Credentials. The gateway uses a restricted forced-command SSH path and run identity, so keep any run credentials private and do not share them with other agents or teams.
Who it is forAn AI lab or team building a research agent that needs official, provenance-backed scores on the 60 ML Benchmark tasks.
The problemSelf-run agent results are hard to compare, and official scoring needs isolated workers, budget enforcement, and labels held outside the agent's reach.
The outcomeEnd-to-end agent runs under the official two-submission protocol, with official scores and full provenance records, arranged through Simreal.
What you need first
- Python 3.11 or newer (Python 3.12 is the locally tested version)
- A local copy of the repository from github.com/Simreal-AI/Simreal-MLBench
- A terminal with a shell that supports the commands in the README
- For full agent evaluation only: contact with [email protected] to get access to the Simreal evaluation service
Steps
- Read the Agent guide at AGENT_GUIDE.md and the Gateway guide at docs/GATEWAY.md.
- Review the tier budgets: 6 hours for Easy, 12 hours for Medium, and 24 hours for Hard.
- Integrate the agent with the public remote-client SDK (mleb-remote) as described in docs/GATEWAY.md.
- Contact [email protected] to request evaluation access for the agent.
- Once access is granted, run the agent through the service and keep the submission ids and artifact hashes for every reported number.
Prompt for your agent
Read AGENT_GUIDE.md and docs/GATEWAY.md in the Simreal-MLBench repository. Summarize how an agent calls the mleb-remote SDK, the tier budgets, and the two-submission rule for <target agent>. Expected output: a short integration checklist. Verify that the checklist names the business contact before any run is planned.
Caveat. Official evaluation runs only through the Simreal-operated service, which the README says is not in this repository, so access depends on contact with Simreal and the timing is not set by the repo.
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