id string | domain string | title string | topic string | domains list | arxiv_id string | venue string | metric_key string | metric_direction string | gpu_required bool | est_wall_clock_sec int64 | synthesis string | num_hypotheses int64 | hypotheses string | experiment_design string | requirements string | rubric string | rubric_num_leaves int64 | manifest_file string | rubric_file string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ML01 | ml | Dropout regularization strategies on shallow tabular MLPs | Comparing dropout regularization strategies (standard, spatial, variational) for preventing overfitting in shallow MLPs on tabular classification benchmarks | [
"machine-learning",
"regularization"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 240 | Dropout-style regularization is a cornerstone of neural-net training for
overfitting control, but its impact on tabular classification with shallow
MLPs remains subtle. Standard element-wise dropout is the default; spatial
(feature-wise) dropout treats correlated input features as a block; MC /
variational dropout keep... | 3 | [{"id": "H1", "statement": "Variational / MC dropout produces lower Expected Calibration Error (ECE) than standard element-wise dropout on at least 2 of 3 tabular classification benchmarks, averaged over ≥5 seeds.", "measurable": true}, {"id": "H2", "statement": "Test-set accuracy differs by <2 absolute percentage poin... | {"research_question": "Does the choice of dropout variant (standard / spatial / variational) matter more for probability calibration than for test accuracy on tabular classification with shallow MLPs?", "conditions": [{"name": "no_dropout", "description": "Shallow MLP (2 hidden layers, 64 units, ReLU) trained with no d... | {"id": "ml01-root", "requirements": "A credible experiment studying whether dropout-variant choice affects calibration more than accuracy on tabular classification: the main dropout variants are implemented, evaluation covers multiple sklearn benchmarks with reasonable seed coverage, and the writeup ties the numeric fi... | 10 | tasks/ml/manifests/ML01.yaml | tasks/ml/rubrics/ML01.json | |
ML02 | ml | Bagging vs boosting vs stacking for noisy non-linear regression | Evaluating ensemble methods (bagging, boosting, stacking) for regression on synthetic non-linear datasets with varying noise levels | [
"machine-learning",
"ensemble-methods"
] | null | ARC-Bench 2026 | rmse | minimize | false | 420 | Ensemble learning is often presented as a broadly reliable way to improve
predictive performance, but different ensemble families can react very
differently to observation noise in regression. Bagging primarily reduces
variance by averaging many weakly correlated models. Boosting is sequential
and can aggressively fit ... | 3 | [{"id": "H1", "statement": "On the high-noise synthetic dataset (noise >= 25), bagging (RandomForestRegressor) achieves lower mean RMSE than boosting (GradientBoostingRegressor), averaged over at least 5 seeds.", "measurable": true}, {"id": "H2", "statement": "On at least 2 of 3 evaluated datasets, at least one ensembl... | {"research_question": "How does RMSE performance of bagging, boosting, and stacking vary with increasing noise in non-linear regression tasks under CPU-only constraints?", "conditions": [{"name": "decision_tree_baseline", "description": "Single DecisionTreeRegressor (max_depth tuned over a small grid) as non-ensemble b... | {"id": "ml02-root", "requirements": "A credible experiment studying bagging vs boosting vs stacking for noisy non-linear regression: the main ensemble families are implemented, execution covers multiple regression datasets with reasonable seed coverage, and the analysis addresses H1/H2/H3 using RMSE-centered evidence."... | 9 | tasks/ml/manifests/ML02.yaml | tasks/ml/rubrics/ML02.json | |
ML03 | ml | CPU comparison of Nelder-Mead, Powell, and CMA-ES on non-convex functions | Comparing gradient-free optimization algorithms (Nelder-Mead, Powell, CMA-ES) for non-convex benchmark functions using only CPU computation | [
"optimization",
"numerical-methods"
] | null | ARC-Bench 2026 | primary_metric | minimize | false | 480 | Gradient-free optimization methods are widely used when derivatives are
unavailable, unreliable, or expensive to compute. In low-to-medium
dimensional non-convex landscapes, direct-search methods such as
Nelder-Mead and Powell are frequently used because they are easy to call
through scipy.optimize. Population-based ap... | 3 | [{"id": "H1", "statement": "CMA-ES achieves lower mean best objective value than both Nelder-Mead and Powell on at least 2 of 3 benchmark functions, averaged over >=10 random starts with the same evaluation budget.", "measurable": true}, {"id": "H2", "statement": "Powell has lower median wall-clock runtime than CMA-ES ... | {"research_question": "Under equal evaluation budgets, how do Nelder-Mead, Powell, and CMA-ES compare on objective quality, runtime, and success probability for non-convex synthetic objectives?", "conditions": [{"name": "nelder_mead", "description": "scipy.optimize.minimize with method='Nelder-Mead', random initial poi... | {"id": "ml03-root", "requirements": "A credible experiment comparing Nelder-Mead, Powell, and CMA-ES on non-convex benchmark functions: the optimizers are implemented under matched evaluation budgets, execution covers multiple benchmark functions with repeated random starts, and results address H1/H2/H3 directionally."... | 9 | tasks/ml/manifests/ML03.yaml | tasks/ml/rubrics/ML03.json | |
ML04 | ml | Effect of standard, min-max, and robust scaling on KNN classification | Analyzing the effect of feature scaling methods (standard, min-max, robust) on k-nearest neighbors classification performance across different data distributions | [
"machine-learning",
"data-preprocessing"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 180 | K-nearest neighbors (KNN) is highly sensitive to feature scale because
distance calculations implicitly weight dimensions by their numeric ranges.
