward (-1.65); detailed: left (-1.49), down (+0.27), backward (-1.65)\n    camera rotation: salient direction: left (yaw -12.1°); detailed: left (yaw -12.1°), down (pitch +6.1°)\nIf no salient movement or rotation is reported, use the magnitude of each axis to determine the most possible movement/rotation direction.\n\nBird's-eye view of the camera trajectory on input_image_1's X-Z plane is rendered in the following image - refer as tool_generated_image_1 (dots = camera positions, forward-pointing arrows = facing direction).\n\nAll vectors are relative to input_image_1's camera frame, using the coordinate system: +X = right, +Y = down, +Z = forward. With this convention, positive yaw (rotation around Y axis) = right and positive pitch (rotation around X axis) = down. If the original question assumes a different coordinate system (e.g. with +Y = up and -Z = forward, which flips the yaw/pitch signs), convert before answering."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_1","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex15/tool_2.jpg"}],"out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_2."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_3","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex15/tool_3.jpg"}],"out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_3."},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Estimate Euclidean distance between red dot at pixel (1141,320) in first 1302x966 image and blue dot at pixel (1060,47) in third image using respective estimated depth maps, camera intrinsics and world-to-camera extrinsics. Print intrinsics, camera transforms, depth local median at each point, derived 3D world coordinates and distance. Be careful depth_result is first image and depth_result_2 is third."},"imgs":[],"code":"import numpy as np\n\n# Extract relevant data\nimage_ids = camera_trajectory_result[\"image_ids\"]\nnum_images = camera_trajectory_result[\"num_images\"]\nintrinsics = camera_trajectory_result[\"intrinsics\"]  # shape: (3, 3, 3)\nextrinsics = camera_trajectory_result[\"extrinsics\"]  # shape: (3, 3, 4)\ndepth_meters_1 = depth_result[\"depth_meters\"]  # first image depth map\ndepth_meters_3 = depth_result_2[\"depth_meters\"]  # third image depth map\n\n# Pixel coordinates (note: depth_result is for first image, depth_result_2 for third)\n# Pixel (1141, 320) in first image (depth_result)\n# Pixel (1060, 47) in third image (depth_result_2)\n# Note: image dimensions are [height, width] = [966, 1302] per depth_result\n\n# Convert pixel coordinates to 0-based indexing (if needed, but usually they are 0-based)\nx1, y1 = 1141, 320  # first image\nx3, y3 = 1060, 47   # third image\n\n# Get depth at each pixel\ndepth_1 = depth_meters_1[y1, x1]  # depth at pixel (1141, 320) in first image\ndepth_3 = depth_meters_3[y3, x3]  # depth at pixel (1060, 47) in third image\n\n# Compute local median depth for each point (for robustness, though not strictly needed for distance)\ndepth_median_1 = np.median(depth_meters_1[y1-1:y1+2, x1-1:x1+2])  # 3x3 neighborhood\ndepth_median_3 = np.median(depth_meters_3[y3-1:y3+2, x3-1:x3+2])  # 3x3 neighborhood\n\n# Print intrinsics (first image)\nprint(\"Intrinsics (first image):\")\nprint(intrinsics[0])\n\n# Print camera transforms (first and third images)\nprint(\"\\nCamera transforms (first image):\")\nprint(extrinsics[0])\nprint(\"\\nCamera transforms (third image):\")\nprint(extrinsics[2])\n\n# Print depth local medians\nprint(f\"\\nDepth local median at pixel (1141, 320) in first image: {depth_median_1:.4f} meters\")\nprint(f\"Depth local median at pixel (1060, 47) in third image: {depth_median_3:.4f} meters\")\n\n# Convert 2D pixel coordinates to 3D world coordinates using camera model\n# For first image (image index 0)\nK = intrinsics[0]  # 3x3 intrinsic matrix\nR1, t1 = extrinsics[0][:3, :3], extrinsics[0][:3, 3]  # rotation and translation (world to camera)\n\n# Convert pixel to normalized image coordinates (assuming 0-based indexing)\nu1, v1 = x1, y1\n# Apply intrinsic matrix to get normalized coordinates\nx_norm1 = (u1 - K[0, 2]) / K[0, 0]  # (u - cx) / fx\ny_norm1 = (v1 - K[1, 2]) / K[1, 1]  # (v - cy) / fy\n\n# Convert normalized image coordinates to 3D camera coordinates\n# [Xc, Yc, Zc] = depth * [x_norm, y_norm, 1]\nXc1 = depth_median_1 * x_norm1\nYc1 = depth_median_1 * y_norm1\nZc1 = depth_median_1\n\n# Convert camera coordinates to world coordinates\n# Xw = R^T * (Xc - t)\nXw1 = np.dot(R1.T, np.array([Xc1, Yc1, Zc1]) - t1)\nXw1 = Xw1.flatten()  # ensure 1D\n\n# For third image (image index 2)\nK = intrinsics[2]  # 3x3 intrinsic matrix\nR3, t3 = extrinsics[2][:3, :3], extrinsics[2][:3, 3]  # rotation and translation (world to camera)\n\n# Convert pixel to normalized image coordinates\nu3, v3 = x3, y3\nx_norm3 = (u3 - K[0, 2]) / K[0, 0]\ny_norm3 = (v3 - K[1, 2]) / K[1, 1]\n\n# Convert normalized image coordinates to 3D camera coordinates\nXc3 = depth_median_3 * x_norm3\nYc3 = depth_median_3 * y_norm3\nZc3 = depth_median_3\n\n# Convert camera coordinates to world coordinates\nXw3 = np.dot(R3.T, np.array([Xc3, Yc3, Zc3]) - t3)\nXw3 = Xw3.flatten()  # ensure 1D\n\n# Print derived 3D world coordinates\nprint(f\"\\n3D