Evaluation Report - codex

📝 ABC

34/45 (75.6%)

📋 Task Description

Load the ABC (Arnold-Beltrami-Childress) flow vector field from "ABC/data/ABC_128x128x128_float32_scalar3.raw", the information about this dataset: ABC Flow (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 128x128x128 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create streamlines using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [73.77, 63.25, 71.65], with 150 seed points and a radius of 75.0. Set integration direction to "BOTH" and maximum streamline length to 150.0. Add a "Tube" filter on the stream tracer to enhance visualization. Set tube radius to 0.57 with 12 sides. Color the tubes by Vorticity magnitude using the 'Cool to Warm (Diverging)' colormap. Show the dataset bounding box as an outline. Use a white background. Render at 1024x1024. Set the viewpoint parameters as: [-150.99, 391.75, 219.64] to position; [32.38, 120.41, 81.63] to focal point; [0.23, -0.31, 0.92] to camera up direction. Save the visualization image as "ABC/results/{agent_mode}/ABC.png". (Optional, but must save if use paraview) Save the paraview state as "ABC/results/{agent_mode}/ABC.pvsm". (Optional, but must save if use python script) Save the python script as "ABC/results/{agent_mode}/ABC.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

26/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Streamline Density: Are the streamline tubes densely distributed throughout the volume, similar to the ground truth?

Judge's Assessment: The result shows a very similar number of streamline tubes and overall spatial coverage within the bounding box compared to the ground truth. The central region is densely populated and the outer regions have comparable streamline presence. Minor differences exist in exact placement/coverage near some corners/edges, but density is essentially matched.

Goal 2

9/10

Criterion: Color Mapping: Are the tubes colored by vorticity magnitude using a blue-white-red diverging colormap, with a similar color distribution as the ground truth?

Judge's Assessment: The tubes are colored with a blue–white–red diverging scheme consistent with the Cool-to-Warm look, and the distribution of reds (higher values) and blues (lower values) across the bundle closely matches the ground truth. The main discrepancy is the darker background in the result, which changes perceived contrast slightly, but the colormap itself and value variation appear correct.

Goal 3

8/10

Criterion: Tube Appearance: Do the streamline tubes have a similar thickness and smooth appearance as the ground truth?

Judge's Assessment: Tube thickness and smoothness are largely consistent with the ground truth (similar radius and rounded appearance). However, the result appears slightly more visually heavy/contrasty due to the dark background and darker outline, making the tubes look a bit thicker/less delicate in places even if geometry is similar.

Overall Assessment

Overall the result is a strong match to the ground truth: streamline density, tube geometry, and cool-to-warm vorticity coloring are all very similar. The main deviation is presentation-related (background not white and darker outline), which slightly affects perceived color contrast and tube appearance but does not fundamentally change the visualization.

📊 Detailed Metrics

Visualization Quality

26/30

Output Generation

5/5

Efficiency

3/10

PSNR

24.16 dB

SSIM

0.9774

LPIPS

0.0491

Input Tokens

736,361

Output Tokens

8,436

Total Tokens

744,797

Total Cost

$3.8083

📝 argon-bubble

29/45 (64.4%)

📋 Task Description

Task: Load the Argon Bubble dataset from "argon-bubble/data/argon-bubble_128x128x256_float32.vtk". Generate a visualization image of the Argon Bubble scalar field dataset with the following visualization settings: 1) Create volume rendering 2) Set the opacity transfer function as a ramp function across values of the volumetric data, assigning opacity 0 to value 0 and assigning opacity 1 to value 1. 3) Set the color transfer function to assign a warm red color [0.71, 0.02, 0.15] to the highest value, a cool color [0.23, 0.29, 0.75] to the lowest value, and a grey color[0.87, 0.87, 0.87] to the midrange value 4) Set the viewpoint parameters as: [0, 450, 0] to position; [0, 0, -15] to focal point; [0, 0, -1] to camera up direction 5) Visualization image resolution is 1024x1024. White background. Shade turned off. Volume rendering ray casting sample distance is 0.1 6) Don't show color/scalar bar or coordinate axes. Save the visualization image as "argon-bubble/results/{agent_mode}/argon-bubble.png". (Optional, but must save if use paraview) Save the paraview state as "argon-bubble/results/{agent_mode}/argon-bubble.pvsm". (Optional, but must save if use pvpython script) Save the python script as "argon-bubble/results/{agent_mode}/argon-bubble.py". (Optional, but must save if use VTK) Save the cxx code script as "argon-bubble/results/{agent_mode}/argon-bubble.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

20/30

Goals

3

Points/Goal

10

Goal 1

7/10

Criterion: Does the visualization image clearly show the regions of cool, warm, and mild regions?

Judge's Assessment: Ground truth shows a mostly cool (blue) volumetric plume with subtle midrange grey highlights and small warm red/orange hotspots embedded inside. The result shows the same overall plume structure and includes faint warm spots, but the midrange grey is less apparent and the overall appearance is more uniformly bluish/washed, reducing the distinct separation between cool/mild/warm regions compared to the ground truth.

Goal 2

6/10

Criterion: Does the blueish region show areas with low opacity?

Judge's Assessment: In the ground truth, the bluish low-value regions appear quite translucent with soft edges and internal visibility consistent with low opacity. In the result, the blue volume looks noticeably more opaque/denser overall (stronger silhouette and less airy translucency), so the low-opacity impression of the blueish region is weaker than in the ground truth.

Goal 3

7/10

Criterion: Does the reddish region show areas with high opacity?

Judge's Assessment: Both images contain small reddish high-value features within the plume. In the ground truth these warm regions read as more distinct and slightly more prominent against the surrounding translucent blue. In the result, the red regions are present but are more muted and less contrasted, making the high-opacity/high-value areas less clearly emphasized than expected.

Overall Assessment

The result largely matches the overall volume shape and includes cool and warm colors in the correct places, but it diverges from the ground truth in rendering balance: the blue region appears too opaque and the midrange grey and warm hotspots are less distinct. This reduces clarity of cool/mild/warm separation and weakens the intended opacity ramp effect.

📊 Detailed Metrics

Visualization Quality

20/30

Output Generation

5/5

Efficiency

4/10

Input Tokens

433,450

Output Tokens

7,014

Total Tokens

440,464

Total Cost

$2.2725

📝 Bernard

34/45 (75.6%)

📋 Task Description

Load the Rayleigh-Benard convection vector field from "Bernard/data/Bernard_128x32x64_float32_scalar3.raw", the information about this dataset: Rayleigh-Benard Convection (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 128x32x64 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create four streamline sets using "Stream Tracer" filters with "Point Cloud" seed type, each with 100 seed points and radius 12.7: - Streamline 1: Seed center at [30.69, 14.61, 47.99]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid blue (RGB: 0.0, 0.67, 1.0). - Streamline 2: Seed center at [91.10, 14.65, 45.70]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid orange (RGB: 1.0, 0.33, 0.0). - Streamline 3: Seed center at [31.87, 12.76, 15.89]. Apply a "Tube" filter (radius 0.3, 12 sides). Color by velocity magnitude using the 'Cool to Warm (Diverging)' colormap. - Streamline 4: Seed center at [92.09, 10.50, 15.32]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid green (RGB: 0.33, 0.67, 0.0). In the pipeline browser panel, hide all stream tracers and only show the tube filters. Use a white background. Render at 1280x1280. Do not show a color bar. Set the viewpoint parameters as: [-81.99, -141.45, 89.86] to position; [65.58, 26.29, 28.48] to focal point; [0.18, 0.20, 0.96] to camera up direction. Save the visualization image as "Bernard/results/{agent_mode}/Bernard.png". (Optional, but must save if use paraview) Save the paraview state as "Bernard/results/{agent_mode}/Bernard.pvsm". (Optional, but must save if use pvpython script) Save the python script as "Bernard/results/{agent_mode}/Bernard.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

24/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Streamline Grouping: Are there four visually separate streamline clusters arranged in a 2x2 grid pattern, similar to the ground truth?

Judge's Assessment: Ground truth shows four distinct streamline/tube clusters arranged as a clear 2x2 block (blue top-left, orange top-right, magnitude-colored bottom-left, green bottom-right). The result image also shows four separate clusters in the same 2x2 arrangement with similar relative placement and spacing. Minor differences: the result clusters appear slightly more tightly packed/overlapping at the boundaries (especially near the center seam), but the grouping is still clearly correct.

Goal 2

6/10

Criterion: Color Assignment: Are the four streamline groups colored in distinct colors (blue, orange, magnitude-mapped, and green), matching the ground truth color scheme?

Judge's Assessment: In the ground truth, three groups are solid-colored (blue, orange, green) and the bottom-left group is colored by velocity magnitude using a cool-to-warm diverging map (visible as a red-dominant warm range with some variation). In the result, the top-left is blue, top-right is orange, and bottom-right is green as expected. However, the bottom-left group appears largely a uniform dark red/maroon with little to no visible cool-to-warm variation, making it look like a solid color rather than magnitude-mapped (or the mapping range is compressed so variation is not apparent). Thus the distinct 'magnitude-mapped' appearance does not match well.

Goal 3

9/10

Criterion: Convection Cell Structure: Do the streamlines within each group show circular or helical looping patterns characteristic of convection cells?

Judge's Assessment: Both ground truth and result show dense, looping/helical streamline tubes within each cluster, consistent with convection-cell circulation. The overall swirling structure and layered loops match closely. Minor differences are limited to density/occlusion (result looks slightly denser and more shadowed), but the convection-cell patterns are clearly present in all four groups.

Overall Assessment

The result matches the ground truth well in terms of having four convection-cell streamline clusters arranged in a 2x2 layout, and the looping/helical structure is strongly consistent. The main discrepancy is the third (bottom-left) group’s coloring: it does not visibly exhibit the cool-to-warm magnitude colormap behavior seen in the ground truth, appearing mostly uniform red instead.

📊 Detailed Metrics

Visualization Quality

24/30

Output Generation

5/5

Efficiency

5/10

Input Tokens

330,913

Output Tokens

7,978

Total Tokens

338,891

Total Cost

$1.7742

📝 bonsai

⚠️ LOW SCORE

24/55 (43.6%)

📋 Task Description

Task: Load the bonsai dataset from "bonsai/data/bonsai_256x256x256_uint8.raw", the information about this dataset: Bonsai (Scalar) Data Scalar Type: unsigned char Data Byte Order: little Endian Data Spacing: 1x1x1 Data Extent: 256x256x256 Then visualize it with volume rendering, modify the transfer function and reach the visualization goal as: "A potted tree with brown pot silver branch and golden leaves." Please think step by step and make sure to fulfill all the visualization goals mentioned above. Use a white background. Render at 1280x1280. Do not show a color bar or coordinate axes. Set the viewpoint parameters as: [-765.09, 413.55, 487.84] to position; [-22.76, 153.30, 157.32] to focal point; [0.30, 0.95, -0.07] to camera up direction. Save the visualization image as "bonsai/results/{agent_mode}/bonsai.png". (Optional, but must save if use paraview) Save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm". (Optional, but must save if use pvpython script) Save the python script as "bonsai/results/{agent_mode}/bonsai.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

17/40

Goals

4

Points/Goal

10

Goal 1

5/10

Criterion: Overall Visualization Goal: How well does the result achieve the overall goal of showing a potted tree with the specified colors?

Judge's Assessment: Ground truth shows a clear potted bonsai with a brown pot, silver/whitish trunk, and distinctly golden leaves. The result shows the bonsai form and similar viewpoint/background, but the color mapping is substantially different: the pot appears yellow-green, the canopy is mostly dark gray/brown rather than golden, and overall the intended tri-color semantic separation is not achieved.

Goal 2

2/10

Criterion: Brown Pot Visualization: Does the result show the pot portion in brown color?

Judge's Assessment: In the ground truth, the pot is clearly brown. In the result, the pot is predominantly yellow/green with some gray shading, not matching the brown appearance.

Goal 3

7/10

Criterion: Silver Branch Visualization: Does the result show the branch/trunk portion in silver color?

Judge's Assessment: The ground truth trunk/branches are silver/whitish. In the result, the main trunk is light gray and reads as somewhat silvery, though the upper branches blend into darker tones and are less distinctly silver than in the ground truth.

Goal 4

3/10

Criterion: Golden Leaves Visualization: Does the result show the leaves portion in golden color?

Judge's Assessment: Ground truth leaves are strongly golden/orange and visually dominant in the canopy. In the result, the canopy is mostly dark gray/brown with only minimal warm tinting, so the golden-leaves goal is largely not met.

Overall Assessment

The result captures the overall bonsai shape and uses a white background with a similar camera view, but the transfer function/colors do not match the target: the pot is not brown (appears yellow-green) and the leaves are not golden (appear dark/gray). The trunk is the closest match, appearing light gray/silver-ish, but overall the intended brown/silver/gold separation is significantly off compared to the ground truth.

📊 Detailed Metrics

Visualization Quality

17/40

Output Generation

5/5

Efficiency

2/10

PSNR

20.32 dB

SSIM

0.9153

LPIPS

0.1127

Input Tokens

801,186

Output Tokens

8,772

Total Tokens

809,958

Total Cost

$4.1375

📝 carp

47/65 (72.3%)

📋 Task Description

Task: Load the carp dataset from "carp/data/carp_256x256x512_uint16.raw", the information about this dataset: Carp (Scalar) Data Scalar Type: unsigned short Data Byte Order: little Endian Data Spacing: 0.78125x0.390625x1 Data Extent: 256x256x512 Instructions: 1. Load the dataset into ParaView. 2. Apply volume rendering to visualize the carp skeleton. 3. Adjust the transfer function to highlight only the bony structures with the original bone color. 4. Optimize the viewpoint to display the full skeleton, ensuring the head, spine, and fins are all clearly visible in a single frame. 5. Analyze the visualization and answer the following questions: Q1: Which of the following options correctly describes the fins visible in the carp skeleton visualization? A. 5 fins: 1 dorsal, 2 pectoral, 2 pelvic B. 6 fins: 1 dorsal, 2 pectoral, 2 pelvic, 1 caudal C. 7 fins: 1 dorsal, 2 pectoral, 2 pelvic, 1 anal, 1 caudal D. 8 fins: 2 dorsal, 2 pectoral, 2 pelvic, 1 anal, 1 caudal Q2: Based on the visualization, what is the approximate ratio of skull length to total body length? A. ~15% B. ~22% C. ~30% D. ~40% 6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. 7. Set the viewpoint parameters as: [265.81, 1024.69, 131.23] to position; [141.24, 216.61, 243.16] to focal point; [0.99, -0.14, 0.07] to camera up direction. 8. Save your work: Save the visualization image as "carp/results/{agent_mode}/carp.png". Save the answers to the analysis questions in plain text as "carp/results/{agent_mode}/answers.txt". (Optional, but must save if use paraview) Save the paraview state as "carp/results/{agent_mode}/carp.pvsm". (Optional, but must save if use pvpython script) Save the python script as "carp/results/{agent_mode}/carp.py". (Optional, but must save if use VTK) Save the cxx code script as "carp/results/{agent_mode}/carp.cxx" Do not save any other files, and always save the visualization image and the text file.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

20/30

Goals

3

Points/Goal

10

Goal 1

7/10

Criterion: Overall Visualization Goal: Does the result match the ground truth visualization of the carp skeleton?

Judge's Assessment: The result shows a full carp skeleton in side view on a white background, matching the overall intent of the ground truth (volume-rendered bony anatomy with head at left and tail at right). However, the result is noticeably fainter and more washed out than the ground truth, and the overall contrast/opacity of bone is lower, making the visualization less similar in appearance.

Goal 2

6/10

Criterion: Bone Visibility: Are the bones clearly visible, similar to the ground truth? Are thin fin rays distinguishable?

Judge's Assessment: Major skeletal elements (skull, spine, ribs) are visible, but bone visibility is weaker than in the ground truth. Thin structures—especially fin rays and distal tail rays—are less distinct and partially lost due to low opacity/contrast. The ground truth has clearer, more continuous fin-ray depiction and stronger bone definition.

Goal 3

7/10

Criterion: Skeletal Structure: Is the entire carp skeleton (head, spine, ribs, fins, tail) visible and similar in appearance to the ground truth?

Judge's Assessment: The entire skeleton is present (head, vertebral column, rib cage, dorsal fin rays, paired fins, and caudal fin). The pose/viewpoint is broadly comparable to the ground truth and keeps the full fish in frame. Differences include reduced clarity of the skull details and less well-defined fins/tail rays, which makes the skeletal structure appear less complete/crisp than the reference.

Overall Assessment

The result largely achieves the correct carp skeleton volume rendering and framing, but it underperforms the ground truth in transfer-function tuning: bones are too transparent/low-contrast, causing fin rays and fine skeletal details to be less visible. Improving opacity and sharpening the bone-only transfer function would bring it closer to the reference.

📝 Text-Based Q&A Evaluation

Score

20/20 (100.0%)

Questions & Correct Answers

Q1 correct answer: C. 7 fins: 1 dorsal, 2 pectoral, 2 pelvic, 1 anal, 1 caudal

Q2 correct answer: B. ~22%

Agent's Answers

Q1: C
Q2: B
skull_ratio_estimate: 0.225

Judge's Evaluation

Evaluation:

Criterion 1 (Q1): The provided answer is C, which matches the correct answer C specifying 7 fins (1 dorsal, 2 pectoral, 2 pelvic, 1 anal, 1 caudal). Score: 10/10. Criterion 2 (Q2): The provided answer is B, which matches the correct answer B (~22%). The skull_ratio_estimate of 0.225 corresponds to 22.5%, consistent with ~22%. Score: 10/10.

📊 Detailed Metrics

Visualization Quality

20/30

Output Generation

5/5

Efficiency

2/10

PSNR

32.98 dB

SSIM

0.9797

LPIPS

0.0186

Text Q&A Score

20/20

100.0%

Input Tokens

1,124,646

Output Tokens

13,842

Total Tokens

1,138,488

Total Cost

$5.8309

📝 chameleon_isosurface

30/45 (66.7%)

📋 Task Description

Task: Load the chameleon dataset from "chameleon_isosurface/data/chameleon_isosurface_256x256x256_float32.vtk". Generate a visualization image of 2 isosurfaces of the Chameleon scalar field dataset with the following visualization settings: 1) Create isosurfaces of Iso_1 with a value of 0.12 and Iso_2 with a value of 0.45 2) Assign RGB color of [0.0, 1.0, 0.0] to Iso_1, and color of [1.0, 1.0, 1.0] to Iso_2 3) Assign opacity of 0.1 to Iso_1, and opacity of 0.99 to Iso_2 4) Set the lighting parameter as: 0.1 to Ambient; 0.7 to Diffuse; 0.6 to Specular 5) Set the viewpoint parameters as: [600, 0, 0] to position; [0, 0, 0] to focal point; [0, -1, 0] to camera up direction 6) White background 7) Visualization image resolution is 1024x1024 8) Don't show color/scalar bar or coordinate axes. Save the visualization image as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.png". (Optional, but must save if use paraview) Save the paraview state as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.pvsm". (Optional, but must save if use pvpython script) Save the python script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.py". (Optional, but must save if use VTK) Save the cxx code script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

23/30

Goals

3

Points/Goal

10

Goal 1

8/10

Criterion: Does the result present 2 isosurfaces, one showing the inner part of the chameleon and one showing the outer part of the chameleon?

