agents-last-exam/agents-last-exam huggingface dataset:
Title: Scene2 Resample
Domain: psychology_neuro — Experimental Psychology & Neuroimaging

Summary: Resample a 1 mm ROI mask onto the 2 mm statistical-map grid and save the required aligned output.

Task prompt:
You are a neuroimaging analyst completing a GUI workflow in 3D Slicer 5.0.3.

## Your Task
Workflow 2: resample a 1mm ROI mask to the 2mm statistical-map grid

Goal:
1. Open the staged ROI mask and statistical map in 3D Slicer.
2. Resample roi_mask_1mm.nii.gz onto statmap_z_2mm.nii.gz using nearest-neighbor interpolation.
3. Save the resampled mask as roi_mask_2mm_nn.nii.gz.
4. Export a screenshot of the resample settings as resample_settings.png.
5. Compute the ROI statistics on the resampled mask and save them to scene2_stats.csv.

Required outputs:
- output/roi_mask_2mm_nn.nii.gz
- output/resample_settings.png
- output/scene2_stats.csv

## Input Files
All task inputs are staged in:
- `base/input`

Key staged files:
- `base/input/statmap_z_2mm.nii.gz`
- `base/input/roi_mask_1mm.nii.gz`

## Software
Launch the correct GUI workflow with:
- `base/software/launch_gui.sh`

The Desktop task folder is a symlink into `/media/user/data/agenthle/...`, so write all outputs under the staged task directory only.

## Required Output Files
Save these outputs exactly under `base/output`:
- `base/output/roi_mask_2mm_nn.nii.gz`
- `base/output/resample_settings.png`
- `base/output/scene2_stats.csv`

## Evaluation
Ensure all required output files exist, are readable, and satisfy the documented output contract.
Focus on this correctness target:
- The output mask must truly land on the 2 mm stat-map grid, the CSV must reflect that mask, and the settings screenshot must be readable.


The agent must:
Launch 3D Slicer from `software/launch_gui.sh`.
Open the staged `roi_mask_1mm.nii.gz` and `statmap_z_2mm.nii.gz` volumes.
Resample the 1 mm ROI mask onto the 2 mm stat-map grid using nearest-neighbor interpolation.
Save the resampled mask to `output/roi_mask_2mm_nn.nii.gz` and export a readable screenshot of the resample settings to `output/resample_settings.png`.
Compute ROI statistics on the resampled mask and save them to `output/scene2_stats.csv`.

Software: Python

Input files:
name: statmap_z_2mm.nii.gz
format: NIfTI volume
path: input/statmap_z_2mm.nii.gz
description: Reference 2 mm grid that the output mask must match.
name: roi_mask_1mm.nii.gz
format: NIfTI mask
path: input/roi_mask_1mm.nii.gz
description: Correct source mask to resample with nearest-neighbor interpolation.
Title: Atwood 2022 Measles Vaccine Coefficient Reproduction
Domain: social_sciences — Economics & Quantitative Social Research

Summary: Reproduce the six Table 2 vaccination-effect coefficient estimates from Atwood (2022) using the published paper, appendix, and archived replication package.

Task prompt:
You are working on a Linux VM as an applied-economics replication analyst.

## Task Directory
`base`

## Visible Inputs
- Task prompt: `base/input/TASK_PROMPT.md`
- Published paper: `base/input/paper.pdf`
- Online appendix: `base/input/paper-online-appendix.pdf`
- Archived replication package: `base/input/replication_package.zip`
- Task metadata: `base/input/task.json`
- Environment manifest: `base/input/environment_manifest.json`
- Allowed repairs: `base/input/allowed_repairs.json`
- Output schemas/examples: `base/input/output_schemas`

## Your Task
Use the staged public materials to reproduce the coefficient report for Alicia Atwood (2022), Table 2, row "Vaccination effect".

