merge chatvis bench and main

.gitignore

@@ -7,4 +7,4 @@ statistics/
 .cache/
 .claude/
 .vscode/
-eval_cases/paraview/main_cases_old.yaml
+backup/

eval_cases/paraview/category_specific_cases.yaml

@@ -1,218 +0,0 @@
-# Medical/Anatomical Data Visualization Test Cases for SciVisAgentBench
-# Tests scalar volume visualization capabilities for medical imaging data
-
-# Test 1: Basic Volume Rendering and Tissue Identification
-- vars:
-    question: |
-      Clear the ParaView pipeline and load the data file "foot/data/foot_256x256x256_uint8.raw".
-      1. Enable volume rendering to visualize the internal structures
-      2. Adjust the opacity transfer function to reveal both bone and soft tissue (bone should be more opaque, soft tissue semi-transparent)
-      3. Set an appropriate color map to differentiate tissue types (e.g., white/beige for bone, reddish for soft tissue)
-      Finally, save the paraview state as "foot/results/{agent_mode}/foot.pvsm"
-  assert:
-    - type: llm-rubric
-      value: |
-        - Successfully load the foot dataset
-        - Enable volume rendering
-        - Adjust opacity to show both bone and soft tissue structures
-        - Apply appropriate color mapping for tissue differentiation
-        - Use screenshot to verify foot bones (metatarsals, phalanges) and soft tissue are visible
-        - Verify the colors match the instruction (white/beige for bone, reddish for soft tissue)
-
-# # Test 2: Multi-Isosurface Segmentation
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "mri_ventricles/data/mri_ventricles_256x256x124_uint8.raw".
-#       1. Create at least 3 different isosurfaces at different threshold values to segment different tissue types
-#       2. Color each isosurface differently to distinguish structures
-#       3. Make appropriate surfaces semi-transparent if needed to show internal structures
-#       Finally, save the paraview state as "mri_ventricles/results/{agent_mode}/mri_ventricles.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-#         - Create multiple isosurfaces (at least 3) at different threshold values
-#         - Apply different colors to each isosurface for clear distinction
-#         - Use screenshot to verify brain ventricles are successfully segmented and visible
-#         - Verify transparency is applied where needed to show nested structures
-#         - Report identification of brain structures (ventricles, grey/white matter boundaries)
-
-# # Test 3: Cross-Sectional Analysis
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "skull/data/skull_256x256x256_uint8.raw".
-#       1. Create three orthogonal slices (axial, sagittal, and coronal planes)
-#       2. Position the slices to show key anatomical features of the skull
-#       3. Apply an appropriate color map to the slices
-#       Finally, save the paraview state as "skull/results/{agent_mode}/skull.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Create exactly three orthogonal slices (axial, sagittal, coronal)
-#         - Use screenshot to verify all three slice planes are visible simultaneously
-#         - Verify slices show key features: cranial cavity, eye sockets, nasal cavity
-#         - Report which anatomical features are visible in each specific plane
-
-# # Test 4: Vascular Structure Visualization
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "aneurism/data/aneurism_256x256x256_uint8.raw".
-#       1. Use appropriate visualization technique to isolate and display the vascular structure
-#       2. Create an isosurface that clearly shows the aneurysm and blood vessels
-#       3. Apply a red or red-gradient color map appropriate for vascular visualization
-#       4. Compute and report the surface area of the vascular structure
-#       Finally, save the paraview state as "aneurism/results/{agent_mode}/aneurism.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Successfully isolate vascular structure using isosurface
-#         - Use screenshot to verify aneurysm bulge is clearly visible
-#         - Verify red/red-gradient coloring is applied to vessels
-#         - Successfully compute and report numerical surface area value
-
-# # Test 5: Histogram Analysis and Tissue Classification
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "pancreas/data/pancreas_240x512x512_int16.raw".
-#       1. Generate a histogram of the intensity values with 256 bins
-#       2. Based on the histogram, identify distinct peaks corresponding to different tissue types
-#       3. Create threshold filters to isolate pancreatic tissue based on the histogram analysis
-#       4. Apply volume rendering with opacity settings based on your histogram findings
-#       Finally, save the paraview state as "pancreas/results/{agent_mode}/pancreas.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Successfully generate histogram with 256 bins
-#         - Report specific intensity values for identified peaks
-#         - Create threshold filters using values derived from histogram peaks
-#         - Apply volume rendering with opacity based on histogram analysis
-#         - Use screenshot to verify pancreatic tissue is properly isolated
-
-# # Test 6: Complex Organ System Visualization
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "mri_woman/data/mri_woman_256x256x109_uint16.raw".
-#       1. Enable volume rendering to show internal anatomy
-#       2. Adjust the transfer functions to visualize multiple organ systems simultaneously
-#       3. Create a clip plane to show a sagittal cross-section while maintaining volume rendering
-#       4. Use appropriate color and opacity settings to distinguish between organs
-#       Finally, save the paraview state as "mri_woman/results/{agent_mode}/mri_woman.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Enable volume rendering successfully
-#         - Adjust transfer functions to reveal multiple organ systems
-#         - Create clip plane in sagittal orientation
-#         - Use screenshots from anterior and lateral views to verify organ visibility
-#         - List identified organs (e.g., heart, lungs, liver, spine)
-#         - Verify clip plane and volume rendering work together
-
-# # Test 7: Small Animal Specimen Analysis
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "frog/data/frog_256x256x44_uint8.raw".
-#       1. Create visualization showing both skeletal and soft tissue structures
-#       2. Use either multiple isosurfaces or volume rendering with careful transfer functions
-#       3. Generate a plot-over-line measurement through the specimen (dorsal to ventral)
-#       Finally, save the paraview state as "frog/results/{agent_mode}/frog.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Choose appropriate technique (isosurfaces or volume rendering)
-#         - Use screenshot to verify both skeleton and soft tissue are visible
-#         - Successfully create plot-over-line from dorsal to ventral
-#         - Report numerical values from plot-over-line measurement
-#         - Verify measurement traverses through the specimen correctly
-
-# # Test 8: Dental Structure Analysis
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "tooth/data/tooth_103x94x161_uint8.raw".
-#       1. Create isosurface to show the tooth enamel (outer layer)
-#       2. Use volume rendering or additional isosurface to show internal structures (dentin, pulp cavity)
-#       3. Apply appropriate colors (white for enamel, yellow for dentin)
-#       Finally, save the paraview state as "tooth/results/{agent_mode}/tooth.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Create isosurface at appropriate threshold for enamel
-#         - Use screenshot to verify white color on enamel and yellow/amber color on dentin
-#         - Identify enamel, dentin, and pulp cavity structures
-
-# # Test 9: Abdominal CT with Vascular Stent Visualization
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "stent/data/stent_512x512x174_uint16.raw".
-#       This is an abdominal/pelvic CT scan containing a stent in the abdominal aorta (no contrast agent used).
-#       1. Create a volume rendering to show the overall abdominal anatomy
-#       2. Adjust the opacity transfer function to visualize bones (spine, pelvis) and soft tissue
-#       3. Create an isosurface at a high threshold value to isolate and highlight the metallic stent
-#       4. Apply appropriate colors - bone white/beige, soft tissue reddish, metallic grey for the stent
-#       Finally, save the paraview state as "stent/results/{agent_mode}/stent.pvsm"
-
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Create volume rendering of full abdomen/pelvis
-#         - Use screenshot to verify spine and pelvis are visible
-#         - Create high-threshold isosurface to isolate metallic stent
-#         - Verify stent appears with metallic/grey coloring
-        
-# # # Test 10: Comparative Analysis with State Saving
-# # - vars:
-# #     question: |
-# #       Clear the ParaView pipeline and load the data file "vis_male/data/vis_male_128x256x256_uint8.raw".
-# #       1. Create two different visualization approaches:
-# #          a) Volume rendering optimized for soft tissue
-# #          b) Isosurface extraction for skeletal system
-# #       2. Save the ParaView state file to preserve your visualization setup
-# #       3. Create a text report comparing the effectiveness of both techniques
-# #   assert:
-# #     - type: llm-rubric
-# #       value: |
-# # 
-# #         - Create volume rendering with soft tissue emphasis
-# #         - Create separate isosurface for skeletal system
-# #         - Use screenshots to document both approaches
-# #         - Successfully save .pvsm state file
-# #         - Write and save comparative analysis as text file
-# #         - Report specific advantages/disadvantages of each technique
-# #         - Verify both visualizations show distinct anatomical features
-
-# # Test 11: Marine Biology Specimen
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "lobster/data/lobster_301x324x56_uint8.raw".
-#       1. Create visualization showing the exoskeleton and internal anatomy
-#       2. Adjust the visualization to show the prominent structure
-#       Finally, save the paraview state as "lobster/results/{agent_mode}/lobster.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - Choose appropriate visualization for exoskeleton (isosurface or volume)
-#         - Use screenshot to verify whether specific features can be identified: claws, abdomen, tail fan
-
-# # Test 12: Fish Anatomy Visualization  
-# - vars:
-#     question: |
-#       Clear the ParaView pipeline and load the data file "carp/data/carp_256x256x512_uint16.raw".
-#       1. Enable volume rendering to show internal fish anatomy
-#       2. Adjust transfer functions to reveal skeletal structure and organs
-#       3. Create a sagittal slice through the fish body
-#       4. Use color mapping that differentiates tissue types
-#       5. Reset camera with appropriate padding to frame the entire fish
-#       Finally, save the paraview state as "carp/results/{agent_mode}/carp.pvsm"
-#   assert:
-#     - type: llm-rubric
-#       value: |
-
-#         - The volume rendering show both skeleton and organs
-#         - Create sagittal slice along fish body length
-#         - Verify sagittal slice shows internal structure along body axis

