name stringlengths 30 55 | instruction stringlengths 2.47k 7.16k | description stringclasses 1
value | keywords listlengths 3 3 | schema_version stringclasses 1
value | config unknown | metadata unknown | files listlengths 24 125 | location stringlengths 24 49 |
|---|---|---|---|---|---|---|---|---|
bespokelabs/act-tensor-sparse-panel-imputation-r2 | # Imputing a severely and heterogeneously incomplete firm-characteristic panel
Recover missing values in unseen firm characteristic panels as accurately as possible. Each panel
is an array with shape `(60 months, N firms, 45 characteristics)`.
Data:
* Every value has been cross-sectionally ranked within its `(month,... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
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"artifacts": [
{
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"task": {
"name": "bespokelabs/act-tensor-sparse-panel-imputation-r2",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"be... | {
"category": "Machine Learning and AI",
"subcategory": "Unsupervised and representation learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev/instance_00.npz",
"environment/app/data/dev/instance_01.npz",
"environment/app/data/dev/instance_02.npz",
"environment/app/data/dev/instance_03.npz",
"environment/app/data/dev/instance_04.npz",
"environment... | tasks/act-tensor-sparse-panel-imputation-r2 | |
bespokelabs/activeprune-al-unlabeled-pool-pruning | # Unlabeled-pool pruning for active learning
An active-learning loop trains a binary sentiment classifier over a large unlabeled pool of
documents. Each round, before the acquisition function runs, your code picks which subset of
the still-unlabeled documents the acquisition function is allowed to consider. Everything... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
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"task": {
"name": "bespokelabs/activeprune-al-unlabeled-pool-pruning",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"be... | {
"category": "Machine Learning and AI",
"subcategory": "Data Engineering and Curation",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_eval.jsonl.gz",
"environment/app/data/dev_eval_y.npy",
"environment/app/data/eval_a_X.npy",
"environment/app/data/eval_b_X.npy",
"environment/app/data/eval_c_X.npy",
"environment/app/data/pool_a.jsonl.gz",
... | tasks/activeprune-al-unlabeled-pool-pruning | |
bespokelabs/budgeted-covtype-dual-market-open | # Dual-market budgeted classification
Build a classification policy. It must learn while label prices move, and it must choose which
feature values to buy while feature prices move. The goal is to maximize balanced accuracy: higher
is always better, with no cutoff where further improvement stops counting. A constant-c... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
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"task": {
"name": "bespokelabs/budgeted-covtype-dual-market-open",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchm... | {
"difficulty": "hard",
"model_difficulty": "hard",
"category": "Machine Learning and AI",
"tags": [
"budgeted-acquisition",
"active-learning",
"dynamic-pricing",
"market-pricing"
],
"task_objective": [
"Optimize"
],
"artifact_type": [
"Generated output artifact"
],
"expert_t... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/POLICY_TEMPLATE.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/server.py",
"environment/are-grad... | tasks/budgeted-covtype-dual-market-open | |
bespokelabs/budgeted-imputation-mcar50 | # Recovering missing values under 50% missingness, on a fixed CPU budget
Fill in the missing values in unseen numeric tables as accurately as possible.
Each value is missing independently with 50% probability, and each call has a 30 second
wall clock limit.
Write an imputer at `/app/output/impute.py`.
## Submission ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
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{
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"task": {
"name": "bespokelabs/budgeted-imputation-mcar50",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
... | {
"category": "Machine Learning and AI",
"subcategory": "Feature engineering",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/cellrunner.py",
"environment/app/cellspec.py",
"environment/app/data/dev/dev1.npz",
"environment/app/data/dev/dev2.npz",
"environment/app/data/dev/dev3.npz",
"environment/app/data/dev/dev4.npz",
"environment/app/d... | tasks/budgeted-imputation-mcar50 | |
bespokelabs/carps-star-discrepancy-subset-select | # Low-discrepancy subset selection
You get a cloud of `n` points in the unit cube `[0,1]^3` and a subset size `k`. Return the `k`
points that fill the cube most evenly, in the L-infinity star discrepancy sense.