Write all final files under `base/output`:
- `execution_log.txt`
- `repair_log.json`
- `paper_coefficients.json`
- `code_coefficients.json`
- `coefficient_matches.csv`
- `verdict.json`

Follow the file contracts in `base/input/TASK_PROMPT.md` and the schemas under `base/input/output_schemas`. Do not modify files under `base/input`.

The agent must:
Read the paper, online appendix, task metadata, and archived replication package.
Extract the six Table 2 "Vaccination effect" point estimates.
Inspect or run the replication package enough to report corresponding code coefficients.
Write the six required structured output files under the runtime output directory.

Software: Python
PDF tools
unzip

Input files:
name: TASK_PROMPT.md
format: md
path: input/TASK_PROMPT.md
description: Detailed solve-time instructions and output contract.
name: paper.pdf
format: pdf
path: input/paper.pdf
description: Published Atwood (2022) paper.
name: paper-online-appendix.pdf
format: pdf
path: input/paper-online-appendix.pdf
description: Online appendix for the paper.
name: replication_package.zip
format: zip
path: input/replication_package.zip
description: Archived author replication package.
name: output_schemas
format: json/csv
path: input/output_schemas/
description: Schemas and sanitized examples for required deliverables.
Title: Hangzhou Metro Passenger Simulation
Domain: transport_safety — Urban & Spatial Planning

Summary: Simulate one operating day of Hangzhou metro passenger trips from staged AFC demand, GIS, and network configuration inputs.

Task prompt:
You are working on a Linux VM to produce a one-day Hangzhou metro passenger simulation output bundle.\n\n## Variant\n`base`: Hangzhou metro daily AFC simulation bundle\n\n## Visible Input Files\n- task prompt: `base/input/task_prompt.md`\n- output contract: `base/input/output_contract.json`\n- AFC demand: `base/input/data/afc_hangzhou.csv`\n- line GeoJSON: `base/input/gis/hangzhou_lines.json`\n- station GeoJSON: `base/input/gis/hangzhou_stations.json`\n- station sequence CSV: `base/input/network_config/station_sequence.csv`\n- operation parameters JSON: `base/input/network_config/operation_parameters.json`\n- runtime manifest: `base/input/runtime_env/`\n\n## Runtime Entry Points\n- bootstrap helper: `base/software/bootstrap_uv_env.sh`\n- python helper: `base/software/python_with_task_deps.sh`\n\n## What You Must Do\n1. Read the staged task prompt and output contract.\n2. Use the staged AFC, GIS, and network configuration files to simulate metro passenger trips.\n3. Write `base/output/passenger_records.csv`.\n4. Write `base/output/validation_report.txt`.\n5. You may also write `base/output/scatter_plot.png`.\n\n## Output Requirements\n- `passenger_records.csv` columns must be exactly: `card_id,o_station,d_station,start_time,end_time_simulate,transfer_times,end_time_real,duration_simulation,duration_real`\n- keep trip identity aligned to the visible AFC rows\n- keep final deliverables at the output-directory root\n

The agent must:
Read the staged task prompt and output contract from `input/`.
Build a one-day Hangzhou metro passenger simulation from the AFC, GIS, and network configuration inputs.
Write `passenger_records.csv` and `validation_report.txt` into `output/` with the required schema.
Use the staged runtime manifest only if extra Python dependencies are needed.

Software: Python
uv
geopandas
matplotlib
networkx
numpy
pandas

Input files:
name: afc_hangzhou.csv
format: CSV
path: input/data/afc_hangzhou.csv
description: Visible AFC trip records for one operating day.
name: hangzhou_lines.json
format: GeoJSON
path: input/gis/hangzhou_lines.json
description: Hangzhou metro line geometry features.
name: hangzhou_stations.json
format: GeoJSON
path: input/gis/hangzhou_stations.json
description: Hangzhou metro station features.
name: station_sequence.csv
format: CSV
path: input/network_config/station_sequence.csv
description: Ordered station sequences by line and direction.
name: operation_parameters.json
format: JSON
path: input/network_config/operation_parameters.json
description: Simulation and routing parameters for the metro task.
name: runtime_env/
format: uv Python environment manifest
path: input/runtime_env/
description: Agent-installable dependency manifest for optional Python tooling.
Title: Capacitated Vehicle Routing Problems
Domain: transport_safety — Mathematical & Operations Research

Summary: Generate VRPLIB-format near-best-known CVRP solutions for three selected benchmark instances.

