update bioimage and molecular yaml

eval_cases/molecular_vis/eval_analysis_tasks.yaml

@@ -2,7 +2,7 @@
 # This test evaluates the ability to complete molecular visualization tasks
 # with detailed requirements and evaluation criteria
 
-#simple licorice visualization of a protein 
+# Case 1: simple licorice visualization of a protein 
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -10,15 +10,15 @@
         3. Visualize the molecular using a licorice representation. 
         4. Take a screenshot of the visualization.
          Q1. Does it show a licorice representation of the protein? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_basic_vis.txt".
+        5. Answer Q1 in a plain text file "case_1/results/{agent_mode}/answers_basic_vis.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_basic_vis.txt"
+      rs-file: "case_1/results/{agent_mode}/answers_basic_vis.txt"
 
-#simple coloring by element of a protein
+# Case 2: simple coloring by element of a protein
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -26,15 +26,15 @@
         3. Visualize the molecular using a CPK or similar representation where atoms are colored by their chemical element.
         4. Take a screenshot of the visualization.
          Q1. Is the molecule colored according to the chemical element of its atoms (e.g., CPK coloring)? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_element_coloring.txt".
+        5. Answer Q1 in a plain text file "case_2/results/{agent_mode}/answers_element_coloring.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_element_coloring.txt"
+      rs-file: "case_2/results/{agent_mode}/answers_element_coloring.txt"
 
-#simple selection and coloring of a protein
+# Case 3: simple selection and coloring of a protein
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -42,15 +42,15 @@
         3. Select all carbon atoms and color them cyan.
         4. Take a screenshot of the visualization.
          Q1. Are all carbon atoms colored cyan? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_selection_coloring.txt".
+        5. Answer Q1 in a plain text file "case_3/results/{agent_mode}/answers_selection_coloring.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_selection_coloring.txt"
+      rs-file: "case_3/results/{agent_mode}/answers_selection_coloring.txt"
 
-#simple coloring by charge of a protein
+# Case 4: simple coloring by charge of a protein
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -58,15 +58,15 @@
         3. Color the molecule according to atomic charge: use one color for positive charges, another for negative charges, and a third for neutral atoms.
         4. Take a screenshot of the visualization.
          Q1. Is the molecule colored by atomic charge (differentiating positive, negative, and neutral)? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_charge_coloring.txt".
+        5. Answer Q1 in a plain text file "case_4/results/{agent_mode}/answers_charge_coloring.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_charge_coloring.txt"
+      rs-file: "case_4/results/{agent_mode}/answers_charge_coloring.txt"
 
-#simple selection and coloring of specific atoms
+# Case 5: simple selection and coloring of specific atoms
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -74,15 +74,15 @@
         3. Select all oxygen atoms in residues 1 to 20 and color them red.
         4. Take a screenshot of the visualization.
          Q1. Are all oxygen atoms in residues 1 to 20 colored red? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_complex_selection.txt".
+        5. Answer Q1 in a plain text file "case_5/results/{agent_mode}/answers_complex_selection.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_complex_selection.txt"
+      rs-file: "case_5/results/{agent_mode}/answers_complex_selection.txt"
 
-#simple selection and coloring of aromatic residues
+# Case 6: simple selection and coloring of aromatic residues
 - vars:
     question: |
         1. I want you to visualize a molecular structure from a CIF file. 
@@ -90,69 +90,70 @@
         3. Select all aromatic residues (PHE, TYR, TRP) and color them purple.
         4. Take a screenshot of the visualization.
          Q1. Are all aromatic residues (PHE, TYR, TRP) colored purple? (yes/no)
-        5. Answer Q1 in a plain text file "md_analysis/results/answers_aromatic_selection.txt".
+        5. Answer Q1 in a plain text file "case_6/results/{agent_mode}/answers_aromatic_selection.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: "md_analysis/results/answers_aromatic_selection.txt"
+      rs-file: "case_6/results/{agent_mode}/answers_aromatic_selection.txt"
 