world coordinates (first image point): ({Xw1[0]:.4f}, {Xw1[1]:.4f}, {Xw1[2]:.4f})\")\nprint(f\"3D world coordinates (third image point): ({Xw3[0]:.4f}, {Xw3[1]:.4f}, {Xw3[2]:.4f})\")\n\n# Compute Euclidean distance between the two 3D points\ndistance = np.linalg.norm(Xw1 - Xw3)\nprint(f\"\\nEuclidean distance between the two points: {distance:.4f} meters\")","out":"Intrinsics (first image):\n[[1.2203016e+03 0.0000000e+00 6.5100000e+02]\n [0.0000000e+00 1.3876421e+03 4.8300000e+02]\n [0.0000000e+00 0.0000000e+00 1.0000000e+00]]\n\nCamera transforms (first image):\n[[ 0.8591257   0.2705432  -0.43440697  0.01150304]\n [-0.12202131  0.93264997  0.33952138  0.15892358]\n [ 0.49700487 -0.23868464  0.8342756  -0.07446227]]\n\nCamera transforms (third image):\n[[ 0.941648    0.22854781 -0.24711327  1.7913343 ]\n [-0.16772765  0.9551071   0.24420871 -0.2721306 ]\n [ 0.29183298 -0.18851092  0.93770844  1.2118572 ]]\n\nDepth local median at pixel (1141, 320) in first image: 2.1547 meters\nDepth local median at pixel (1060, 47) in third image: 4.0840 meters\n\n3D world coordinates (first image point): (1.8916, -0.6854, 1.3490)\n3D world coordinates (third image point): (0.5929, -1.6351, 2.5497)\n\nEuclidean distance between the two points: 2.0076 meters"}]},{"id":"ex16","num":16,"inPaper":true,"dataset":"SparBench","model":"GPT-6 Luna","domain":"spatial","tags":["camera-trajectory","code","depth"],"composition":true,"slug":"camera-to-cabinet-distance","question":"From the person’s position who took the photo, what is the distance to the center of cabinet (red point)? Express it in meters. Calculate or judge based on the 3D center points of these objects. The first image becomes the primary perspective, correlating with the observer's view. Only one number should be included in your response.\nPlease answer the question using a single word or phrase.","pred":"2.4","gt":"2.4","score":1.0,"correct":true,"overview":{"task":"Three photographs of a basement storage area are taken from nearby positions; the first is the observer's view. A red point marks a cabinet, visible only in the third photo. Give the distance from the observer to the cabinet in metres.","why it is hard":"The cabinet is seen from a different position than the one the question asks about, so its depth in the third photo is not the answer; without tools the model answers 2.0 m.","how the agent solves it":"It recovers the camera positions, reads the cabinet's depth in the third photo, and has the coding agent move that point into the observer's frame: 2.40 m, exactly the ground truth."},"notool":"2.0 m","final":"","inputs":[{"h":"input_image_1","src":"static/examples/ex16/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex16/input_2.jpg"},{"h":"input_image_3","src":"static/examples/ex16/input_3.jpg"}],"steps":[{"tool":"Camera_Trajectory_Tool","cap":"camera","args":{"image_ids":["input_image_1","input_image_2","input_image_3"]},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex16/tool_1.jpg"}],"out":"Camera trajectory summary:\n- input_image_1: reference camera and reference view.\n- input_image_2:\n    camera movement: salient direction: none (all movements < 1 meter); detailed: left (-0.22), down (+0.12), backward (-0.38)\n    camera rotation: salient direction: right (yaw +12.7°); detailed: right (yaw +12.7°), down (pitch +9.4°)\n- input_image_3:\n    camera movement: salient direction: none (all movements < 1 meter); detailed: left (-0.53), down (+0.11), backward (-0.70)\n    camera rotation: salient direction: none (all rotations < 10°); detailed: right (yaw +5.7°), down (pitch +8.8°)\nIf no salient movement or rotation is reported, use the magnitude of each axis to determine the most possible movement/rotation direction.\n\nBird's-eye view of the camera trajectory on input_image_1's X-Z plane is rendered in the following image - refer as tool_generated_image_1 (dots = camera positions, forward-pointing arrows = facing direction).\n\nAll vectors are relative to input_image_1's camera frame, using the coordinate system: +X = right, +Y = down, +Z = forward. With this convention, positive yaw (rotation around Y axis) = right and positive pitch (rotation around X axis) = down. If the original question assumes a different coordinate system (e.g. with +Y = up and -Z = forward, which flips the yaw/pitch signs), convert before answering."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_3","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex16/tool_2.jpg"}],"say":"I’ll estimate the marked cabinet’s depth relative to the camera using a depth map of the image containing the red point.","out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_2."},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Extract the estimated depth in meters at the red point marking the cabinet in input_image_3 (roughly x=1110, y=923 in a 1302x966 image). Report the local median depth in a small surrounding patch, ignoring invalid values."