eval_cases/paraview/chatvis_bench_cases.yaml

@@ -1,465 +0,0 @@
-# 1. line-plot
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "line-plot/results/{agent_mode}/line-plot.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Line Visualization Quality: Are multiple distinct lines clearly visible and properly rendered showing the evolution of different variables along the specified path?
-
-          2. Variable Differentiation: Are all dataset variables visually distinguishable through distinct colors or line styles with clear separation between curves?
-
-          3. Axis and Scale Appropriateness: Do the plot axes display appropriate ranges and scaling that effectively show the data trends and variations?
-
-          4. Legend and Readability: Is there a clear legend identifying each variable line with readable labels and proper visual organization?
-
-# 2. ml-dvr
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "ml-dvr/results/{agent_mode}/ml-dvr.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Volume Rendering Quality: Is the volume rendering properly generated with appropriate opacity and color mapping that reveals internal structures?
-
-          2. Transfer Function Application: Does the default transfer function effectively highlight meaningful data features and provide good visual contrast?
-
-          3. Isometric View Setup: Is the visualization displayed from an isometric viewpoint that provides a clear three-dimensional perspective of the volume?
-
-          4. Visual Clarity and Detail: Are the volume details clearly visible with proper lighting and shading that enhances depth perception?
-
-# 3. ml-iso
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "ml-iso/results/{agent_mode}/ml-iso.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Isosurface Generation: Is the isosurface properly generated at the specified value (0.5) with correct topology and continuity?
-
-          2. Surface Rendering Quality: Does the isosurface display smooth surfaces with appropriate shading and lighting that reveals the 3D structure?
-
-          3. Geometric Accuracy: Are the surface features geometrically correct and free from artifacts or discontinuities?
-
-          4. Visual Presentation: Is the isosurface clearly visible with good contrast and coloring that enhances the understanding of the data structure?
-
-# 4. ml-slice-iso
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "ml-slice-iso/results/{agent_mode}/ml-slice-iso.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Slice Generation: Is the y-z plane slice properly generated at x=0 position showing the correct cross-section of the volume?
-
-          2. Contour on Slice: Are the contour lines at value 0.5 correctly extracted from the slice and properly displayed?
-
-          3. Red Color Application: Is the contour visualization properly colored red as specified in the requirements?
-
-          4. View Direction: Is the visualization displayed from the correct +x direction view that provides clear visibility of the slice and contours?
-
-# 5. points-surf-clip
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "points-surf-clip/results/{agent_mode}/points-surf-clip.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Delaunay Triangulation Quality: Is the 3D Delaunay triangulation properly generated creating a valid mesh structure from the point data?
-
-          2. 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?
-
-          3. Wireframe Representation: Is the result displayed as a clear wireframe showing the triangulated mesh structure with visible edges?
-
-          4. Geometric Integrity: Does the clipped wireframe maintain proper connectivity and show the expected geometric features without artifacts?
-
-# 6. shrink-sphere
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm".
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Sphere Creation and Resolution: Is the sphere created with doubled theta resolution providing higher geometric detail and smoother curvature?
-
-          2. Shrink Filter Application: Is the shrink filter properly applied with halved shrink factor creating visible separation between mesh elements?
-
-          3. Dual Representation: Are both the wireframe sphere and shrink filter results simultaneously visible and properly grouped together?
-
-          4. Visual Quality: Does the visualization clearly show the contrast between the wireframe structure and the shrunken elements with appropriate white background?
-
-# 7. stream-glyph
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm".
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Streamline Generation: Are streamlines properly traced following the V variable flow field with appropriate seeding from the point cloud?
-
-          2. Tube and Glyph Rendering: Are streamlines rendered as tubes with cone glyphs properly attached showing flow direction and magnitude?
-
-          3. Temperature Color Mapping: Are both streamlines and glyphs correctly colored by the Temp variable with appropriate color scaling?
-
-          4. View Configuration: Is the visualization displayed from the correct +x view direction providing clear visibility of the flow patterns and structures?
-
-# 8. time-varying
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "time-varying/results/{agent_mode}/time-varying.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Temporal Animation Quality: Does the animation smoothly progress through all time steps showing the evolution of the EQPS variable over time?
-
-          2. Dual View Configuration: Are both render views properly configured with the second view showing temporal interpolation effects compared to the first?
-
-          3. Color Mapping and Legend: Is the EQPS variable properly color-mapped with an appropriate color bar legend visible throughout the animation?
-
-          4. View Direction and Layout: Is the +y direction view properly set and are both views arranged side-by-side in the correct layout configuration?
-
-# 9. chart-opacity
-- vars:
-    question: |
-          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 a screenshot in "chart-opacity/results/{agent_mode}/chart-opacity.png".
-          Finally, save the ParaView state as "chart-opacity/results/{agent_mode}/chart-opacity.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Chart Generation: Is the plot over line chart properly created from the wavelet data?
-
-          2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted, showing all three specified variables and distinguishable in the chart?
-
-          3. Opacity Settings: Is the arc_length variable displayed with full opacity (1.0) while Points_Z and RTData show reduced opacity (0.3)?
-
-          4. Chart Clarity: Does the chart provide clear visualization of the data trends with appropriate axis scaling and readable formatting?
