Add AiScientist MLE-Bench Lite results

[survivi]

Leaderboard Submission Disclosure

• The scaffold/harness used in this leaderboard submission does not expose to the agent signals derived from the held-out test set during rollouts.

Hello MLE-Bench team,

We are the AweAI Team, and we would like to submit the latest public evaluation results of our open-source framework, AiScientist.

AiScientist is described in our paper here:

• Paper: https://arxiv.org/pdf/2604.13018

As part of this pull request, we provide:

• Three independent grading reports under runs/aiscientist_glm5_lite_group1-3
• The corresponding run_group_experiments.csv entries
• A short runs/README.md note for this experiment
• A proposed leaderboard row for AiScientist

This submission is for MLE-Bench Lite, i.e. the 22-task low-complexity split.
This is also the MLE setting reported in our paper. We chose the Lite setting because it is the benchmark's recommended reduced-cost evaluation protocol while still enabling fair comparison on the Low == Lite column.

For this submission:

• Framework: AiScientist
• Team: AweAI Team
• Model: GLM-5
• Runtime budget: 24 hours per task
• Hardware: 1 H20 GPU
• Lite score (Low == Lite Any Medal %): 81.82 ± 0.00

We would like to be explicit that this PR only reports Lite / low-split results. We have therefore proposed placing this row under Additional Leaderboard Submissions with a Lite only note rather than presenting it as a full Low / Medium / High / All submission. If you would prefer a different formatting for Lite-only entries, we are happy to adjust the PR accordingly.

Regarding data visibility and evaluation integrity: during solving, the agent does not use held-out test-set signals. Only public competition data is exposed to the agent, while hidden / held-out information remains outside the agent-visible solving context and is only used for grading.

AiScientist is fully open-sourced, and the paper provides a system-level description of the framework, including its MLE-Bench Lite evaluation setting.

Thank you very much for building and maintaining MLE-Bench. We appreciate the benchmark and would be happy to revise the formatting or metadata if you have a preferred convention for Lite-only submissions.