},"imgs":[],"code":"# Extract depth at the red point (x=1110, y=923) in input_image_3\n# Note: depth_meters is in meters, and the coordinates are 0-indexed in the depth map\nx, y = 1110, 923\ndepth_at_point = depth_result[\"depth_meters\"][y, x]\n\n# Define a small surrounding patch (e.g., 5x5 window centered at the point)\npatch_size = 5\nhalf_patch = patch_size // 2\nvalid_depths = []\n\n# Check bounds and collect valid depth values in the patch\nfor dy in range(-half_patch, half_patch + 1):\n    for dx in range(-half_patch, half_patch + 1):\n        ny, nx = y + dy, x + dx\n        if 0 <= ny < depth_result[\"depth_shape\"][0] and 0 <= nx < depth_result[\"depth_shape\"][1]:\n            depth_val = depth_result[\"depth_meters\"][ny, nx]\n            if not math.isnan(depth_val) and not math.isinf(depth_val):\n                valid_depths.append(depth_val)\n\n# Compute local median depth (ignoring invalid values)\nif valid_depths:\n    local_median_depth = statistics.median(valid_depths)\nelse:\n    local_median_depth = None  # No valid depth values in patch\n\n# Output the depth at the point and the local median\nprint(f\"Depth at point (x=1110, y=923): {depth_at_point:.4f} meters\")\nprint(f\"Local median depth in 5x5 patch: {local_median_depth:.4f} meters\")","out":"Execution error: name 'math' is not defined"},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Read depth_result depth_meters at red point x≈1110 y≈923, image3 is 1302x966. Compute median finite values in the nearby 7x7 patch. Use no imports."},"imgs":[],"code":"# Get depth_meters array and dimensions\ndepth_map = depth_result[\"depth_meters\"]\nheight, width = depth_result[\"depth_shape\"]\n\n# Target point (x≈1110, y≈923) - note: image coordinates are (width, height) = (1302, 966)\nx, y = 1110, 923\n\n# Define 7x7 patch around the point (centered at (x, y))\npatch_start_row = max(0, y - 3)\npatch_end_row = min(height, y + 4)\npatch_start_col = max(0, x - 3)\npatch_end_col = min(width, x + 4)\n\n# Collect finite values in the 7x7 patch\nfinite_values = []\nfor row in range(patch_start_row, patch_end_row):\n    for col in range(patch_start_col, patch_end_col):\n        val = depth_map[row, col]\n        if not (val == 0 or val == float('inf') or val == float('-inf')):\n            finite_values.append(val)\n\n# Compute median of finite values\nif len(finite_values) > 0:\n    sorted_values = sorted(finite_values)\n    n = len(sorted_values)\n    median = sorted_values[n // 2] if n % 2 == 1 else (sorted_values[n // 2 - 1] + sorted_values[n // 2]) / 2\nelse:\n    median = None\n\nprint(median)","out":"2.1564598"},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Estimate distance from reference camera (image1) to cabinet red-point center in image3. Use its depth (local median around x=1110,y=923), intrinsics and relative camera extrinsics/centers from camera_trajectory_result. Unproject the pixel into image3 camera coordinates, transform the 3D point into image1 camera coordinates, and calculate Euclidean distance. Report the computed value and relevant transform conventions."},"imgs":[],"code":"import numpy as np\n\n# Extract reference image index (image1) and target image index (image3)\nref_idx = 0  # 'input_image_1'\ntarget_idx = 2  # 'input_image_3'\n\n# Get depth at pixel (x=1110, y=923) in image3 (note: depth is in meters)\ndepth_at_pixel = depth_result[\"depth_meters\"][923, 1110]  # y, x indexing\n\n# Compute local median depth around the pixel for robustness (3x3 neighborhood)\ny_center, x_center = 923, 1110\ny_start, y_end = max(0, y_center - 1), min(depth_result[\"depth_shape\"][0], y_center + 2)\nx_start, x_end = max(0, x_center - 1), min(depth_result[\"depth_shape\"][1], x_center + 2)\nlocal_depth = depth_result[\"depth_meters\"][y_start:y_end, x_start:x_end]\nmedian_depth = np.median(local_depth)\n\n# Use median depth for robustness\ndepth = median_depth\n\n# Unproject pixel (x=1110, y=923) in image3 to 3D point in image3 camera coordinates\n# Using intrinsics and depth\nintrinsics = camera_trajectory_result[\"intrinsics\"][target_idx]  # 3x3\nK = intrinsics  # camera matrix\n\n# pixel coordinates (0-indexed, so subtract 1 if needed, but depth_result uses 0-indexed)\nx_pixel = x_center\ny_pixel = y_center\n\n# Unproject: (X, Y, Z) = depth * (u - cx) / fx, depth * (v - cy) / fy, depth\nfx, fy = K[0, 0], K[1, 1]\ncx, cy = K[0, 2], K[1, 2]\n\nX_3d = depth * (x_pixel - cx) / fx\nY_3d = depth * (y_pixel - cy) / fy\nZ_3d = depth\n\npoint_3d_in_target_camera = np.array([X_3d, Y_3d, Z_3d])\n\n# Transform point from image3 camera coordinates to image1 camera coordinates\n# Use extrinsics: world_to_camera = [R | t], so camera_to_world = R^T * (-t)\n# We need to transform from image3 camera frame to image1 camera frame\n# So: point_in_ref_camera = R_ref_to_target @ point_in_target_camera + t_ref_to_target\n\n# Get extrinsics for image3 (target) and image1 (reference)\nextrinsics_target = camera_trajectory_result[\"extrinsics\"][target_idx]  # 3x4\nextrinsics_ref = camera_trajectory_result[\"extrinsics\"][ref_idx]  # 3x4\n\n# Extract rotation matrix and translation vector for target and reference\nR_target = extrinsics_target[:, :3]  # 3x3 rotation matrix\nt_target = extrinsics_target[:, 3]   # 3x1 translation vector\n\nR_ref = extrinsics_ref[:, :3]  # 3x3 rotation matrix\nt_ref = extrinsics_ref[:, 3]   # 3x1 translation vector\n\n# Transform point from target camera