Task prompt:
You are working on a Linux VM.

## Your Task
Build a reproducible CVRP-solving workflow and generate VRPLIB-format solutions for exactly three selected instances.

## Visible Inputs
- Problem specification: `base/input/problem_spec.md`
- Instance directory: `base/input/instances/`
- Runtime manifest: `base/input/runtime_env/pyproject.toml`
- Runtime lockfile: `base/input/runtime_env/uv.lock`
- Python wrapper: `base/software/python_cvrp_env.sh`

## What You Must Produce
Write these required files:
- `base/output/solutions/M-n101-k10.sol`
- `base/output/solutions/X-n101-k25.sol`
- `base/output/solutions/X-n106-k14.sol`

You may also write helper scripts and logs anywhere under `base/output`.

## Rules
- Read `base/input/problem_spec.md` first.
- Do not modify files under `base/input`.
- Do not write outside `base/output`.
- Use only the staged local inputs and software already available on this VM.


The agent must:
Inspect the three staged `.vrp` files and implement a CVRP-solving workflow.
Use the staged wrapper if needed to run Python with the pinned task runtime.
Write the three required `.sol` files under `output/solutions/`.

Software: Python
uv
PyVRP
VRPLIB

Input files:
name: Problem specification
format: Markdown
path: input/problem_spec.md
description: Public benchmark instructions and output contract.
name: Selected CVRP instances
format: VRPLIB
path: input/instances/
description: Three benchmark instances that must all be solved.
name: Runtime manifest
format: pyproject.toml + uv.lock
path: input/runtime_env/
description: Pinned Python runtime manifest for task-local solving.
name: Python wrapper
format: shell script
path: software/python_cvrp_env.sh
description: Runs Python with the staged runtime and keeps the resolved environment under output.
Title: Atlas Outpost Graybox Navigation
Domain: visual_media — 3D, Animation & Interactive Media

Summary: Finish a Windows GPU Blender/Godot graybox level blockout so the exported Atlas Outpost layout supports the required visible navigation routes and review constraints.

Task prompt:
You are working on a Windows GPU VM.

## Variant
`base`: Atlas Outpost graybox navigation blockout

## Your Task
Finish the Atlas Outpost graybox level-design starter project. This is a level-blockout workflow: author route geometry, re-export the glTF, wire the Godot validation scene, and write the visible handoff metrics.

## Input Files
- Task prompt: `base\input\TASK_PROMPT.md`
- Starter project root: `base\input`
- Blender blockout: `base\input\blender\atlas_outpost_blockout.blend`
- Exported glTF starter: `base\input\exports\atlas_outpost_blockout.gltf`
- Godot validation scene: `base\input\godot\scenes\atlas_outpost_validation.tscn`
- Optional Blender launcher: `base\software\open_blender.bat`
- Optional Godot launcher: `base\software\open_godot.bat`

## What You Must Do
1. Read `base\input\TASK_PROMPT.md` and the visible specs under `base\input`.
2. Complete the graybox route layout in the project files.
3. Re-export `exports/atlas_outpost_blockout.gltf` and any referenced sidecar files.
4. Update `godot/scenes/atlas_outpost_validation.tscn` with the required review markers.
5. Fill `docs/layout_handoff.md` with the visible route metrics.
6. Save the completed project under `base\output` using the same relative paths as the starter.