-#simple RMSD and RMSF calculation of a protein
+# Case 7: simple RMSD and RMSF calculation of a protein
 - vars:
     question: |
         1. I want you to perform a structural analysis on a molecular structure from a CIF file. 
         2. Load the data/1CRN.cif. 
         3. Calculate the Root Mean Square Deviation (RMSD) of the structure against itself.
         4. Calculate the Root Mean Square Fluctuation (RMSF) for the structure.
-        5. Save the computed RMSD and RMSF values as plain text to "md_analysis/results/answers_rmsd_rmsf.txt".
+        5. Save the computed RMSD and RMSF values as plain text to "case_7/results/{agent_mode}/answers_rmsd_rmsf.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Does the output report the calculated RMSD?
           2. Does the output report the calculated RMSF values or state that it requires a trajectory?
-      rs-file: "md_analysis/results/answers_rmsd_rmsf.txt"
+      rs-file: "case_7/results/{agent_mode}/answers_rmsd_rmsf.txt"
 
-#simple radius of gyration calculation of a protein
+# Case 8: simple radius of gyration calculation of a protein
 - vars:
     question: |
         1. I want you to calculate the compactness of a protein from a CIF file. 
         2. Load the data/1CRN.cif. 
         3. Calculate the Radius of Gyration (Rg) of the protein structure.
-        4. Save the calculated Radius of Gyration as plain text to "md_analysis/results/answers_rg.txt".
+        4. Save the calculated Radius of Gyration as plain text to "case_8/results/{agent_mode}/answers_rg.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Does the output report a numeric value for the calculated Radius of Gyration?
-      rs-file: "md_analysis/results/answers_rg.txt"
+      rs-file: "case_8/results/{agent_mode}/answers_rg.txt"
 
+# Case 9: calculate specific geometric properties
 - vars:
     question: |
-        1. I want you to calculate specific geometric properties of a molecular structure from a CIF file. 
-        2. Load the data/1CRN.cif. 
+        1. I want you to calculate specific geometric properties of a molecular structure from a CIF file.
+        2. Load the data/1CRN.cif.
         3. Calculate the distance between the alpha carbons of residue 1 and residue 10.
         4. Calculate the backbone dihedral angles (phi and psi) for residue 5.
-        5. Save the computed distance and angles as plain text to "md_analysis/results/answers_distances_angles.txt".
+        5. Save the computed distance and angles as plain text to "case_9/results/{agent_mode}/answers_distances_angles.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Does the output report the calculated distance between the alpha carbons of residue 1 and 10?
           2. Does the output report the calculated phi and psi dihedral angles for residue 5?
-      rs-file: "md_analysis/results/answers_distances_angles.txt"
+      rs-file: "case_9/results/{agent_mode}/answers_distances_angles.txt"
 
-#simple contact calculation of a protein
+# Case 10: simple contact calculation of a protein
 - vars:
     question: |
         1. I want you to calculate the number of contacts in a folded protein from a CIF file.
         2. Load the data/1CRN.cif.
         3. Calculate the number of contacts within an 8 Angstrom cutoff.
-        4. Save the total count of contacts as plain text to "md_analysis/results/answers_native_contacts.txt".
+        4. Save the total count of contacts as plain text to "case_10/results/{agent_mode}/answers_native_contacts.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Does the output report a numeric count of contacts?
-      rs-file: "md_analysis/results/answers_native_contacts.txt"
+      rs-file: "case_10/results/{agent_mode}/answers_native_contacts.txt"

eval_cases/napari/eval_visualization_tasks.yaml

@@ -1,7 +1,7 @@
 # Basic Visualization Workflow Tests
 # Use https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD573.html IM1 to test the workflows.
 