frame to reference camera frame\n# point_in_ref_camera = R_ref_to_target @ point_in_target_camera + t_ref_to_target\n# R_ref_to_target = R_target^T (since extrinsics are world_to_camera)\n# t_ref_to_target = -R_target^T @ t_target + t_ref\n\nR_ref_to_target = R_target.T  # rotation from target to ref\nt_ref_to_target = -R_ref_to_target @ t_target + t_ref\n\n# Apply transformation\npoint_3d_in_ref_camera = R_ref_to_target @ point_3d_in_target_camera + t_ref_to_target\n\n# Compute Euclidean distance from reference camera center to the point\ncamera_center_ref = t_ref  # camera center in reference frame is the translation vector\ndistance = np.linalg.norm(point_3d_in_ref_camera - camera_center_ref)\n\n# Print results\nprint(f\"Estimated distance from reference camera to cabinet red-point center in image3: {distance:.4f} meters\")\nprint(f\"Transform conventions: All vectors are relative to {camera_trajectory_result['reference_image_id']}'s camera frame, using the coordinate system: {camera_trajectory_result['coordinate_system']}\")","out":"Estimated distance from reference camera to cabinet red-point center in image3: 2.3995 meters\nTransform conventions: All vectors are relative to input_image_1's camera frame, using the coordinate system: All vectors are relative to input_image_1's camera frame, using the coordinate system: +X = right, +Y = down, +Z = forward. With this convention, positive yaw (rotation around Y axis) = right and positive pitch (rotation around X axis) = down. If the original question assumes a different coordinate system (e.g. with +Y = up and -Z = forward, which flips the yaw/pitch signs), convert before answering."}]},{"id":"ex17","num":17,"inPaper":true,"dataset":"SparBench","model":"Qwen3-VL-32B","domain":"spatial","tags":["camera-trajectory","code","depth","grounding"],"composition":true,"slug":"window-to-cabinet-distance","question":"In meters, how far is the center of window (red point) from the center of cabinet (blue point)? Calculate or judge based on the 3D center points of these objects. Only one numeric value is required for your response.\nPlease answer the question using a single word or phrase.","pred":"1.1957","gt":"1.1","score":0.9,"correct":true,"overview":{"task":"Three photographs of an office corner are taken from nearby positions. A red point marks a window in the first photo and a blue point marks a cabinet in the third. Give the 3D distance between them in metres. Without tools the same model answers 1.7 m.","how the agent approaches it":"It chains four kinds of tool: camera trajectory to relate the views, depth in all three views, grounding to locate the window and the cabinet, and the coding agent to back-project both points into one frame and measure between them.","caveat":"The pipeline is the right one, but its last step does not use the marked points. The generated code reads each box as position plus width and height, although the boxes are corner coordinates, and it takes the first “cabinet” box, an upper wall cabinet at the top edge of the photo, instead of the lower cabinet with the blue point. The answer, 1.20 m against a ground truth of 1.1 m, is therefore close partly by coincidence."},"notool":"","final":"Based on the computation, the Euclidean distance between the center of the window (red point) and the center of the cabinet (blue point) is 1.1957 meters. This was calculated by estimating the 3D coordinates of both points using depth maps and camera trajectory data, then computing the distance in the world coordinate system.","inputs":[{"h":"input_image_1","src":"static/examples/ex17/input_1.jpg"},{"h":"input_image_2","src":"static/examples/ex17/input_2.jpg"},{"h":"input_image_3","src":"static/examples/ex17/input_3.jpg"}],"steps":[{"tool":"Camera_Trajectory_Tool","cap":"camera","args":{"image_ids":["input_image_1","input_image_2","input_image_3"]},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/ex17/tool_1.jpg"}],"say":"To calculate the distance between the center of the window (red point) and the center of the cabinet (blue point), I need to estimate their 3D positions. Since the images are from different viewpoints, I will first use the Camera_Trajectory_Tool to understand the camera motion between the frames. Then, I can use depth estimation and object grounding to get approximate 3D coordinates for both points. Finally, I'll compute the Euclidean distance between them.","out":"Camera trajectory summary:\n- input_image_1: reference camera and reference view.\n- input_image_2:\n    camera movement: salient direction: none (all movements < 1 meter); detailed: left (-0.40), down (+0.07), backward (-0.29)\n    camera rotation: salient direction: right (yaw +40.7°); detailed: right (yaw +40.7°), down (pitch +1.0°)\n- input_image_3:\n    camera movement: salient direction: none (all movements < 1 meter); detailed: left (-0.26), down (+0.03), backward (-0.15)\n    camera rotation: salient direction: right (yaw +28.8°); detailed: right (yaw +28.8°), up (pitch -4.1°)\nIf no salient movement or rotation is reported, use the magnitude of each axis to determine the most possible movement/rotation direction.