In practice, preprocessing decisions such as StandardScaler, MinMaxScaler,
or RobustScaler can alter neighborhood structure enough to change
classification performance as mu... | 3 | [{"id": "H1", "statement": "RobustScaler + KNN achieves higher mean test accuracy than StandardScaler + KNN on the outlier-heavy synthetic dataset by at least 0.03 absolute accuracy, averaged over ≥5 random splits.", "measurable": true}, {"id": "H2", "statement": "Across the evaluated datasets, at least one real datase... | {"research_question": "How does scaler choice (standard, min-max, robust) affect KNN classification accuracy and stability across datasets with different feature distributions and outlier profiles?", "conditions": [{"name": "no_scaling_knn", "description": "KNeighborsClassifier with fixed k and distance metric on raw f... | {"id": "ml04-root", "requirements": "A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address... | 9 | tasks/ml/manifests/ML04.yaml | tasks/ml/rubrics/ML04.json | |
ML05 | ml | Dimensionality reduction methods for preserving cluster structure in synthetic high-dimensional data | Investigating dimensionality reduction techniques (PCA, t-SNE, UMAP) for preserving cluster structure in high-dimensional synthetic datasets | [
"machine-learning",
"unsupervised-learning"
] | null | ARC-Bench 2026 | silhouette_score | maximize | false | 600 | Dimensionality reduction is frequently used before visualization and
clustering, but different methods optimize different objectives and can
distort neighborhood and global geometry in distinct ways. PCA is linear and
emphasizes variance preservation, while t-SNE and UMAP are nonlinear manifold
methods designed to pres... | 3 | [{"id": "H1", "statement": "On nonlinear manifold data (noisy two-moons embedded into 50D), at least one nonlinear reducer (t-SNE or UMAP) achieves silhouette_score at least 0.10 higher than PCA after 2D reduction and KMeans clustering, averaged over >=5 seeds.", "measurable": true}, {"id": "H2", "statement": "On linea... | {"research_question": "Which reducer best balances cluster-structure preservation and compute cost across linear and nonlinear synthetic high-dimensional datasets?", "conditions": [{"name": "pca_2d", "description": "PCA with n_components=2 using sklearn.decomposition.PCA."}, {"name": "tsne_2d", "description": "t-SNE wi... | {"id": "ml05-root", "requirements": "A credible experiment studying how linear (PCA) vs non-linear (t-SNE / UMAP-like) dimensionality reduction preserves cluster structure on synthetic high-dimensional datasets: methods share a consistent evaluation pipeline, execution covers multiple datasets with repeated seeds, and ... | 9 | tasks/ml/manifests/ML05.yaml | tasks/ml/rubrics/ML05.json | |
ML06 | ml | Adaptive learning-rate schedules for logistic-regression convergence on binary classification | Comparing adaptive learning rate schedules (cosine annealing, step decay, exponential decay, warm restarts) for logistic regression convergence on binary classification | [
"optimization",
"machine-learning"
] | null | ARC-Bench 2026 | convergence_epochs | minimize | false | 420 | Logistic regression is often treated as a solved baseline, yet its practical
training behavior still depends strongly on optimization details. In
first-order methods, the learning-rate schedule can dominate time-to-quality:
a constant step size may converge slowly or oscillate, while adaptive
schedules can accelerate e... | 3 | [{"id": "H1", "statement": "Cosine annealing or cosine warm restarts achieves at least 20% lower mean convergence_epochs than fixed learning rate on at least 2 of 3 evaluated binary datasets, averaged over >=5 seeds.", "measurable": true}, {"id": "H2", "statement": "Among adaptive schedules (step decay, exponential dec... | {"research_question": "Which learning-rate schedule yields the fastest convergence for mini-batch logistic regression on binary sklearn tasks, and do faster schedules preserve validation/test performance?", "conditions": [{"name": "fixed_lr", "description": "Mini-batch logistic regression trained with constant learning... | {"id": "ml06-root", "requirements": "A credible experiment studying whether adaptive learning-rate schedules improve logistic-regression convergence on binary classification: schedule conditions are implemented as distinct update rules, runs are executed on multiple datasets with multiple seeds, and results address H1/... | 10 | tasks/ml/manifests/ML06.yaml | tasks/ml/rubrics/ML06.json | |