-
-# 10. color-blocks
-- vars:
-    question: |
-          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 a screenshot of the visualization in the file "color-blocks/results/{agent_mode}/color-blocks.png".
-          Finally, save the ParaView state as "color-blocks/results/{agent_mode}/color-blocks.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Block Color Mapping: Is the dataset properly colored by vtkBlockColors field with distinct block visualization?
-
-          2. Individual Block Coloring: Is block_2 correctly colored using the x component of the ACCL variable with appropriate scaling?
-
-          3. Color Transfer Functions: Are the color transfer functions properly applied with cool to warm coloring for the ACCL variable?
-
-          4. View Configuration: Is the dataset displayed from the -y direction with blue-gray background and visible color bar legend?
-
-# 11. color-data
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "color-data/results/{agent_mode}/color-data.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
-
-          2. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
-
-          3. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?
-
-# 12. export-gltf
-- vars:
-    question: |
-          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 a screenshot to the file "export-gltf/results/{agent_mode}/export-gltf.png".
-          Finally, save the ParaView state as "export-gltf/results/{agent_mode}/export-gltf.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. GLTF Export Quality: Is the wavelet object properly exported to GLTF format with correct surface representation and RTData coloring?
-
-          2. GLTF Import and Display: Is the exported GLTF file successfully loaded and displayed as a surface with proper geometry?
-
-          3. Texture Coordinate Coloring: Is the imported GLTF object correctly colored by TEXCOORD_0 with Cool to Warm colormap?
-
-          4. Clean Presentation: Are the color bar and orientation axes properly hidden for a clean visualization appearance?
-
-# 13. import-gltf
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "import-gltf/results/{agent_mode}/import-gltf.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. GLTF Import Success: Are the specified GLTF nodes properly imported and displayed as separate geometric components?
-
-          2. Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?
-
-          3. 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.
-
-          4. Layout Configuration: Is the view properly sized to 300x300 pixels with correct rendering and background palette?
-
-# 14. render-histogram
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "render-histogram/results/{agent_mode}/render-histogram.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Wavelet Visualization: Is the wavelet object properly rendered with RTDATA coloring and visible color bar?
-
-          2. Split View Layout: Is the view correctly split with the wavelet visualization on the left and histogram on the right?
-
-          3. Histogram Generation: Is the histogram properly generated from RTDATA showing the data distribution?
-
-          4. Color Map Consistency: Are both the wavelet visualization and histogram using the same Cool to Warm color map?
-
-# 15. reset-camera-direction
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "reset-camera-direction/results/{agent_mode}/reset-camera-direction.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Wavelet Creation: Is the Wavelet object properly created and displayed in the scene?
-
-          2. Surface with Edges Representation: Is the wavelet correctly displayed with "Surface with Edges" representation showing both surface and wireframe?
-
-          3. Camera Direction: Is the camera positioned according to the specified direction vector [0.5, 1, 0.5]?
-
-          4. View Quality: Does the visualization provide a clear view of the wavelet structure from the specified camera angle?
-
-# 16. save-transparent
-- vars:
-    question: |
-          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.
-          Finally, save the ParaView state as "save-transparent/results/{agent_mode}/save-transparent.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Object Creation: Is the wavelet object properly created and displayed in the scene? Looking similar to the GT image?
-
-          2. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?
-
-# 17. subseries-of-time-series
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "subseries-of-time-series/results/{agent_mode}/subseries-of-time-series.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Data Loading and Block Selection: Are the specified element blocks properly loaded and the slice plane correctly applied?
-
-          2. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
-
-          3. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?
-
-# 18. write-ply
-- vars:
-    question: |
-          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 a screenshot to the file "write-ply/results/{agent_mode}/write-ply.png".
-          Finally, save the ParaView state as "write-ply/results/{agent_mode}/write-ply.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Cube Creation: Is the cube object properly created and displayed with correct geometry?
-
-          2. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
-
-          3. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?
-
-# 19. climate
-- vars:
-    question: |
-          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".
-          Finally, save the ParaView state as "climate/results/{agent_mode}/climate.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
-
-          2. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
-
-          3. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?
-
-# 20. materials
-- vars:
-    question: |
-          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 a screenshot of both views in "materials/results/{agent_mode}/materials.png".
-          Finally, save the ParaView state as "materials/results/{agent_mode}/materials.pvsm"
-
-  assert:
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Side-by-Side Comparison: Are both datasets properly displayed in side-by-side views with correct dimensions and labeling?
-
-          2. Data Conversion and Filtering: Are the Intensity and Phase variables correctly converted to point data and isovolume filtering applied?
-
-          3. Clipping and Color Mapping: Is the plane clipping correctly applied and Viridis colormap properly used for Phase variable?
-
-          4. Camera and Layout: Is the camera positioned correctly in (-1, 0, -1) direction with appropriate fitting and legends visible?
-