-# Test: Multi-channel Overlay with Colormaps with channels
+# Case 1: Multi-channel Overlay with Colormaps with channels
 - vars:
     question: |
       1. Load the "data/dataset_002/dataset_002_ch0.tif" dataset into napari as channel 0 and "data/dataset_002/dataset_002_ch1.tif" as channel 1. 
@@ -10,8 +10,8 @@
       4. Use additive blending for all channels to create an overlay visualization.
       5. Go the timestep 14. 
         Q1: Does the cell show protrusions? (Yes/No)
-      6. Take a screenshot of the result, save it to "eval_visualization_workflows/screenshot_1.png"
-      7. Answer Q1 in a plain text file "eval_visualization_workflows/multi_channel_answer.txt".
+      6. Take a screenshot of the result, save it to "eval_visualization_tasks/case_1/results/{agent_mode}/screenshot_1.png"
+      7. Answer Q1 in a plain text file "eval_visualization_tasks/case_1/results/{agent_mode}/multi_channel_answer.txt".
   assert:
     - type: llm-rubric
       subtype: vision
@@ -19,70 +19,70 @@
           1. Does the visualization show a green cell with red blobs on the inside? 
           2. Does the result rendering look similar to ground truth?
       gs-file: GS/dataset_002_1.png
-      rs-file: eval_visualization_workflows/screenshot_1.png
+      rs-file: eval_visualization_tasks/case_1/results/{agent_mode}/screenshot_1.png
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: eval_visualization_workflows/multi_channel_answer.txt                    
+      rs-file: eval_visualization_tasks/case_1/results/{agent_mode}/multi_channel_answer.txt                    
   options:
     cache: false
     runSerially: true
 
 
-# Test: ingesting points
+# Case 2: ingesting points
 - vars:
     question: |
       1. Load the "data/dataset_002/Points.csv" dataset into napari.
       2. Check if the points layer has been created.
         Q1: Was the points layer created successfully? (Yes/No)
-      3. Answer Q1 in a plain text file "eval_visualization_workflows/points_answer.txt".
+      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_2/results/{agent_mode}/points_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: eval_visualization_workflows/points_answer.txt
+      rs-file: eval_visualization_tasks/case_2/results/{agent_mode}/points_answer.txt
   options:
     cache: false
     runSerially: true
 
-# Test: ingesting shapes
+# Case 3: ingesting shapes
 - vars:
     question: |
       1. Load the "data/dataset_002/Shapes.csv" dataset into napari.
       2. Check if the shapes layer has been created.
         Q1: Was the shapes layer created successfully? (Yes/No)
-      3. Answer Q1 in a plain text file "eval_visualization_workflows/shapes_answer.txt".
+      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_3/results/{agent_mode}/shapes_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: eval_visualization_workflows/shapes_answer.txt
+      rs-file: eval_visualization_tasks/case_3/results/{agent_mode}/shapes_answer.txt
   options:
     cache: false
     runSerially: true
 
-# Test: ingesting labels
+# Case 4: ingesting labels
 - vars:
     question: |
       1. Load the "data/dataset_002/Labels.tif" dataset into napari.
       2. Check if a new layer called "Labels" has been created.
         Q1: Was the layer created successfully? (Yes/No)
-      3. Answer Q1 in a plain text file "eval_visualization_workflows/labels_answer.txt".
+      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_4/results/{agent_mode}/labels_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
         1. Q1 correct answer: Yes
-      rs-file: eval_visualization_workflows/labels_answer.txt
+      rs-file: eval_visualization_tasks/case_4/results/{agent_mode}/labels_answer.txt
   options:
     cache: false
     runSerially: true
 