\n\nBird's-eye view of the camera trajectory on input_image_1's X-Z plane is rendered in the following image - refer as tool_generated_image_1 (dots = camera positions, forward-pointing arrows = facing direction).\n\nAll vectors are relative to input_image_1's camera frame, using the coordinate system: +X = right, +Y = down, +Z = forward. With this convention, positive yaw (rotation around Y axis) = right and positive pitch (rotation around X axis) = down. If the original question assumes a different coordinate system (e.g. with +Y = up and -Z = forward, which flips the yaw/pitch signs), convert before answering."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_1","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/ex17/tool_2.jpg"}],"say":"Now that I have the camera trajectory, I will estimate the 3D positions of the window (red point) and cabinet (blue point). I'll use depth estimation on each image to get depth values at these points, then combine with the camera positions and orientations to compute their 3D coordinates. Finally, I'll calculate the Euclidean distance between them.","out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_2."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_2","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_3","src":"static/examples/ex17/tool_3.jpg"}],"say":"Next, I will estimate the depth at the red point (window center) and blue point (cabinet center) in each image. Since the points are marked in different images, I'll need to use the depth maps from each respective image to get their depths. Then, using the camera positions and orientations from the trajectory, I can compute the 3D coordinates of each point and calculate the distance between them.","out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_3."},{"tool":"Depth_Estimation_Tool","cap":"depth","args":{"image_id":"input_image_3","colormap":"inferno"},"imgs":[{"h":"tool_generated_image_4","src":"static/examples/ex17/tool_4.jpg"}],"say":"Now, I will estimate the depth at the red point (window center) and blue point (cabinet center) in each image. Since the points are marked in different images, I'll need to use the depth maps from each respective image to get their depths. Then, using the camera positions and orientations from the trajectory, I can compute the 3D coordinates of each point and calculate the distance between them.","out":"Depth estimation complete. Shape: [966, 1302], colormap: inferno.\nMetric depth in meters available as `depth_meters` (float32 ndarray).\nColorized depth map rendered below; refer to it as tool_generated_image_4."},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"window","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_5","src":"static/examples/ex17/tool_5.jpg"}],"say":"Now I have depth maps for all three images. Next, I will use the Visual_Grounding_Tool to locate the window (red point) in input_image_1 and the cabinet (blue point) in input_image_3. This will give me the pixel coordinates of these points, which I can then use with the depth maps to get their 3D coordinates relative to each camera. After that, I'll use the camera trajectory data to transform these points into a common coordinate system and compute the distance between them.","out":"Visual_Grounding_Tool grounded 1 objects for the prompt 'window'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [0, 0, 353.92, 389.56]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_5.","boxes":[[0,0,354,390]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"cabinet","image_id":"input_image_3"},"imgs":[{"h":"tool_generated_image_6","src":"static/examples/ex17/tool_6.jpg"}],"say":"Now I will ground the cabinet (blue point) in input_image_3 to get its pixel coordinates. After that, I'll use the depth maps and camera trajectory data to compute the 3D coordinates of both points and calculate the distance between them.","out":"Visual_Grounding_Tool grounded 5 objects for the prompt 'cabinet'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: {'object 1': [194.95, 0, 373.1, 39.21], 'object 2': [252.05, 590.15, 416.32, 983.2], 'object 3': [382.59, 0, 593.34, 61.06], 'object 4': [423.93, 649.71, 721.75, 1000], 'object 5': [599.26, 0, 864.26, 83.16]}. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_6.","boxes":[[195,0,373,39],[252,590,416,983],[383,0,593,61],[424,650,722,1000],[599,0,864,83]]},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Compute the 3D coordinates of the window center (red point) in input_image_1 and the cabinet center (blue point) in input_image_3 using the depth maps and camera trajectory data. Then, calculate the Euclidean distance between these two 3D points."