| --- |
| 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} |
| } |
| ``` |
|
|