 
-#Test: Recreate a figure from a dataset. 
+# Case 5: Recreate a figure from a dataset. 
 - vars:
     question: |
       1. Load the dataset into napari: data/dataset_001/dataset_001.tiff
@@ -93,7 +93,7 @@
       6. Take a screenshot of your recreation.
       7. If the recreation does not match the target figure, adjust the visualization settings and take a screenshot again.
       8. Stop when the recreation matches the target figure or you have tried five different visualization settings.
-      9. Save the final screenshot to "eval_figure_recreation/screenshot.png".
+      9. Save the final screenshot to "eval_visualization_tasks/case_5/results/{agent_mode}/screenshot.png".
   assert:
     - type: llm-rubric
       subtype: vision
@@ -102,12 +102,12 @@
           2. Are the same colormaps and blending modes used as in the target figure?
           3. Is the contrast and gamma adjusted to match the target figure?
       gs-file: GS/dataset_001.png
-      rs-file: eval_figure_recreation/screenshot.png                 
+      rs-file: eval_visualization_tasks/case_5/results/{agent_mode}/screenshot.png                 
   options:
     cache: false
     runSerially: true
 
-# Test: Iso surface determination for a target
+# Case 6: Iso surface determination for a target
 - vars:
     question: |
       1. Read the file "data/dataset_003/eval_iso_surface_determination_target_1.txt" to get the target iso-surface values for different tooth structures.
@@ -117,7 +117,7 @@
       5. Rotate the camera to several angles and take a screenshot of the result each time to check if the target structure is clearly visible from different angles.
       6. If the target structure is not clearly visible, adjust the iso surface value and take a screenshot again.
       7. Stop when the target structure is clearly visible or you have tried five different iso surface values.
-      8. Save the final screenshot to "eval_iso_surface_determination/screenshot.png".
+      8. Save the final screenshot to "eval_visualization_tasks/case_6/results/{agent_mode}/screenshot.png".
   assert:
     - type: llm-rubric
       subtype: vision
@@ -125,31 +125,31 @@
           1. Does the result rendering look similar to ground truth? 
           2. Does the visualization show the target structure clearly?
       gs-file: GS/dataset_003.png
-      rs-file: eval_iso_surface_determination/screenshot.png                          
+      rs-file: eval_visualization_tasks/case_6/results/{agent_mode}/screenshot.png                          
   options:
     cache: false
     runSerially: true
 
 
-# Test: Cell Counting and Measurement Analysis
+# Case 7: Cell Counting and Measurement Analysis
 - vars:
     question: |
       1. Load the image "data/dataset_002/dataset_002_ch0.tif" and set channel 0 to a magenta colormap.
       2. Switch to a 3D MIP view. 
       3. Take a screenshot and analyze it to count how many complete cells are visible (not cut off by edges).
       Q1: answer with the number of complete cells you counted, for example "5" if you see 5 complete cells.
-      4. Save the answer of Q1 to the questions in plain text as "eval_analysis_workflows/Q1_answer.txt".
+      4. Save the answer of Q1 to the questions in plain text as "eval_visualization_tasks/case_7/results/{agent_mode}/Q1_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
         1. Q1 correct answer: 2
-      rs-file: eval_analysis_workflows/Q1_answer.txt
+      rs-file: eval_visualization_tasks/case_7/results/{agent_mode}/Q1_answer.txt
   options:
     cache: false
     runSerially: true
 
-# Test: Statistical Analysis and Data Export
+# Case 8: Statistical Analysis and Data Export
 - vars:
     question: |
       1. Load the image "data/dataset_001/dataset_001.tiff". 
@@ -157,36 +157,36 @@
       3. Extract the raw layer data and examine its properties.
       4. Save the current layer to a file for further analysis.
        Q1: Was the statistical analysis and data export successful? (Yes/No)
-      6. Save the answer of Q1 in plain text as "eval_analysis_workflows/layer_statistics_answer.txt".
+      6. Save the answer of Q1 in plain text as "eval_visualization_tasks/case_8/results/{agent_mode}/layer_statistics_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
         1. Q1 correct answer: Yes
-      rs-file: eval_analysis_workflows/layer_statistics_answer.txt
+      rs-file: eval_visualization_tasks/case_8/results/{agent_mode}/layer_statistics_answer.txt
   options:
     cache: false
     runSerially: true
 
