FrontierChallenge
FrontierChallenge provides 97 scientific workflow tasks with plaintext English instructions, inputs, Harbor definitions, domain labels, and the redistributable open runtime image.
| Path | Contents |
|---|---|
manifest.jsonl |
Dataset Viewer rows with task ID, taxonomy, difficulty, runtime, and instruction |
tasks/<task-id>/ |
instruction.md, task metadata, environment definition, and agent-visible inputs |
images/ |
Verified linux/amd64 Docker archive for the 81 open-image tasks |
This repository contains no graders, rubrics, fixtures, or reference outputs.
Those are stored as encrypted archives in the separate
apodex/FrontierChallenge-reference
dataset.
Use the FrontierChallenge runtime to download both datasets, verify their shared registry, load the image archive, run Harbor, and score a task:
git clone https://github.com/ApodexAI/FrontierAgent.git
cd FrontierAgent/benchmarks/frontierchallenge
python -m pip install -e .
HF_TOKEN=hf_... ./scripts/setup.sh --track open
cp .env.example .env
./scripts/run_eval.sh \
--agent claude-code --model <model> \
--include-task-name task_011_cell_migration_wound_healing
The 16 ORCA task definitions and inputs are included normally. ORCA itself and an ORCA-configured image are never distributed; evaluators obtain ORCA from its official provider and follow the runtime repository's local-image tutorial.
Verify a downloaded solve package with:
python tools/verify_dataset.py
Citation
@misc{apodex11,
title = {Apodex 1.1: Scaling Agentic Intelligence for Complex Work},
author = {{Apodex Team}},
year = {2026},
eprint = {2608.23283},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2608.23283}
}
@misc{frontierchallenge,
title = {FrontierChallenge: Evaluating Scientific Workflow Completion},
author = {Liangcai Su and Zhaopeng Feng and Zhuo Chen and Zhen Zhang
and Xiang Lin and Ruilin Li and Handuo Zhang and Ning Wang
and Kailong Wen and Yueqi Guo and Feng Xing and Yiling Guo
and Chenxiong Qian and Simon Shaolei Du and Lidong Bing
and Xinyu Wang},
year = {2026},
eprint = {2608.24979},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2608.24979}
}
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