For a set `P` of `k` points, the star discrepancy is:
$$d^*(P) = \sup_{q \in [0,1]^3} \left| \frac{\#\{p \... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
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{
"source": "/var/lib/are",
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"task": {
"name": "bespokelabs/carps-star-discrepancy-subset-select",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"ben... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Evaluation infrastructure",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 3,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/examples/cloud_00.npy",
"environment/app/data/examples/cloud_01.npy",
"environment/app/data/examples/cloud_02.npy",
"environment/app/data/examples/cloud_03.npy",
"environment/app/data/examples/cloud_04.npy",
"e... | tasks/carps-star-discrepancy-subset-select | |
bespokelabs/causalpfn-cate-pehe-ihdp-surfaceb | # Heterogeneous treatment-effect estimation from observational data
Given observational data where treatment was not randomly assigned, estimate how much the
treatment would change the outcome for each held out row.
## Task
Write `/app/output/estimator.py` with a single entry point:
```python
def estimate_cate(X_tr... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
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{
"source": "/var/lib/are",
"service": "grader"
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"task": {
"name": "bespokelabs/causalpfn-cate-pehe-ihdp-surfaceb",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchm... | {
"category": "Machine Learning and AI",
"subcategory": "Model inference and prediction",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev/realization_000.npz",
"environment/app/data/dev/realization_001.npz",
"environment/app/data/dev/realization_002.npz",
"environment/app/data/dev/realization_003.npz",
"environment/app/data/dev/realization_004.... | tasks/causalpfn-cate-pehe-ihdp-surfaceb | |
bespokelabs/causalrivers-heldout-station-graph-auroc | # Directed edge scores for river gauge subgraphs
A river gauge network is a directed graph over discharge measuring stations. An edge `i -> j`
means water measured at station `i` flows onward to station `j`, with no gauged station in
between. Given five years of discharge data from five stations at a time, score how l... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/causalrivers-heldout-station-graph-auroc",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
... | {
"category": "Machine Learning and AI",
"subcategory": "Model inference and prediction",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 6,
"metric_name": "Mean per-subgraph AUROC of directed-edge scores over a se... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_a_edges.csv",
"environment/app/data/dev_b_edges.csv",
"environment/app/dev_data.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/dra... | tasks/causalrivers-heldout-station-graph-auroc | |
bespokelabs/coreset-selection-group-robust-waterbirds | # Coreset selection against an unlabeled slice
You're given embeddings for a pool of bird photos, produced once by a frozen image backbone.
Each embedding is 512-dimensional. Your job is to pick 240 of these rows to form the training
set for a fixed downstream classifier.
The classifier is scored on a held-out evalua... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
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"task": {
"name": "bespokelabs/coreset-selection-group-robust-waterbirds",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Data loading and training pipelines",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_draws.npy",
"environment/app/data/dev_pool_features.npy",
"environment/app/data/dev_pool_labels.npy",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-g... | tasks/coreset-selection-group-robust-waterbirds | |
bespokelabs/cpu-decoder-graph-executor | # A faster executor for transformer-decoder compute graphs
`/app/reference_executor.py` runs transformer-decoder compute graphs on CPU. Write an
executor that computes the same thing faster.
## Submission format
- Deliverable: `/app/output/executor.py`, plus any helper modules placed beside it in
`/app/output/`.
-... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/cpu-decoder-graph-executor",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Systems and Efficiency",
"task_objective": [
"Optimize",
"Implement"
],
"artifact_type": [
"Single script or program",
"Codebase"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/bench.py",
"environment/app/graph_spec.py",
"environment/app/harness.py",
"environment/app/reference_executor.py",
"environment/app/worker.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/... | tasks/cpu-decoder-graph-executor | |
bespokelabs/cpu-llm-decode-throughput | # Serve a concurrent generation workload faster
Build a generation engine that serves the concurrent workload faster while returning the same
greedy token ids as the reference engine.
