Quick Start¶
Before You Start¶
You need Python 3.11+ and an agent that can read a prepared workspace and write the requested output files. Custom file-exchange commands use linux_ns on Linux or none for local testing only. For an official seed42 Results submission, use a compatible built-in adapter with Docker-based os isolation.
1. Install the Runner¶
2. Pull Public Task Instances¶
This downloads public prompts and input data. Private reference answers and scoring configuration are not included.
3. Run Your Agent¶
asibench run --no-score \
--instances-dir hf_instances/ \
--agent-cmd 'python my_agent.py --workspace {workspace}' \
--sandbox linux_ns \
--output-dir out/
The framework prepares each task workspace, runs your command, collects declared outputs, and records provenance for the submission bundle. All four prompt levels are evaluated unless you explicitly pass --prompt-levels. This custom-command example is for local testing; for public seed42 Results, use a compatible built-in adapter with --sandbox os because the Portal rejects other effective sandbox modes.
4. Submit for Official Scoring¶
With an upload endpoint configured, the command prints a confirmation link. Open it, check the parsed run summary, and click Confirm. Uploading alone leaves the run as a private draft. Without an endpoint, it creates a local archive and reports NOT SUBMITTED — nothing was uploaded.
Contributing a task is a separate workflow
If you want to add a new scientific problem to ASI-Bench, start with Contribute a Task. Do not use the result-submission workflow.