Judge's Assessment: The ground truth shows two distinct isosurfaces: a translucent green outer skin and an opaque white/gray inner skeletal structure. The result image also clearly shows an outer green shell and an inner bright skeletal/bony structure with similar overall geometry and visibility. However, the result differs in overall rendering/contrast (notably the background and brightness), making the separation and appearance less faithful to the ground truth even though the two-surface concept is present.

Goal 2

7/10

Criterion: Is the skin of the Chameleon object of green color?

Judge's Assessment: In the ground truth, the chameleon skin appears as a light translucent green layer. In the result, the outer surface is green and translucent as well, but it appears more saturated and the overall scene is brighter/washed in places, which reduces the match to the ground truth’s subtler green skin appearance.

Goal 3

8/10

Criterion: Is the bone of the Chameleon object of white color?

Judge's Assessment: The ground truth bone/inner structure is rendered as a light gray/white opaque surface. The result shows the internal structure as very bright white, clearly conveying the bone isosurface. Minor mismatch comes from the stronger whiteness/overexposure and different overall lighting/background compared to the ground truth.

Overall Assessment

The result largely captures the intended visualization: two isosurfaces with a green translucent outer skin and a white inner skeletal structure. The main discrepancies versus the ground truth are the background (black instead of white) and overall brightness/contrast, which affects how closely the colors and surface separation match the reference.

📊 Detailed Metrics

Visualization Quality

23/30

Output Generation

5/5

Efficiency

2/10

Input Tokens

843,336

Output Tokens

10,178

Total Tokens

853,514

Total Cost

$4.3693

📝 chart-opacity

43/55 (78.2%)

📋 Task Description

Create a wavelet object. Create a plot over line chart from the wavelet with three paths: arc_length, Points_Z, and RTData variables with opacity for arc_length 1 and opacity for Points_Z and RTData 0.3. Save the visualization image as "chart-opacity/results/{agent_mode}/chart-opacity.png". (Optional, but must save if use paraview) Save the paraview state as "chart-opacity/results/{agent_mode}/chart-opacity.pvsm". (Optional, but must save if use python script) Save the python script as "chart-opacity/results/{agent_mode}/chart-opacity.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

33/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Chart Generation: Is the plot over line chart properly created from the wavelet data?

Judge's Assessment: The result shows a proper plot-over-line style chart with three line series matching the overall shapes seen in the ground truth (arc_length increasing linearly, Points_Z slightly increasing near zero, RTData large varying curve). The main difference is formatting/layout (larger canvas and fonts), not the chart construction itself.

Goal 2

10/10

Criterion: Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted, showing all three specified variables and distinguishable in the chart?

Judge's Assessment: All three required variables (arc_length, Points_Z, RTData) are present and clearly distinguishable by color and legend entries, matching the ground truth series content.

Goal 3

6/10

Criterion: Opacity Settings: Is the arc_length variable displayed with full opacity (1.0) while Points_Z and RTData show reduced opacity (0.3)?

Judge's Assessment: In the ground truth, arc_length appears fully opaque while Points_Z and RTData are visibly more transparent (fainter). In the result, Points_Z and RTData do not appear as reduced-opacity relative to arc_length to the same degree; they look closer to normal opacity, so the 0.3 opacity requirement is only partially met.

Goal 4

8/10

Criterion: Chart Clarity: Does the chart provide clear visualization of the data trends with appropriate axis scaling and readable formatting?

Judge's Assessment: The result chart is clear with readable axes and legend, and the scaling matches the ground truth (x ~0–35, y ~-100–300). However, the oversized fonts/legend and thicker visual emphasis slightly reduce the similarity to the ground truth’s cleaner, more compact presentation.

Overall Assessment

The generated visualization correctly plots the three requested variables with appropriate trends and scaling, closely matching the ground truth in content. The main shortcoming is the opacity styling: Points_Z and RTData are not as visibly translucent as in the reference, making the opacity requirement the weakest match.

📊 Detailed Metrics

Visualization Quality

33/40

Output Generation

5/5

Efficiency

5/10

Input Tokens

377,040

Output Tokens

3,808

Total Tokens

380,848

Total Cost

$1.9423

📝 climate

27/45 (60.0%)

📋 Task Description

I would like to use ParaView to visualize a dataset of ocean currents. Read in the file named "climate/data/climate.vtp". Apply a calculator filter to compute the following function: (-velocity_X*sin(coordsX*0.0174533) + velocity_Y*cos(coordsX*0.0174533)) * iHat + (-velocity_X * sin(coordsY*0.0174533) * cos(coordsX*0.0174533) - velocity_Y * sin(coordsY*0.0174533) * sin(coordsX*0.0174533) + velocity_Z * cos(coordsY*0.0174533)) * jHat + 0*kHat Render the computed values using a tube filter with 0.05 as the tube radius. Color the tubes by the magnitude of the velocity. Light the tubes with the maximum shininess and include normals in the lighting. Add cone glyphs to show the direction of the velocity. The glyphs are composed of 10 polygons, having a radius 0 0.15, a height of 0.5, and a scaling factor of 0.5. View the result in the -z direction. Adjust the view so that the tubes occupy the 90% of the image. Save a screenshot of the result, 2294 x 1440 pixels, white background, in the filename "climate/results/{agent_mode}/climate.png". (Optional, but must save if use paraview) Save the paraview state as "climate/results/{agent_mode}/climate.pvsm". (Optional, but must save if use python script) Save the python script as "climate/results/{agent_mode}/climate.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

21/30

Goals

3

Points/Goal

10

Goal 1

8/10

Criterion: Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?

Judge's Assessment: The result shows the same tube-based streamline geometry as the ground truth, with a similar apparent tube thickness and a blue-to-red colormap indicating velocity magnitude (including the higher-magnitude orange/red region on the left). Lighting/specular highlights look comparable, suggesting normals and shininess are largely correct. Main mismatch: the color legend/transfer function presentation differs (large, intrusive color bar overlay in the result), which affects the overall visual match though not the tube rendering itself.

Goal 2

7/10

Criterion: Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?

Judge's Assessment: Cone glyphs indicating direction are present in both and appear similarly distributed and oriented along the flow. However, in the result they read slightly heavier/more visually dominant (likely due to rendering/scale or overlap), and the overall glyph appearance is less clean because of the large legend overlay. Direction depiction is broadly consistent with the ground truth, but not a near-identical match.

Goal 3

6/10

Criterion: View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?

Judge's Assessment: Both images are viewed from the -Z direction with a white background and similar framing of the circulation pattern. The result does not match the ground truth’s view composition because the color legend is much larger and placed over the data, and there is also an extra large white numeric overlay near the right side. These overlays reduce the effective data occupancy and deviate from the expected clean layout.

Overall Assessment

The core visualization (tubes colored by velocity magnitude with directional cone glyphs, viewed from -Z on a white background) matches the ground truth well. The main discrepancies are presentation/layout issues: an oversized, centrally placed color bar and an additional numeric overlay in the result that are not present in the ground truth, making the final render notably less faithful despite similar underlying geometry and coloring.

📊 Detailed Metrics

Visualization Quality

21/30

Output Generation

5/5

Efficiency

1/10

Input Tokens

1,387,830

Output Tokens

14,698

Total Tokens

1,402,528

Total Cost

$7.1596

📝 color-blocks

⚠️ LOW SCORE

25/55 (45.5%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Set the background to a blue-gray palette. Read the file "color-blocks/data/color-blocks.ex2". This is a multiblock dataset. Color the dataset by the vtkBlockColors field. Retrieve the color map for vtkBlockColors. Retrieve the opacity transfer function for vtkBlockColors. Retrieve the 2D transfer function for vtkBlockColors. Set block coloring for the block at /IOSS/element_blocks/block_2 using the variable ACCL on the x component of the points. Rescale the block's color and opacity maps to match the current data range of block_2. Retrieve the color transfer function for the ACCL variable of block_2. Enable the color bar for block_2. Apply a cool to warm color preset to the color map for block_2. Set the camera to look down the -y direction and to see the entire dataset. Save the visualization image as "color-blocks/results/{agent_mode}/color-blocks.png". (Optional, but must save if use paraview) Save the paraview state as "color-blocks/results/{agent_mode}/color-blocks.pvsm". (Optional, but must save if use python script) Save the python script as "color-blocks/results/{agent_mode}/color-blocks.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

20/40

Goals

4

Points/Goal

10

Goal 1

6/10

Criterion: Block Color Mapping: Is the dataset properly colored by vtkBlockColors field with distinct block visualization?

Judge's Assessment: Ground truth shows a multiblock dataset where most blocks are neutral/gray while the selected top block is colored by a scalar field (ACCL X), implying block-based coloring is in effect. The result image does show distinct blocks (a gray cylinder and a separate top rectangular block), but the top block is rendered as a single solid red color rather than showing the expected per-point/per-cell variation. This suggests the overall block coloring/multiblock handling is only partially correct compared to ground truth.

Goal 2

3/10

Criterion: Individual Block Coloring: Is block_2 correctly colored using the x component of the ACCL variable with appropriate scaling?

Judge's Assessment: In the ground truth, block_2 (the top rectangular block) is colored with a cool-to-warm gradient pattern (visible red/blue banding), consistent with ACCL x-component variation and rescaled range. In the result, the top block is uniformly red with no visible gradient/variation, indicating block_2 is not correctly colored by ACCL (x component) or the mapping is not applied/rescaled properly.

Goal 3

4/10

Criterion: Color Transfer Functions: Are the color transfer functions properly applied with cool to warm coloring for the ACCL variable?

Judge's Assessment: Ground truth uses a cool-to-warm transfer function with both blue and red present on block_2, and the color bar shows a range including negative to positive values (e.g., -5.0e-07 to 3.2e-07) labeled 'ACCL X'. The result does show a cool-to-warm style color bar, but it is labeled 'ACCL0' and spans 0 to 1, and the rendered data does not exhibit the expected diverging colors on the block. This indicates the intended transfer function/preset and scaling are not correctly applied to the ACCL x-component for block_2.

Goal 4

7/10

Criterion: View Configuration: Is the dataset displayed from the -y direction with blue-gray background and visible color bar legend?

Judge's Assessment: Background in the result is a blue-gray tone similar to the ground truth, and a color bar is visible. The camera orientation appears consistent with looking along approximately the -y direction (axis triad shows Y pointing toward the viewer), and the dataset is fully in view. However, the color bar labeling differs (ACCL0 vs ACCL X) and the overall framing/zoom differs somewhat from the ground truth.

Overall Assessment

The result matches the requested background, general view direction, and includes a color legend, but it fails to reproduce the key scientific visualization intent: block_2 should be colored by the ACCL x-component with a cool-to-warm diverging map and correct data-range scaling. Instead, block_2 appears as a uniform solid red and the legend range/labeling does not match the ground truth.

📊 Detailed Metrics

Visualization Quality

20/40

Output Generation

5/5

Efficiency

0/10

Input Tokens

1,712,414

Output Tokens

15,483

Total Tokens

1,727,897

Total Cost

$8.7943

📝 color-data

37/45 (82.2%)

📋 Task Description

Create a wavelet object. Create a new calculator with the function 'RTData*iHat + ln(RTData)*jHat + coordsZ*kHat'. Get a color transfer function/color map and opacity transfer function/opacity map for the result of the calculation, scaling the color and/or opacity maps to the data range. For a surface representation, color by the x coordinate of the result using a cool to warm color map, show the color bar/color legend, and save a screenshot of size 1158 x 833 pixels in "color-data/results/{agent_mode}/color-data.png". (Optional, but must save if use paraview) Save the paraview state as "color-data/results/{agent_mode}/color-data.pvsm". (Optional, but must save if use python script) Save the python script as "color-data/results/{agent_mode}/color-data.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

27/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?

Judge's Assessment: The result uses the same cool-to-warm (blue through light/neutral to red) color transfer function as the ground truth, with a very similar data range shown on the legend (approximately 3.7e+01 to 2.8e+02). The overall hue progression and scaling appear consistent; only minor differences in background/contrast make the colors look slightly different.

Goal 2

8/10

Criterion: Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?

Judge's Assessment: The surface is colored by the x-component of the calculator output (legend label shows "Result X"), matching the intended variable. The spatial pattern (blue edges with warmer bands in the interior) matches the ground truth well. The main difference is the framing/zoom: the result shows a larger, more zoomed-in surface occupying more of the view, which slightly changes perceived gradients but not the underlying coloring intent.

Goal 3

10/10

Criterion: Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?

Judge's Assessment: A vertical color bar/legend is clearly visible on the right in the result, labeled "Result X" with matching tick marks and endpoints comparable to the ground truth. Placement and readability are effectively the same.

Overall Assessment

The result closely matches the ground truth: correct cool-to-warm colormap scaled to the data range, correct surface coloring by Result X, and a properly displayed color legend. The only notable discrepancy is the camera/framing and background color, which slightly affects visual similarity but not the required visualization elements.

📊 Detailed Metrics

Visualization Quality

27/30

Output Generation

5/5

Efficiency

5/10

Input Tokens

417,614

Output Tokens

3,817

Total Tokens

421,431

Total Cost

$2.1453

📝 crayfish_streamline

27/45 (60.0%)

📋 Task Description

Load the Crayfish flow vector field from "crayfish_streamline/data/crayfish_streamline_322x162x119_float32_scalar3.raw", the information about this dataset: Crayfish Flow (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 322x162x119 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create two streamline sets using "Stream Tracer" filters with "Point Cloud" seed type, each with 100 seed points and radius 32.2: - Streamline 1: Seed center at [107.33, 81.0, 59.5]. Apply a "Tube" filter (radius 0.5, 12 sides). Color by Velocity magnitude using a diverging colormap with the following RGB control points: - Value 0.0 -> RGB(0.231, 0.298, 0.753) (blue) - Value 0.02 -> RGB(0.865, 0.865, 0.865) (white) - Value 0.15 -> RGB(0.706, 0.016, 0.149) (red) - Streamline 2: Seed center at [214.67, 81.0, 59.5]. Apply a "Tube" filter (radius 0.5, 12 sides). Color by Velocity magnitude using the same colormap. Show the dataset bounding box as an outline (black). In the pipeline browser panel, hide all stream tracers and only show the tube filters and the outline. Use a white background. Render at 1280x1280. Set the viewpoint parameters as: [436.67, -370.55, 562.71] to position; [160.5, 80.5, 59.07] to focal point; [-0.099, 0.714, 0.693] to camera up direction Save the paraview state as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.pvsm". Save the visualization image as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.png". (Optional, if use python script) Save the python script as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

20/30

Goals

3

Points/Goal

10

Goal 1

7/10

Criterion: Overall Visualization Goal: Does the result show streamline tubes colored by velocity magnitude within a rectangular bounding box, similar to the ground truth?

Judge's Assessment: The result shows streamline tubes inside a rectangular bounding-box outline, matching the overall intended content and camera framing reasonably well. However, the background is dark/gray instead of white, and the outline appears heavier/darker than in the ground truth, which changes the overall look.

Goal 2

6/10

Criterion: Streamline Clusters: Are there two distinct clusters that matches the ground truth layout?

Judge's Assessment: Two main streamline regions are present (left and right), broadly consistent with the ground truth’s two-cluster structure. That said, in the result the two sets appear more blended into a continuous band across the center, making the separation between the two seeded clusters less distinct than in the ground truth.

Goal 3

7/10

Criterion: Color Mapping: Are the tubes colored by velocity magnitude using a blue-white-red diverging colormap, with a similar distribution as the ground truth?

Judge's Assessment: The tubes are colored with a blue–white–red diverging scheme and show a similar qualitative distribution (blue on outer/low-speed regions, more white/red in higher-speed core areas). Differences in overall brightness/contrast (likely from background/lighting) make the white midpoint and reds look less like the ground truth, but the mapping is largely correct.

Overall Assessment

The visualization largely captures the required elements: two streamline tube sets within an outline box and colored by velocity magnitude with a diverging palette. The main mismatches are the non-white background and reduced visual separation between the two streamline clusters, plus some contrast differences affecting perceived colormap fidelity.

📊 Detailed Metrics

Visualization Quality

20/30

Output Generation

5/5

Efficiency

2/10

PSNR

18.24 dB

SSIM

0.8982

LPIPS

0.1064

Input Tokens

886,139

Output Tokens

8,153

Total Tokens

894,292

Total Cost

$4.5530

📝 engine

⚠️ LOW SCORE

18/55 (32.7%)

📋 Task Description

Task: Load the vortex dataset from "engine/data/engine_256x256x128_uint8.raw", the information about this dataset: engine (Scalar) Data Scalar Type: float Data Byte Order: little Endian Data Extent: 256x256x128 Number of Scalar Components: 1 Instructions: 1. Load the dataset into ParaView. 2. Apply the volume rendering to visualize the engine dataset 3. Adjust the transfer function, let the outer part more transparent and the inner part more solid. Use light blue for the outer part and orange for the inner part. 4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. 5. Set the viewpoint parameters as: [-184.58, 109.48, -431.72] to position; [134.05, 105.62, 88.92] to focal point; [0.01, 1.0, -0.001] to camera up direction. 6. Save your work: Save the visualization image as "engine/results/{agent_mode}/engine.png". (Optional, but must save if use paraview) Save the paraview state as "engine/results/{agent_mode}/engine.pvsm". (Optional, but must save if use pvpython script) Save the python script as "engine/results/{agent_mode}/engine.py". (Optional, but must save if use VTK) Save the cxx code script as "engine/results/{agent_mode}/engine.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

11/40

Goals

4

Points/Goal

10

Goal 1

4/10

Criterion: Overall Visualization Goal: How well does the result use volume rendering to clearly present the internal and external structures of the engine dataset?

