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crosscodeeval-csharp-0000
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45278328b81d5c5e981be12875cd577833a3a480a429a396e9cca0efb9fd9b59
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0002
[ 46, 108, 97, 105, 111, 110, 95, 118, 52, 95, 112, 97, 116, 99, 104, 101, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
79baa76e34dc0b2bc9031a510cc72074c1802badbd6e90e6af46a140fb011e77
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0004
[ 46, 108, 97, 105, 111, 110, 95, 118, 52, 95, 112, 97, 116, 99, 104, 101, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
41ab0e3e418663da91ccf6417813ce756f7257fdadb60467620d4573a404c7fe
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0005
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
d995681598f44c811df620b2acad0ff8aa0acf3e60c317e4db4ca101937e3dcf
1,3,4,5
null
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runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0006
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
003ce44deec3a04b0f95a2b14b988b04f87e0305b55d2b69b507fcec79dd5d50
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0007
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
4ba9ea37028ebf9c69701398f875e19bb96906ea666fbc41f3c448075140c7ac
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0008
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
1d14d5b7499add3f1e42dcd8be1433fad0d559a3b91f5de6edc55d4b73d8a1cf
1,3,4,5
null
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runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0009
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
10f032f7243905e30b5457581be68721aa8ab7c39867ba115203082c65b4e2d5
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0010
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
186a7400429abb361539822445617a3fd90602ba09d5640bc8c058c2dabada7d
1,3,4,5
null
null
runs/2026-10-03-c536bae24db8.json
crosscodeeval-csharp-0012
"LmxhaW9uX3Y0X3BhdGNoZWQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED)
38b27541d1a1be362ad246eee600adeb4fc584a4de5be1807f71ed33a6258869
1,3,4,5
null
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runs/2026-10-03-c536bae24db8.json
End of preview. Expand in Data Studio

Validated tasks

Agent tasks in Harbor format, together with the result of validating them. The validation is described in the OT-next-data validation README.

Layout

Each data source has its own folder with three files:

File Content
README.md what the tasks are and where they come from
tasks.parquet the tasks that are kept
archive.parquet the tasks that were excluded, with the reason

runs/ holds one file per validation run: the contract of the run and the number of tasks kept and archived at each stage.

Load the kept tasks, or the archived ones:

from datasets import load_dataset
kept = load_dataset("FWeindel/validated-tasks", split="train")
archived = load_dataset("FWeindel/validated-tasks", "archive", split="train")

Download one datasource and its images

To run one datasource, download its original Parquet. Replace SOURCE with its folder name and choose a local workspace:

from huggingface_hub import hf_hub_download

parquet = hf_hub_download(
    repo_id="FWeindel/validated-tasks",
    repo_type="dataset",
    filename="SOURCE/tasks.parquet",
    local_dir="/path/to/workspace/dataset",
)

This downloads the tasks only. When you run that file with the OT-next-data launcher, its image downloader automatically downloads the published Apptainer images needed by your selected tasks, verifies them, and caches them for later runs. You do not need to download other datasources' images.

This works for releases with image references in their Parquet metadata. Keep the original file to preserve those references. Tasks without published images are built as usual. See the download and cache guide for details and Docker support.

Columns

Column Meaning
path task ID
task_binary the task as a tar archive, as validated
content_sha256 hash of the task's files
stages_passed the validation stages the task has passed, e.g. 1,3,4,5
archive_stage the stage that excluded the task; empty for kept tasks
archive_reason why the task was excluded; empty for kept tasks
run the file in runs/ of the run that decided this

A changed task has a new content_sha256 and is validated again from stage 1.

Keep or archive

Stage Rule
1, static checks archive if any check fails
3, build archive if the container does not build or start
4 and 5, oracle and NOP archive if the reward is wrong: the reference solution must get 1, doing nothing must get 0
2, 6, 7 and 8 never archive; a person judges

Updates

Each validation run is proposed as one pull request. It adds the run file, updates the two Parquets of each affected data source, and states in its description how many tasks were kept and archived, and why.

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