`/app/reference/reference_engine.py` serves generation requests one at a time on a 12-layer
causal language model (GPT-2 124M geometry... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/cpu-llm-decode-throughput",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Systems and Efficiency",
"task_objective": [
"Optimize",
"Implement"
],
"artifact_type": [
"Codebase",
"Single script or program"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_workload_0.json",
"environment/app/data/dev_workload_1.json",
"environment/app/data/dev_workload_2.json",
"environment/app/data/dev_workload_3.json",
"environment/app/data/warmup_workload.json",
"environmen... | tasks/cpu-llm-decode-throughput | |
bespokelabs/dctabeval-aeac-pooled-cat-statistics | # Access-request risk scoring from high-cardinality ID columns
Rank unseen access requests by how likely they are to be granted.
Employee access requests are recorded as nine nominal ID columns (resource, manager,
role rollups, department, title, family) and a binary outcome: the request was granted
or it was not. No... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/dctabeval-aeac-pooled-cat-statistics",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"ben... | {
"category": "Machine Learning and AI",
"subcategory": "Feature engineering",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 3,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev.csv",
"environment/app/local_panel.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/serve... | tasks/dctabeval-aeac-pooled-cat-statistics | |
bespokelabs/fast-adv-budgeted-pgd50-robust-cifar10 | # Train an adversarially robust CIFAR 10 model
Train a `PreActResNet18` within 180 seconds. The grader attacks the trained model and measures how many unseen CIFAR 10 images remain correctly classified.
## Deliverable
Write `/app/output/train.py`. It must be a single Python file no larger than 102,400 bytes.
The gr... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/fast-adv-budgeted-pgd50-robust-cifar10",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"b... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Training loops",
"task_objective": [
"Implement",
"Optimize",
"Secure or harden"
],
"artifact_type": [
"Single script or program",
"Generated output artifact"
],
"expert_time_estimate_hours": 4,
"metric_name": "PGD... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/preact_resnet.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/server.py",
"environment/are-grader... | tasks/fast-adv-budgeted-pgd50-robust-cifar10 | |
bespokelabs/fastercache-budgeted-video-dit-cache-policy | # Reduce video diffusion transformer evaluations
Video diffusion models are slow because they run a large transformer many times while refining
each video. CogVideoX 2b uses 50 diffusion steps and evaluates its transformer twice at each step,
once for the conditional prompt and once for the unconditional prompt. An un... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
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"task": {
"name": "bespokelabs/fastercache-budgeted-video-dit-cache-policy",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
... | {
"category": "Machine Learning and AI",
"subcategory": "Model inference and prediction",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 5,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/examples/example_policy.py",
"environment/app/harness/__init__.py",
"environment/app/harness/channel.py",
"environment/app/harness/hcore.py",
"environment/app/harness/run_eval.py",
"environment/app/harness/worker.py... | tasks/fastercache-budgeted-video-dit-cache-policy | |
bespokelabs/fastergcg-candidate-token-rank-ccc | # Rank token substitutions by true loss reduction
A prompt optimizer selects 64 possible token replacements using the gradient of its target loss. It would normally run the model once for every candidate to find which replacements help.
Build a ranker that predicts the same ordering without running the model at grade... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/fastergcg-candidate-token-rank-ccc",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"bench... | {
"category": "Machine Learning and AI",
"subcategory": "Interpretability and model inspection",
"task_objective": [
"Implement",
"Optimize",
"Analyze"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_behaviors.csv",
"environment/app/data/token_allowlist.npy",
"environment/app/example_ranker.py",
"environment/app/gcg_states.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerf... | tasks/fastergcg-candidate-token-rank-ccc | |
bespokelabs/grpo-rl-halfcheetah-advantage-estimator | # Advantage estimation for a frozen PPO-clip harness
Design the advantage values used by a fixed PPO training program in Gymnasium's
`HalfCheetah-v4` environment. Your goal is to maximize the trained policy's average episode return
across hidden training and evaluation seeds.