ML07 | ml | Text feature extraction trade-offs: TF-IDF vs count vs hashing with NB/SVM | Evaluating text feature extraction methods (TF-IDF, count vectors, hashing) combined with Naive Bayes and SVM for document classification on 20newsgroups | [
"natural-language-processing",
"machine-learning"
] | null | ARC-Bench 2026 | f1_score | maximize | false | 600 | Sparse bag-of-words pipelines remain strong baselines for document
classification, especially under strict CPU and latency constraints.
Three common feature extractors — CountVectorizer, TfidfVectorizer, and
HashingVectorizer — define different bias/variance and efficiency trade-offs.
Count vectors preserve raw frequen... | 3 | [{"id": "H1", "statement": "TF-IDF + linear SVM achieves the highest macro-F1 among implemented conditions on at least 2 of 3 evaluated text datasets.", "measurable": true}, {"id": "H2", "statement": "HashingVectorizer-based pipelines reduce vectorization+fit wall-clock time by at least 20% versus the corresponding TF-... | {"research_question": "Across sklearn-accessible document datasets, what are the accuracy-efficiency trade-offs between count, TF-IDF, and hashing features when paired with Multinomial Naive Bayes and linear SVM classifiers?", "conditions": [{"name": "count_mnb", "description": "CountVectorizer (word unigrams, min_df=2... | {"id": "ml07-root", "requirements": "A credible experiment studying text-feature-extraction trade-offs (count, TF-IDF, hashing) with Naive Bayes and linear SVM for document classification: multiple vectorizer+classifier pipelines are implemented, runs execute on multiple datasets, and results address H1/H2/H3 direction... | 9 | tasks/ml/manifests/ML07.yaml | tasks/ml/rubrics/ML07.json | |
ML08 | ml | Impact of imbalance-handling strategies on sklearn binary classifiers | Analyzing the impact of class imbalance handling strategies (SMOTE, random oversampling, class weights, undersampling) on binary classification with scikit-learn | [
"machine-learning",
"imbalanced-learning"
] | null | ARC-Bench 2026 | balanced_accuracy | maximize | false | 360 | Class imbalance is one of the most common practical failure modes in binary
classification: a model can achieve high raw accuracy by predicting the
majority class while missing the minority class almost entirely. In
scikit-learn workflows, practitioners often choose among simple data-level
resampling (random oversampli... | 3 | [{"id": "H1", "statement": "At least one of {class_weight_balanced, smote} achieves higher balanced_accuracy than no_handling by >=0.03 absolute on at least 2 of 3 datasets (mean over >=5 seeds).", "measurable": true}, {"id": "H2", "statement": "Random undersampling yields the highest minority recall among compared str... | {"research_question": "Which imbalance-handling strategy provides the most consistent gains in balanced accuracy for binary classification with fixed sklearn models under controlled train/test splits?", "conditions": [{"name": "no_handling", "description": "Baseline logistic regression trained on imbalanced data with n... | {"id": "ml08-root", "requirements": "A credible experiment studying imbalance-handling strategies (e.g., class weights, SMOTE, random over-/under-sampling) for binary classification: methods are implemented as distinct conditions, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 usin... | 8 | tasks/ml/manifests/ML08.yaml | tasks/ml/rubrics/ML08.json | |