eval_cases/paraview/{main_cases.yaml → paraview_cases.yaml}

@@ -937,3 +937,510 @@
           3. Surface Appearance: Does the surface color and shading appear similar to the ground truth?
 
           4. Visualization Clarity: Are the vortex features clearly visible and comparable to the ground truth?
+
+# Case 29: line-plot
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Line Visualization Quality: Are multiple distinct lines clearly visible and properly rendered showing the evolution of different variables along the specified path?
+
+          2. Variable Differentiation: Are all dataset variables visually distinguishable through distinct colors or line styles with clear separation between curves?
+
+          3. Axis and Scale Appropriateness: Do the plot axes display appropriate ranges and scaling that effectively show the data trends and variations?
+
+          4. Legend and Readability: Is there a clear legend identifying each variable line with readable labels and proper visual organization?
+
+# Case 30: ml-dvr
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Volume Rendering Quality: Is the volume rendering properly generated with appropriate opacity and color mapping that reveals internal structures?
+
+          2. Transfer Function Application: Does the default transfer function effectively highlight meaningful data features and provide good visual contrast?
+
+          3. Isometric View Setup: Is the visualization displayed from an isometric viewpoint that provides a clear three-dimensional perspective of the volume?
+
+          4. Visual Clarity and Detail: Are the volume details clearly visible with proper lighting and shading that enhances depth perception?
+
+# Case 31: ml-iso
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Isosurface Generation: Is the isosurface properly generated at the specified value (0.5) with correct topology and continuity?
+
+          2. Surface Rendering Quality: Does the isosurface display smooth surfaces with appropriate shading and lighting that reveals the 3D structure?
+
+          3. Geometric Accuracy: Are the surface features geometrically correct and free from artifacts or discontinuities?
+
+          4. Visual Presentation: Is the isosurface clearly visible with good contrast and coloring that enhances the understanding of the data structure?
+
+# Case 32: ml-slice-iso
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Slice Generation: Is the y-z plane slice properly generated at x=0 position showing the correct cross-section of the volume?
+
+          2. Contour on Slice: Are the contour lines at value 0.5 correctly extracted from the slice and properly displayed?
+
+          3. Red Color Application: Is the contour visualization properly colored red as specified in the requirements?
+
+          4. View Direction: Is the visualization displayed from the correct +x direction view that provides clear visibility of the slice and contours?
+
+# Case 33: points-surf-clip
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Delaunay Triangulation Quality: Is the 3D Delaunay triangulation properly generated creating a valid mesh structure from the point data?
+
+          2. 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?
+
+          3. Wireframe Representation: Is the result displayed as a clear wireframe showing the triangulated mesh structure with visible edges?
+
+          4. Geometric Integrity: Does the clipped wireframe maintain proper connectivity and show the expected geometric features without artifacts?
+
+# Case 34: shrink-sphere
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Sphere Creation and Resolution: Is the sphere created with doubled theta resolution providing higher geometric detail and smoother curvature?
+
+          2. Shrink Filter Application: Is the shrink filter properly applied with halved shrink factor creating visible separation between mesh elements?
+
+          3. Dual Representation: Are both the wireframe sphere and shrink filter results simultaneously visible and properly grouped together?
+
+          4. Visual Quality: Does the visualization clearly show the contrast between the wireframe structure and the shrunken elements with appropriate white background?
+
+# Case 35: stream-glyph
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Streamline Generation: Are streamlines properly traced following the V variable flow field with appropriate seeding from the point cloud?
+
+          2. Tube and Glyph Rendering: Are streamlines rendered as tubes with cone glyphs properly attached showing flow direction and magnitude?
+
+          3. Temperature Color Mapping: Are both streamlines and glyphs correctly colored by the Temp variable with appropriate color scaling?
+
+          4. View Configuration: Is the visualization displayed from the correct +x view direction providing clear visibility of the flow patterns and structures?
+
+# Case 36: time-varying
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Temporal Animation Quality: Does the animation smoothly progress through all time steps showing the evolution of the EQPS variable over time?
+
+          2. Dual View Configuration: Are both render views properly configured with the second view showing temporal interpolation effects compared to the first?
+
+          3. Color Mapping and Legend: Is the EQPS variable properly color-mapped with an appropriate color bar legend visible throughout the animation?
+
+          4. View Direction and Layout: Is the +y direction view properly set and are both views arranged side-by-side in the correct layout configuration?
+
+# Case 37: chart-opacity
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Chart Generation: Is the plot over line chart properly created from the wavelet data?
+
+          2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted, showing all three specified variables and distinguishable in the chart?
+
+          3. Opacity Settings: Is the arc_length variable displayed with full opacity (1.0) while Points_Z and RTData show reduced opacity (0.3)?
+
+          4. Chart Clarity: Does the chart provide clear visualization of the data trends with appropriate axis scaling and readable formatting?
+
+# Case 38: color-blocks
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Block Color Mapping: Is the dataset properly colored by vtkBlockColors field with distinct block visualization?
+
+          2. Individual Block Coloring: Is block_2 correctly colored using the x component of the ACCL variable with appropriate scaling?
+
+          3. Color Transfer Functions: Are the color transfer functions properly applied with cool to warm coloring for the ACCL variable?
+
+          4. View Configuration: Is the dataset displayed from the -y direction with blue-gray background and visible color bar legend?
+
+# Case 39: color-data
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
+
+          2. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
+
+          3. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?
+
+# Case 40: export-gltf
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. GLTF Export Quality: Is the wavelet object properly exported to GLTF format with correct surface representation and RTData coloring?
+
+          2. GLTF Import and Display: Is the exported GLTF file successfully loaded and displayed as a surface with proper geometry?
+
+          3. Texture Coordinate Coloring: Is the imported GLTF object correctly colored by TEXCOORD_0 with Cool to Warm colormap?
+
+          4. Clean Presentation: Are the color bar and orientation axes properly hidden for a clean visualization appearance?
+
+# Case 41: import-gltf
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. GLTF Import Success: Are the specified GLTF nodes properly imported and displayed as separate geometric components?
+
+          2. Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?
+
+          3. 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.
+
+          4. Layout Configuration: Is the view properly sized to 300x300 pixels with correct rendering and background palette?
+
+# Case 42: render-histogram
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Wavelet Visualization: Is the wavelet object properly rendered with RTDATA coloring and visible color bar?
+
+          2. Split View Layout: Is the view correctly split with the wavelet visualization on the left and histogram on the right?
+
+          3. Histogram Generation: Is the histogram properly generated from RTDATA showing the data distribution?
+
+          4. Color Map Consistency: Are both the wavelet visualization and histogram using the same Cool to Warm color map?
+
+# Case 43: reset-camera-direction
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Wavelet Creation: Is the Wavelet object properly created and displayed in the scene?
+
+          2. Surface with Edges Representation: Is the wavelet correctly displayed with "Surface with Edges" representation showing both surface and wireframe?
+
+          3. Camera Direction: Is the camera positioned according to the specified direction vector [0.5, 1, 0.5]?
+
+          4. View Quality: Does the visualization provide a clear view of the wavelet structure from the specified camera angle?
+
+# Case 44: save-transparent
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Object Creation: Is the wavelet object properly created and displayed in the scene? Looking similar to the GT image?
+
+          2. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?
+
+# Case 45: subseries-of-time-series
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Data Loading and Block Selection: Are the specified element blocks properly loaded and the slice plane correctly applied?
+
+          2. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
+
+          3. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?
+
+# Case 46: write-ply
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Cube Creation: Is the cube object properly created and displayed with correct geometry?
+
+          2. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
+
+          3. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?
+
+# Case 47: climate
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
+
+          2. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
+
+          3. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?
+
+# Case 48: materials
+- vars:
+    question: |
+          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.
+
+  assert:
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          1. Side-by-Side Comparison: Are both datasets properly displayed in side-by-side views with correct dimensions and labeling?
+
+          2. Data Conversion and Filtering: Are the Intensity and Phase variables correctly converted to point data and isovolume filtering applied?
+
+          3. Clipping and Color Mapping: Is the plane clipping correctly applied and Viridis colormap properly used for Phase variable?
+
+          4. Camera and Layout: Is the camera positioned correctly in (-1, 0, -1) direction with appropriate fitting and legends visible?

eval_cases/paraview/what_obj_cases.yaml

@@ -114,39 +114,7 @@
       value: |
           The visualization image shows a carp or fish anatomy with a reasonable transfer function, and the object is shown clearly
 
