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AutoResearchExam

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AutoResearchExam
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---
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/).

<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 with the harness

Download the tasks and harness into separate folders:

```bash
mkdir autoresearch-exam-run && cd autoresearch-exam-run
git clone https://github.com/bespokelabsai/AutoResearchExam-Terminus.git harness
cd harness
uv sync --python 3.12 --extra modal
uv run hf download bespokelabs/AutoResearchExam \
  --repo-type dataset \
  --local-dir ../hf-tasks
```

Run one task on Modal:

```bash
uv run harbor run \
  --path ../hf-tasks/tasks/cpu-decoder-graph-executor \
  --agent autoresearchexam-terminus \
  --model openai/gpt-5.6-sol \
  --env modal \
  --plugin autoresearch-exam \
  --pk max_iterations=1 \
  --pk max_duration_seconds=3600
```

Use `--path ../hf-tasks/tasks` to run all tasks. Use `--env docker` to run with local Docker.

[AutoResearchExam-Terminus](https://github.com/bespokelabsai/AutoResearchExam-Terminus) provides the harness and the code used to compute AUARC.

## 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}
}
```