ML09 | ml | Bayesian optimization vs grid vs random search for random-forest tuning | Comparing Bayesian optimization vs grid search vs random search for hyperparameter tuning of random forests on UCI benchmark datasets | [
"automl",
"machine-learning"
] | null | ARC-Bench 2026 | best_cv_score | maximize | false | 780 | Hyperparameter tuning often dominates practical AutoML performance, yet in
CPU-limited settings the search strategy itself can matter as much as the
model family. For random forests, common tunables (number of trees,
max_depth, min_samples_split, min_samples_leaf, max_features) define a mixed
discrete-continuous space ... | 3 | [{"id": "H1", "statement": "With an equal budget of 20 hyperparameter evaluations per dataset, Bayesian optimization achieves higher mean best_cv_score than random search on at least 2 of 3 datasets.", "measurable": true}, {"id": "H2", "statement": "Random search matches or exceeds grid search in best_cv_score on at le... | {"research_question": "Under a fixed evaluation budget, which tuning strategy (Bayesian optimization, grid search, random search) yields the best random-forest cross-validation and test performance on sklearn UCI-style datasets?", "conditions": [{"name": "grid_search_budget20", "description": "Grid search over a compac... | {"id": "ml09-root", "requirements": "A credible experiment comparing Bayesian optimization, grid search, and random search for RandomForest hyperparameter tuning: methods are implemented with comparable budgets, executed on multiple datasets, and results are mapped directionally to H1/H2/H3.", "judging_note": "Score on... | 10 | tasks/ml/manifests/ML09.yaml | tasks/ml/rubrics/ML09.json | |
ML10 | ml | Cross-validation strategy reliability for small-sample model selection | Investigating the effectiveness of different cross-validation strategies (k-fold, stratified, repeated, leave-one-out) for model selection with small sample sizes | [
"machine-learning",
"model-selection"
] | null | ARC-Bench 2026 | estimation_bias | minimize | false | 600 | With small datasets, model selection can become highly sensitive to the
validation protocol. Practitioners often choose among k-fold,
stratified k-fold, repeated k-fold, and leave-one-out cross-validation
(LOOCV), but these strategies trade off bias, variance, and compute in
different ways. A method that appears best u... | 3 | [{"id": "H1", "statement": "RepeatedStratifiedKFold (5 folds × 3 repeats) achieves lower mean absolute estimation bias than plain KFold (5 folds) on at least 2 of 3 evaluated small-sample datasets, averaged over ≥5 random seeds.", "measurable": true}, {"id": "H2", "statement": "StratifiedKFold (5 folds) yields lower ac... | {"research_question": "For small-sample classification model selection, how do KFold, StratifiedKFold, RepeatedStratifiedKFold, and LOOCV compare in estimation bias, stability, and compute cost?", "conditions": [{"name": "kfold_5", "description": "Model selection via 5-fold KFold (shuffle=True) on the training split."}... | {"id": "ml10-root", "requirements": "A credible experiment studying how cross-validation strategy choice (k-fold, stratified k-fold, repeated stratified k-fold, LOOCV) affects small-sample model-selection reliability: strategies are implemented consistently, runs cover multiple small-sample datasets with multiple seeds... | 8 | tasks/ml/manifests/ML10.yaml | tasks/ml/rubrics/ML10.json | |
ML11 | ml | Benchmarking classical unsupervised outlier detectors under controlled anomaly injection | Benchmarking unsupervised outlier detection methods (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope) on UCI datasets with injected anomalies | [
"anomaly-detection",
"machine-learning"
] | null | ARC-Bench 2026 | roc_auc | maximize | false | 480 | Classical unsupervised anomaly-detection methods remain widely used in
production tabular pipelines because they are interpretable enough,
CPU-efficient, and available in standard libraries. Yet their relative
behavior can change sharply as anomaly prevalence and feature scaling vary.