Judge's Assessment: Ground truth shows a clean volume rendering with a translucent light-blue exterior and clearly visible orange internal components on a pure white background. The result image looks more like a shaded/edge-enhanced rendering with heavy surface emphasis and grainy appearance; the volume effect is much less apparent and the internal structures are not revealed in the same clear way.

Goal 2

3/10

Criterion: Structural Clarity: Does the visualization emphasize depth so that the outer layers do not obscure the inner structures?

Judge's Assessment: In the ground truth, depth layering is strong: the outer shell is transparent enough that inner orange parts (cylindrical features) are clearly visible. In the result, the outer material appears largely opaque and textured, obscuring internal structures; depth cues come mostly from surface shading rather than volumetric translucency.

Goal 3

2/10

Criterion: Transfer Function Transparency: Is the outer region rendered with higher transparency and the inner region more solid, achieving a clear layering effect?

Judge's Assessment: Ground truth achieves the requested transparency relationship (outer very transparent, inner more solid). The result does not: the exterior is not rendered as a light, transparent shell and instead dominates the view, with only small hints of brighter interior regions.

Goal 4

2/10

Criterion: Transfer Function Color Mapping: Are colors correctly assigned so that the outer part is light blue and the inner part is orange, enhancing structural contrast?

Judge's Assessment: Ground truth uses light blue for the outer region and orange for the inner region. The result uses mostly tan/gray for the bulk of the object with limited orange highlights; the light-blue outer shell appearance is missing, so the intended color separation is not met.

Overall Assessment

Compared to the ground truth, the result fails to reproduce the key volume-rendering look: the outer shell is not light-blue and transparent, and the inner orange structures are not clearly exposed. The rendering appears more surface/edge shaded with different color mapping and insufficient transparency layering.

📊 Detailed Metrics

Visualization Quality

11/40

Output Generation

5/5

Efficiency

2/10

Input Tokens

994,134

Output Tokens

11,350

Total Tokens

1,005,484

Total Cost

$5.1409

📝 export-gltf

46/55 (83.6%)

📋 Task Description

Create a wavelet object. Create a surface rendering of the wavelet object and color by RTData. Scale the color map to the data, and don't display the color bar or the orientation axes. Export the view to "export-gltf/results/{agent_mode}/ExportedGLTF.gltf". Next load the file "export-gltf/results/{agent_mode}/ExportedGLTF.gltf" and display it as a surface. Color this object by TEXCOORD_0. Scale the color map to the data, and don't display the color bar or the orientation axes. Use the 'Cool to Warm' colormap. Set the background color to white. Save the visualization image as "export-gltf/results/{agent_mode}/export-gltf.png". (Optional, but must save if use paraview) Save the paraview state as "export-gltf/results/{agent_mode}/export-gltf.pvsm". (Optional, but must save if use python script) Save the python script as "export-gltf/results/{agent_mode}/export-gltf.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

38/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: GLTF Export Quality: Is the wavelet object properly exported to GLTF format with correct surface representation and RTData coloring?

Judge's Assessment: The result shows the expected wavelet surface appearance with the characteristic banded structure and smooth shading consistent with a properly exported surface. While we cannot directly verify the intermediate RTData-colored export step from the final image alone, the successful presence of coherent texture-like coloring suggests the GLTF export preserved surface attributes well. Minor differences in sharpness/contrast vs. ground truth are visible but do not indicate a major export issue.

Goal 2

10/10

Criterion: GLTF Import and Display: Is the exported GLTF file successfully loaded and displayed as a surface with proper geometry?

Judge's Assessment: The imported GLTF is clearly displayed as a single square surface with the same geometry and framing as the ground truth. No missing geometry, triangulation artifacts, or incorrect representation are apparent.

Goal 3

9/10

Criterion: Texture Coordinate Coloring: Is the imported GLTF object correctly colored by TEXCOORD_0 with Cool to Warm colormap?

Judge's Assessment: Coloring matches the ground truth’s Cool-to-Warm style (blue outer regions, warm/red central bands) and appears scaled appropriately to the data range. The pattern alignment is very similar; slight differences in saturation/contrast and smoothness compared to the ground truth prevent a perfect match.

Goal 4

10/10

Criterion: Clean Presentation: Are the color bar and orientation axes properly hidden for a clean visualization appearance?

Judge's Assessment: Clean presentation matches the ground truth: white background, no visible color bar, and no orientation axes.

Overall Assessment

The result closely matches the ground truth: correct wavelet surface geometry after GLTF import, correct TEXCOORD_0-based cool-to-warm coloring, and a clean white-background render with no extra UI elements. Only minor color/contrast differences are observed.

📊 Detailed Metrics

Visualization Quality

38/40

Output Generation

5/5

Efficiency

3/10

Input Tokens

667,557

Output Tokens

6,053

Total Tokens

673,610

Total Cost

$3.4286

📝 foot

25/45 (55.6%)

📋 Task Description

Task: Load the Foot dataset from "foot/data/foot_256x256x256_uint8.raw", the information about this dataset: Foot Description: Rotational C-arm x-ray scan of a human foot. Tissue and bone are present in the dataset. Data Type: uint8 Data Byte Order: little Endian Data Spacing: 1x1x1 Data Extent: 256x256x256 Data loading is very important, make sure you correctly load the dataset according to their features. Visualize the anatomical structures: 1. Apply volume rendering with an X-ray transfer function that distinguishes soft tissues and bones. Bones with darker color, and soft tissue with lighter color. 2. Analyze the visualization and answer the following questions: Q1: Based on the X-ray style volume rendering of the foot dataset, which of the following best describes the visibility of bony structures? A. Both the phalanges and metatarsals are fully visible B. The phalanges are fully visible, but the metatarsals are only partially visible C. The metatarsals are fully visible, but the phalanges are only partially visible D. Neither the phalanges nor the metatarsals are clearly visible 3. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. 4. Set the viewpoint parameters as: [-576.41, -264.41, -153.48] to position; [127.5, 127.5, 127.5] to focal point; [-0.52, 0.38, 0.76] to camera up direction. 5. Save your work: Save the visualization image as "foot/results/{agent_mode}/foot.png". Save the answers to the analysis questions in plain text as "foot/results/{agent_mode}/answers.txt". (Optional, but must save if use paraview) Save the paraview state as "foot/results/{agent_mode}/foot.pvsm". (Optional, but must save if use pvpython script) Save the python script as "foot/results/{agent_mode}/foot.py". (Optional, but must save if use VTK) Save the cxx code script as "foot/results/{agent_mode}/foot.cxx" Do not save any other files, and always save the visualization image and the text file.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

18/20

Goals

2

Points/Goal

10

Goal 1

9/10

Criterion: Overall Goal: Does the visualization effectively distinguish between different tissue types in the foot dataset?

Judge's Assessment: The result image matches the ground truth very closely in how it separates and emphasizes anatomical structures: the bony elements (phalanges/metatarsals) appear as darker, denser regions while surrounding soft tissue is much lighter and more transparent. The overall visibility and contrast of the bones relative to soft tissue is essentially the same as the ground truth, with only minor differences in intensity/opacity (the result looks slightly more contrasty/darker in some bone shafts).

Goal 2

9/10

Criterion: X-ray Appearance: Does the visualization resemble an X-ray (monochrome or grayscale, transparent look, consistent lighting)?

Judge's Assessment: The rendering strongly resembles an X-ray style volume rendering: grayscale/monochrome appearance, translucent soft tissue, and darker bone attenuation on a white background. Lighting and transparency are consistent with the ground truth. Minor deviation: the result has slightly stronger darkening in the metatarsal shafts and a slightly crisper look, but it still reads as the same X-ray aesthetic.

Overall Assessment

Overall, the result is an excellent match to the ground truth: correct X-ray-like grayscale volume rendering on white background with clear differentiation between soft tissue (light/transparent) and bone (darker/denser). Only small contrast/opacity differences are noticeable.

📝 Text-Based Q&A Evaluation

Score

0/10 (0.0%)

Questions & Correct Answers

1. Q1 correct answer: B. The phalanges are fully visible, but the metatarsals are only partially visible

Agent's Answers

Q1: C

Judge's Evaluation

Evaluation:

Criterion 1 requires Q1 to be answered as B (phalanges fully visible, metatarsals only partially visible). The provided answer is C, which does not match the required correct answer. Therefore, it does not meet the criterion and receives 0/10.

📊 Detailed Metrics

Visualization Quality

18/20

Output Generation

5/5

Efficiency

2/10

PSNR

27.94 dB

SSIM

0.9874

LPIPS

0.0314

Text Q&A Score

0/10

0.0%

Input Tokens

864,975

Output Tokens

8,607

Total Tokens

873,582

Total Cost

$4.4540

📝 import-gltf

37/55 (67.3%)

📋 Task Description

Load the "BlueGrayBackground" palette. Read the file "import-gltf/data/import-gltf.glb" and import the nodes "/assembly/Axle", "assembly/OuterRing/Torus002", and "assembly/OuterRing/MiddleRing/InnerRing". Set the layout size to 300x300 pixels. Point the camera in the positive Y direction and zoom to fit. Make sure all views are rendered, then save a screenshot to "import-gltf/results/{agent_mode}/import-gltf.png". (Optional, but must save if use paraview) Save the paraview state as "import-gltf/results/{agent_mode}/import-gltf.pvsm". (Optional, but must save if use python script) Save the python script as "import-gltf/results/{agent_mode}/import-gltf.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

32/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: GLTF Import Success: Are the specified GLTF nodes properly imported and displayed as separate geometric components?

Judge's Assessment: The result image shows the GLTF geometry successfully imported and rendered as distinct components: a large outer ring, a smaller inner ring, and a long thin axle/rod passing horizontally through the center. This matches the ground truth structure; any differences are not indicative of import failure.

Goal 2

9/10

Criterion: Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?

Judge's Assessment: All three specified nodes appear present and visible in the result: the Axle (thin horizontal rod), Torus002 (large outer torus/ring), and InnerRing (small central ring). Their relative placement matches the ground truth. Minor differences in shading/background do not obscure any node.

Goal 3

8/10

Criterion: Camera Positioning: Is the camera positioned in the positive Y direction with appropriate zoom to fit all imported geometry? Carefully compare the camera position of GT and result images.

Judge's Assessment: Camera orientation and framing are very similar to the ground truth (viewed along +Y with the axle appearing as a horizontal line and the rings centered). The zoom-to-fit is close, though the result appears slightly differently framed/centered compared to the ground truth (subtle scale/position difference).

Goal 4

6/10

Criterion: Layout Configuration: Is the view properly sized to 300x300 pixels with correct rendering and background palette?

Judge's Assessment: Layout appears to be 300x300 in both. However, the background palette differs: the ground truth has a white background, while the result uses a dark blue-gray background consistent with 'BlueGrayBackground'. If the GT is the reference, this is a noticeable mismatch in background appearance, though rendering quality and size look correct.

Overall Assessment

Geometry import and node visibility match the ground truth well, and the camera view is largely consistent with only minor framing differences. The main discrepancy is the background color/palette compared to the ground truth, which is visually significant.

📊 Detailed Metrics

Visualization Quality

32/40

Output Generation

5/5

Efficiency

0/10

Input Tokens

1,409,072

Output Tokens

15,651

Total Tokens

1,424,723

Total Cost

$7.2801

📝 line-plot

⚠️ LOW SCORE

22/55 (40.0%)

📋 Task Description

Read the dataset in the file "line-plot/data/line-plot.ex2", and print the number of components and the range of all the variables. Show a default view of the dataset, colored by the variable Pres. Create a line plot over all the variables in the dataset, from (0,0,0) to (0,0,10). Write the values of the line plot in the file "line-plot/results/{agent_mode}/line-plot.csv", and save a screenshot of the line plot in "line-plot/results/{agent_mode}/line-plot.png". (Optional, but must save if use paraview) Save the paraview state as "line-plot/results/{agent_mode}/line-plot.pvsm". (Optional, but must save if use python script) Save the python script as "line-plot/results/{agent_mode}/line-plot.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

15/40

Goals

4

Points/Goal

10

Goal 1

4/10

Criterion: Line Visualization Quality: Are multiple distinct lines clearly visible and properly rendered showing the evolution of different variables along the specified path?

Judge's Assessment: Ground truth shows multiple variable curves along the line (most near zero plus a prominent decaying curve). The result plot is dominated by a large step-like black curve (likely an ID field) and one decaying curve; most other variables are not visibly present (compressed at the bottom), so the multi-line visualization quality does not match the ground truth.

Goal 2

3/10

Criterion: Variable Differentiation: Are all dataset variables visually distinguishable through distinct colors or line styles with clear separation between curves?

Judge's Assessment: In the ground truth, variables are differentiated and the main varying curve is clearly visible while others are at least represented. In the result, inclusion of large-magnitude ID variables (e.g., PedigreeElementId/GlobalElementId/ObjectId) overwhelms the scale, causing most physical variables (e.g., species, Pres/Temp) to be indistinguishable/overplotted near zero. Differentiation between dataset variables is therefore poor compared to ground truth.

Goal 3

2/10

Criterion: Axis and Scale Appropriateness: Do the plot axes display appropriate ranges and scaling that effectively show the data trends and variations?

Judge's Assessment: Ground truth uses a y-range (~0–1000) appropriate for the main varying variable(s), making trends readable. The result uses a much larger y-range (~0–5500) driven by ID-like fields, which flattens the meaningful curves and obscures variation; axis scaling is not appropriate relative to the ground truth intent.

Goal 4

6/10

Criterion: Legend and Readability: Is there a clear legend identifying each variable line with readable labels and proper visual organization?

Judge's Assessment: Both plots include a legend with readable labels. However, the result legend contains extra ID variables not present in the ground truth legend, and the visual dominance of the black curve reduces practical readability/interpretability of the other legend entries.

Overall Assessment

The result does not match the ground truth line-plot appearance: it includes large-magnitude ID fields that dominate the plot, leading to inappropriate scaling and making most intended variables hard to see. Legend is present and readable, but variable visibility and differentiation are substantially worse than in the ground truth.

📊 Detailed Metrics

Visualization Quality

15/40

Output Generation

5/5

Efficiency

2/10

Input Tokens

837,945

Output Tokens

10,768

Total Tokens

848,713

Total Cost

$4.3512

📝 lobster

⚠️ LOW SCORE

24/55 (43.6%)

📋 Task Description

Task: Load the Lobster dataset from "lobster/data/lobster_301x324x56_uint8.raw", the information about this dataset: Lobster Description: CT scan of a lobster contained in a block of resin. Data Type: uint8 Data Byte Order: little Endian Data Spacing: 1x1x1.4 Data Extent: 301x324x56 Data loading is very important, make sure you correctly load the dataset according to their features. Visualize the scanned specimen: 1. Create an isosurface at the specimen boundary, find a proper isovalue to show the whole structure. 2. Use natural colors appropriate for the specimen (red-orange for lobster) 3. Analyze the visualization and answer the following questions: Q1: Based on the isosurface visualization of the lobster specimen, how many walking legs are visible? A. 6 walking legs B. 7 walking legs C. 8 walking legs D. 10 walking legs 4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. 5. Set the viewpoint parameters as: [543.52, -957.0, 1007.87] to position; [150.0, 161.5, 38.5] to focal point; [-0.15, 0.62, 0.77] to camera up direction. 6. Save your work: Save the visualization image as "lobster/results/{agent_mode}/lobster.png". Save the answers to the analysis questions in plain text as "lobster/results/{agent_mode}/answers.txt". (Optional, but must save if use paraview) Save the paraview state as "lobster/results/{agent_mode}/lobster.pvsm". (Optional, but must save if use pvpython script) Save the python script as "lobster/results/{agent_mode}/lobster.py". (Optional, but must save if use VTK) Save the cxx code script as "lobster/results/{agent_mode}/lobster.cxx" Do not save any other files, and always save the visualization image and the text file.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

17/30

Goals

3

Points/Goal

10

Goal 1

4/10

Criterion: Overall Goal: Does the visualization clearly show the structure and details of the Lobster?

Judge's Assessment: Ground truth shows the lobster filling most of the frame with a clear, optimal close view of the full specimen. The result image shows the lobster extremely small and far away in the center, with most of the image being empty background, so the overall structure is not clearly presented and does not match the intended framing/view.

Goal 2

6/10

Criterion: Boundary Clearity: Are surface details and boundaries of the lobster well-defined?

Judge's Assessment: In the ground truth, the isosurface boundary is continuous and the surface features (carapace ridges, claws, legs) are well defined. In the result, the boundary detail on the lobster itself appears reasonably extracted, but the small scale makes fine surface details and leg boundaries hard to inspect; there are also slight speckly/edge artifacts around the silhouette. Overall, boundary extraction seems mostly correct but not comparably clear to the ground truth due to the view/scale.

Goal 3

7/10

Criterion: Correct Color: Is the color of the lobster mimic a real one? (red-orange)

Judge's Assessment: The ground truth uses a natural red-orange lobster color on a white background. The result uses a similar red-orange material for the lobster, but the background is black (not white as required), which reduces similarity to the ground truth presentation even though the specimen color itself is close.

Overall Assessment

The result captures a lobster-like red-orange isosurface, but it diverges strongly from the ground truth in presentation: the camera/framing is not optimal (specimen is tiny), and the background is black instead of white. These issues significantly reduce the clarity and comparability of the visualization despite a mostly reasonable isosurface extraction.

📝 Text-Based Q&A Evaluation

Score

0/10 (0.0%)

Questions & Correct Answers

1. Q1 correct answer: B. 7 walking legs

Agent's Answers

Isovalue: 65
Q1: C (8 walking legs)

Judge's Evaluation

Evaluation:

Criterion 1 (Q1 correct answer: B, 7 walking legs): The provided answer for Q1 is "C (8 walking legs)", which does not match the required correct answer "B (7 walking legs)". Therefore, it does not meet the evaluation criterion and receives 0/10. The isovalue (65) is not evaluated by the given criterion.

📊 Detailed Metrics

Visualization Quality

17/30

Output Generation

5/5

Efficiency

2/10

Text Q&A Score

0/10

0.0%

Input Tokens

922,472

Output Tokens

10,985

Total Tokens

933,457

Total Cost

$4.7771

📝 materials

46/55 (83.6%)

📋 Task Description

Compare two datasets in two views side by side each 900 pixels wide x 1400 pixels high. Read the dataset "materials/data/materials_prediction.vtr" in the left view and "materials/data/materials_ground_truth.vtr" in the right view. In both views, convert the "Intensity" and "Phase" variables from cell to point data. In both views, take an isovolume of the "Intensity" variable in the range of [0.2, 1.0], clipped with a plane at (32.0, 32.0, 32.0) and +x normal direction. Color both views with the Viridis (matplotlib) color map for the "Phase" variable, scaled to the data range, including a colormap legend in both views. Label the left view "NN Prediction" and the right view "Ground Truth". Orient the camera to look in the (-1, 0, -1) direction, with the datasets fitting in the views. Save the visualization image as "materials/results/{agent_mode}/materials.png". (Optional, but must save if use paraview) Save the paraview state as "materials/results/{agent_mode}/materials.pvsm". (Optional, but must save if use python script) Save the python script as "materials/results/{agent_mode}/materials.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

39/40

Goals

4

Points/Goal

10

Goal 1

10/10

Criterion: Side-by-Side Comparison: Are both datasets properly displayed in side-by-side views with correct dimensions and labeling?