-# Test: Annotation Workflow
+# Case 9: Annotation Workflow
 - vars:
     question: |
       1. Load the image "data/dataset_001/dataset_001.tiff". 
       2. Add point annotations at random locations on the image.
       3. Add shape annotations (rectangles or circles) at random locations on the image.
        Q1: Check if layers have been generated. (Yes/No)
-      4. Save the answer of Q1 in plain text as "eval_analysis_workflows/annotation_answer.txt".
+      4. Save the answer of Q1 in plain text as "eval_visualization_tasks/case_9/results/{agent_mode}/annotation_answer.txt".
   assert:
     - type: llm-rubric
       subtype: text
       value: |
           1. Q1 correct answer: Yes
-      rs-file: eval_analysis_workflows/annotation_answer.txt
+      rs-file: eval_visualization_tasks/case_9/results/{agent_mode}/annotation_answer.txt
   options:
     cache: false
     runSerially: true
 
-# Test: Advanced Annotation Workflow: Cell Surface Trace (This will likely fail)
+# Case 10: Advanced Annotation Workflow: Cell Surface Trace (This will likely fail)
 - vars:
     question: |
       1. Load the image "data/dataset_002/dataset_002_ch0.tif" into napari.
@@ -194,19 +194,19 @@
       3. Use a screenshot to validate whether the polygon correctly traces the cell surface.
       4. If the trace is not accurate, adjust the polygon and take a new screenshot to validate.
       5. Stop when the trace is accurate or you have tried five different attempts.
-      6. Save the results and the final screenshot to "eval_annotation_workflows/cell_surface_trace.png".
+      6. Save the results and the final screenshot to "eval_visualization_tasks/case_10/results/{agent_mode}/cell_surface_trace.png".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
           1. Does the final screenshot show a polygon shape that accurately traces the outline of the cell surface?
           2. Is the polygon layer correctly overlaid on the image?
-      rs-file: eval_annotation_workflows/cell_surface_trace.png
+      rs-file: eval_visualization_tasks/case_10/results/{agent_mode}/cell_surface_trace.png
   options:
     cache: false
     runSerially: true
 
-# Test: Camera Operations (Zoom and Rotate)
+# Case 11: Camera Operations (Zoom and Rotate)
 - vars:
     question: |
       1. Load the "data/dataset_002/dataset_002_ch0.tif" dataset into napari as channel 0 and "data/dataset_002/dataset_002_ch1.tif" as channel 1.
@@ -214,8 +214,8 @@
       3. Switch to the 3D view. 
       4. Zoom in to the cell in the middle.
       5. Rotate the camera to a side view.
-      6. Take a screenshot of the zoomed-in view and save it to "eval_camera_operations/zoom_screenshot.png".
-      7. Take a screenshot of the side view and save it to "eval_camera_operations/rotate_screenshot.png".
+      6. Take a screenshot of the zoomed-in view and save it to "eval_visualization_tasks/case_11/results/{agent_mode}/zoom_screenshot.png".
+      7. Take a screenshot of the side view and save it to "eval_visualization_tasks/case_11/results/{agent_mode}/rotate_screenshot.png".
   assert:
     - type: llm-rubric
       subtype: vision
@@ -223,14 +223,14 @@
           1. Does the visualization show a zoomed-in view of the cell in the middle?
           2. Does the result rendering look similar to ground truth?
       gs-file: GS/dataset_002_zoom.jpg
-      rs-file: eval_camera_operations/zoom_screenshot.png
+      rs-file: eval_visualization_tasks/case_11/results/{agent_mode}/zoom_screenshot.png
     - type: llm-rubric
       subtype: vision
       value: |
           1. Does the visualization show a side view of the cell?
           2. Does the result rendering look similar to ground truth?
       gs-file: GS/dataset_002_camera_side.png
-      rs-file: eval_camera_operations/rotate_screenshot.png
+      rs-file: eval_visualization_tasks/case_11/results/{agent_mode}/rotate_screenshot.png
   options:
     cache: false
     runSerially: true