},"imgs":[],"say":"Now I have the bounding boxes for both the window and cabinet. The red point (window center) is in input_image_1, and the blue point (cabinet center) is in input_image_3. I will use the depth maps for each image to get the depth at these points, then combine with the camera positions and orientations from the trajectory to compute their 3D coordinates. Finally, I'll calculate the Euclidean distance between them.","code":"import numpy as np\nfrom PIL import Image\nimport io\nimport base64\n\n# Step 1: Extract camera intrinsics and extrinsics for input_image_1 and input_image_3\n# Note: camera_trajectory_result[\"image_ids\"] is ['input_image_1', 'input_image_2', 'input_image_3']\n# So index 0 = input_image_1, index 2 = input_image_3\n\nK1 = camera_trajectory_result[\"intrinsics\"][0]  # 3x3 intrinsic matrix for input_image_1\nK3 = camera_trajectory_result[\"intrinsics\"][2]  # 3x3 intrinsic matrix for input_image_3\n\n# World-to-camera extrinsic matrices (OpenCV/COLMAP format: [R | t])\n# Note: extrinsics are 3x4 matrices, where the first 3x3 is rotation and last column is translation\n# We need to convert to camera-to-world for point reconstruction\n# But for now, we'll use them as is for projecting 3D points to 2D, or reconstructing 3D from 2D+depth\n\n# For 3D reconstruction from 2D pixel + depth, we use:\n# X = depth * (x - cx) / fx\n# Y = depth * (y - cy) / fy\n# Z = depth\n# But this is in camera coordinates.\n\n# Step 2: Get depth maps for input_image_1 and input_image_3\ndepth1 = depth_result[\"depth_meters\"]  # depth for input_image_1\ndepth3 = depth_result_3[\"depth_meters\"]  # depth for input_image_3\n\n# Step 3: Get window bounding box from segment_window (for input_image_1)\n# The bounding box is given in normalized coordinates [x1, y1, w, h]\n# But we need to convert to pixel coordinates in the original image\n# segment_window[\"orig_img_h\"] = 968, \"orig_img_w\" = 1296\n# depth maps are 966x1302, which is close to 968x1296, so we can use the same scale\n\n# Convert normalized box to pixel coordinates\nbox_window = segment_window[\"pred_boxes\"][0]\nx1_norm, y1_norm, w_norm, h_norm = box_window\nh_orig, w_orig = segment_window[\"orig_img_h\"], segment_window[\"orig_img_w\"]\n\n# Convert to pixel coordinates (assuming same scale as depth map)\n# Since depth map is 966x1302 and original image is 968x1296, we can scale\n# But for simplicity, we'll use the original image dimensions to compute center\nx1 = int(x1_norm * w_orig)\ny1 = int(y1_norm * h_orig)\nw = int(w_norm * w_orig)\nh = int(h_norm * h_orig)\n\n# Compute center of window in pixel coordinates\ncenter_x_window = x1 + w // 2\ncenter_y_window = y1 + h // 2\n\n# Step 4: Get cabinet bounding box from segment_cabinet (for input_image_3)\n# We need the cabinet center in input_image_3\n# There are 5 cabinets, we'll use the first one (highest confidence)\nbox_cabinet = segment_cabinet[\"pred_boxes\"][0]\nx1_norm_cab, y1_norm_cab, w_norm_cab, h_norm_cab = box_cabinet\n\n# Convert to pixel coordinates\nx1_cab = int(x1_norm_cab * w_orig)\ny1_cab = int(y1_norm_cab * h_orig)\nw_cab = int(w_norm_cab * w_orig)\nh_cab = int(h_norm_cab * h_orig)\n\n# Compute center of cabinet in pixel coordinates\ncenter_x_cab = x1_cab + w_cab // 2\ncenter_y_cab = y1_cab + h_cab // 2\n\n# Step 5: Get depth at center points\n# For window in input_image_1\ndepth_window = depth1[center_y_window, center_x_window]\n\n# For cabinet in input_image_3\ndepth_cab = depth3[center_y_cab, center_x_cab]\n\n# Step 6: Compute 3D coordinates in camera coordinates\n# For window in input_image_1\nfx1, fy1 = K1[0, 0], K1[1, 1]\ncx1, cy1 = K1[0, 2], K1[1, 2]\n\n# 3D point in camera 1 coordinates\nX1 = depth_window * (center_x_window - cx1) / fx1\nY1 = depth_window * (center_y_window - cy1) / fy1\nZ1 = depth_window\n\n# For cabinet in input_image_3\nfx3, fy3 = K3[0, 0], K3[1, 1]\ncx3, cy3 = K3[0, 2], K3[1, 2]\n\n# 3D point in camera 3 coordinates\nX3 = depth_cab * (center_x_cab - cx3) / fx3\nY3 = depth_cab * (center_y_cab - cy3) / fy3\nZ3 = depth_cab\n\n# Step 7: Convert 3D points to world coordinates using extrinsics\n# Extrinsics are world-to-camera: [R | t], so to get world coordinates from camera coordinates:\n# P_world = R^T * (P_camera - t)  [since extrinsic = [R | t], and P_camera = R * P_world + t]\n\n# Extract R and t for camera 1 and camera 3\n# extrinsics shape: (N, 3, 4) -> for each image, 3x4 matrix [R | t]\n# R is 3x3, t is 3x1 (last column)\n\nR1 = camera_trajectory_result[\"extrinsics\"][0][:, :3]  # 3x3 rotation matrix\nt1 = camera_trajectory_result[\"extrinsics\"][0][:, 3]   # 3x1 translation vector\n\nR3 = camera_trajectory_result[\"extrinsics\"][2][:, :3]\nt3 = camera_trajectory_result[\"extrinsics\"][2][:, 3]\n\n# Convert camera 1 point to world coordinates\nP_cam1 = np.array([X1, Y1, Z1])\nP_world1 = R1.T @ (P_cam1 - t1)\n\n# Convert camera 3 point to world coordinates\nP_cam3 = np.array([X3, Y3, Z3])\nP_world3 = R3.T @ (P_cam3 - t3)\n\n# Step 8: Compute Euclidean distance between the two 3D points in world coordinates\ndistance = np.linalg.norm(P_world1 - P_world3)\n\n# Output results\nprint(f\"Window center 3D coordinates (world frame): {P_world1}\")\nprint(f\"Cabinet center 3D coordinates (world frame): {P_world3}\")\nprint(f\"Euclidean distance between window and cabinet: {distance:.3f} meters\")\n\n# Assign to variables for result namespace\nwindow_3d_world = P_world1\ncabinet_3d_world = P_world3\ndistance_between = distance","out":"Execution error: index 182417 is out of bounds for axis 0 with size 966"},{"tool":"Python_Coding_Agent_Tool","cap":"code","args":{"objective":"Compute the 3D coordinates of the window center (red point) in input_image_1 and the cabinet center (blue point) in input_image_3 using the depth maps and camera trajectory data. Clamp the pixel coordinates to the depth map dimensions to avoid out-of-bounds errors. Then, calculate the Euclidean distance between these two 3D points."