Judge's Assessment: Result matches the ground truth layout: two side-by-side views with the left labeled "NN Prediction" and the right labeled "Ground Truth". The split, relative sizing, and placement of the objects and annotations are consistent with the reference.

Goal 2

9/10

Criterion: Data Conversion and Filtering: Are the Intensity and Phase variables correctly converted to point data and isovolume filtering applied?

Judge's Assessment: The extracted geometry and internal banding patterns match the ground truth closely, consistent with correct cell-to-point conversion and an isovolume on Intensity in the specified range. Any differences are negligible and not visually apparent beyond tiny smoothing/aliasing variations.

Goal 3

10/10

Criterion: Clipping and Color Mapping: Is the plane clipping correctly applied and Viridis colormap properly used for Phase variable?

Judge's Assessment: Clipping appears correct (flat cut face visible in both views in the same location/orientation as ground truth). Coloring uses the Viridis-like purple-to-yellow ramp for Phase, and both views include matching Phase color legends with the same approximate range and styling.

Goal 4

10/10

Criterion: Camera and Layout: Is the camera positioned correctly in (-1, 0, -1) direction with appropriate fitting and legends visible?

Judge's Assessment: Camera orientation and framing match the ground truth: both objects are viewed from the same (-1,0,-1)-like direction, fit well in each viewport, and the colorbars and axes triads are visible and positioned consistently.

Overall Assessment

The result is an excellent match to the ground truth across layout, filtering/clipping, colormap/legends, and camera. Only imperceptible rendering differences (if any) separate it from a perfect reproduction.

📊 Detailed Metrics

Visualization Quality

39/40

Output Generation

5/5

Efficiency

2/10

Input Tokens

1,157,869

Output Tokens

11,125

Total Tokens

1,168,994

Total Cost

$5.9562

📝 mhd-magfield_streamribbon

33/55 (60.0%)

📋 Task Description

Load the MHD magnetic field dataset from "mhd-magfield_streamribbon/data/mhd-magfield_streamribbon.vti" (VTI format, 128x128x128 grid with components bx, by, bz). Generate a stream ribbon seeded from a line source along the y-axis at x=64, z=64 (from y=20 to y=108), with 30 seed points. The stream ribbon should be traced along the magnetic field lines. Color the stream ribbon by magnetic field magnitude using the 'Cool to Warm' colormap. Enable surface lighting with specular reflection for better 3D perception. Add a color bar labeled 'Magnetic Field Magnitude'. Use a white background. Set an isometric camera view. Render at 1024x1024 resolution. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.png". (Optional, but must save if use paraview) Save the paraview state as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.pvsm". (Optional, but must save if use pvpython script) Save the python script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.py". (Optional, but must save if use VTK) Save the cxx code script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

24/40

Goals

4

Points/Goal

10

Goal 1

6/10

Criterion: Overall Visualization Goal: Does the result match the ground truth stream ribbon visualization of the MHD magnetic field?

Judge's Assessment: The result clearly shows a 3D stream-ribbon rendering colored with a cool-to-warm style colormap and includes a color bar, matching the intended visualization type. However, the overall presentation differs notably from the ground truth: the result uses a black background (ground truth is white), the camera framing is much closer/zoomed-in, and the color bar styling/placement differs (ground truth has a smaller bar with numeric range ~0.089–1.1 and clean labeling).

Goal 2

7/10

Criterion: Surface Patterns: Does the stream ribbon show similar flow patterns and structures as the ground truth?

Judge's Assessment: The ribbon flow structures (tangled, looping ribbons with similar twisting/curving behavior) are broadly consistent with the ground truth. The same general bundle-like topology appears, suggesting the underlying streamline integration is similar. Differences arise mainly from the tighter crop and perspective, which makes it harder to confirm that all ribbon paths match one-to-one.

Goal 3

5/10

Criterion: Surface Coverage: Is the spatial extent and shape of the stream ribbon similar to the ground truth?

Judge's Assessment: Spatial extent and coverage do not match well: the ground truth shows the full ribbon set centered in the frame with ample white space, while the result is zoomed in and clipped at the image boundaries (large ribbons cut off at left/top). This changes the perceived overall shape and reduces visible coverage compared to the full structure in the ground truth.

Goal 4

6/10

Criterion: Color Mapping: Is the color distribution across the surface visually similar to the ground truth?

Judge's Assessment: The cool-to-warm mapping is present and the ribbons show similar blue/white/red variation, but the distribution appears different due to lighting/exposure and the close-up view (more saturated highlights and large white specular regions). The color bar range/ticks also differ from the ground truth, and the label is less clean/partially obscured, reducing similarity.

Overall Assessment

The result captures the core idea (stream ribbons colored by magnitude with lighting and a color bar), and the flow patterns look broadly similar. Major mismatches are the black background instead of white, significantly different camera framing (zoom/cropping), and differences in colorbar presentation and apparent color scaling/lighting, leading to only moderate overall agreement with the ground truth.

📊 Detailed Metrics

Visualization Quality

24/40

Output Generation

5/5

Efficiency

4/10

Input Tokens

428,623

Output Tokens

6,378

Total Tokens

435,001

Total Cost

$2.2388

📝 mhd-turbulence_pathline

39/55 (70.9%)

📋 Task Description

Load the MHD turbulence velocity field time series "mhd-turbulence_pathline/data/mhd-turbulence_pathline_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each). Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.5) to extend particle travel and smooth trajectories. Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80. Render pathlines as tubes with radius 0.3. Color by velocity magnitude using the 'Viridis (matplotlib)' colormap. Add a color bar for velocity magnitude. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Use a white background. Set an isometric camera view. Render at 1024x1024. Save the visualization image as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.png". (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.pvsm". (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.py". (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

34/40

Goals

4

Points/Goal

10

Goal 1

8/10

Criterion: Overall Visualization Goal: Does the result match the ground truth pathline visualization of the MHD turbulence velocity field?

Judge's Assessment: The result shows the same kind of tube-rendered pathlines with a Viridis-like colormap and a similar isometric view. However, the background is dark gray/blue instead of white as in the ground truth, which changes the overall appearance and contrast. The colorbar placement is similar, but the overall styling differs due to background and label presentation.

Goal 2

9/10

Criterion: Pathline Patterns: Do the pathlines show similar particle trajectories and flow structures as the ground truth?

Judge's Assessment: The pathline trajectories and overall flow structures match very closely: a tall, sparse upper cluster of curved lines and a denser, tangled lower cluster with similar bends and crossings. No major discrepancies in the shapes/patterns are apparent.

Goal 3

9/10

Criterion: Pathline Coverage: Is the spatial extent and distribution of pathlines similar to the ground truth?

Judge's Assessment: Spatial extent and distribution are highly consistent with the ground truth: pathlines occupy the same vertical span and have similar density (more concentrated near the bottom, fewer in the middle/upper region). Any differences are minimal and likely due to rendering/camera framing nuances.

Goal 4

8/10

Criterion: Color Mapping: Is the color distribution along pathlines visually similar to the ground truth?

Judge's Assessment: Color mapping along the tubes appears consistent (purple/blue low values to green/yellow higher values) and the range on the colorbar is very similar. The main difference is perceptual: the dark background alters perceived brightness/contrast of the same colors, and the colorbar labeling differs slightly (ground truth shows ticks without the prominent vertical label).

Overall Assessment

The result largely reproduces the ground-truth pathline visualization in geometry, coverage, and colormap behavior. The primary mismatch is the non-white background (and slightly different colorbar/annotation styling), which affects the overall look but not the underlying pathline computation/structure.

📊 Detailed Metrics

Visualization Quality

34/40

Output Generation

5/5

Efficiency

0/10

PSNR

22.94 dB

SSIM

0.9867

LPIPS

0.0532

Input Tokens

5,196,465

Output Tokens

27,481

Total Tokens

5,223,946

Total Cost

$26.3945

📝 mhd-turbulence_pathribbon

28/45 (62.2%)

📋 Task Description

Load the MHD turbulence velocity field time series "mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each). Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.1) for dense, smooth trajectories. Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80. Create ribbon surfaces from the pathlines using the Ribbon filter with width 1.5 and a fixed default normal to prevent twisting. Apply Smooth filter (500 iterations) and generate surface normals for smooth shading. Set surface opacity to 0.85. Color by velocity magnitude using the 'Cool to Warm' colormap (range 0.1-0.8). Add specular highlights (0.5). Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view. Render at 1024x1024. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.png". (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.pvsm". (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.py". (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

23/30

Goals

3

Points/Goal

10

Goal 1

8/10

Criterion: Surface Patterns: Does the path ribbon show similar flow patterns and structures as the ground truth?

Judge's Assessment: The result shows the same overall ribbon/pathline structures as the ground truth: a vertically oriented bundle with similar curling/looping features in the upper section and a broader, more tangled cluster near the bottom. The main flow patterns and relative placement of the prominent bends match well. Minor differences appear in fine-scale surface texture/shading (the result shows more visible banding/striations on some ribbons), but the core trajectory patterns are consistent.

Goal 2

8/10

Criterion: Surface Coverage: Is the spatial extent and shape of the path ribbon similar to the ground truth?

Judge's Assessment: Spatial extent and overall shape are very similar: the ribbons occupy a comparable vertical range and have similar lateral spread, with the same separated upper strands and denser lower region. The framing/camera appears essentially the same. Small differences in perceived thickness/opacity and the visibility of some ribbon segments (likely due to lighting/background/opacity differences) slightly change the apparent coverage, but not dramatically.

Goal 3

7/10

Criterion: Color Mapping: Is the color distribution across the surface visually similar to the ground truth?

Judge's Assessment: The cool-to-warm mapping and approximate value range look consistent (blue low speeds, red/orange high speeds in similar locations along the ribbons). However, the result’s darker background changes the perceived color balance and contrast compared to the white-background ground truth, and the ribbons appear a bit more washed/gray in midtones. The colorbar is present and similar, but overall color distribution match is somewhat reduced by the background/lighting differences.

Overall Assessment

The result closely reproduces the main path-ribbon geometry and flow structures and has largely matching spatial coverage. The biggest deviation is presentation: a dark background (instead of white) and slightly different shading/contrast, which affects perceived color similarity and surface appearance.

📊 Detailed Metrics

Visualization Quality

23/30

Output Generation

5/5

Efficiency

0/10

PSNR

21.06 dB

SSIM

0.9612

LPIPS

0.1071

Input Tokens

1,383,855

Output Tokens

12,188

Total Tokens

1,396,043

Total Cost

$7.1021

📝 mhd-turbulence_streamline

32/55 (58.2%)

📋 Task Description

Load the MHD turbulence velocity field dataset "mhd-turbulence_streamline/data/mhd-turbulence_streamline.vti" (VTI format, 128x128x128 grid). Generate 3D streamlines seeded from a line source along the z-axis at x=64, y=64 (from z=0 to z=127), with 50 seed points. Color the streamlines by velocity magnitude using the 'Turbo' colormap. Set streamline tube radius to 0.3 using the Tube filter. Add a color bar labeled 'Velocity Magnitude'. Use a white background. Set an isometric camera view. Render at 1024x1024. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.png". (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.pvsm". (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.py". (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

24/40

Goals

4

Points/Goal

10

Goal 1

6/10

Criterion: Overall Visualization Goal: Does the result match the ground truth streamline visualization of the MHD turbulence velocity field?

Judge's Assessment: The result shows 3D streamlines colored by velocity magnitude with a color bar and an isometric-like view, matching the general intent. However, the background is dark gray instead of white, and the overall streamline extent/appearance differs noticeably from the ground truth (ground truth fills a broader region with more visible tubes).

Goal 2

6/10

Criterion: Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth?

Judge's Assessment: Major flow structures are somewhat similar (a central bundle with streamlines arcing outward), but the result has a more vertically concentrated, narrower plume-like structure. The ground truth exhibits more sprawling, intertwined loops and lateral spread, especially toward the left and lower regions.

Goal 3

5/10

Criterion: Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?

Judge's Assessment: Streamline coverage is significantly reduced compared to the ground truth: fewer visible trajectories and less spatial spread. The ground truth has denser coverage and more streamlines occupying the full volume around the center, while the result appears sparser and more confined.

Goal 4

7/10

Criterion: Color Mapping: Is the color distribution along streamlines visually similar to the ground truth?

Judge's Assessment: The color mapping looks broadly consistent (Turbo-like progression with blues/greens to yellows/reds and a similar scalar range up to ~1.1). Still, the distribution differs because the result contains fewer high-magnitude (orange/red) segments and overall less variation across the scene than the ground truth.

Overall Assessment

The result captures the basic streamline visualization with magnitude coloring and a labeled color bar, but it diverges from the ground truth in key presentation and content aspects: incorrect background color, noticeably sparser and more vertically confined streamline coverage, and somewhat different overall flow pattern/spatial extent.

📊 Detailed Metrics

Visualization Quality

24/40

Output Generation

5/5

Efficiency

3/10

PSNR

16.67 dB

SSIM

0.8901

LPIPS

0.1645

Input Tokens

497,090

Output Tokens

6,083

Total Tokens

503,173

Total Cost

$2.5767

📝 miranda

37/45 (82.2%)

📋 Task Description

Task: Load the Rayleigh-Taylor Instability dataset from "miranda/data/miranda_256x256x256_float32.vtk". Generate a visualization image of the Rayleigh-Taylor Instability dataset, a time step of a density field in a simulation of the mixing transition in Rayleigh-Taylor instability, with the following visualization settings: 1) Create volume rendering 2) Set the opacity transfer function as a ramp function from value 0 to 1 of the volumetric data, assigning opacity 0 to value 0 and assigning opacity 1 to value 1. 3) Set the color transfer function following the 7 rainbow colors and assign a red color [1.0, 0.0, 0.0] to the highest value, a purple color [0.5, 0.0, 1.0] to the lowest value. 4) Set the viewpoint parameters as: [650, 650, 650] to position; [128, 128, 128] to focal point; [1, 0, 0] to camera up direction 5) Volume rendering ray casting sample distance is 0.1 6) White background 7) Visualization image resolution is 1024x1024 8) Don't show color/scalar bar or coordinate axes. Save the visualization image as "miranda/results/{agent_mode}/miranda.png". (Optional, but must save if use paraview) Save the paraview state as "miranda/results/{agent_mode}/miranda.pvsm". (Optional, but must save if use pvpython script) Save the python script as "miranda/results/{agent_mode}/miranda.py". (Optional, but must save if use VTK) Save the cxx code script as "miranda/results/{agent_mode}/miranda.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

29/30

Goals

3

Points/Goal

10

Goal 1

10/10

Criterion: Does the visualization image clearly show the regions from low to high intensity?

Judge's Assessment: The result image matches the ground truth very closely: the full rainbow range (purple/blue through green/yellow to red) is clearly visible across the volume, and the spatial distribution of low-to-high intensity regions (blue/purple concentrated toward the upper/left areas and strong red/orange in the lower/right/front regions) appears essentially identical.

Goal 2

9/10

Criterion: Does the purple region show areas with low opacity?

Judge's Assessment: Low-value regions in the result appear in purple/blue tones and are relatively faint/less visually dominant compared to the high-value red/orange regions, consistent with low opacity. Compared to the ground truth, the translucency/whitened edges in the purple areas looks extremely similar, with only negligible differences (if any) in how washed-out the very lowest values appear near the top boundary.

Goal 3

10/10

Criterion: Does the red region show areas with high opacity?

Judge's Assessment: High-value regions are rendered in strong red and appear most opaque/dense in both images. The large red mass on the right/front face and the saturated red/orange structures match the ground truth in both placement and visual prominence, indicating the high-opacity mapping is correctly reproduced.

Overall Assessment

The agent-generated visualization is an excellent match to the ground truth. The intensity progression is clear, low values are shown in purple/blue with comparatively low visual dominance (consistent with low opacity), and high values are shown in saturated red with high opacity. Any differences are minimal and do not materially affect the intended transfer-function behavior.

📊 Detailed Metrics

Visualization Quality

29/30

Output Generation

5/5

Efficiency

3/10

Input Tokens

554,847

Output Tokens

5,921

Total Tokens

560,768

Total Cost

$2.8630

📝 ml-dvr

37/55 (67.3%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Read in the file named "ml-dvr/data/ml-dvr.vtk". Generate a volume rendering using the default transfer function. Rotate the view to an isometric direction. Save a screenshot of the result in the filename "ml-dvr/results/{agent_mode}/ml-dvr.png". The rendered view and saved screenshot should be 1920 x 1080 pixels. (Optional, but must save if use paraview) Save the paraview state as "ml-dvr/results/{agent_mode}/ml-dvr.pvsm". (Optional, but must save if use python script) Save the python script as "ml-dvr/results/{agent_mode}/ml-dvr.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

26/40

Goals

4

Points/Goal

10

Goal 1

7/10

Criterion: Volume Rendering Quality: Is the volume rendering properly generated with appropriate opacity and color mapping that reveals internal structures?

Judge's Assessment: Both ground truth and result show a semi-transparent volume-rendered cube with visible internal banding/ring-like structures, indicating volume rendering is enabled and opacity is nonzero. However, the result appears more uniformly opaque/filled in the upper region and less balanced in revealing the same internal structure distribution seen in the ground truth (which shows a clearer left-right color/structure separation).

Goal 2

6/10

Criterion: Transfer Function Application: Does the default transfer function effectively highlight meaningful data features and provide good visual contrast?

Judge's Assessment: The ground truth uses a purple-to-red appearance with a strong lateral gradient, while the result uses a blue-to-red mapping with a strong vertical gradient (blue top, red bottom). This indicates the transfer function/color mapping in the result does not match the ground truth default look for this dataset (or is applied to a different scalar/range), reducing visual correspondence and contrast placement relative to the expected image.

Goal 3

6/10

Criterion: Isometric View Setup: Is the visualization displayed from an isometric viewpoint that provides a clear three-dimensional perspective of the volume?

