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d0e79c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | # Selected 15 Cases for Human Evaluation
# These cases represent diverse visualization capabilities across the benchmark
#
# Each case specifies:
# - name: The case directory name
# - path: Path to the case directory (relative to workspace root)
# - yaml: Path to the YAML file containing evaluation criteria
# - description: Brief description of what the case tests
cases:
- name: argon-bubble
path: SciVisAgentBench-tasks/paraview/argon-bubble
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Color & Opacity Mapping, Volume Rendering
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: richtmyer
path: SciVisAgentBench-tasks/paraview/richtmyer
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Color & Opacity Mapping, Volume Rendering
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: foot
path: SciVisAgentBench-tasks/paraview/foot
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Volume Rendering
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: crayfish_streamline
path: SciVisAgentBench-tasks/paraview/crayfish_streamline
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Surface & Contour Extraction
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: twoswirls_streamribbon
path: SciVisAgentBench-tasks/paraview/twoswirls_streamribbon
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Surface & Contour Extraction
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: tornado
path: SciVisAgentBench-tasks/paraview/tornado
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Surface & Contour Extraction, Glyph & Marker Placement
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: tgc-velocity_contour
path: SciVisAgentBench-tasks/paraview/tgc-velocity_contour
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Surface & Contour Extraction
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: rti-velocity_slices
path: SciVisAgentBench-tasks/paraview/rti-velocity_slices
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: View & Camera Control, Data Subsetting & Extraction
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: rti-velocity_glyph
path: SciVisAgentBench-tasks/paraview/rti-velocity_glyph
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Glyph & Marker Placement, Data Subsetting & Extraction
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: supernova_isosurface
path: SciVisAgentBench-tasks/paraview/supernova_isosurface
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Surface & Contour Extraction (isosurface)
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: time-varying
path: SciVisAgentBench-tasks/paraview/time-varying
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Temporal Processing
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: chart-opacity
path: SciVisAgentBench-tasks/paraview/chart-opacity
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Plot & Chart Generation
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: climate
path: SciVisAgentBench-tasks/paraview/climate
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Field Computation
agent_mode: chatvis_claude-sonnet-4-5_exp1
# - name: subseries-of-time-series
# path: SciVisAgentBench-tasks/paraview/subseries-of-time-series
# yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
# description: Dataset Restructuring
# agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: shrink-sphere
path: SciVisAgentBench-tasks/paraview/shrink-sphere
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Geometric & Topological Transformation
agent_mode: chatvis_claude-sonnet-4-5_exp1
- name: import-gltf
path: SciVisAgentBench-tasks/paraview/import-gltf
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Dataset Restructuring, View & Camera Control
agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
- name: render-histogram
path: SciVisAgentBench-tasks/paraview/render-histogram
yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
description: Plot & Chart Generation, Color & Opacity Mapping
agent_mode: chatvis_claude-sonnet-4-5_exp1
# From molecular_vis/workflows (2 cases)
- name: curved-membrane
path: SciVisAgentBench-tasks/molecular_vis/workflows/curved-membrane
yaml: benchmark/eval_cases/molecular_vis/workflows/eval_analysis_workflows.yaml
description: Data Subsetting & Extraction
agent_mode: gmx_vmd_mcp_claude-sonnet-4-5_exp1
- name: ras-raf-membrane
path: SciVisAgentBench-tasks/molecular_vis/workflows/ras-raf-membrane
yaml: benchmark/eval_cases/molecular_vis/workflows/eval_analysis_workflows.yaml
description: View & Camera Control
agent_mode: gmx_vmd_mcp_claude-sonnet-4-5_exp1
- name: bio_isosurface-determination
path: SciVisAgentBench-tasks\bioimage_data\eval_iso_surface_determination\operation_1
yaml: benchmark\eval_cases\napari\1_workflows\eval_iso_surface_determination.yaml
description: Surface & Contour Extraction (isosurface)
agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
task_description:
1. Read the file "data/dataset_003/eval_iso_surface_determination_target_1.txt" to get the target iso-surface values for different tooth structures.
2. Load data/dataset_003/dataset_003.tif into napari.
3. Switch to 3D view mode and set the rendering to iso.
4. Find the iso surface value that shows the target clearly.
5. Rotate the camera to several angles and take a screenshot of the result each time to check if the target structure is clearly visible from different angles.
6. If the target structure is not clearly visible, adjust the iso surface value and take a screenshot again.
7. Stop when the target structure is clearly visible or you have tried five different iso surface values.
8. Save the final screenshot to "eval_iso_surface_determination/screenshot.png".
vision-rubrics:
1. Does the result rendering look similar to ground truth?
2. Does the visualization show the target structure clearly?
- name: bio_visualization-workflows
path: SciVisAgentBench-tasks\bioimage_data\eval_visualization_workflows\operation_1
yaml: benchmark\eval_cases\napari\1_workflows\eval_visualization_workflows.yaml
description: Color & Opacity Mapping, Volume Rendering, Temporal Processing
agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
task_description:
1. Load the "data/dataset_002/dataset_002.tif" dataset into napari.
2. Depending on the number of channels, set the colormap for the first channel 0 to red and channel 1 to green.
3. Switch to the 3D view.
4. Use additive blending for all channels to create an overlay visualization.
5. Go the timestep 14.
Q1. Does the cell show protrusions? (Yes/No)
6. Take a screenshot of the result, save it to "eval_visualization_workflows/screenshot_1.png"
7. Answer Q1 in a plain text file "eval_visualization_workflows/Q1_answer.txt".
vision-rubrics:
1. Does the visualization show a green cell with red blobs on the inside?
2. Does the result rendering look similar to ground truth?
- name: bio_figure-recreation
path: SciVisAgentBench-tasks\bioimage_data\eval_figure_recreation\operation_1
yaml: benchmark\eval_cases\napari\1_workflows\eval_figure_recreation.yaml
description: Color & Opacity Mapping, Volume Rendering
agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
task_description:
1. Load the dataset into napari "data/dataset_001/dataset_001.tiff"
2. Read the target figure "data/dataset_001/dataset_001.png" but don't load it into napari.
3. Read the dataset description "data/dataset_001/dataset_001.yaml".
4. Set the same colormaps and blending modes as the target figure.
5. Adjust contrast and gamma as needed to match the target figure.
6. Take a screenshot of your recreation.
7. If the recreation does not match the target figure, adjust the visualization settings and take a screenshot again.
8. Stop when the recreation matches the target figure or you have tried five different visualization settings.
9. Save the final screenshot to "eval_figure_recreation/screenshot.png".
vision-rubrics:
1. Does the visualization show a green cell with red blobs on the inside?
2. Does the result rendering look similar to ground truth? |