The only part you control is the advantag... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/grpo-rl-halfcheetah-advantage-estimator",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"... | {
"name": "advantage-estimation-half-cheetah",
"task_slug": "grpo-rl-halfcheetah-advantage-estimator",
"expert_time_estimate_hours": 4,
"title": "Advantage estimation for a frozen PPO-clip harness on HalfCheetah-v4 at a 1M-step budget",
"category": "Model Training and ML Infrastructure",
"subcategory": "Rei... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/harness/estimator_worker.py",
"environment/app/harness/eval_policy.py",
"environment/app/harness/ppo_harness.py",
"environment/app/harness/train.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-... | tasks/grpo-rl-halfcheetah-advantage-estimator | |
bespokelabs/hicard-latent-encoder | # Fixed-width encoding for a very high-cardinality categorical column
A tabular regression dataset has 10 numeric covariates `X` and one categorical column `G`.
`G` has `n_categories = 1600` levels with very different frequencies. Write an encoder that
replaces `G` with **at most 8 numeric columns** without ever seein... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
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"task": {
"name": "bespokelabs/hicard-latent-encoder",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
],
... | {
"category": "Machine Learning and AI",
"subcategory": "Feature engineering",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev/instance_00000.npz",
"environment/app/data/dev/instance_00001.npz",
"environment/app/data/dev/instance_00002.npz",
"environment/app/data/dev/instance_00003.npz",
"environment/app/data/dev/instance_00004.npz",... | tasks/hicard-latent-encoder | |
bespokelabs/label-efficient-risk-estimator | # Label-efficient risk estimation
A classifier `f` assigns a probability distribution over `C` classes to each of `N = 2000`
unlabelled pool examples. Its risk on that pool is
$$
R = \frac{1}{N} \sum_i -\log \text{target\_probs}[i, y_i]
$$
where `y_i` is the true (unknown) class of example `i`. You may buy at most `... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/label-efficient-risk-estimator",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark... | {
"category": "Machine Learning and AI",
"subcategory": "Evaluation, Calibration, and Robustness",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev/dev00/labels.npy",
"environment/app/data/dev/dev00/meta.json",
"environment/app/data/dev/dev00/surrogate_probs.npy",
"environment/app/data/dev/dev00/target_probs.npy",
"environment/app/data/dev/dev01/labels.n... | tasks/label-efficient-risk-estimator | |
bespokelabs/less-is-more-pretrain-token-budget-selector | # Spend a pretraining token budget
Build a selector that chooses which web documents to use for language model pretraining under a fixed token budget.
## Goal
The document pool contains about 250 million GPT-2 BPE tokens. Your selector may spend 125,000,000 tokens.
The grader trains the same 124M parameter decoder-... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/less-is-more-pretrain-token-budget-selector",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
... | {
"task_slug": "less-is-more-pretrain-token-budget-selector",
"title": "Spend a pretraining token budget: write the corpus selector that minimises held-out perplexity",
"category": "Model Training and ML Infrastructure",
"subcategory": "Data loading and training pipelines",
"expert_time_estimate_hours": 8,
... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/harness/dev_cycle.py",
"environment/app/harness/pretrain_harness.py",
"environment/app/harness/train_config.json",
"environment/app/output/select.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader... | tasks/less-is-more-pretrain-token-budget-selector | |
bespokelabs/mlh-coco-16bit-hash-head-map5000 | # 16-bit hash codes for multi-label image retrieval
Build a binary encoder that maps a frozen 512-dimensional image feature vector to a 16-bit code,
so that the Hamming distance between two codes reflects how much semantic content the two images
share.
## Data
- `/app/data/train_features.npy`: shape `(10000, 512)`, ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/mlh-coco-16bit-hash-head-map5000",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchma... | {
"category": "Machine Learning and AI",
"subcategory": "Unsupervised and representation learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/train_features.npy",
"environment/app/data/train_labels.npy",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-gr... | tasks/mlh-coco-16bit-hash-head-map5000 | |
bespokelabs/reppo-reliable-onpolicy-control-trainer | # Train control policies that hit a performance bar on every seed and hold it
Write a reinforcement-learning training procedure at `/app/solution/trainer.py`. The score is
the **reliable-success fraction**. It is the fraction of runs whose policy reaches 90% of a
reference level and stays there for the last ten evalua... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/reppo-reliable-onpolicy-control-trainer",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"... | {
"task_slug": "reppo-reliable-onpolicy-control-trainer",
"category": "Model Training and ML Infrastructure",
"subcategory": "Model Training and Alignment",
"metric_name": "reliable_success_fraction",
"objective": "maximize",
"graded_deliverable": "/app/solution/trainer.py",
"device": "cpu",
"expert_tim... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/child_main.py",
"environment/app/envs.py",
"environment/app/panel.py",
"environment/app/public_panel.json",
"environment/app/run_public.py",
"environment/app/solution/trainer.py",
"environment/are-grader-ctx/Docke... | tasks/reppo-reliable-onpolicy-control-trainer | |
bespokelabs/sae-sparse-dict-nmse-frontier | # Sparse coding of GPT-2 activations
Build a sparse dictionary that reconstructs GPT-2 small layer 8 residual stream activations.