Judge's Assessment: Both images are shown in a 3D corner-on view of the cube (an isometric-like presentation). However, the cube orientation differs: the ground truth shows a different rotation (color features concentrated toward the right face), while the result is rotated such that the red region is predominantly at the bottom and blue at the top. So it is isometric-ish but not the same isometric direction as the reference.

Goal 4

7/10

Criterion: Visual Clarity and Detail: Are the volume details clearly visible with proper lighting and shading that enhances depth perception?

Judge's Assessment: Overall clarity is good in both: smooth shading, clean background, and visible internal banding. The result is slightly less informative in matching the expected depth/feature emphasis because the color/opacity distribution is different (more uniform blue region and strong bottom red), making the internal structures less comparable to the ground truth’s feature placement.

Overall Assessment

The result successfully produces a clean volume rendering of the cube with visible internal structure and an isometric-style view, but it does not match the ground truth’s transfer function appearance and rotation. The main mismatches are the color mapping (purple-red vs blue-red and different gradient direction) and the camera orientation relative to the volume features.

📊 Detailed Metrics

Visualization Quality

26/40

Output Generation

5/5

Efficiency

6/10

Input Tokens

329,674

Output Tokens

3,986

Total Tokens

333,660

Total Cost

$1.7082

📝 ml-iso

42/55 (76.4%)

📋 Task Description

Read in the file named "ml-iso/data/ml-iso.vtk", and generate an isosurface of the variable var0 at value 0.5. Use a white background color. Save a screenshot of the result, size 1920 x 1080 pixels, in "ml-iso/results/{agent_mode}/ml-iso.png". (Optional, but must save if use paraview) Save the paraview state as "ml-iso/results/{agent_mode}/ml-iso.pvsm". (Optional, but must save if use python script) Save the python script as "ml-iso/results/{agent_mode}/ml-iso.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

32/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Isosurface Generation: Is the isosurface properly generated at the specified value (0.5) with correct topology and continuity?

Judge's Assessment: The result shows the same concentric-ring isosurface structure and overall silhouette as the ground truth (including the four corner protrusions), indicating the isosurface was generated correctly and with matching topology. Any difference in isovalue is not evident from the geometry; the main mismatch is presentation rather than surface extraction.

Goal 2

9/10

Criterion: Surface Rendering Quality: Does the isosurface display smooth surfaces with appropriate shading and lighting that reveals the 3D structure?

Judge's Assessment: Surface shading and lighting are very similar to the ground truth: smooth specular highlights and clear depth cues across the concentric ridges. No obvious faceting or poor normals; rendering quality appears essentially equivalent.

Goal 3

9/10

Criterion: Geometric Accuracy: Are the surface features geometrically correct and free from artifacts or discontinuities?

Judge's Assessment: Geometric features (ring spacing, central bulge, and edge/corner shapes) match the ground truth closely with no visible cracks, holes, or discontinuities. No apparent artifacts beyond minor framing/scale differences.

Goal 4

5/10

Criterion: Visual Presentation: Is the isosurface clearly visible with good contrast and coloring that enhances the understanding of the data structure?

Judge's Assessment: The ground truth uses a white background, while the result uses a dark gray background. This significantly changes contrast and does not meet the specified white background requirement. The isosurface remains visible, but the presentation diverges clearly from the expected look.

Overall Assessment

The extracted isosurface geometry and rendering quality closely match the ground truth, with smooth, artifact-free concentric features. The primary issue is visual presentation: the background color is incorrect (dark gray instead of white), causing a notable mismatch with the expected output.

📊 Detailed Metrics

Visualization Quality

32/40

Output Generation

5/5

Efficiency

5/10

Input Tokens

382,537

Output Tokens

3,959

Total Tokens

386,496

Total Cost

$1.9721

📝 ml-slice-iso

39/55 (70.9%)

📋 Task Description

Please generate a ParaView Python script for the following operations. Read in the file named "ml-slice-iso/data/ml-slice-iso.vtk". Slice the volume in a plane parallel to the y-z plane at x=0. Take a contour through the slice at the value 0.5. Color the contour red. Use a white background. Rotate the view to look at the +x direction. Save a screenshot of the result in the filename "ml-slice-iso/results/{agent_mode}/ml-slice-iso.png". The rendered view and saved screenshot should be 1920 x 1080 pixels. (Optional, but must save if use paraview) Save the paraview state as "ml-slice-iso/results/{agent_mode}/ml-slice-iso.pvsm". (Optional, but must save if use python script) Save the python script as "ml-slice-iso/results/{agent_mode}/ml-slice-iso.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

29/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Slice Generation: Is the y-z plane slice properly generated at x=0 position showing the correct cross-section of the volume?

Judge's Assessment: The result shows the same single contour-like curve in the same position/shape as the ground truth, consistent with a y–z slice at x=0 viewed edge-on from +x. The slice surface itself is not visible in either, but the extracted geometry matches closely, indicating the slice location/orientation is effectively correct.

Goal 2

10/10

Criterion: Contour on Slice: Are the contour lines at value

Judge's Assessment: The contour extracted from the slice matches the ground truth very closely in shape, extent, and placement, consistent with an isovalue of 0.5 on the slice.

Goal 3

1/10

Criterion: 5 correctly extracted from the slice and properly displayed?

Judge's Assessment: Ground truth contour is red, while the result contour is clearly blue/purple. This does not satisfy the required red coloring.

Goal 4

9/10

Criterion: Red Color Application: Is the contour visualization properly colored red as specified in the requirements?

Judge's Assessment: The view direction appears consistent with looking along +x (showing the slice/contour as a line in the y–z plane). Orientation triad placement differs slightly, but the camera/view alignment matches the ground truth well.

Overall Assessment

Geometry and camera match the ground truth well: the correct slice/contour appears to be generated and viewed from +x. The major failure is the contour color: it is blue in the result instead of red.

📊 Detailed Metrics

Visualization Quality

29/40

Output Generation

5/5

Efficiency

5/10

Input Tokens

331,517

Output Tokens

3,913

Total Tokens

335,430

Total Cost

$1.7163

📝 points-surf-clip

⚠️ LOW SCORE

26/55 (47.3%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Read in the file named "points-surf-clip/data/points-surf-clip.ex2". Generate an 3d Delaunay triangulation of the dataset. Clip the data with a y-z plane at x=0, keeping the -x half of the data and removing the +x half. Render the image as a wireframe. Save a screenshot of the result in the filename "points-surf-clip/results/{agent_mode}/points-surf-clip.png". The rendered view and saved screenshot should be 1920 x 1080 pixels. Use a white background color. (Optional, but must save if use paraview) Save the paraview state as "points-surf-clip/results/{agent_mode}/points-surf-clip.pvsm". (Optional, but must save if use python script) Save the python script as "points-surf-clip/results/{agent_mode}/points-surf-clip.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

16/40

Goals

4

Points/Goal

10

Goal 1

4/10

Criterion: Delaunay Triangulation Quality: Is the 3D Delaunay triangulation properly generated creating a valid mesh structure from the point data?

Judge's Assessment: Ground truth shows a full 3D Delaunay tetrahedralized volume rendered in wireframe (dense interior triangulation along the length). The result image shows only a single clipped cross-section-like shape with heavy overdraw and missing the long 3D volume appearance, suggesting the 3D Delaunay volume is not properly represented (or the view/representation is not showing the volumetric triangulation).

Goal 2

3/10

Criterion: Clipping Accuracy: Is the mesh correctly clipped by the y-z plane at x=0, with only the -x half of the data remaining visible?

Judge's Assessment: In the ground truth, clipping at x=0 leaves a long half-volume (the -x side) with a clear planar cut face at the right end. The result instead shows a quarter/sector-shaped piece without the expected long extent, indicating the clip is incorrect (wrong plane/origin and/or wrong side kept) or the camera is not showing the remaining half-volume.

Goal 3

6/10

Criterion: Wireframe Representation: Is the result displayed as a clear wireframe showing the triangulated mesh structure with visible edges?

Judge's Assessment: Both images use a wireframe-like depiction with visible edges. However, the result has very thick/overlapping white lines and looks visually saturated, reducing clarity compared to the crisp black wireframe in the ground truth.

Goal 4

3/10

Criterion: Geometric Integrity: Does the clipped wireframe maintain proper connectivity and show the expected geometric features without artifacts?

Judge's Assessment: The ground truth maintains clean connectivity and a coherent clipped half-cylinder/duct-like volume. The result shows strong artifacts/overplotting (streaky vertical/horizontal bands) and lacks the expected connected 3D structure, indicating poor geometric integrity relative to the reference.

Overall Assessment

The result does not match the expected clipped 3D Delaunay wireframe half-volume. While it is rendered in a wireframe style, the clipping outcome and overall geometry differ substantially from the ground truth and exhibit significant visual artifacts.

📊 Detailed Metrics

Visualization Quality

16/40

Output Generation

5/5

Efficiency

5/10

Input Tokens

458,652

Output Tokens

4,387

Total Tokens

463,039

Total Cost

$2.3591

📝 render-histogram

33/55 (60.0%)

📋 Task Description

Create a wavelet object and render it as a surface colored by RTDATA with a visible color bar. Rescale the colors to the data range and use the 'Cool to Warm' color map. Next, split the view horizontally to the right and create a histogram view from the wavelet RTDATA. Apply the same 'Cool to Warm' color map to the histogram. Save a screenshot of both views (wavelet rendering on the left and histogram on the right) in the file "render-histogram/results/{agent_mode}/render-histogram.png". (Optional, but must save if use paraview) Save the paraview state as "render-histogram/results/{agent_mode}/render-histogram.pvsm". (Optional, but must save if use python script) Save the python script as "render-histogram/results/{agent_mode}/render-histogram.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

28/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Wavelet Visualization: Is the wavelet object properly rendered with RTDATA coloring and visible color bar?

Judge's Assessment: Result shows the wavelet rendered as a surface/plane colored by RTData with a visible vertical color bar. The color bar range and cool-to-warm appearance match the ground truth closely. Minor differences: the wavelet appears slightly larger/zoomed and the color bar tick labeling differs a bit, but the requirement is clearly met.

Goal 2

10/10

Criterion: Split View Layout: Is the view correctly split with the wavelet visualization on the left and histogram on the right?

Judge's Assessment: Layout matches the ground truth: a horizontal split with the wavelet render on the left and the histogram view on the right in a single screenshot.

Goal 3

4/10

Criterion: Histogram Generation: Is the histogram properly generated from RTDATA showing the data distribution?

Judge's Assessment: Ground truth histogram is a filled/bar-style histogram with many bins colored by the colormap. The result instead shows a single thin gray line/outline (triangle-like distribution) with legend 'bin_values', not a proper binned histogram display of RTData counts. It conveys some distribution shape but does not match the expected histogram rendering.

Goal 4

5/10

Criterion: Color Map Consistency: Are both the wavelet visualization and histogram using the same Cool to Warm color map?

Judge's Assessment: Wavelet uses the cool-to-warm colormap correctly. However, the histogram in the result is essentially gray/monochrome (not visibly mapped with cool-to-warm across bins as in the ground truth), so colormap consistency is only partially satisfied.

Overall Assessment

The wavelet rendering and split-view layout match the ground truth very well, including a visible RTData color bar with cool-to-warm colors. The main mismatch is the histogram: it is not rendered as a colored binned histogram (appears as a gray line plot), and therefore does not apply the same cool-to-warm colormap as expected.

📊 Detailed Metrics

Visualization Quality

28/40

Output Generation

5/5

Efficiency

0/10

Input Tokens

1,932,560

Output Tokens

14,659

Total Tokens

1,947,219

Total Cost

$9.8827

📝 reset-camera-direction

45/55 (81.8%)

📋 Task Description

Create a Wavelet object, set its representation to "Surface with Edges", and set the camera direction to [0.5, 1, 0.5]. Save a screenshot to the file "reset-camera-direction/results/{agent_mode}/reset-camera-direction.png". (Optional, but must save if use paraview) Save the paraview state as "reset-camera-direction/results/{agent_mode}/reset-camera-direction.pvsm". (Optional, but must save if use python script) Save the python script as "reset-camera-direction/results/{agent_mode}/reset-camera-direction.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

34/40

Goals

4

Points/Goal

10

Goal 1

10/10

Criterion: Wavelet Creation: Is the Wavelet object properly created and displayed in the scene?

Judge's Assessment: Both ground truth and result show the standard ParaView Wavelet cube dataset rendered in the view. The object is present, centered, and fills most of the frame similarly.

Goal 2

10/10

Criterion: Surface with Edges Representation: Is the wavelet correctly displayed with "Surface with Edges" representation showing both surface and wireframe?

Judge's Assessment: The result clearly uses "Surface with Edges": solid faces with a visible blue wireframe grid overlaid, matching the ground truth’s surface+edge appearance.

Goal 3

6/10

Criterion: Camera Direction: Is the camera positioned according to the specified direction vector [0.5, 1,

Judge's Assessment: The camera direction in the result is similar (viewing a cube corner with three faces visible), but it does not match the ground truth closely: the result is more zoomed-in and the apparent orientation/tilt differs (faces occupy different proportions and the corner is framed differently), indicating the camera direction is not set to the same [0.5, 1, 0.5] view as the reference.

Goal 4

8/10

Criterion: 5]?

Judge's Assessment: Despite the camera mismatch, the wavelet structure is clearly visible and the edges are crisp. The view is slightly too close compared to ground truth, but still provides a clear 3D perspective of the cube.

Overall Assessment

Wavelet creation and the "Surface with Edges" representation match the ground truth very well. The main discrepancy is the camera: the result’s framing/tilt differs noticeably from the reference direction, though the visualization remains clear and interpretable.

📊 Detailed Metrics

Visualization Quality

34/40

Output Generation

5/5

Efficiency

6/10

Input Tokens

214,467

Output Tokens

3,283

Total Tokens

217,750

Total Cost

$1.1216

📝 richtmyer

29/45 (64.4%)

📋 Task Description

Task: Load the Richtmyer dataset from "richtmyer/data/richtmyer_256x256x240_float32.vtk". Generate a visualization image of the Richtmyer dataset, Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation, with the following visualization settings: 1) Create volume rendering 2) Set the opacity transfer function as a ramp function from value 0.05 to 1 of the volumetric data, assigning opacity 0 to value less than 0.05 and assigning opacity 1 to value 1. 3) Set the color transfer function following the 7 rainbow colors and assign a red color [1.0, 0.0, 0.0] to the highest value, a purple color [0.5, 0.0, 1.0] to the lowest value. 4) Visualization image resolution is 1024x1024 5) Set the viewpoint parameters as: [420, 420, -550] to position; [128, 128, 150] to focal point; [-1, -1, 1] to camera up direction 6) Turn on the shade and set the ambient, diffuse and specular as 1.0 7) White background. Volume rendering ray casting sample distance is 0.1 8) Don't show color/scalar bar or coordinate axes. Save the visualization image as "richtmyer/results/{agent_mode}/richtmyer.png". (Optional, but must save if use paraview) Save the paraview state as "richtmyer/results/{agent_mode}/richtmyer.pvsm". (Optional, but must save if use pvpython script) Save the python script as "richtmyer/results/{agent_mode}/richtmyer.py". (Optional, but must save if use VTK) Save the cxx code script as "richtmyer/results/{agent_mode}/richtmyer.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

22/30

Goals

3

Points/Goal

10

Goal 1

8/10

Criterion: Does the visualization show a clear surface with peaks and valleys?

Judge's Assessment: The result image shows the same overall wedge/pyramidal volume with a clearly textured, bumpy surface structure (many small peaks/valleys) similar to the ground truth. However, the result appears more uniformly shaded and darker, with less of the bright multi-hue surface detail seen in the ground truth, so the perceived peak/valley contrast is somewhat reduced though still clearly present.

Goal 2

6/10

Criterion: Are the peaks highlighted with the reddish color?

Judge's Assessment: In the ground truth, higher-value regions are frequently highlighted with warm colors (yellow/orange/red accents) across the surface, and the outer faces are strongly red. In the result, the outer faces still show red/black tones, but the main surface is dominated by blue/teal with far fewer warm (reddish) highlights on the peaks. This suggests the high-end of the colormap is underrepresented or mapped differently compared to the ground truth.

Goal 3

8/10

Criterion: Are the valleys highlighted with the bluish color?

Judge's Assessment: Valley/low-value regions in the ground truth are represented with cooler colors (cyan/blue/purple) interspersed among warmer highlights. The result strongly emphasizes cool colors (blue/teal) across much of the surface, which does correspond well to valleys/low values, though it may be overly dominant compared to the ground truth’s broader rainbow spread.

Overall Assessment

The result matches the overall geometry and the presence of a detailed peak/valley surface reasonably well. The main discrepancy is color mapping: compared to the ground truth’s balanced rainbow with noticeable warm (reddish) peak highlights, the result is heavily skewed toward blue/teal, making peaks less clearly emphasized in red while valleys remain well represented in cool colors.

📊 Detailed Metrics

Visualization Quality

22/30

Output Generation

5/5

Efficiency

2/10

Input Tokens

851,425

Output Tokens

11,043

Total Tokens

862,468

Total Cost

$4.4228

📝 rotstrat

⚠️ LOW SCORE

18/45 (40.0%)

📋 Task Description

Task: Load the rotstrat dataset from "rotstrat/data/rotstrat_256x256x256_float32.vtk". Generate a visualization image of the Rotstrat dataset, temperature field of a direct numerical simulation of rotating stratified turbulence, with the following visualization settings: 1) Create volume rendering 2) Set the opacity transfer function as a step function jumping from 0 to 1 at value 0.12 3) Set the color transfer function to assign a warm red color [0.71, 0.02, 0.15] to the highest value, a cool color [0.23, 0.29, 0.75] to the lowest value, and a grey color[0.87, 0.87, 0.87] to the midrange value 4) Set the viewpoint parameters as: [800, 128, 128] to position; [0, 128, 128] to focal point; [0, 1, 0] to camera up direction 5) Volume rendering ray casting sample distance is 0.1 6) White background 7) Visualization image resolution is 1024x1024 8) Don't show color/scalar bar or coordinate axes. Save the visualization image as "rotstrat/results/{agent_mode}/rotstrat.png". (Optional, but must save if use paraview) Save the paraview state as "rotstrat/results/{agent_mode}/rotstrat.pvsm". (Optional, but must save if use pvpython script) Save the python script as "rotstrat/results/{agent_mode}/rotstrat.py". (Optional, but must save if use VTK) Save the cxx code script as "rotstrat/results/{agent_mode}/rotstrat.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

12/30

Goals

3

Points/Goal

10

Goal 1

5/10

Criterion: Does the visualization image clearly show the shape of turbulence compared to ground truth?

