| --- |
| 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](https://benchmarks.bespokelabs.ai/autoresearchexam/). |
| Version 1.1 runs natively on [Harbor](https://github.com/harbor-framework/harbor) with any |
| agent harness. Leaderboard scores come from the official harness on version 1.0. See |
| [Run](#run) for both. |
|
|
| ## News |
|
|
| - **2026-10: v1.1.** Run AutoResearchExam with any agent harness using plain `harbor run` |
| on [Harbor](https://github.com/harbor-framework/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 |
|
|
| <details> |
| <summary>View tasks by area →</summary> |
|
|
| ### Model training |
|
|
| #### Representation learning |
|
|
| - [Compact encoding for a column with many categories](./tasks/hicard-latent-encoder) |
| - [Compact item embeddings for recommendations](./tasks/sparse-elsa-item-embeddings-8nnz) |
| - [Short binary codes for image search](./tasks/mlh-coco-16bit-hash-head-map5000) |
|
|
| #### Reinforcement learning |
|
|
| - [Advantage estimation for policy training](./tasks/grpo-rl-halfcheetah-advantage-estimator) |
| - [Reliable control policy trainer](./tasks/reppo-reliable-onpolicy-control-trainer) |
|
|
| #### Predictive modeling |
|
|
| - [Predict valid actions for a game policy](./tasks/vas-maskless-deployment-feasibility) |
| - [Rank employee access requests from ID codes](./tasks/dctabeval-aeac-pooled-cat-statistics) |
|
|
| #### Model merging and distillation |
|
|
| - [Eight domain vision model merging](./tasks/ties-merging-clip-vitl14-eight-task-merge) |
|
|
| ### Algorithms and optimization |
|
|
| #### Statistical methods |
|
|
| - [Estimate uncertainty in a streaming optimizer](./tasks/sketched-newton-cov-estimator) |
| - [Estimate individual treatment effects](./tasks/causalpfn-cate-pehe-ihdp-surfaceb) |
| - [Recover river flow links](./tasks/causalrivers-heldout-station-graph-auroc) |
|
|
| #### Constrained optimization |
|
|
| - [Dual market budgeted classification](./tasks/budgeted-covtype-dual-market-open) |
| - [MILP branching policy](./tasks/tgat-milp-branching-node-count) |
| - [Online chance constrained policy](./tasks/sopcc-online-chance-constrained-policy) |
|
|
| ### Data engineering and curation |
|
|
| #### Imputation |
|
|
| - [Budgeted imputation](./tasks/budgeted-imputation-mcar50) |
| - [Sparse panel imputation](./tasks/act-tensor-sparse-panel-imputation-r2) |
|
|
| #### Data selection |
|
|
| - [Unlabeled pool pruning](./tasks/activeprune-al-unlabeled-pool-pruning) |
| - [Low discrepancy subset selection](./tasks/carps-star-discrepancy-subset-select) |
| - [Group robust coreset selection](./tasks/coreset-selection-group-robust-waterbirds) |
| - [Pretraining token budget selection](./tasks/less-is-more-pretrain-token-budget-selector) |
|
|
| ### Systems and efficiency |
|
|
| #### Inference |
|
|
| - [CPU language model decoding](./tasks/cpu-llm-decode-throughput) |
| - [CPU decoder graph execution](./tasks/cpu-decoder-graph-executor) |
| - [Video diffusion transformer caching](./tasks/fastercache-budgeted-video-dit-cache-policy) |
|
|
| #### Compression |
|
|
| - [SVDQuant PixArt Sigma quantization](./tasks/svdquant-w4a4-psnr) |
|
|
| ### Evaluation, calibration, and robustness |
|
|
| #### Metric estimation |
|
|
| - [Shortest valid confidence interval](./tasks/shortest-valid-ci-l2-ece) |
| - [Label efficient risk estimation](./tasks/label-efficient-risk-estimator) |
|
|
| #### Robustness |
|
|
| - [Adversarially robust CIFAR 10 training](./tasks/fast-adv-budgeted-pgd50-robust-cifar10) |
|
|
| ### AI safety and alignment |
|
|
| - [Candidate token loss ranker](./tasks/fastergcg-candidate-token-rank-ccc) |
|
|
| ### Interpretability |
|
|
| - [Sparse dictionary coding](./tasks/sae-sparse-dict-nmse-frontier) |
|
|
| </details> |
|
|
| ## 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](https://github.com/bespokelabsai/AutoResearchExam/tree/v1.0) with the official |
| [AutoResearchExam-Terminus](https://github.com/bespokelabsai/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](https://github.com/harbor-framework/harbor), start Docker, and set up your |
| model provider's credentials. Then run any agent harness on a task: |
|
|
| ```bash |
| 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](https://github.com/bespokelabsai/AutoResearchExam-Terminus/blob/main/scripts/custom_harness.md) |
| 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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