## Goal
The grader measures normalized reconstruction error on 1,048,576 hidden activations:
$$
\frac{\sum_i \lVert x_i-\hat{x}_i\rVert^2}
{\sum_i \lVert x_i-\bar{x}\rVert^2}.
$$
Lower ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/sae-sparse-dict-nmse-frontier",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"... | {
"category": "Machine Learning and AI",
"subcategory": "Unsupervised and representation learning",
"task_objective": [
"Optimize",
"Implement"
],
"artifact_type": [
"Single script or program",
"Generated output artifact"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budge... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/extract_acts.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/server.py",
"environment/are-grader-... | tasks/sae-sparse-dict-nmse-frontier | |
bespokelabs/shortest-valid-ci-l2-ece | # Shortest valid confidence interval for the squared l2 calibration error
Given predicted class probabilities and their true labels, return a confidence interval for the
squared calibration error defined below. The interval should be as short as possible while still
covering the true value in at least 88% of repeated ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/shortest-valid-ci-l2-ece",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
... | {
"category": "Machine Learning and AI",
"subcategory": "Model evaluation and benchmarking",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/dev_panel.json",
"environment/app/panel.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/serv... | tasks/shortest-valid-ci-l2-ece | |
bespokelabs/sketched-newton-cov-estimator | # Limiting covariance of an online sketched Newton method
Estimate the optimizer's limiting covariance matrix $\Xi$ from the records of one run.
The fixed optimizer in `/app/harness/runner.py` processes one linear regression sample at a time.
Its final estimate varies across repeated runs. After scaling by the final ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/sketched-newton-cov-estimator",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Evaluation infrastructure",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/harness/dev/inst_0.json",
"environment/app/harness/dev/inst_0_xi_star.npy",
"environment/app/harness/dev/inst_1.json",
"environment/app/harness/dev/inst_1_xi_star.npy",
"environment/app/harness/runner.py",
"environm... | tasks/sketched-newton-cov-estimator | |
bespokelabs/sopcc-online-chance-constrained-policy | # Online stochastic orienteering under a budget-violation cap
A robot moves over a complete graph on 40 vertices, starting at vertex 0 and finishing at vertex
1, collecting the reward of every distinct vertex it visits along the way. Vertex rewards,
coordinates, and the distance matrix are known exactly before the run... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/sopcc-online-chance-constrained-policy",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"b... | {
"category": "Machine Learning and AI",
"subcategory": "Reinforcement learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/harness/baseline_policy.py",
"environment/app/harness/dev_eval.py",
"environment/app/harness/policy_worker.py",
"environment/app/harness/runner.py",
"environment/app/harness/sopcc.py",
"environment/are-grader-ctx/Do... | tasks/sopcc-online-chance-constrained-policy | |
bespokelabs/sparse-elsa-item-embeddings-8nnz | # Item embeddings under a hard 8-nonzero budget
Build compact item embeddings that retrieve a user's held-out items from their known interactions.
`/app/data/train.npz` holds a binarized user-item interaction matrix. `scipy.sparse.load_npz`
loads it as a `csr_matrix` of shape `(48366, 10000)`, dtype float32, every st... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/sparse-elsa-item-embeddings-8nnz",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchma... | {
"category": "Machine Learning and AI",
"subcategory": "Unsupervised and representation learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/check_contract.py",
"environment/app/data/train.npz",
"environment/app/data/val.npz",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
... | tasks/sparse-elsa-item-embeddings-8nnz | |
bespokelabs/svdquant-w4a4-psnr | # Improve W4A4 PixArt Sigma image quality
Improve the image quality of a W4A4 quantized PixArt Sigma model. Write your solution to `/app/output/quantize.py`. This must be the only file you create or modify.