Judge's Assessment: The ground truth shows high-contrast, sharply defined turbulent filaments and layered structures (with dark edge-like boundaries and clear swirling detail). The result image shows a similar overall swirling/turbulent pattern, but it is much more blurred/low-contrast and lacks the crisp filamentary texture seen in the ground truth. Additionally, the result has a large dark-gray border/background area around a smaller central rendering, which changes the overall presentation compared to the ground truth’s full-frame view.

Goal 2

4/10

Criterion: Does the visualization show the shape of a vortex in the upper right part of the image?

Judge's Assessment: In the ground truth, the upper-right region contains a distinct vortex-like swirl with clear curved bands and sharp boundaries. In the result, there is a swirl in the upper-right area, but it is significantly softer and less defined; the vortex structure is only partially discernible and does not match the clarity/shape prominence of the ground truth.

Goal 3

3/10

Criterion: Does the visualization show the shape of a vortex in the bottom left corner of the image?

Judge's Assessment: The ground truth shows a noticeable vortex feature in the bottom-left corner with strong curvature and defined streaks. In the result, the bottom-left corner region is present but the vortex is not clearly formed; the structure is washed out and lacks the distinct curled shape and boundary definition, making the corner vortex hard to identify compared to the ground truth.

Overall Assessment

The result captures the general turbulent swirling character but deviates substantially in visual sharpness/contrast and framing. Key vortex features (upper-right and bottom-left) are much less distinct than in the ground truth, suggesting differences in transfer functions, sampling/opacity behavior, or rendering settings that reduce structural definition.

📊 Detailed Metrics

Visualization Quality

12/30

Output Generation

5/5

Efficiency

1/10

Input Tokens

1,209,617

Output Tokens

12,652

Total Tokens

1,222,269

Total Cost

$6.2379

📝 rti-velocity_glyph

44/55 (80.0%)

📋 Task Description

Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_glyph/data/rti-velocity_glyph.vti" (VTI format, 128x128x128 grid). Create a slice at y=64 through the volume. Place arrow glyphs on the slice, oriented by the velocity vector. Use uniform arrow size (no magnitude scaling, scale factor 3.0). Color the arrows by velocity magnitude using the 'Viridis (matplotlib)' colormap. Use a sampling stride of 3. Add a color bar labeled 'Velocity Magnitude'. Use a white background. Set the camera to view along the negative y-axis. Render at 1024x1024. Set the viewpoint parameters as: [63.5, 250.0, 63.5] to position; [63.5, 64.0, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.png". (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.pvsm". (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.py". (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

34/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Overall Visualization Goal: Does the result match the ground truth glyph visualization of the RTI velocity field?

Judge's Assessment: The result shows the same RTI velocity glyph slice visualization as the ground truth: a dense field of arrow glyphs on a horizontal band with a white background and a Viridis-like colorbar at the right. Composition and camera view (looking along -y) appear consistent. Minor differences include slightly different framing/margins and colorbar annotation styling (ground truth shows numeric range labels more clearly).

Goal 2

9/10

Criterion: Glyph Patterns: Do the arrow glyphs show similar orientation and spatial patterns as the ground truth?

Judge's Assessment: Arrow orientations and the overall flow structures match closely: the same large-scale upward/downward sweeping patterns above and below the central turbulent mixing region, and similar vortex-like curls within the middle band. Any differences are subtle and likely due to small sampling/placement or rendering differences rather than incorrect vector orientation.

Goal 3

8/10

Criterion: Glyph Appearance: Do the glyphs appear with similar uniform sizing as the ground truth?

Judge's Assessment: Glyphs appear uniformly sized in both images (no obvious magnitude scaling), and the density/stride looks very similar. The result’s arrows look marginally thicker/longer in places compared to the ground truth, but still consistent with a uniform scale factor presentation.

Goal 4

8/10

Criterion: Color Mapping: Is the color distribution across glyphs visually similar to the ground truth?

Judge's Assessment: The color distribution matches well: mostly dark purple arrows with green/yellow highlights concentrated in the central mixing region, consistent with Viridis magnitude coloring. The result’s colorbar appears to have fewer/less visible tick labels than the ground truth and the exact mapping/contrast looks slightly different, but the spatial pattern of magnitudes is very similar.

Overall Assessment

Overall, the result is an excellent match to the ground truth: same slice glyph visualization, very similar vector patterns and magnitude coloring. Differences are minor and mostly related to presentation details (colorbar labeling/format and slight framing/glyph rendering thickness).

📊 Detailed Metrics

Visualization Quality

34/40

Output Generation

5/5

Efficiency

5/10

PSNR

19.87 dB

SSIM

0.9283

LPIPS

0.0594

Input Tokens

377,597

Output Tokens

4,787

Total Tokens

382,384

Total Cost

$1.9598

📝 rti-velocity_slices

37/45 (82.2%)

📋 Task Description

Load the Rayleigh-Taylor instability velocity field from "rti-velocity_slices/data/rti-velocity_slices.vti" (VTI format, 128x128x128). Create three orthogonal slices: at x=64 (YZ-plane), y=64 (XZ-plane), and z=64 (XY-plane). Color all three slices by velocity magnitude using the 'Turbo' colormap. Add a color bar labeled 'Velocity Magnitude'. Use a white background. Set an isometric camera view that shows all three slices. Render at 1024x1024. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.png". (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.pvsm". (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.py". (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

29/30

Goals

3

Points/Goal

10

Goal 1

10/10

Criterion: Slice Count and Orientation: Are there exactly three perpendicular slices (one horizontal XY-plane and two vertical XZ and YZ planes), matching the ground truth arrangement?

Judge's Assessment: The result shows exactly three mutually orthogonal slices: one horizontal XY slice and two vertical slices (XZ and YZ) intersecting at the volume center, matching the ground truth geometry and arrangement closely.

Goal 2

9/10

Criterion: Color Mapping: Are the slices colored using a Turbo-like colormap (blue to green to yellow to red) mapped to velocity magnitude, with a similar color distribution as the ground truth?

Judge's Assessment: The colormap on the slices matches the Turbo-like blue→green→yellow→red progression and the value distribution looks very similar to the ground truth. The colorbar is present and labeled. Minor mismatch: the background is dark/gray in the result instead of white, which slightly changes perceived contrast but not the mapping itself.

Goal 3

10/10

Criterion: Mixing Zone Pattern: Does the horizontal (XY) slice show a chaotic, high-velocity-magnitude mixing pattern in its center region, similar to the ground truth?

Judge's Assessment: The horizontal (XY) slice exhibits the same central chaotic mixing-zone pattern with similar high-magnitude yellow/red patches and surrounding lower-magnitude blues/greens as in the ground truth.

Overall Assessment

The visualization content (three orthogonal center slices, Turbo-like magnitude coloring, and the characteristic RTI mixing pattern) matches the ground truth extremely well. The main deviation is the non-white background in the result, which affects presentation but not the core slice/magnitude visualization.

📊 Detailed Metrics

Visualization Quality

29/30

Output Generation

5/5

Efficiency

3/10

PSNR

19.27 dB

SSIM

0.9609

LPIPS

0.0414

Input Tokens

545,402

Output Tokens

6,649

Total Tokens

552,051

Total Cost

$2.8267

📝 rti-velocity_streakline

⚠️ LOW SCORE

21/45 (46.7%)

📋 Task Description

Load the Rayleigh–Taylor instability velocity field time series from "rti-velocity_streakline/data/rti-velocity_streakline_{timestep}.nc", where "timestep" in {0030, 0031, 0032, 0033, 0034, 0035, 0036, 0037, 0038, 0039, 0040} (11 timesteps, NetCDF format, 128×128×128 grid each, with separate vx, vy, vz arrays). Construct the time-varying velocity field u(x,t) by merging vx, vy, vz into a single vector field named "velocity", and compute the velocity magnitude "magnitude" = |velocity| for coloring. Compute streaklines as a discrete approximation of continuous particle injection: continuously release particles from fixed seed points at every sub-timestep into the time-varying velocity field using the StreakLine filter. Apply TemporalShiftScale (scale=20) to extend particle travel time, and apply TemporalInterpolator with a sub-timestep interval of 0.25 (or smaller) to approximate continuous injection over time. Seed 26 static points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use StaticSeeds=True, ForceReinjectionEveryNSteps=1 (reinjection at every sub-timestep), and set TerminationTime=200. Render the resulting streaklines as tubes with radius 0.3. Color the tubes by velocity magnitude ("magnitude") using the 'Cool to Warm (Extended)' colormap. Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view and render at 1024×1024. Set the viewpoint parameters as: [200.0, 200.0, 200.0] to position; [63.5, 63.5, 63.5] to focal point; [0.0, 0.0, 1.0] to camera up direction. Save the visualization image as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.png". (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.pvsm". (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.py". (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

15/30

Goals

3

Points/Goal

10

Goal 1

4/10

Criterion: Streak Line Patterns: Do the streak lines show similar flow patterns and structures as the ground truth?

Judge's Assessment: Ground truth shows a relatively sparse set of streaklines with distinct zig-zag/looping segments and visible separation between individual tubes. The result image shows a much denser, more cluttered bundle with many overlapping/stacked segments, giving a different apparent flow structure (more like a thick column of trajectories rather than separated streakline paths). Core idea (streaklines in a vertical column) is present, but the detailed patterns/structures do not match well.

Goal 2

5/10

Criterion: Streak Line Coverage: Is the spatial extent and distribution of streak lines similar to the ground truth?

Judge's Assessment: Both images concentrate streaklines in a tall, narrow region centered in the domain, spanning much of the vertical extent. However, the result has substantially greater coverage/density (fills in the column with many more lines) and appears slightly more compact/occluded, whereas the ground truth has more empty space and clearer individual paths. Spatial extent is roughly similar, but distribution is notably different.

Goal 3

6/10

Criterion: Color Mapping: Is the color distribution along streak lines visually similar to the ground truth?

Judge's Assessment: Both use a cool-to-warm style mapping with blues for lower magnitudes and reds for higher magnitudes, and both show mostly blue/cyan with occasional warm (orange/red) segments. The result appears overall darker/more uniformly blue and the background is not white (affecting perceived contrast), but the general color progression and presence of warm highlights are reasonably consistent with the ground truth.

Overall Assessment

The result captures the general concept (vertical streakline bundle colored by velocity magnitude) but differs substantially from the ground truth in streakline pattern clarity and density/coverage. Color mapping is broadly similar, though contrast/background and overall color balance differ.

📊 Detailed Metrics

Visualization Quality

15/30

Output Generation

5/5

Efficiency

1/10

PSNR

18.01 dB

SSIM

0.9335

LPIPS

0.0758

Input Tokens

1,539,556

Output Tokens

10,789

Total Tokens

1,550,345

Total Cost

$7.8596

📝 save-transparent

30/35 (85.7%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Create a wavelet object and show it. Color the rendering by the variable ‘RTData’. Render the wavelet as a surface. Hide the color bar. Next, set the layout size to be 300 pixels by 300 pixels. Next, move the camera with the following settings. The camera position should be [30.273897726939246, 40.8733980301544, 43.48927935675712]. The camera view up should be [-0.3634544237682163, 0.7916848767068606, -0.49105594165731975]. The camera parallel scale should be 17.320508075688775. Save a screenshot to the file “save-transparent/results/{agent_mode}/save-transparent.png”, set the image resolution to 300x300, and set the background to transparent. (Optional, but must save if use paraview) Save the paraview state as “save-transparent/results/{agent_mode}/save-transparent.pvsm”. (Optional, but must save if use python script) Save the python script as “save-transparent/results/{agent_mode}/save-transparent.py”. Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

19/20

Goals

2

Points/Goal

10

Goal 1

9/10

Criterion: Object Creation: Is the wavelet object properly created and displayed in the scene? Looking similar to the GT image?

Judge's Assessment: The result shows a single wavelet cube rendered as a surface and colored with a blue-to-tan scalar pattern consistent with RTData. The camera framing and orientation appear very close to the ground truth (same cube view and lighting). Minor differences in shading/contrast are visible but the object and coloring match well overall.

Goal 2

10/10

Criterion: Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?

Judge's Assessment: Both the ground truth and result images have a transparent background (they appear on a black page here, but the background itself is transparent in the PNG). No solid background color is present, matching the requirement.

Overall Assessment

The visualization closely matches the expected wavelet surface colored by RTData, and the screenshot background is correctly transparent. Only slight visual differences in tone/shading prevent a perfect object-match score.

📊 Detailed Metrics

Visualization Quality

19/20

Output Generation

5/5

Efficiency

6/10

Input Tokens

392,729

Output Tokens

3,049

Total Tokens

395,778

Total Cost

$2.0094

📝 shrink-sphere

49/55 (89.1%)

📋 Task Description

Create a default sphere and then hide it. Create a shrink filter from the sphere. Double the sphere's theta resolution. Divide the shrink filter's shrink factor in half. Extract a wireframe from the sphere. Group the shrink filter and wireframe together and show them. Save a screenshot of the result in the filename "shrink-sphere/results/{agent_mode}/shrink-sphere.png". The rendered view and saved screenshot should be 1920 x 1080 pixels and have a white background. (Optional, but must save if use paraview) Save the paraview state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm". (Optional, but must save if use python script) Save the python script as "shrink-sphere/results/{agent_mode}/shrink-sphere.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

36/40

Goals

4

Points/Goal

10

Goal 1

9/10

Criterion: Sphere Creation and Resolution: Is the sphere created with doubled theta resolution providing higher geometric detail and smoother curvature?

Judge's Assessment: The result shows a finely tessellated sphere with many small facets/edges, consistent with a doubled theta resolution compared to a default low-res sphere. The geometric detail and curvature appearance closely match the ground truth; any difference is negligible.

Goal 2

9/10

Criterion: Shrink Filter Application: Is the shrink filter properly applied with halved shrink factor creating visible separation between mesh elements?

Judge's Assessment: The shrink output is clearly visible as separated, inward-shrunken triangular elements across the sphere surface, matching the expected halved shrink factor look. The spacing and element separation appear very similar to the ground truth, with only minor visual differences (primarily due to coloring).

Goal 3

10/10

Criterion: Dual Representation: Are both the wireframe sphere and shrink filter results simultaneously visible and properly grouped together?

Judge's Assessment: Both representations are simultaneously present: a wireframe sphere (thin line network) and the shrunken cell faces/elements overlaid. This dual display matches the ground truth composition and indicates the intended grouping/showing behavior was achieved.

Goal 4

8/10

Criterion: Visual Quality: Does the visualization clearly show the contrast between the wireframe structure and the shrunken elements with appropriate white background?

Judge's Assessment: Background is white and the wireframe vs shrunken elements contrast is clear. However, the result uses a strong blue color for the shrunken elements whereas the ground truth appears gray/neutral, making the overall visual styling noticeably different even though structure is correct.

Overall Assessment

The result matches the ground truth well in geometry and filter outcomes: high-resolution sphere, correct shrink appearance, and simultaneous wireframe + shrink display on a white background. The main discrepancy is the color/styling of the shrunken elements (blue vs gray), which affects visual fidelity but not the underlying visualization goals.

📊 Detailed Metrics

Visualization Quality

36/40

Output Generation

5/5

Efficiency

8/10

Input Tokens

189,667

Output Tokens

3,409

Total Tokens

193,076

Total Cost

$0.9995

📝 solar-plume

⚠️ LOW SCORE

23/55 (41.8%)

📋 Task Description

Task: Load the solar plume dataset from "solar-plume/data/solar-plume_126x126x512_float32_scalar3.raw", the information about this dataset: solar-plume (Vector) Data Scalar Type: float Data Byte Order: little Endian Data Extent: 126x126x512 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Add a "stream tracer" filter under the solar plume data to display streamline, set the "Seed type" to "Point Cloud" and set the center of point cloud to 3D position [50, 50, 320] with a radius 30, then hide the point cloud sphere. Add a "tube" filter under the "stream tracer" filter to enhance the streamline visualization. Set the radius to 0.5. In the pipeline browser panel, hide everything except the "tube" filter. Please think step by step and make sure to fulfill all the visualization goals mentioned above. Set the viewpoint parameters as: [62.51, -984.78, 255.45] to position; [62.51, 62.46, 255.45] to focal point; [0, 0, 1] to camera up direction. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. Save the visualization image as "solar-plume/results/{agent_mode}/solar-plume.png". (Optional, but must save if use paraview) Save the paraview state as "solar-plume/results/{agent_mode}/solar-plume.pvsm". (Optional, but must save if use pvpython script) Save the python script as "solar-plume/results/{agent_mode}/solar-plume.py". (Optional, but must save if use VTK) Save the cxx code script as "solar-plume/results/{agent_mode}/solar-plume.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

15/40

Goals

4

Points/Goal

10

Goal 1

4/10

Criterion: Overall Visualization Goal: Does the result match the ground truth streamline visualization of solar-plume flow structures?

Judge's Assessment: Ground truth shows a sparse set of tube streamlines with a clear central vertical plume core and a few large looping trajectories around it on a white background. The result shows a much denser, nearly solid mass of streamlines filling the volume, with different styling (solid red) and a dark/gray background. Overall the visualization does not match the expected look and balance of structures.

Goal 2

5/10

Criterion: Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth, particularly in the plume region?

Judge's Assessment: Both images depict a tall plume-like structure with upward flow and curling/arching streamlines near the top. However, the ground truth has distinct, separated loops and a visible central column, while the result’s extreme density obscures individual plume-region structures and changes the apparent pattern (looks more like a packed bundle than discrete streamlines).

Goal 3

2/10

Criterion: Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?

Judge's Assessment: Streamline coverage/density is drastically different: ground truth is relatively sparse with many open white regions and only some outer loops; the result is heavily over-seeded/over-integrated, producing a very high density of lines throughout the plume and surrounding region.

Goal 4

4/10

Criterion: Visual Appearance: Do the streamline tubes appear similar in thickness and visibility to the ground truth?

Judge's Assessment: Ground truth tubes appear thin and lightly shaded (blue/gray) with good separation and visibility of individual trajectories. The result appears as uniformly colored red lines/tubes with much less discernible thickness variation, and the high density causes severe occlusion, reducing visibility of tube geometry compared to the ground truth.

Overall Assessment

While the result captures the general idea of a vertical solar-plume streamline field, it diverges strongly from the ground truth in streamline density/coverage and overall appearance (background and coloring), which significantly changes the perceived flow structures and does not match the expected visualization.