## Required Output Paths
- `base\output\blender\atlas_outpost_blockout.blend`
- `base\output\exports\atlas_outpost_blockout.gltf`
- `base\output\exports\atlas_outpost_blockout.bin` if referenced by the exported glTF
- `base\output\godot\scenes\atlas_outpost_validation.tscn`
- `base\output\docs\layout_handoff.md`

## Important
- Keep your final deliverables under `base\output`.
- Do not modify `input\` or any non-output data.
- Do not rely on internet access.


The agent must:
Read the visible task prompt and starter project under `input/`.
Complete the Blender/Godot graybox project and exported glTF route geometry.
Update the Godot validation scene and visible handoff notes.
Save the completed project with the same relative paths under `output/`.

Software: Blender 4.5.1 LTS
Godot Engine 4.6.2
Python
Windows

Input files:
name: TASK_PROMPT.md
format: Markdown
path: input/TASK_PROMPT.md
description: Agent-visible task brief with visible route, zone, cover, and LOS requirements.
name: atlas_outpost_blockout.blend
format: Blender file
path: input/blender/atlas_outpost_blockout.blend
description: Starter graybox blockout file.
name: atlas_outpost_blockout.gltf
format: glTF
path: input/exports/atlas_outpost_blockout.gltf
description: Starter exported geometry.
name: atlas_outpost_validation.tscn
format: Godot scene
path: input/godot/scenes/atlas_outpost_validation.tscn
description: Starter Godot review scene.
Title: Blender Character Reconstruction From Multiview
Domain: visual_media — 3D, Animation & Interactive Media

Summary: Reconstruct a stylized full-body character in Blender from five fixed clay reference views plus scale/orientation metadata, then save the scene and export the required deliverables under the exact submission layout.

Task prompt:
You are a 3D artist using Blender on a Windows VM.

Reconstruct the provided stylized character from the staged multiview references.

Agent-visible input:
- Modeling brief: `base\input\modeling_brief.md`
- Scale/orientation guide: `base\input\scale_orientation_guide.json`
- Validation camera guide: `base\input\validation_views.json`
- Views: `view_front.png`, `view_side.png`, `view_back.png`, `view_front_three_quarter.png`, `view_back_three_quarter.png`
- Blender launcher: `base\software\open_blender.bat`

Required submission under `base\output\submission`:
- `final.blend`
- `reconstructed_character.glb`
- `reconstructed_character.obj`
- `modeling_report.md`

Requirements:
- Preserve the major full-body silhouette, proportions, and component structure.
- Match the coordinate and scale guidance from `scale_orientation_guide.json`.
- Treat the five staged views as the visual target.
- Write the required deliverables only under the designated `output/submission/` path.


The agent must:
Open Blender through the staged launcher and reconstruct the stylized full-body character from the five staged clay reference views.
Use `scale_orientation_guide.json` to keep the character upright, centered, and scaled to the intended world-space frame.
Use `validation_views.json` as the fixed camera/render contract that the final geometry must satisfy.
Save `final.blend` and export `reconstructed_character.glb`, `reconstructed_character.obj`, and `modeling_report.md` under `output/submission/`.

Software: Blender

Input files:
name: modeling_brief.md
format: Markdown
path: input/modeling_brief.md
description: High-level modeling objective, required components, allowed simplifications, and required output files.
name: scale_orientation_guide.json
format: JSON
path: input/scale_orientation_guide.json
description: Visible world-space orientation, bounding-box, centering, and scale tolerances.
name: validation_views.json
format: JSON
path: input/validation_views.json
description: Visible fixed camera, lighting, and render settings for the five validation views.
name: view_*.png
format: PNG images
path: input/
description: Five clay-rendered multiview targets: front, side, back, front-three-quarter, and back-three-quarter.
name: open_blender.bat
format: BAT launcher
path: software/open_blender.bat
description: Stable agent-visible Blender launcher. Probes C:\Softwares\Blender-5.0.1\blender.exe first, then Program Files Blender 5.0 / 5.1.
Title: butterfly_flap_animation
Domain: visual_media — 3D, Animation & Interactive Media

Summary: Animate a supplied butterfly image in Adobe After Effects with four wing-flap cycles and a smooth curved 2.5D flight path.