The file must define `quantize_model(pipe, rank=32) -> None`. The grader passes in a fresh FP16 `diffusers.PixA... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/svdquant-w4a4-psnr",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark"
],
... | {
"category": "Machine Learning and AI",
"subcategory": "ML serving and deployment",
"task_objective": [
"Optimize",
"Transform",
"Implement"
],
"artifact_type": [
"Codebase",
"Generated output artifact"
],
"expert_time_estimate_hours": 4,
"are_version": "1.1",
"budget_seconds": 86... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/calibration_prompts.jsonl",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/app_data/calibration_prompts.jsonl",
"environment/are-grader-ctx/are/d... | tasks/svdquant-w4a4-psnr | |
bespokelabs/tgat-milp-branching-node-count | # Branching policy for a mixed integer linear program
Build a branching policy that lets a mixed integer linear programming (MILP) solver prove
optimality in as few search nodes as possible on unseen set covering instances.
The solver uses branch and bound. At each search node, it asks which fractional variable to br... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/tgat-milp-branching-node-count",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benchmark... | {
"category": "Machine Learning and AI",
"subcategory": "Reinforcement Learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program",
"Generated output artifact"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/README.md",
"environment/app/bnbenv/__init__.py",
"environment/app/bnbenv/features.py",
"environment/app/bnbenv/host.py",
"environment/app/bnbenv/protocol.py",
"environment/app/bnbenv/runner.py",
"environment/app/... | tasks/tgat-milp-branching-node-count | |
bespokelabs/ties-merging-clip-vitl14-eight-task-merge | # Merge eight vision models into one encoder
Create one CLIP ViT-L/14 vision encoder that performs well across eight image classification datasets.
## Goal
You receive one pretrained vision encoder and eight copies fine-tuned on:
`sun397`, `cars`, `resisc45`, `eurosat`, `svhn`, `gtsrb`, `mnist`, and `dtd`.
Return ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/ties-merging-clip-vitl14-eight-task-merge",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
... | {
"category": "Machine Learning and AI",
"subcategory": "Computer vision",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program",
"Generated output artifact"
],
"expert_time_estimate_hours": 6,
"are_version": "1.1",
"budget_seconds": 86400
} | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/eval_dev.py",
"environment/are-grader-ctx/Dockerfile",
"environment/are-grader-ctx/Dockerfile.are",
"environment/are-grader-ctx/are/drain.py",
"environment/are-grader-ctx/are/server.py",
"environment/are-grader-ctx/... | tasks/ties-merging-clip-vitl14-eight-task-merge | |
bespokelabs/vas-maskless-deployment-feasibility | # Supply the validity mask a mask-trained policy acts under
MiniDungeon is a procedurally generated dungeon crawler with 24 discrete actions. Before each
choice, the policy needs a Boolean mask that marks which actions the environment will accept.
During training, the policy received the true mask and ignored invalid ... | [
"autoresearch",
"machine-learning",
"benchmark"
] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
{
"source": "/var/lib/are",
"service": "grader"
}
],
"task": {
"name": "bespokelabs/vas-maskless-deployment-feasibility",
"version": "1.1.0",
"description": "",
"keywords": [
"autoresearch",
"machine-learning",
"benc... | {
"category": "Model Training and ML Infrastructure",
"subcategory": "Reinforcement learning",
"task_objective": [
"Implement",
"Optimize"
],
"artifact_type": [
"Single script or program",
"Generated output artifact"
],
"expert_time_estimate_hours": 3,
"are_version": "1.1",
"budget_sec... | [
"README.md",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/app/data/train_traj.npz",
"environment/app/data/val_traj.npz",
"environment/app/mdobs.py",
"environment/app/policy_weights.npz",
"environment/app/reference_policy.py",
"environment/are-grader-ctx/Dockerfile",
"environ... | tasks/vas-maskless-deployment-feasibility |
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