📊 Detailed Metrics

Visualization Quality

15/40

Output Generation

5/5

Efficiency

3/10

PSNR

16.00 dB

SSIM

0.9122

LPIPS

0.1004

Input Tokens

840,287

Output Tokens

7,834

Total Tokens

848,121

Total Cost

$4.3189

📝 stream-glyph

35/55 (63.6%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Read in the file named "stream-glyph/data/stream-glyph.ex2". Trace streamlines of the V data array seeded from a default point cloud. Render the streamlines with tubes. Add cone glyphs to the streamlines. Color the streamlines and glyphs by the Temp data array. View the result in the +X direction. Save a screenshot of the result in the filename "stream-glyph/results/{agent_mode}/stream-glyph.png". The rendered view and saved screenshot should be 1920 x 1080 pixels. (Optional, but must save if use paraview) Save the paraview state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm". (Optional, but must save if use python script) Save the python script as "stream-glyph/results/{agent_mode}/stream-glyph.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

26/40

Goals

4

Points/Goal

10

Goal 1

6/10

Criterion: Streamline Generation: Are streamlines properly traced following the V variable flow field with appropriate seeding from the point cloud?

Judge's Assessment: Ground truth shows a compact bundle of streamlines arcing upward and looping back down, seeded so the flow forms a dome-like curtain. The result does show many streamlines following a coherent flow, but the overall pattern is quite different: it forms a tall cylindrical/columnar volume with many long, nearly vertical strands and a prominent horizontal band in the middle. This suggests different seeding and/or integration settings compared to the ground truth.

Goal 2

7/10

Criterion: Tube and Glyph Rendering: Are streamlines rendered as tubes with cone glyphs properly attached showing flow direction and magnitude?

Judge's Assessment: Both images clearly use tube-like streamlines and have cone glyphs along the lines indicating direction. However, the result’s tubes/glyphs appear thinner and less uniformly “tubed” than the ground truth, and the glyph density/placement differs (ground truth has a more regular, tightly packed cone pattern along the arcing tubes). Core requirement is met but not a close match.

Goal 3

8/10

Criterion: Temperature Color Mapping: Are both streamlines and glyphs correctly colored by the Temp variable with appropriate color scaling?

Judge's Assessment: Both streamlines and glyphs are colored with a blue-white-red temperature ramp. The result includes a Temp colorbar and shows a similar hot (red/orange) region and cooler (blue) regions. The spatial distribution differs from the ground truth (hot region appears as a mid-height band rather than concentrated near the bottom), indicating different streamline sampling/coverage, but the coloring-by-Temp requirement is satisfied.

Goal 4

5/10

Criterion: View Configuration: Is the visualization displayed from the correct +x view direction providing clear visibility of the flow patterns and structures?

Judge's Assessment: Ground truth is viewed from +X with a clean white background and the flow structure centered in that orientation. The result appears to be from a different camera orientation (the axis triad and the visible structure suggest not the same +X view), and it uses a dark background. The viewpoint does not match the ground truth well.

Overall Assessment

The result successfully produces streamlines with cone glyphs and colors them by Temp, but the streamline seeding/integration and overall flow geometry differ substantially from the ground truth, and the camera/view configuration does not match the required +X view. Rendering style (background, tube/glyph appearance) also differs, reducing visual similarity.

📊 Detailed Metrics

Visualization Quality

26/40

Output Generation

5/5

Efficiency

4/10

Input Tokens

462,920

Output Tokens

6,046

Total Tokens

468,966

Total Cost

$2.4053

📝 subseries-of-time-series

⚠️ LOW SCORE

11/45 (24.4%)

📋 Task Description

Read the file "subseries-of-time-series/data/subseries-of-time-series.ex2". Load two element blocks: the first is called 'Unnamed block ID: 1 Type: HEX', the second is called 'Unnamed block ID: 2 Type: HEX'. Next, slice this object with a plane with origin at [0.21706008911132812, 4.0, -5.110947132110596] and normal direction [1.0, 0.0, 0.0]. The plane should have no offset. Next, save this time series to a collection of .vtm files. The base file name for the time series is "subseries-of-time-series/results/{agent_mode}/canslices.vtm" and the suffix is '_%d'. Only save time steps with index between 10 and 20 inclusive, counting by 3. Next, load the files "subseries-of-time-series/results/{agent_mode}/canslices_10.vtm", "subseries-of-time-series/results/{agent_mode}/canslices_13.vtm", "subseries-of-time-series/results/{agent_mode}/canslices_16.vtm", and "subseries-of-time-series/results/{agent_mode}/canslices_19.vtm" in multi-block format. Finally, show the multi-block data set you just loaded. Save a screenshot to the file "subseries-of-time-series/results/{agent_mode}/subseries-of-time-series.png". (Optional, but must save if use paraview) Save the paraview state as "subseries-of-time-series/results/{agent_mode}/subseries-of-time-series.pvsm". (Optional, but must save if use python script) Save the python script as "subseries-of-time-series/results/{agent_mode}/subseries-of-time-series.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

4/30

Goals

3

Points/Goal

10

Goal 1

2/10

Criterion: Data Loading and Block Selection: Are the specified element blocks properly loaded and the slice plane correctly applied?

Judge's Assessment: Ground truth shows a visible sliced geometry (a horizontal slab-like piece with a thin vertical feature beneath it) on a dark background. The result image is essentially empty/white with only the orientation axes visible, indicating the slice output is not being displayed (or camera/visibility is incorrect). This suggests the specified blocks and/or slice plane result is not correctly shown.

Goal 2

1/10

Criterion: Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?

Judge's Assessment: The ground truth implies the multi-block time-step slices were loaded and rendered. In the result, no multi-block geometry is visible at all, so there is no visual evidence that the exported VTM subseries (10, 13, 16, 19) was successfully loaded back as a multi-block dataset.

Goal 3

1/10

Criterion: Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?

Judge's Assessment: Final visualization should display the sliced geometry from the selected time steps. The result shows a blank scene (white background) with no rendered dataset, so the final multi-block display requirement is not met.

Overall Assessment

The result does not match the ground truth: the expected sliced geometry is missing entirely in the rendered view. This indicates failures in displaying the slice and/or reloading/rendering the multi-block time-series subset.

📊 Detailed Metrics

Visualization Quality

4/30

Output Generation

5/5

Efficiency

2/10

Input Tokens

892,031

Output Tokens

11,903

Total Tokens

903,934

Total Cost

$4.6387

📝 supernova_isosurface

24/45 (53.3%)

📋 Task Description

Task: Load the supernova dataset from "supernova_isosurface/data/supernova_isosurface_256x256x256_float32.raw", the information about this dataset: supernova (Scalar) Data Scalar Type: float Data Byte Order: little Endian Data Spacing: 1x1x1 Data Extent: 256x256x256 Data loading is very important, make sure you correctly load the dataset according to their features. Then visualize it and extract two isosurfaces. One of them use color red, showing areas with low density (isovalue 40 and opacity 0.2), while the other use color light blue, showing areas with high density (isovalue 150 and opacity 0.4). Please think step by step and make sure to fulfill all the visualization goals mentioned above. Only make the two isosurfaces visible. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. Set the viewpoint parameters as: [567.97, 80.17, 167.28] to position; [125.09, 108.83, 121.01] to focal point; [-0.11, -0.86, 0.50] to camera up direction. Save the visualization image as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.png". (Optional, but must save if use paraview) Save the paraview state as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.pvsm". (Optional, but must save if use pvpython script) Save the python script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.py". (Optional, but must save if use VTK) Save the cxx code script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

15/30

Goals

3

Points/Goal

10

Goal 1

5/10

Criterion: Overall Visualization Goal: How well does the result achieve the overall goal of showing the supernova structure with two distinct isosurfaces representing different density regions?

Judge's Assessment: Compared to the ground truth (top image), the result (bottom image) shows the same overall supernova-like structure with an outer shell and an inner complex surface, but the appearance is substantially different: the inner surface is rendered in a dark gray rather than light blue, and the overall contrast/visibility of the two-surface composition is weaker. The view/background match reasonably (white background, similar framing), but the key visual distinction between the two isosurfaces is not achieved.

Goal 2

8/10

Criterion: Does the red isosurface show low density areas (outside regions) with lower opacity?

Judge's Assessment: The low-density isosurface appears as a translucent red/pink outer envelope in the result, similar to the ground truth. Opacity looks low and the surface reads as an enclosing shell. There are minor differences in saturation/opacity (result looks a bit more uniformly pink and slightly heavier), but the requirement is largely met.

Goal 3

2/10

Criterion: Does the blue isosurface show high density areas (inside regions) with higher opacity?

Judge's Assessment: The high-density isosurface in the ground truth is clearly light blue/cyan and more opaque, standing out strongly against the red shell. In the result, the corresponding inner structure is rendered as dark gray/black with no light-blue coloration, and it does not visually match the intended higher-opacity light-blue surface. This fails the color requirement and significantly reduces the intended inside-vs-outside density cue.

Overall Assessment

The result captures the presence of two nested isosurfaces and roughly the correct geometry/view, and the red low-density outer shell is mostly correct. However, the high-density isosurface is not light blue and appears dark/gray, so the key visual encoding (blue high-density vs red low-density with different opacities) does not match the ground truth.

📊 Detailed Metrics

Visualization Quality

15/30

Output Generation

5/5

Efficiency

4/10

PSNR

22.81 dB

SSIM

0.9761

LPIPS

0.0608

Input Tokens

492,166

Output Tokens

5,379

Total Tokens

497,545

Total Cost

$2.5415

📝 supernova_streamline

34/45 (75.6%)

📋 Task Description

Load the Supernova velocity vector field from "supernova_streamline/data/supernova_streamline_100x100x100_float32_scalar3.raw", the information about this dataset: Supernova Velocity (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 100x100x100 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create streamlines using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [50, 50, 50], with 200 seed points and a radius of 45.0. Set maximum streamline length to 100.0. Add a "Tube" filter on the stream tracer. Set tube radius to 0.3 with 12 sides. Color the tubes by Vorticity magnitude using a diverging colormap with the following RGB control points: - Value 0.0 -> RGB(0.231, 0.298, 0.753) (blue) - Value 0.05 -> RGB(0.865, 0.865, 0.865) (white) - Value 0.5 -> RGB(0.706, 0.016, 0.149) (red) Show the dataset bounding box as an outline (black). In the pipeline browser panel, hide the stream tracer and only show the tube filter and the outline. Use a white background. Render at 1280x1280. Set the viewpoint parameters as: [41.38, 73.91, -282.0] to position; [49.45, 49.50, 49.49] to focal point; [0.01, 1.0, 0.07] to camera up direction. Save the visualization image as "supernova_streamline/results/{agent_mode}/supernova_streamline.png". (Optional, but must save if use paraview) Save the paraview state as "supernova_streamline/results/{agent_mode}/supernova_streamline.pvsm". (Optional, but must save if use pvpython script) Save the python script as "supernova_streamline/results/{agent_mode}/supernova_streamline.py". (Optional, but must save if use VTK) Save the cxx code script as "supernova_streamline/results/{agent_mode}/supernova_streamline.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

26/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Central Structure: Is there a dense, chaotic cluster of streamlines near the center of the volume, matching the ground truth?

Judge's Assessment: Ground truth shows a dense, tangled/chaotic bundle of stream-tubes concentrated around the volume center with many curved paths. The result reproduces this central cluster very closely in location, density, and overall chaotic structure. Minor differences in apparent contrast/visibility (likely due to background/lighting) slightly change perceived density but the core structure matches well.

Goal 2

9/10

Criterion: Radial Extensions: Are there long, straight streamline tubes extending radially outward from the central region, similar to the ground truth?

Judge's Assessment: In the ground truth, many long, relatively straight tubes extend radially outward from the central region in all directions, forming a spiky halo. The result shows the same strong radial extensions with similar lengths and distribution. Any differences are small (slight variation in prominence of some rays), but the radial pattern is clearly preserved.

Goal 3

8/10

Criterion: Color Mapping: Are the tubes colored by vorticity magnitude using a blue-white-red diverging colormap, with warm colors concentrated near the center and cool colors on the extended lines?

Judge's Assessment: Ground truth uses a blue-white-red diverging map with red/orange concentrated in the central high-vorticity region and blue on the long outward tubes, with some whitish transition in between. The result follows the same overall color logic (warm center, cool outer rays) and similar range. However, the darker background and slightly different midtone appearance make the white/neutral band less crisp and the overall colormap impression a bit less faithful than the geometry.

Overall Assessment

The result matches the ground truth very well in streamline/tube geometry: a dense central turbulent cluster plus strong radial outward tubes. Color mapping is largely correct (warm center, cool periphery) but the presentation/background/contrast makes the diverging blue-white-red transition look slightly less like the ground truth.

📊 Detailed Metrics

Visualization Quality

26/30

Output Generation

5/5

Efficiency

3/10

PSNR

23.71 dB

SSIM

0.9806

LPIPS

0.0521

Input Tokens

774,250

Output Tokens

7,592

Total Tokens

781,842

Total Cost

$3.9851

📝 tangaroa_streamribbon

40/55 (72.7%)

📋 Task Description

Task: Load the tangaroa dataset from "tangaroa_streamribbon_300x180x120_float32_scalar3.raw", the information about this dataset: tangaroa (Vector) Data Scalar Type: float Data Byte Order: little Endian Data Extent: 300x180x120 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Apply "streamline tracer" filter, set the "Seed Type" to point cloud, turn off the "show sphere", set the center to [81.6814, 80.708, 23.5093], and radius to 29.9 Add "Ribbon" filter to the streamline tracer results and set width to 0.3, set the Display representation to Surface. In pipeline browser panel, hide everything except the ribbon filter results. Please think step by step and make sure to fulfill all the visualization goals mentioned above. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. Set the viewpoint parameters as: [372.27, 278.87, 214.44] to position; [169.85, 76.46, 12.02] to focal point; [-0.41, 0.82, -0.41] to camera up direction. Save the visualization image as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.png". (Optional, but must save if use paraview) Save the paraview state as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.pvsm". (Optional, but must save if use pvpython script) Save the python script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.py". (Optional, but must save if use VTK) Save the cxx code script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

32/40

Goals

4

Points/Goal

10

Goal 1

8/10

Criterion: Overall Visualization Goal: Does the result match the ground truth visualization of tangaroa flow structures using ribbon surfaces?

Judge's Assessment: The result shows the same overall ribbon/stream structure as the ground truth: a dense, tangled region on the left transitioning into long, mostly parallel ribbons extending to the right on a white background. The camera/viewpoint appears consistent. The main mismatch is visual styling: the ground truth uses a blue–orange diverging coloration, while the result is largely grayscale/low-saturation, changing the intended appearance though the geometry matches well.

Goal 2

8/10

Criterion: Flow Surface Patterns: Do the ribbon surfaces show similar flow patterns and structures as the ground truth?

Judge's Assessment: Flow patterns are very similar: the left-side recirculating/turbulent knot and the downstream stretched ribbons align closely in shape and curvature. Fine-scale differences exist in perceived contrast and separation of strands (likely due to different coloring/lighting), but the underlying flow structures match well.

Goal 3

9/10

Criterion: Surface Coverage: Is the spatial distribution and coverage of the flow surfaces similar to the ground truth?

Judge's Assessment: Spatial distribution and coverage are highly consistent: similar density of ribbons in the core region and similar extent/length of downstream ribbons. The overall footprint and occupied region in the frame match the ground truth closely, with only minor differences in visibility of some strands due to the muted coloring.

Goal 4

7/10

Criterion: Visual Appearance: Do the ribbon surfaces appear similar in width and structure to the ground truth?

Judge's Assessment: Ribbon width and surface-like appearance are generally consistent, but the result looks more like thin gray strands with less clear ribbon surface shading compared to the ground truth’s more visually distinct colored ribbons. This makes the ribbon structure appear slightly less pronounced even if the geometry is similar.

Overall Assessment

The result largely reproduces the correct streamline-ribbon geometry, viewpoint, and spatial coverage seen in the ground truth. The primary discrepancy is the lack of the ground truth’s strong blue/orange color mapping and associated visual contrast, which reduces clarity of ribbon surfaces and strand separation but does not substantially alter the flow structure depiction.

📊 Detailed Metrics

Visualization Quality

32/40

Output Generation

5/5

Efficiency

3/10

PSNR

29.94 dB

SSIM

0.9855

LPIPS

0.0177

Input Tokens

573,363

Output Tokens

6,425

Total Tokens

579,788

Total Cost

$2.9632

📝 tgc-velocity_contour

41/55 (74.5%)

📋 Task Description

Load the turbulence-gravity-cooling velocity field dataset from "tgc-velocity_contour/data/tgc-velocity_contour.vti" (VTI format, 64x64x64). Extract a slice at z=32 and color it by velocity magnitude using 'Viridis (matplotlib)' colormap. Also add contour lines of velocity magnitude on the same slice at values [0.3, 0.6, 0.9, 1.2] using the Contour filter on the slice output. Display contour lines in white. Add a color bar labeled 'Velocity Magnitude'. Light gray background (RGB: 0.9, 0.9, 0.9). Top-down camera. Render at 1024x1024. Set the viewpoint parameters as: [31.5, 31.5, 100.0] to position; [31.5, 31.5, 32.0] to focal point; [0.0, 1.0, 0.0] to camera up direction. Save the visualization image as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.png". (Optional, but must save if use paraview) Save the paraview state as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.pvsm". (Optional, but must save if use pvpython script) Save the python script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.py". (Optional, but must save if use VTK) Save the cxx code script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

30/40

Goals

4

Points/Goal

10

Goal 1

7/10

Criterion: Overall Visualization Goal: Does the result match the ground truth slice and contour visualization of the TGC velocity field?

Judge's Assessment: The result shows a Viridis-colored scalar slice with white contour lines overlaid and a vertical color bar, matching the intended visualization type. However, the framing/camera differs from the ground truth: the result is zoomed in/cropped to a subregion rather than showing the full slice extent, and the overall layout (margins/placement) does not match.

Goal 2

7/10

Criterion: Slice Pattern: Does the colored slice show similar patterns and structures as the ground truth?

Judge's Assessment: Within the visible region, the slice’s structures (large purple low-magnitude region with surrounding teal/green areas) resemble the ground truth patterns. But because the result is significantly zoomed/cropped, it’s hard to verify full-slice correspondence and some features present in the ground truth context are missing.

Goal 3

8/10

Criterion: Contour Lines: Are the contour lines positioned and shaped similarly to the ground truth?

Judge's Assessment: Contour lines appear in white and generally follow the same major boundaries seen in the ground truth (notably the large enclosing contour around the central low-magnitude region and smaller interior loops). Minor differences in exact placement/extent are present, likely due to the different zoom/framing and possibly slight contour level/processing differences.