eval_cases/selected_cases.yaml

@@ -1,200 +0,0 @@
-# Selected 15 Cases for Human Evaluation
-# These cases represent diverse visualization capabilities across the benchmark
-#
-# Each case specifies:
-#   - name: The case directory name
-#   - path: Path to the case directory (relative to workspace root)
-#   - yaml: Path to the YAML file containing evaluation criteria
-#   - description: Brief description of what the case tests
-
-cases:
-  - name: argon-bubble
-    path: SciVisAgentBench-tasks/paraview/argon-bubble
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Color & Opacity Mapping, Volume Rendering
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: richtmyer
-    path: SciVisAgentBench-tasks/paraview/richtmyer
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Color & Opacity Mapping, Volume Rendering
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: foot
-    path: SciVisAgentBench-tasks/paraview/foot
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Volume Rendering
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: crayfish_streamline
-    path: SciVisAgentBench-tasks/paraview/crayfish_streamline
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Surface & Contour Extraction
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: twoswirls_streamribbon
-    path: SciVisAgentBench-tasks/paraview/twoswirls_streamribbon
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Surface & Contour Extraction
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: tornado
-    path: SciVisAgentBench-tasks/paraview/tornado
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Surface & Contour Extraction, Glyph & Marker Placement
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: tgc-velocity_contour
-    path: SciVisAgentBench-tasks/paraview/tgc-velocity_contour
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Surface & Contour Extraction
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: rti-velocity_slices
-    path: SciVisAgentBench-tasks/paraview/rti-velocity_slices
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: View & Camera Control, Data Subsetting & Extraction
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: rti-velocity_glyph
-    path: SciVisAgentBench-tasks/paraview/rti-velocity_glyph
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Glyph & Marker Placement, Data Subsetting & Extraction
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: supernova_isosurface
-    path: SciVisAgentBench-tasks/paraview/supernova_isosurface
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Surface & Contour Extraction (isosurface)
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: time-varying
-    path: SciVisAgentBench-tasks/paraview/time-varying
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Temporal Processing
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: chart-opacity
-    path: SciVisAgentBench-tasks/paraview/chart-opacity
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Plot & Chart Generation
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: climate
-    path: SciVisAgentBench-tasks/paraview/climate
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Field Computation
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  # - name: subseries-of-time-series
-  #   path: SciVisAgentBench-tasks/paraview/subseries-of-time-series
-  #   yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-  #   description: Dataset Restructuring
-  #   agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: shrink-sphere
-    path: SciVisAgentBench-tasks/paraview/shrink-sphere
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Geometric & Topological Transformation
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  - name: import-gltf
-    path: SciVisAgentBench-tasks/paraview/import-gltf
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Dataset Restructuring, View & Camera Control
-    agent_mode: paraview_mcp_claude-sonnet-4-5_exp1
-
-  - name: render-histogram
-    path: SciVisAgentBench-tasks/paraview/render-histogram
-    yaml: benchmark/eval_cases/paraview/paraview_cases.yaml
-    description: Plot & Chart Generation, Color & Opacity Mapping
-    agent_mode: chatvis_claude-sonnet-4-5_exp1
-
-  # From molecular_vis/workflows (2 cases)
-  - name: curved-membrane