-# 8. CSAFE Heptane Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/csafe_heptane/data/csafe_heptane_302x302x302_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/csafe_heptane/results/{agent_mode}/csafe_heptane.pvsm"
-        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/csafe_heptane/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should recognize combustion or heptane simulation data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows combustion or heptane simulation data with a reasonable transfer function, and the object is shown clearly
-
-# 9. Duct Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/duct/data/duct_193x194x1000_float32.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/duct/results/{agent_mode}/duct.pvsm"
-        Take a screenshot and provide a textual report describing what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/duct/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify flow patterns in a duct geometry
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows flow patterns in a duct geometry with a reasonable transfer function, and the object is shown clearly
-
-# 10. Engine Dataset
+# 8. Engine Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/engine/data/engine_256x256x128_uint8.raw".
@@ -162,7 +130,7 @@
       value: |
           The visualization image shows an engine or mechanical components with a reasonable transfer function, and the object is shown clearly
 
-# 11. Foot Dataset
+# 9. Foot Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/foot/data/foot_256x256x256_uint8.raw".
@@ -178,7 +146,7 @@
       value: |
           The visualization image shows a foot with bone and tissue structures with a reasonable transfer function, and the object is shown clearly
 
-# 12. Frog Dataset
+# 10. Frog Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/frog/data/frog_256x256x44_uint8.raw".
@@ -194,7 +162,7 @@
       value: |
           The visualization image shows a frog specimen with internal anatomy with a reasonable transfer function, and the object is shown clearly
 
-# 13. Fuel Dataset
+# 11. Fuel Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/fuel/data/fuel_64x64x64_uint8.raw".
@@ -210,7 +178,7 @@
       value: |
           The visualization image shows fuel combustion or related simulation with a reasonable transfer function, and the object is shown clearly
 
-# 14. Hydrogen Atom Dataset
+# 12. Hydrogen Atom Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/hydrogen_atom/data/hydrogen_atom_128x128x128_uint8.raw".
@@ -226,7 +194,7 @@
       value: |
           The visualization image shows hydrogen atom orbital or probability distribution with a reasonable transfer function, and the object is shown clearly
 
-# 15. Lobster Dataset
+# 13. Lobster Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/lobster/data/lobster_301x324x56_uint8.raw".
@@ -242,23 +210,7 @@
       value: |
           The visualization image shows a lobster or crustacean anatomy with a reasonable transfer function, and the object is shown clearly
 
-# 16. Marschner-Lobb Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/marschner_lobb/data/marschner_lobb_41x41x41_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/marschner_lobb/results/{agent_mode}/marschner_lobb.pvsm"
-        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/marschner_lobb/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should recognize Marschner-Lobb synthetic test pattern
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows Marschner-Lobb synthetic test pattern with a reasonable transfer function, and the object is shown clearly
-
-# 17. MRI Ventricles Dataset
+# 14. MRI Ventricles Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/mri_ventricles/data/mri_ventricles_256x256x124_uint8.raw".
@@ -274,7 +226,7 @@
       value: |
           The visualization image shows brain ventricles or ventricular structures with a reasonable transfer function, and the object is shown clearly
 
-# 18. MRI Woman Dataset
+# 15. MRI Woman Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/mri_woman/data/mri_woman_256x256x109_uint16.raw".
@@ -290,7 +242,7 @@
       value: |
           The visualization image shows human anatomical structures from MRI scan with a reasonable transfer function, and the object is shown clearly
 
-# 19. MRT Angio Dataset
+# 16. MRT Angio Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/mrt_angio/data/mrt_angio_416x512x112_uint16.raw".
@@ -306,23 +258,7 @@
       value: |
           The visualization image shows angiography or vascular structures with a reasonable transfer function, and the object is shown clearly
 
-# 20. Neghip Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/neghip/data/neghip_64x64x64_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/neghip/results/{agent_mode}/neghip.pvsm"
-        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/neghip/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should visualize and describe molecule structure
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows molecule structure with a reasonable transfer function, and the object is shown clearly
-
-# 21. Neocortical Layer 1 Axons Dataset
+# 17. Neocortical Layer 1 Axons Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/neocortical_layer_1_axons/data/neocortical_layer_1_axons_1464x1033x76_uint8.raw".
@@ -338,7 +274,7 @@
       value: |
           The visualization image shows neural axons or neocortical network structures with a reasonable transfer function, and the object is shown clearly
 
-# 22. Nucleon Dataset
+# 18. Nucleon Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/nucleon/data/nucleon_41x41x41_uint8.raw".
@@ -354,7 +290,7 @@
       value: |
           Should visualize nucleon or particle physics data with a reasonable transfer function, and the object is shown clearly
 
-# 23. Pancreas Dataset
+# 19. Pancreas Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/pancreas/data/pancreas_240x512x512_int16.raw".
@@ -370,23 +306,7 @@
       value: |
           The visualization image shows pancreas or pancreatic anatomy with a reasonable transfer function, and the object is shown clearly
 
-# 24. Shockwave Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/shockwave/data/shockwave_64x64x512_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/shockwave/results/{agent_mode}/shockwave.pvsm"
-        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/shockwave/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify shockwave or wave propagation patterns
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows shockwave or wave propagation patterns with a reasonable transfer function, and the object is shown clearly
-
-# 25. Silicium Dataset
+# 20. Silicium Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/silicium/data/silicium_98x34x34_uint8.raw".
@@ -402,7 +322,7 @@
       value: |
           The visualization image shows silicon crystal or material structure with a reasonable transfer function, and the object is shown clearly
 
-# 26. Skull Dataset
+# 21. Skull Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/skull/data/skull_256x256x256_uint8.raw".
@@ -418,7 +338,7 @@
       value: |
           1. Should identify skull or cranial bone structures
 
-# 27. Statue Leg Dataset
+# 22. Statue Leg Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/statue_leg/data/statue_leg_341x341x93_uint8.raw".
@@ -434,7 +354,7 @@
       value: |
           The visualization image shows a statue leg or sculptural form with a reasonable transfer function, and the object is shown clearly
 