},"imgs":[],"say":"The error occurred because the pixel coordinates for the window center exceeded the depth map dimensions. I need to adjust the pixel coordinates to fit within the depth map bounds. I'll use the original image dimensions to compute the center, then clamp the coordinates to the depth map size before accessing the depth value.","code":"import numpy as np\nfrom PIL import Image\nimport io\nimport base64\n\n# Step 1: Extract camera intrinsics and extrinsics for input_image_1 and input_image_3\n# Note: camera_trajectory_result[\"image_ids\"] is ['input_image_1', 'input_image_2', 'input_image_3']\n# So index 0 is input_image_1, index 2 is input_image_3\n\nK1 = camera_trajectory_result[\"intrinsics\"][0]  # 3x3 intrinsic matrix for input_image_1\nK3 = camera_trajectory_result[\"intrinsics\"][2]  # 3x3 intrinsic matrix for input_image_3\n\n# World-to-camera extrinsic matrices (OpenCV/COLMAP format: [R | t])\n# Note: extrinsics are 3x4 matrices, where the first 3x3 is rotation and last column is translation\n# We need to convert to camera-to-world for 3D point reconstruction\n# But for now, we'll use the extrinsics as is for projecting 3D points to camera space\n\n# Extract extrinsics for input_image_1 and input_image_3\nE1 = camera_trajectory_result[\"extrinsics\"][0]  # world-to-camera for input_image_1\nE3 = camera_trajectory_result[\"extrinsics\"][2]  # world-to-camera for input_image_3\n\n# Step 2: Get window bounding box from segment_window (for input_image_1)\n# The bounding box is in [x1, y1, w, h] format, normalized? Let's check the values\n# Preview: [[-0.7693090438842773, -0.09972977638244629, 458.6855163574219, 377.0949401855469]]\n# The x1, y1 are negative? That suggests they are normalized or relative to center?\n# But orig_img_h=968, orig_img_w=1296, and depth map is 966x1302 — very close.\n\n# Let's assume the bounding box is in pixel coordinates, but the negative values suggest it's not.\n# Since the values are large (458, 377) and the image is 1296x968, it's likely absolute pixel coordinates.\n# But x1 is negative? That's odd. Let's check if it's normalized.\n\n# Actually, looking at the values: x1=-0.769, y1=-0.099 — these are very small, likely normalized.\n# But then w=458, h=377 — these are large, so probably not normalized.\n\n# Let's assume the bounding box is in absolute pixel coordinates, and clamp to image size.\n# We'll use the original image dimensions from segment_window for clamping.\n\norig_h = segment_window[\"orig_img_h\"]  # 968\norig_w = segment_window[\"orig_img_w\"]  # 1296\n\n# Get window box\nwindow_box = segment_window[\"pred_boxes\"][0]\nx1, y1, w, h = window_box\n\n# Convert to center coordinates\nwindow_center_x = x1 + w / 2\nwindow_center_y = y1 + h / 2\n\n# Clamp to image dimensions\nwindow_center_x = np.clip(window_center_x, 0, orig_w - 1)\nwindow_center_y = np.clip(window_center_y, 0, orig_h - 1)\n\n# Step 3: Get depth for window center in input_image_1\n# depth_result is for input_image_1 (since it's the first depth result and we're using it for input_image_1)\ndepth_map_1 = depth_result[\"depth_meters\"]\n\n# Clamp to depth map dimensions (966, 1302)\ndepth_h, depth_w = depth_map_1.shape\nwindow_center_x_clamped = np.clip(window_center_x, 0, depth_w - 1)\nwindow_center_y_clamped = np.clip(window_center_y, 0, depth_h - 1)\n\n# Get depth at window center\ndepth_window = depth_map_1[int(window_center_y_clamped), int(window_center_x_clamped)]\n\n# Step 4: Convert 2D pixel + depth to 3D point in camera coordinate system (input_image_1)\n# We need to use the intrinsic matrix K1 to unproject\n\n# Convert pixel to normalized coordinates\nu = window_center_x_clamped\nv = window_center_y_clamped\n\n# Unproject using depth and intrinsic matrix\n# 3D point in camera coordinates: [X, Y, Z] = depth * [ (u - cx) / fx, (v - cy) / fy, 1 ]\n# But we need to invert the intrinsic matrix to get the 3D point\n\n# Intrinsic matrix K1 is 3x3\nfx, fy = K1[0, 0], K1[1, 1]\ncx, cy = K1[0, 2], K1[1, 2]\n\n# Compute 3D point in camera coordinates\nX_cam = (u - cx) * depth_window / fx\nY_cam = (v - cy) * depth_window / fy\nZ_cam = depth_window\n\n# So 3D point in camera 1 coordinate system is [X_cam, Y_cam, Z_cam]\npoint_window_cam1 = np.array([X_cam, Y_cam, Z_cam])\n\n# Step 5: Get cabinet bounding box from segment_cabinet (for input_image_3)\n# There are 5 detections, we'll use the first one (highest score)\ncabinet_box = segment_cabinet[\"pred_boxes\"][0]\nx1_cab, y1_cab, w_cab, h_cab = cabinet_box\n\n# Convert to center\ncabinet_center_x = x1_cab + w_cab / 2\ncabinet_center_y = y1_cab + h_cab / 2\n\n# Clamp to image dimensions\ncabinet_center_x = np.clip(cabinet_center_x, 0, orig_w - 1)\ncabinet_center_y = np.clip(cabinet_center_y, 0, orig_h - 1)\n\n# Step 6: Get depth for cabinet center in input_image_3\ndepth_map_3 = depth_result_3[\"depth_meters\"]\n\n# Clamp to depth map dimensions\ncabinet_center_x_clamped = np.clip(cabinet_center_x, 0, depth_w - 1)\ncabinet_center_y_clamped = np.clip(cabinet_center_y, 0, depth_h - 1)\n\n# Get depth at cabinet center\ndepth_cabinet = depth_map_3[int(cabinet_center_y_clamped), int(cabinet_center_x_clamped)]\n\n# Step 7: Convert 2D pixel + depth to 3D point in camera coordinate system (input_image_3)\n# Use intrinsic matrix K3\n\nfx3, fy3 = K3[0, 0], K3[1, 1]\ncx3, cy3 = K3[0, 2], K3[1, 2]\n\n# Unproject\nX_cam3 = (cabinet_center_x_clamped - cx3) * depth_cabinet / fx3\nY_cam3 = (cabinet_center_y_clamped - cy3) * depth_cabinet / fy3\nZ_cam3 = depth_cabinet\n\npoint_cabinet_cam3 = np.array([X_cam3, Y_cam3, Z_cam3])\n\n# Step 8: Convert both 3D points to world coordinates using extrinsics\n# Extrinsics are world-to-camera: E = [R | t], so to get world point from camera point:\n# P_world = R^T * (P_cam - t)  [since E * P_world = P_cam, so P_world = E^{-1} * P_cam]\n\n# For input_image_1: E1 = [R1 | t1], so E1^{-1} = [R1^T | -R1^T * t1]\nR1 = E1[:, :3]  # 3x3 rotation matrix\nt1 = E1[:, 3]   # 3x1 translation vector\n\n# Invert E1: E1_inv = [R1^T, -R1^T * t1]\nR1_T = R1.T\nt1_inv = -R1_T @ t1\n\n# Convert point_window_cam1 to world coordinates\npoint_window_world = R1_T @ (point_window_cam1 - t1)\n\n# For input_image_3: E3 = [R3 | t3]\nR3 = E3[:, :3]\nt3 = E3[:, 3]\n\nR3_T = R3.T\nt3_inv = -R3_T @ t3\n\n# Convert point_cabinet_cam3 to world coordinates\npoint_cabinet_world = R3_T @ (point_cabinet_cam3 - t3)\n\n# Step 9: Compute Euclidean distance between the two 3D points in world coordinates\ndistance = np.linalg.norm(point_window_world …","out":"Window center 3D in world coordinates: [-0.59107007 -0.35877492  1.67238153]\nCabinet center 3D in world coordinates: [ 0.5192192  -0.77874663  1.52856489]\nEuclidean distance between them: 1.1957 meters"}]},{"id":"extra2","num":"extra2","inPaper":false,"dataset":"FSCD-147","model":"GPT-6 Sol","domain":"count","tags":["grounding"],"composition":false,"slug":"detect-pills","question":"Locate all instances of 'pill' in this image. For each instance, output a bounding box as [x1, y1, x2, y2] where coordinates are normalised on a 0-1000 scale (with (0,0) as the top-left corner and (1000,1000) as the bottom-right corner). Your answer should be a list of lists. If there are no instances of 'pill' in the image, output an empty list [] as your answer.","pred":"16 boxes","gt":"16 boxes","score":null,"correct":null,"overview":{},"notool":"","final":"","inputs":[{"h":"input_image_1","src":"static/examples/extra2/input_1.jpg"}],"steps":[{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"pill","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_1","src":"static/examples/extra2/tool_1.jpg"}],"say":"I’ll check the pill outlines and count each one before giving the boxes.","out":"Visual_Grounding_Tool grounded 10 objects for the prompt 'pill'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [443, 579, 540, 964], \"label\": \"pill\"},\n\t{\"bbox_2d\": [453, 23, 555, 299], \"label\": \"pill\"},\n\t{\"bbox_2d\": [540, 582, 629, 966], \"label\": \"pill\"},\n\t{\"bbox_2d\": [567, 28, 669, 303], \"label\": \"pill\"},\n\t{\"bbox_2d\": [633, 575, 723, 957], \"label\": \"pill\"},\n\t{\"bbox_2d\": [671, 27, 781, 299], \"label\": \"pill\"},\n\t{\"bbox_2d\": [726, 566, 819, 951], \"label\": \"pill\"},\n\t{\"bbox_2d\": [822, 570, 914, 958], \"label\": \"pill\"},\n\t{\"bbox_2d\": [896, 41, 1000, 314], \"label\": \"pill\"},\n\t{\"bbox_2d\": [915, 583, 1000, 979], \"label\": \"pill\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_1.","boxes":[[443,579,540,964],[453,23,555,299],[540,582,629,966],[567,28,669,303],[633,575,723,957],[671,27,781,299],[726,566,819,951],[822,570,914,958],[896,41,1000,314],[915,583,1000,979]]},{"tool":"Visual_Grounding_Tool","cap":"grounding","args":{"query":"dark blue oval pill","image_id":"input_image_1"},"imgs":[{"h":"tool_generated_image_2","src":"static/examples/extra2/tool_2.jpg"}],"say":"The first pass missed several dark-blue pills. I’ll check those separately.","out":"Visual_Grounding_Tool grounded 10 objects for the prompt 'dark blue oval pill'. Their corresponding bounding boxes (on a 0-1000 scale, [x1, y1, x2, y2]) are: ```json\n[\n\t{\"bbox_2d\": [452, 301, 555, 571], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [453, 24, 556, 299], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [558, 309, 663, 580], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [567, 28, 669, 302], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [665, 307, 769, 579], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [671, 27, 781, 299], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [772, 305, 877, 577], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [784, 27, 893, 303], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [879, 312, 986, 588], \"label\": \"dark blue oval pill\"},\n\t{\"bbox_2d\": [896, 40, 1000, 314], \"label\": \"dark blue oval pill\"}\n]```. The object numbers correspond to the labeled masks rendered in the following image, which you can refer to as tool_generated_image_2.","boxes":[[452,301,555,571],[453,24,556,299],[558,309,663,580],[567,28,669,302],[665,307,769,579],[671,27,781,299],[772,305,877,577],[784,27,893,303],[879,312,986,588],[896,40,1000,314]]}]}];