README.md

metadata

pretty_name: AutoResearchExam
license: apache-2.0
size_categories:
  - n<1K
tags:
  - autoresearch
  - machine-learning
  - benchmark

AutoResearchExam

This repository contains the 29 tasks from AutoResearchExam. Version 1.1 runs natively on Harbor with any agent harness. Leaderboard scores come from the official harness on version 1.0. See Run for both.

News

• 2026-10: v1.1. Run AutoResearchExam with any agent harness using plain harbor run on Harbor main, without depending on any custom harness. Same tasks, data and graders as v1.0. Leaderboard scores still come from v1.0 with the official harness.
• 2026-09: v1.0. Initial release with the official harness and leaderboard.

Tasks by area

View tasks by area →

Model training

Representation learning

• Compact encoding for a column with many categories
• Compact item embeddings for recommendations
• Short binary codes for image search

Reinforcement learning

• Advantage estimation for policy training
• Reliable control policy trainer

Predictive modeling

• Predict valid actions for a game policy
• Rank employee access requests from ID codes

Model merging and distillation

• Eight domain vision model merging

Algorithms and optimization

Statistical methods

• Estimate uncertainty in a streaming optimizer
• Estimate individual treatment effects
• Recover river flow links

Constrained optimization

• Dual market budgeted classification
• MILP branching policy
• Online chance constrained policy

Data engineering and curation

Imputation

• Budgeted imputation
• Sparse panel imputation

Data selection

• Unlabeled pool pruning
• Low discrepancy subset selection
• Group robust coreset selection
• Pretraining token budget selection

Systems and efficiency

Inference

• CPU language model decoding
• CPU decoder graph execution
• Video diffusion transformer caching

Compression

• SVDQuant PixArt Sigma quantization

Evaluation, calibration, and robustness

Metric estimation

• Shortest valid confidence interval
• Label efficient risk estimation

Robustness

• Adversarially robust CIFAR 10 training

AI safety and alignment

• Candidate token loss ranker

Interpretability

• Sparse dictionary coding

Run

Each run has a 24 hour research budget, and the agent should keep working until it ends.

With the official harness

The leaderboard uses the v1.0 tasks with the official AutoResearchExam-Terminus harness. To reproduce leaderboard scores, follow the instructions in that repository.

With your own harness

The official harness keeps the agent working and submitting until the budget ends. With your own harness, that is up to the agent. An agent can stop early, but then it scores lower, because it did not follow the instruction to keep going.

Install Harbor, start Docker, and set up your model provider's credentials. Then run any agent harness on a task:

git clone https://github.com/bespokelabsai/AutoResearchExam.git
harbor run \
  --path AutoResearchExam/cpu-decoder-graph-executor \
  --agent <agent> --model <model> --env docker \
  --ak prompt_template_path=AutoResearchExam/are_protocol.md.j2

Use --path AutoResearchExam to run all tasks. are_protocol.md.j2 adds the research protocol after each task instruction: submit regularly, keep improving the solution, and keep going until the run ends.

During the run, the agent can reach api.openai.com and api.anthropic.com. If your harness uses another host, add --allow-agent-host <host>.

GPU tasks need an NVIDIA GPU and the NVIDIA Container Toolkit. Grading shares GPU 0 with the agent by default. On a machine with a spare GPU, set ARE_GRADER_GPU=1 to give grading its own GPU.

How autoresearch submissions and intermediate grading work

Each task runs the agent next to two grader containers.

• The agent submits with the submit tool of the are MCP server, or over HTTP at http://grader:8000/submit. The first grader scores the submission on the public validation split. The agent gets the score and grader output from the result tool.
• The second grader scores the same submission on the private test split. The agent cannot reach it and never sees these scores.
• Grading does not pause the agent. The agent keeps working while a submission is graded. Only one grade runs at a time, validation or test. A new submission is refused until the previous one has its validation score.
• When the agent stops, the final workspace is graded too, unless it matches the last submission.

How scores can differ from v1.0

In v1.0, the official harness keeps the agent submitting for the full 24 hours. In v1.1, that is your agent's responsibility.

In v1.0, grading paused the agent. Now the agent keeps working while a submission is graded, so it has more working time. Because only one submission is graded at a time, an agent cannot flood the grader.

Grading runs on the same machine as the agent and can compete with it for compute. A grader uses as many cores as the task gives the agent, and the two graders never run at the same time, so plan for about twice the cores each task requests.

Scores

Each trial writes verifier/reward.json. Its reward is the private test score of the submission with the best validation score. Its auarc uses each task's own score scale. To put scores on the leaderboard scale, follow the scoring guide to normalize scores and compute AUARC. Every submission's scores and times are in the trial's artifacts/var/lib/are/ledger.jsonl.

Citation

If you use AutoResearchExam in your research, please cite:

@misc{ramesh2026autoresearchexam,
  author = {Ramesh, Anirudha and
            Devic, Siddartha and
            Garg, Shivank and
            Parulekar, Advait and
            Mahnot, Drish and
            Pimpalgaonkar, Shreyas and
            Suresh, Vishnu and
            Dimakis, Alex and
            Sathiamoorthy, Maheswaran},
  title = {{AutoResearchExam}},
  year = {2026},
  month = sep,
  url = {https://benchmarks.bespokelabs.ai/autoresearchexam/},
  note = {Published September 9, 2026}
}