-# 28. Stent Dataset
+# 23. Stent Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/stent/data/stent_512x512x174_uint16.raw".
@@ -445,7 +365,7 @@
       value: |
           Should identify a stent or medical device mesh structure
 
-# 29. Supernova Dataset
+# 24. Supernova Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/supernova/data/supernova_256x256x256_float32.raw".
@@ -461,23 +381,7 @@
       value: |
           The visualization image shows supernova or astrophysical explosion simulation with a reasonable transfer function, and the object is shown clearly
 
-# 30. TACC Turbulence Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/tacc_turbulence/data/tacc_turbulence_256x256x256_float32.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/tacc_turbulence/results/{agent_mode}/tacc_turbulence.pvsm"
-        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/tacc_turbulence/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify turbulence or vortex flow structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows turbulence or vortex flow structures with a reasonable transfer function, and the object is shown clearly
-
-# 31. Tooth Dataset
+# 25. Tooth Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/tooth/data/tooth_103x94x161_uint8.raw".
@@ -493,7 +397,7 @@
       value: |
           The visualization image shows tooth or dental anatomy with a reasonable transfer function, and the object is shown clearly
 
-# 32. Tornado Dataset
+# 26. Tornado Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/tornado/data/tornado_64x64x64_float32_scalar3.raw".
@@ -509,7 +413,7 @@
       value: |
           The visualization image shows tornado or vortex flow pattern with a reasonable transfer function, and the object is shown clearly
 
-# 33. Visible Male Dataset
+# 27. Visible Male Dataset
 - vars:
     question: |
         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/vis_male/data/vis_male_128x256x256_uint8.raw".