Goal 4

8/10

Criterion: Color Mapping: Is the color distribution on the slice visually similar to the ground truth?

Judge's Assessment: The Viridis mapping and overall color distribution (purple lows, teal mid, green/yellow highs) are consistent with the ground truth, and the color bar range looks similar. Differences are mainly due to the cropped view emphasizing different parts of the range rather than a fundamentally different colormap or scaling.

Overall Assessment

The result largely achieves the correct slice+contour visualization with appropriate colormap and white contours, and the local patterns match well. The main mismatch versus ground truth is the camera/framing: the result is zoomed in and does not show the full slice extent, reducing overall similarity.

📊 Detailed Metrics

Visualization Quality

30/40

Output Generation

5/5

Efficiency

6/10

PSNR

21.40 dB

SSIM

0.9741

LPIPS

0.0338

Input Tokens

325,498

Output Tokens

4,424

Total Tokens

329,922

Total Cost

$1.6939

📝 time-varying

31/55 (56.4%)

📋 Task Description

Read the dataset in the file "time-varying/data/time-varying.ex2", and color the data by the EQPS variable. Viewing in the +y direction, play an animation through the time steps, with visible color bar legend. Rescale the data range to last time step, and play the animation again. Create a second linked render view to the right of the first, applying a temporal interpolator to the second view. Play the animation simultaneously in both views, and save the animation of both views in "time-varying/results/{agent_mode}/time-varying.avi". Print the following statistics: average value of EQPS over all locations and all time steps, average value of EQPS over all locations in the first half of the time steps, average value of EQPS over all locations in the even numbered time steps, and variance of EQPS over all locations and all the time steps. Save the last frame of the visualization image as "time-varying/results/{agent_mode}/time-varying.png". (Optional, but must save if use paraview) Save the paraview state as "time-varying/results/{agent_mode}/time-varying.pvsm". (Optional, but must save if use python script) Save the python script as "time-varying/results/{agent_mode}/time-varying.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

26/40

Goals

4

Points/Goal

10

Goal 1

3/10

Criterion: Temporal Animation Quality: Does the animation smoothly progress through all time steps showing the evolution of the EQPS variable over time?

Judge's Assessment: Only a single static frame is shown for the result (no evidence of time progression). The ground truth expectation is an animation through time steps (and replay after rescaling to last time step). From the provided result imagery, temporal evolution/animation quality cannot be verified and appears not demonstrated.

Goal 2

8/10

Criterion: Dual View Configuration: Are both render views properly configured with the second view showing temporal interpolation effects compared to the first?

Judge's Assessment: The result shows two side-by-side render views with very similar geometry and coloring, consistent with the required dual-view layout. However, there is no visible difference between the two views that would indicate the right view has a temporal interpolator applied (they look essentially identical in the provided frame), so the interpolation effect is not evident.

Goal 3

9/10

Criterion: Color Mapping and Legend: Is the EQPS variable properly color-mapped with an appropriate color bar legend visible throughout the animation?

Judge's Assessment: EQPS is clearly used for color mapping in the result, and both views include a visible color bar labeled "EQPS" with a 0 to ~2.9 range. This matches the requirement well. Minor differences from ground truth may exist (e.g., exact placement/formatting), but the legend and mapping are present and readable.

Goal 4

6/10

Criterion: View Direction and Layout: Is the +y direction view properly set and are both views arranged side-by-side in the correct layout configuration?

Judge's Assessment: The result correctly arranges two views side-by-side. The camera/view direction appears similar between the two result views, but compared to the ground truth the object orientation/framing differs (ground truth shows a different apparent viewpoint/pose of the geometry). Thus the +y viewing direction match is uncertain/likely off, though the general layout requirement is met.

Overall Assessment

The result succeeds on showing a dual, linked-style side-by-side layout with EQPS coloring and visible color legends. However, the key time-varying requirements (playing through time steps, replay after rescaling to last time step, and demonstrating temporal interpolation differences in the second view) are not verifiable from the provided result and are not visually evidenced; additionally, the camera/view orientation does not closely match the ground truth.

📊 Detailed Metrics

Visualization Quality

26/40

Output Generation

5/5

Efficiency

0/10

Input Tokens

3,914,882

Output Tokens

26,099

Total Tokens

3,940,981

Total Cost

$19.9659

📝 tornado

33/45 (73.3%)

📋 Task Description

Load the Tornado vector field from "tornado/data/tornado_64x64x64_float32_scalar3.raw", the information about this dataset: Tornado (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 64x64x64 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create a streamline visualization using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [31.5, 31.5, 47.25], radius 12.6, and maximum streamline length to 512.0. Add a "Tube" filter (radius 0.25) on the stream tracer. Color the tubes by Velocity magnitude using the 'Cool to Warm (Diverging)' colormap. Also display the stream tracer directly with line width 5.0 and "Render Lines As Tubes" enabled. Add a "Glyph" filter on the original data using Arrow glyph type. Orient arrows by the Velocity vector and scale by Velocity magnitude with a scale factor of 25.0. Set maximum number of sample points to 2500. Color glyphs by Velocity magnitude using the same colormap. Add an "Outline" filter to display the dataset bounding box (black). Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Show both color bar and coordinate axes. Set the viewpoint parameters as: [142.01, -36.46, 93.96] to position; [31.5, 31.5, 31.5] to focal point; [-0.35, 0.25, 0.90] to camera up direction. Save the visualization image as "tornado/results/{agent_mode}/tornado.png". (Optional, but must save if use paraview) Save the paraview state as "tornado/results/{agent_mode}/tornado.pvsm". (Optional, but must save if use pvpython script) Save the python script as "tornado/results/{agent_mode}/tornado.py". (Optional, but must save if use VTK) Save the cxx code script as "tornado/results/{agent_mode}/tornado.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

25/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Vortex Structure: Is a funnel-shaped vortex core visible with streamlines spiraling around a central vertical axis, matching the ground truth?

Judge's Assessment: Ground truth shows a clear funnel-shaped tornado with a tight, spiraling core and broader spiral flow near the top. The result image also shows a well-defined vertical vortex core with streamlines/tubes spiraling upward into a wide, circular cap region. Minor differences: the result’s overall contrast/background is darker and the top spiral appears slightly less layered/less dense than the ground truth, but the key funnel/vortex structure matches strongly.

Goal 2

8/10

Criterion: Glyph Presence: Are arrow glyphs distributed throughout the volume showing velocity direction, similar to the ground truth?

Judge's Assessment: In the ground truth, arrow glyphs are visible throughout the volume, especially in the lower half and around the periphery. The result also contains many arrow glyphs distributed across the domain, including around the vortex and in the surrounding flow. However, glyphs in the result are somewhat harder to distinguish against the darker background and appear a bit less uniformly prominent than in the ground truth.

Goal 3

8/10

Criterion: Color Mapping: Are both the streamline tubes and arrow glyphs colored by velocity magnitude using a blue-to-red diverging colormap, matching the ground truth color distribution?

Judge's Assessment: Both images use a blue-to-red diverging scheme where higher speeds are red/orange (notably near the upper ring and core) and lower speeds are blue (outer/lower regions). The result’s color distribution is broadly consistent with the ground truth for both tubes and glyphs. Differences: the result’s background and lighting make midtones look slightly muted, and the overall balance appears a bit more orange/gray in some regions compared to the ground truth’s clearer cool-to-warm separation.

Overall Assessment

The result closely reproduces the expected tornado visualization: a recognizable funnel vortex with spiraling streamlines, volume-distributed arrow glyphs, and appropriate cool-to-warm magnitude coloring. Main deviations are presentation-related (darker background/contrast) and slightly reduced clarity of glyphs and color separation compared to the ground truth.

📊 Detailed Metrics

Visualization Quality

25/30

Output Generation

5/5

Efficiency

3/10

PSNR

16.82 dB

SSIM

0.7857

LPIPS

0.1555

Input Tokens

774,642

Output Tokens

8,514

Total Tokens

783,156

Total Cost

$4.0009

📝 twoswirls_streamribbon

32/45 (71.1%)

📋 Task Description

Load the Two Swirls vector field from "twoswirls_streamribbon/data/twoswirls_streamribbon_64x64x64_float32_scalar3.raw", the information about this dataset: Two Swirls (Vector) Data Scalar Type: float Data Byte Order: Little Endian Data Extent: 64x64x64 Number of Scalar Components: 3 Data loading is very important, make sure you correctly load the dataset according to their features. Create two stream ribbons using "Stream Tracer" filters with "Line" seed type (resolution 25 points each), and apply a "Ribbon" filter (width 2.5) to each: - Stream Ribbon 1: Line seed from [16, 10, 32] to [16, 54, 32]. Ribbon colored solid green (RGB: 0.2, 0.7, 0.3) with opacity 0.35. - Stream Ribbon 2: Line seed from [48, 10, 32] to [48, 54, 32]. Ribbon colored solid blue (RGB: 0.2, 0.4, 0.85) with opacity 0.35. Show the dataset bounding box as an outline (black, opacity 0.3). In the pipeline browser panel, hide all stream tracers and only show the ribbon filters and the outline. Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. Set the viewpoint parameters as: [30.51, -154.18, 144.99] to position; [30.51, 31.5, 30.91] to focal point; [0.0, 0.53, 0.85] to camera up direction. Save the visualization image as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.png". (Optional, but must save if use paraview) Save the paraview state as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.pvsm". (Optional, but must save if use pvpython script) Save the python script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.py". (Optional, but must save if use VTK) Save the cxx code script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

23/30

Goals

3

Points/Goal

10

Goal 1

9/10

Criterion: Swirl Separation: Are there two visually distinct swirl structures (one on the left and one on the right), matching the spatial arrangement in the ground truth?

Judge's Assessment: Ground truth shows two clearly separated swirl/ribbon structures: a green swirl on the left half of the box and a blue swirl on the right. The result image also shows two distinct swirls in the same left/right arrangement with similar relative placement inside the bounding box. Separation is slightly reduced because the camera/background/opacity make the volumes feel more merged, but the two structures are still clearly distinguishable.

Goal 2

8/10

Criterion: Stream Ribbon Shape: Do the ribbon surfaces show wrapped, swirling sheet-like structures similar to the ground truth?

Judge's Assessment: In the ground truth, both objects appear as ribbon-like, sheet surfaces that wrap and loop, with the blue ribbon having prominent large loops and the green ribbon showing a denser core with some protruding ribbon segments. The result reproduces the overall swirling ribbon-sheet character and major loops reasonably well, but the shapes look thicker/less crisp and somewhat more blob-like due to darker rendering and reduced contrast, making fine ribbon layering less readable than in the ground truth.

Goal 3

6/10

Criterion: Color and Transparency: Are the stream ribbons rendered with distinct colors (green and blue) and semi-transparency, similar to the ground truth?

Judge's Assessment: Ground truth uses a white background and semi-transparent solid green/blue ribbons (light, airy appearance) with a faint black outline box. The result has a dark gray/blue background (not white), making the ribbons appear much darker and less like the intended semi-transparent pastel tones. Green and blue are still distinct, and transparency is present, but the overall color/opacity impression deviates noticeably from the ground truth.

Overall Assessment

The result captures the key structural intent: two separated swirling ribbon structures (green left, blue right) with broadly similar ribbon wrapping. The main mismatch is appearance: the background is dark instead of white, which changes perceived color and transparency and reduces contrast, making the ribbons look heavier and less like the ground-truth semi-transparent sheets.

📊 Detailed Metrics

Visualization Quality

23/30

Output Generation

5/5

Efficiency

4/10

PSNR

20.51 dB

SSIM

0.9015

LPIPS

0.1017

Input Tokens

446,405

Output Tokens

5,188

Total Tokens

451,593

Total Cost

$2.3098

📝 vortex

40/55 (72.7%)

📋 Task Description

Task: Load the vortex dataset from "vortex/data/vortex_128x128x128_float32.raw", the information about this dataset: vortex (Scalar) Data Scalar Type: float Data Byte Order: little Endian Data Extent: 128x128x128 Number of Scalar Components: 1 Instructions: 1. Load the dataset into ParaView. 2. Leverage "contour" filter to achieve iso-surface rendering. In pipeline browser panel, hide everything except the "contour" fileter. 3. In properties panel of "contour" filter, set isosurface value to -0.2, use Solid Color and set the color as beige. 4. Enable Ambient occlusion by toggle the "Use Ambient Occlusion" button in the Render Passes. 5. Add head light with light inspector, set "Coords" as Camera, "Intentsity" to 0.2, Type to "Directional". 6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. 7. Set the viewpoint parameters as: [308.85, 308.85, 308.85] to position; [63.5, 63.5, 63.5] to focal point; [-0.41, 0.82, -0.41] to camera up direction. 8. Save your work: Save the visualization image as "vortex/results/{agent_mode}/vortex.png". (Optional, but must save if use paraview) Save the paraview state as "vortex/results/{agent_mode}/vortex.pvsm". (Optional, but must save if use pvpython script) Save the python script as "vortex/results/{agent_mode}/vortex.py". (Optional, but must save if use VTK) Save the cxx code script as "vortex/results/{agent_mode}/vortex.cxx" Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

32/40

Goals

4

Points/Goal

10

Goal 1

8/10

Criterion: Overall Visualization Goal: Does the result match the ground truth isosurface rendering of the vortex scalar field?

Judge's Assessment: The result shows the same kind of single beige/solid isosurface rendering of the vortex field as the ground truth, with a very similar overall composition and camera framing. However, the background is dark bluish/gray instead of white, which is a clear deviation from the specified/ground-truth look.

Goal 2

9/10

Criterion: Isosurface Structure: Does the isosurface show the same vortex structure and topology as the ground truth?

Judge's Assessment: The isosurface geometry/topology matches very closely: the same collection of tubular/curved sheet-like vortex structures, similar holes/open ends, and the same spatial arrangement (large curved tube on the right, elongated structure on the left, central void). Only minor differences in perceived thickness/contrast due to lighting/background.

Goal 3

7/10

Criterion: Surface Appearance: Does the surface color and shading appear similar to the ground truth?

Judge's Assessment: Surface shading and material response are broadly similar (smooth solid color with soft shadowing/occlusion), but the color appears more gray/less warm-beige than the ground truth, and the darker background changes the perceived brightness and contrast. This makes the appearance noticeably different even though the lighting style is comparable.

Goal 4

8/10

Criterion: Visualization Clarity: Are the vortex features clearly visible and comparable to the ground truth?

Judge's Assessment: Vortex features are clearly visible and readable, and the view is comparable to the ground truth. Clarity is slightly reduced relative to the ground truth because the non-white background and cooler surface tone reduce the clean separation and the intended bright, airy look.

Overall Assessment

The result largely matches the ground truth in terms of isosurface structure and viewpoint, producing a very similar vortex topology. The main mismatches are stylistic: the background is not white and the surface appears cooler/less beige, which affects overall appearance and contrast.

📊 Detailed Metrics

Visualization Quality

32/40

Output Generation

5/5

Efficiency

3/10

PSNR

32.94 dB

SSIM

0.9808

LPIPS

0.0136

Input Tokens

636,827

Output Tokens

6,740

Total Tokens

643,567

Total Cost

$3.2852

📝 write-ply

⚠️ LOW SCORE

20/45 (44.4%)

📋 Task Description

I would like to use ParaView to visualize a dataset. Create a wavelet object. Change the view size to 400x400. Show the wavelet object and reset the camera to fit the data. Next, create a contour of wavelet object from the dataset "RTData". The contour should have isosurfaces at the following values: 97.222075, 157.09105, 216.96002500000003, and 276.829. Show the contour and color it with the same colormap that is used for coloring "RTData". Finally, save the contour in PLY format to the file "write-ply/results/{agent_mode}/PLYWriterData.ply". Save the visualization image as "write-ply/results/{agent_mode}/write-ply.png". (Optional, but must save if use paraview) Save the paraview state as "write-ply/results/{agent_mode}/write-ply.pvsm". (Optional, but must save if use python script) Save the python script as "write-ply/results/{agent_mode}/write-ply.py". Do not save any other files, and always save the visualization image.

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Total Score

10/30

Goals

3

Points/Goal

10

Goal 1

1/10

Criterion: Cube Creation: Is the cube object properly created and displayed with correct geometry?

Judge's Assessment: The ground truth shows a multi-surface contour (blue outer fragments, gray middle bands, red inner bands) within a visible bounding box/outline. The result image shows a similar multi-surface contour, but there is no cube object displayed (no distinct cube geometry; only the contour surfaces are visible). Thus the cube creation/display requirement is not met in the result compared to ground truth.

Goal 2

2/10

Criterion: PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?

Judge's Assessment: There is no visual evidence in the result that an exported PLY was re-imported and displayed as a separate dataset (e.g., a second pipeline item, duplicated geometry, or any import artifacts). The result only shows the contour-like geometry. Compared to the ground truth (which at least shows the expected contour scene framing), the PLY import goal is largely unaddressed/unclear.

Goal 3

7/10

Criterion: Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?

Judge's Assessment: The rendered surface geometry in the result broadly matches the ground truth contour appearance: blue outer shell-like pieces, gray layered middle, and red inner isosurfaces with similar overall shape and camera framing. Main differences: the result has a white background instead of the darker background and lacks the visible bounding box/outline seen in the ground truth, which slightly reduces fidelity to the expected rendering presentation.

Overall Assessment

The result matches the expected contour visualization reasonably well in terms of surface geometry and coloring, but it does not show the cube object and provides no clear indication that the PLY was imported back and visualized. Presentation differences (background and missing outline) are also present.

📊 Detailed Metrics

Visualization Quality

10/30

Output Generation

5/5

Efficiency

5/10

Input Tokens

411,689

Output Tokens

4,778

Total Tokens

416,467

Total Cost

$2.1301

📊 Overall Performance

Overall Score

Test Cases

Avg Vision Score

PSNR (Scaled)

SSIM (Scaled)

LPIPS (Scaled)

Completion Rate

ℹ️ About Scaled Metrics

🔧 Configuration

📝 ABC

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 argon-bubble

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 Bernard

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 bonsai

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 carp

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📝 Text-Based Q&A Evaluation

Score

Questions & Correct Answers

Agent's Answers

Judge's Evaluation

📊 Detailed Metrics

📝 chameleon_isosurface

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 chart-opacity

📋 Task Description

🖼️ Visualization Comparison

Ground Truth

Agent Result

📏 Vision Evaluation Rubrics

Score Summary

Overall Assessment

📊 Detailed Metrics

📝 climate

📋 Task Description