-    path: SciVisAgentBench-tasks/molecular_vis/workflows/curved-membrane
-    yaml: benchmark/eval_cases/molecular_vis/workflows/eval_analysis_workflows.yaml
-    description: Data Subsetting & Extraction
-    agent_mode: gmx_vmd_mcp_claude-sonnet-4-5_exp1
-
-  - name: ras-raf-membrane
-    path: SciVisAgentBench-tasks/molecular_vis/workflows/ras-raf-membrane
-    yaml: benchmark/eval_cases/molecular_vis/workflows/eval_analysis_workflows.yaml
-    description: View & Camera Control
-    agent_mode: gmx_vmd_mcp_claude-sonnet-4-5_exp1
-
-  - name: bio_isosurface-determination
-    path: SciVisAgentBench-tasks\bioimage_data\eval_iso_surface_determination\operation_1
-    yaml: benchmark\eval_cases\napari\1_workflows\eval_iso_surface_determination.yaml
-    description: Surface & Contour Extraction (isosurface)
-    agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
-    task_description:
-      1. Read the file "data/dataset_003/eval_iso_surface_determination_target_1.txt" to get the target iso-surface values for different tooth structures.
-
-      2. Load data/dataset_003/dataset_003.tif into napari. 
-
-      3. Switch to 3D view mode and set the rendering to iso. 
-
-      4. Find the iso surface value that shows the target clearly.
-
-      5. Rotate the camera to several angles and take a screenshot of the result each time to check if the target structure is clearly visible from different angles.
-
-      6. If the target structure is not clearly visible, adjust the iso surface value and take a screenshot again.
-
-      7. Stop when the target structure is clearly visible or you have tried five different iso surface values.
-
-      8. Save the final screenshot to "eval_iso_surface_determination/screenshot.png".
-    vision-rubrics:
-        1. Does the result rendering look similar to ground truth? 
-        2. Does the visualization show the target structure clearly?
-
-  - name: bio_visualization-workflows
-    path: SciVisAgentBench-tasks\bioimage_data\eval_visualization_workflows\operation_1
-    yaml: benchmark\eval_cases\napari\1_workflows\eval_visualization_workflows.yaml
-    description: Color & Opacity Mapping, Volume Rendering, Temporal Processing
-    agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
-    task_description:
-      1. Load the "data/dataset_002/dataset_002.tif" dataset into napari. 
-
-      2. Depending on the number of channels, set the colormap for the first channel 0 to red and channel 1 to green.
-
-      3. Switch to the 3D view. 
-
-      4. Use additive blending for all channels to create an overlay visualization.
-
-      5. Go the timestep 14. 
-        Q1. Does the cell show protrusions? (Yes/No)
-
-      6. Take a screenshot of the result, save it to "eval_visualization_workflows/screenshot_1.png"
-
-      7. Answer Q1 in a plain text file "eval_visualization_workflows/Q1_answer.txt".
-    vision-rubrics:
-        1. Does the visualization show a green cell with red blobs on the inside? 
-        2. Does the result rendering look similar to ground truth?
-
-  - name: bio_figure-recreation
-    path: SciVisAgentBench-tasks\bioimage_data\eval_figure_recreation\operation_1
-    yaml: benchmark\eval_cases\napari\1_workflows\eval_figure_recreation.yaml
-    description: Color & Opacity Mapping, Volume Rendering
-    agent_mode: napari_mcp_claude-sonnet-4-5_exp_default
-    task_description:
-      1. Load the dataset into napari "data/dataset_001/dataset_001.tiff"
-
-      2. Read the target figure "data/dataset_001/dataset_001.png" but don't load it into napari.
-
-      3. Read the dataset description "data/dataset_001/dataset_001.yaml".
-
-      4. Set the same colormaps and blending modes as the target figure.
-
-      5. Adjust contrast and gamma as needed to match the target figure.
-
-      6. Take a screenshot of your recreation.
-
-      7. If the recreation does not match the target figure, adjust the visualization settings and take a screenshot again.
-
-      8. Stop when the recreation matches the target figure or you have tried five different visualization settings.
-
-      9. Save the final screenshot to "eval_figure_recreation/screenshot.png".
-    vision-rubrics:
-        1. Does the visualization show a green cell with red blobs on the inside? 
-        2. Does the result rendering look similar to ground truth?