eval_cases/paraview/what_obj_cases_anonymized.yaml

@@ -1,526 +0,0 @@
-# Comprehensive Data Identification Test Cases for SciVisAgentBench
-# This test evaluates the ability to identify and understand data content
-# without prior knowledge from metadata or naming patterns
-
-# 1. Aneurism Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_001/data/data_001_256x256x256_uint8.raw".
-        Use visualization tools to determine what object or structure is contained in this dataset. Save the paraview state as "anonymized_datasets/dataset_001/results/{agent_mode}/dataset_001.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_001/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          The assistant should identify the data shows an aneurism or vascular structure
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows an aneurism or vascular structure with a reasonable transfer function, and the object is shown clearly
-
-# 2. Backpack Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_002/data/data_002_512x512x373_uint16.raw".
-        Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "anonymized_datasets/dataset_002/results/{agent_mode}/dataset_002.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_002/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          The assistant should identify that the data shows a backpack
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a backpack with a reasonable transfer function, and the object is shown clearly
-
-# 3. Blunt Fin Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_003/data/data_003_256x128x64_uint8.raw".
-        Use visualization tools to determine what object or structure is in this dataset. Save the paraview state as "anonymized_datasets/dataset_003/results/{agent_mode}/dataset_003.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_003/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          The assistant should identify a fin or aerodynamic strcuture or simulation result in the data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a fin or aerodynamic strcuture or simulation result in the data with a reasonable transfer function, and the object is shown clearly
-
-# 4. Bonsai Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_004/data/data_004_256x256x256_uint8.raw".
-        Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "anonymized_datasets/dataset_004/results/{agent_mode}/dataset_004.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_004/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a bonsai tree or botanical structure in the data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a bonsai tree or botanical structure in the data with a reasonable transfer function, and the object is shown clearly
-
-# 5. Boston Teapot Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_005/data/data_005_256x256x178_uint8.raw".
-        Use visualization tools to determine what object is in this dataset. Save the paraview state as "anonymized_datasets/dataset_005/results/{agent_mode}/dataset_005.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_005/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a teapot in the visualization
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a teapot in the visualization with a reasonable transfer function, and the object is shown clearly
-
-# 6. Bunny Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_006/data/data_006_512x512x361_uint16.raw".
-        Use visualization tools to determine what object is in this dataset. Save the paraview state as "anonymized_datasets/dataset_006/results/{agent_mode}/dataset_006.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_006/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a bunny or rabbit in the 3D scanned data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a bunny or rabbit in the 3D scanned data with a reasonable transfer function, and the object is shown clearly
-
-# 7. Carp Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_007/data/carp_256x256x512_uint16.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_007/results/{agent_mode}/dataset_007.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_007/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a carp or fish anatomy
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a carp or fish anatomy with a reasonable transfer function, and the object is shown clearly
-
-# 8. CSAFE Heptane Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_008/data/data_007_302x302x302_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_008/results/{agent_mode}/dataset_008.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_008/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should recognize combustion or heptane simulation data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows combustion or heptane simulation data with a reasonable transfer function, and the object is shown clearly
-
-# 9. Duct Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_009/data/data_008_193x194x1000_float32.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_009/results/{agent_mode}/dataset_009.pvsm"
-        Take a screenshot and provide a textual report describing what you observe and save it to "anonymized_datasets/dataset_009/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify flow patterns in a duct geometry
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows flow patterns in a duct geometry with a reasonable transfer function, and the object is shown clearly
-
-# 10. Engine Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_010/data/data_009_256x256x128_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_010/results/{agent_mode}/dataset_010.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_010/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify an engine or mechanical components
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows an engine or mechanical components with a reasonable transfer function, and the object is shown clearly
-
-# 11. Foot Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_011/data/data_010_256x256x256_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_011/results/{agent_mode}/dataset_011.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_011/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a foot with bone and tissue structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a foot with bone and tissue structures with a reasonable transfer function, and the object is shown clearly
-
-# 12. Frog Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_012/data/data_011_256x256x44_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_012/results/{agent_mode}/dataset_012.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_012/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a frog specimen with internal anatomy
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a frog specimen with internal anatomy with a reasonable transfer function, and the object is shown clearly
-
-# 13. Fuel Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_013/data/fuel_64x64x64_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_013/results/{agent_mode}/dataset_013.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_013/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify fuel combustion or related simulation
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows fuel combustion or related simulation with a reasonable transfer function, and the object is shown clearly
-
-# 14. Hydrogen Atom Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_014/data/data_012_128x128x128_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_014/results/{agent_mode}/dataset_014.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_014/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should recognize hydrogen atom orbital or probability distribution
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows hydrogen atom orbital or probability distribution with a reasonable transfer function, and the object is shown clearly
-
-# 15. Lobster Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_015/data/data_013_301x324x56_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_015/results/{agent_mode}/dataset_015.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_015/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a lobster or crustacean anatomy
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a lobster or crustacean anatomy with a reasonable transfer function, and the object is shown clearly
-
-# 16. Marschner-Lobb Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_016/data/marschner_lobb_41x41x41_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_016/results/{agent_mode}/dataset_016.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_016/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should recognize Marschner-Lobb synthetic test pattern