Task prompt:
Create a butterfly wing-flap animation in Adobe After Effects.

Remote workspace:
- Task folder: base

Software:
- Launch Adobe After Effects using: base\software\launch_after_effects.bat
- The installed After Effects version may differ from the original submission; use the available AE version opened by this launcher.
- After Effects is the intended tool, but if AE is not available in the environment, any other way to achieve the goal is welcome (the output MP4 is what gets evaluated).

Visible inputs:
- Source butterfly artwork: base\input\butterfly.jpeg
- Detailed task spec: base\input\task_spec.md

What to do:
1. Import the butterfly image into After Effects.
2. Split or mask the butterfly into a central body plus left and right wings.
3. Set wing anchor points near the wing-body joints.
4. Animate the wings with repeated Y-rotation or equivalent perspective deformation.
5. Produce exactly 4 complete wing flap cycles: open -> closed -> open.
6. Move the butterfly along a smooth curved 2.5D-style path through the frame with at least 2 Y-direction turning points.
7. Keep the butterfly texture recognizably tied to the source image at fully open moments.
8. Use a plain dark or black background so the butterfly motion remains clear.

Export requirements:
- Export one H.264 MP4.
- Frame rate: 30 fps.
- Duration: 4.0 to 5.5 seconds.
- Save the final render exactly here: base\output\output.mp4

Do not modify files under input/. Write only the final MP4 under output/.


The agent must:
Launch Adobe After Effects from `software/launch_after_effects.bat` (intended tool); if AE is unavailable in the environment, any other approach that produces the required output is acceptable.
Import `input/butterfly.jpeg` and use it as the source butterfly artwork.
Split or mask the butterfly into a central body plus left and right wings.
Animate the wings with repeated Y-rotation or equivalent perspective deformation for exactly 4 complete flap cycles.
Move the butterfly along a smooth curved 2.5D-style path with at least 2 Y-direction turning points.
Use a plain dark or black background for clear motion detection.
Export one 30 fps H.264 MP4, 4.0 to 5.5 seconds long, to `output/output.mp4`.

Software: Adobe After Effects

Input files:
name: Butterfly artwork
format: JPEG image
path: input/butterfly.jpeg
description: Agent-visible source artwork to animate
name: Task specification
format: Markdown
path: input/task_spec.md
description: Public animation and export requirements
Title: chroma_key_from_reference
Domain: visual_media — 3D, Animation & Interactive Media

Summary: Isolate the correct foreground subject from an existing scene and rebuild it as a green-screen plate in DaVinci Resolve.

Task prompt:
Goal: Isolate the correct foreground subject from the existing scene in the source clip, create a new Resolve project yourself, place that subject over a uniform bright-green background as indicated by the first-frame target screenshot, and export the finished green-screen plate.

Remote workspace:
- Task folder: crane_flyingsky

Software:
- Launch DaVinci Resolve using: crane_flyingsky\software\DaVinci Resolve.lnk
- No starter `.drp` project is packaged with this task. Create a new Resolve project yourself for the task run.

Inputs (already on remote desktop):
- Input assets live under: crane_flyingsky\input
- Source composite containing the subject in an existing scene: crane_flyingsky\input\input.mp4
- First-frame target screenshot showing the intended subject-on-green result: crane_flyingsky\input\input.png

What to do:
- Create a new Resolve project and import the task assets from the task folder.
- Isolate the foreground subject shown in the target screenshot from the source clip's existing scene using Resolve masking, rotoscoping, or other appropriate tools.
- Remove the original scene background and place the preserved subject over a uniform bright-green background.
- Use the screenshot to determine which subject to preserve and match its placement, scale, framing, and overall composition.
- Treat the screenshot as the visual specification for the finished green-screen plate.
- Do not export the untouched source clip, retain the original scene background, or omit the green target background.

Output:
- Export/render to: crane_flyingsky\output\output.mp4

The agent must:
Launch DaVinci Resolve from `software/DaVinci Resolve.lnk`
Create a new Resolve project for the task run
Isolate the target foreground subject from the existing scene in `input/input.mp4`
Replace the original scene background with a uniform bright-green background in Resolve
Use `input/input.png` as the first-frame target screenshot for subject identity, placement, scale, framing, and the intended green-screen result
Export the final output to `output/output.mp4`

Software: DaVinci Resolve

Input files:
name: Input clip
format: `.mp4`
path: input/input.mp4
description: Source composite containing the target foreground subject in an existing scene
name: Input target screenshot
format: `.png`
path: input/input.png
description: First-frame target screenshot showing which foreground subject to preserve and the intended subject-on-green placement, scale, framing, and composition; not a packaged project or output asset
Title: Compress A 3D Gaussian Splatting Scene
Domain: visual_media — 3D, Animation & Interactive Media

Summary: Compress a pretrained 3D Gaussian Splatting scene while preserving rendering quality on hidden LLFF holdout views, then report the resulting quality metrics and compression ratio.

Task prompt:
You are working on a Windows GPU neural-rendering benchmark.

Read these staged inputs first:
- `base\input\task_prompt.md`
- `base\input\baseline_results.json`
- `base\input\scene_manifest.json`

Core visible asset roots:
- `base\input\scene\point_cloud_30000.ply`
- `base\input\scene\sparse\0`
- `base\input\scene\images`

Pre-installed 3DGS rendering runtime (software entry points):
- `base\software\python_3dgs.bat <script.py>` — Python with the official gaussian-splatting repo on PYTHONPATH and CUDA configured
- `base\software\render_3dgs.bat --model_path <path> [...]` — official render.py wrapper
- `base\software\README.md` — details on the installed runtime

Write your outputs only under `base\output`.
Required output files:
- `base\output\point_cloud_30000_compressed.ply`
- `base\output\rendered_test_views\<holdout filename>.jpg`
- `base\output\results.json`
- `base\output\compression_report.json`

Important:
- the holdout filenames are public
- the visible JPGs for those holdout names are placeholders only
- do not modify files under `base\input`


The agent must:
Read the staged prompt, baseline metrics, manifest, source PLY, sparse COLMAP bundle, and visible scene images.
Compress `input/scene/point_cloud_30000.ply` into `output/point_cloud_30000_compressed.ply`.
Render one holdout-view image for every filename listed in `input/scene_manifest.json` and save them under `output/rendered_test_views/`.
Write `output/results.json` with average PSNR, SSIM, LPIPS, and per-view PSNR/SSIM entries.
Write `output/compression_report.json` with the gaussian counts before and after compression.

Software: Gaussian Splatting
Python

Input files:
name: task_prompt.md
format: Markdown
path: input/task_prompt.md
description: Agent-facing task contract and reminders.
name: baseline_results.json
format: JSON
path: input/baseline_results.json
description: Baseline average metrics for the uncompressed source scene.
name: scene_manifest.json
format: JSON
path: input/scene_manifest.json
description: Lists the holdout filenames, image counts, and source gaussian count.
name: point_cloud_30000.ply
format: PLY
path: input/scene/point_cloud_30000.ply
description: Source 3D Gaussian scene to compress.
name: sparse/0/
format: COLMAP bundle
path: input/scene/sparse/0/
description: Camera intrinsics and poses for rendering the holdout views.
name: images/
format: JPG directory
path: input/scene/images/
description: Visible training images plus placeholder JPGs for the public holdout filenames.