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows Marschner-Lobb synthetic test pattern with a reasonable transfer function, and the object is shown clearly
-
-# 17. MRI Ventricles Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_017/data/data_014_256x256x124_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_017/results/{agent_mode}/dataset_017.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_017/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify brain ventricles or ventricular structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows brain ventricles or ventricular structures with a reasonable transfer function, and the object is shown clearly
-
-# 18. MRI Woman Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_018/data/data_015_256x256x109_uint16.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_018/results/{agent_mode}/dataset_018.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_018/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify human anatomical structures from MRI scan
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows human anatomical structures from MRI scan with a reasonable transfer function, and the object is shown clearly
-
-# 19. MRT Angio Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_019/data/data_016_416x512x112_uint16.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_019/results/{agent_mode}/dataset_019.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_019/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify angiography or vascular structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows angiography or vascular structures with a reasonable transfer function, and the object is shown clearly
-
-# 20. Neghip Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_020/data/data_017_64x64x64_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_020/results/{agent_mode}/dataset_020.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_020/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should visualize and describe molecule structure
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows molecule structure with a reasonable transfer function, and the object is shown clearly
-
-# 21. Neocortical Layer 1 Axons Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_021/data/neocortical_layer_1_axons_1464x1033x76_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_021/results/{agent_mode}/dataset_021.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_021/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify neural axons or neocortical network structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows neural axons or neocortical network structures with a reasonable transfer function, and the object is shown clearly
-
-# 22. Nucleon Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_022/data/data_018_41x41x41_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_022/results/{agent_mode}/dataset_022.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_022/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should visualize nucleon or particle physics data
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          Should visualize nucleon or particle physics data with a reasonable transfer function, and the object is shown clearly
-
-# 23. Pancreas Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_023/data/pancreas_240x512x512_int16.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_023/results/{agent_mode}/dataset_023.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_023/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify pancreas or pancreatic anatomy
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows pancreas or pancreatic anatomy with a reasonable transfer function, and the object is shown clearly
-
-# 24. Shockwave Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_024/data/shockwave_64x64x512_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_024/results/{agent_mode}/dataset_024.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_024/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify shockwave or wave propagation patterns
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows shockwave or wave propagation patterns with a reasonable transfer function, and the object is shown clearly
-
-# 25. Silicium Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_025/data/silicium_98x34x34_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_025/results/{agent_mode}/dataset_025.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_025/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify silicon crystal or material structure
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows silicon crystal or material structure with a reasonable transfer function, and the object is shown clearly
-
-# 26. Skull Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_026/data/skull_256x256x256_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_026/results/{agent_mode}/dataset_026.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_026/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          1. Should identify skull or cranial bone structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Should identify skull or cranial bone structures
-
-# 27. Statue Leg Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_027/data/data_019_341x341x93_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_027/results/{agent_mode}/dataset_027.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_027/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify a statue leg or sculptural form
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows a statue leg or sculptural form with a reasonable transfer function, and the object is shown clearly
-
-# 28. Stent Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_028/data/stent_512x512x174_uint16.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_028/results/{agent_mode}/dataset_028.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_028/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      value: |
-          Should identify a stent or medical device mesh structure
-
-# 29. Supernova Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_029/data/data_020_256x256x256_float32.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_029/results/{agent_mode}/dataset_029.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_029/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify supernova or astrophysical explosion simulation
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows supernova or astrophysical explosion simulation with a reasonable transfer function, and the object is shown clearly
-
-# 30. TACC Turbulence Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_030/data/tacc_turbulence_256x256x256_float32.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_030/results/{agent_mode}/dataset_030.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_030/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify turbulence or vortex flow structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows turbulence or vortex flow structures with a reasonable transfer function, and the object is shown clearly
-
-# 31. Tooth Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_031/data/data_021_103x94x161_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_031/results/{agent_mode}/dataset_031.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_031/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify tooth or dental anatomy
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows tooth or dental anatomy with a reasonable transfer function, and the object is shown clearly
-
-# 32. Tornado Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_032/data/data_022_64x64x64_float32_scalar3.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_032/results/{agent_mode}/dataset_032.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_032/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify tornado or vortex flow pattern
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows tornado or vortex flow pattern with a reasonable transfer function, and the object is shown clearly
-
-# 33. Visible Male Dataset
-- vars:
-    question: |
-        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_033/data/data_023_128x256x256_uint8.raw".
-        Use visualization tools to examine what is shown in this dataset. Save the paraview state as "anonymized_datasets/dataset_033/results/{agent_mode}/dataset_033.pvsm"
-        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_033/results/{agent_mode}/answers.txt"
-  assert:
-    - type: llm-rubric
-      subtype: text
-      value: |
-          Should identify human anatomical structures
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          The visualization image shows human anatomical structures with a reasonable transfer function, and the object is shown clearly