IsolationForest is often robust in... | 3 | [{"id": "H1", "statement": "IsolationForest achieves higher mean ROC-AUC than OneClassSVM on at least 2 of 3 datasets when averaged over anomaly rates {0.05, 0.10} and ≥3 seeds.", "measurable": true}, {"id": "H2", "statement": "At anomaly rate 0.10, LocalOutlierFactor attains the best PR-AUC (rank 1) on at least 1 of 3... | {"research_question": "Which among IsolationForest, LocalOutlierFactor, OneClassSVM, and EllipticEnvelope is most robust across datasets and injected anomaly rates for unsupervised outlier detection under CPU constraints?", "conditions": [{"name": "isolation_forest", "description": "sklearn IsolationForest with contami... | {"id": "ml11-root", "requirements": "A credible experiment benchmarking unsupervised outlier detectors (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope, or equivalents) with injected anomalies: methods are implemented as distinct code paths, runs cover multiple datasets across anomaly rates, and resu... | 9 | tasks/ml/manifests/ML11.yaml | tasks/ml/rubrics/ML11.json | |
ML12 | ml | Comparing clustering algorithms on synthetic non-convex and anisotropic shapes | Comparing clustering algorithms (k-means, agglomerative, DBSCAN, HDBSCAN, spectral) on synthetic shapes (moons, circles, blobs, anisotropic) with ground-truth labels | [
"machine-learning",
"clustering"
] | null | ARC-Bench 2026 | adjusted_rand_score | maximize | false | 420 | Clustering algorithms encode very different geometric assumptions. K-means
prefers spherical, equal-variance groups; agglomerative clustering can adapt
to hierarchical structure depending on linkage; DBSCAN finds dense regions
and can recover non-convex shapes while labeling outliers as noise; spectral
clustering can s... | 3 | [{"id": "H1", "statement": "On the two non-convex datasets (moons and circles), at least one of {DBSCAN, SpectralClustering} achieves mean ARI that is at least 0.15 higher than k-means, averaged over ≥5 seeds.", "measurable": true}, {"id": "H2", "statement": "On the anisotropic-blobs dataset, agglomerative clustering (... | {"research_question": "How strongly does dataset geometry (non-convex vs anisotropic) determine which clustering algorithm achieves the best agreement with ground-truth labels under a fixed CPU-budget protocol?", "conditions": [{"name": "kmeans_k2_or_k3", "description": "KMeans baseline with n_clusters set to dataset g... | {"id": "ml12-root", "requirements": "A credible experiment comparing clustering algorithms across synthetic geometric datasets: clustering conditions are implemented, execution covers multiple datasets with repeated seeds and ARI-centric reporting, and the analysis addresses H1/H2/H3 directionally.", "judging_note": "S... | 9 | tasks/ml/manifests/ML12.yaml | tasks/ml/rubrics/ML12.json | |
ML13 | ml | Evaluating kernel choices for Gaussian Process regression on synthetic 1-D and 5-D functions | Evaluating kernel choices (RBF, polynomial, Matern-3/2, Matern-5/2) for Gaussian Process regression on synthetic 1-D and 5-D functions with homoscedastic noise | [
"machine-learning",
"kernel-methods"
] | null | ARC-Bench 2026 | test_nll | minimize | false | 360 | Gaussian Process (GP) regression performance depends strongly on the kernel,
which encodes assumptions about function smoothness and local structure.
Practitioners often default to the RBF kernel because it is easy to optimize
and widely available, but this choice can underperform when the target
function has roughness... | 3 | [{"id": "H1", "statement": "Across the two synthetic datasets, at least one of {RBF, Matern-5/2} achieves the lowest mean test NLL on each dataset (averaged over >=5 seeds).", "measurable": true}, {"id": "H2", "statement": "The polynomial kernel has mean test NLL at least 10% worse than the best kernel on at least 1 of... | {"research_question": "How do RBF, polynomial, Matern-3/2, and Matern-5/2 kernels compare for Gaussian Process regression on synthetic 1-D and 5-D noisy functions when judged primarily by test NLL?", "conditions": [{"name": "gp_rbf", "description": "GaussianProcessRegressor with ConstantKernel * RBF + WhiteKernel, hype... | {"id": "ml13-root", "requirements": "A credible experiment studying kernel-choice effects (RBF, polynomial, Matern-3/2, Matern-5/2, or equivalents) for Gaussian Process regression on synthetic noisy functions: conditions are implemented, execution covers multiple datasets with repeated seeds, and results address H1/H2/... | 9 | tasks/ml/manifests/ML13.yaml | tasks/ml/rubrics/ML13.json | |
ML14 | ml | Comparing split-conformal, Mondrian conformal, and CQR-style intervals on heteroscedastic regression | Comparing conformal prediction procedures (split-conformal, Mondrian, CQR) for achieving target coverage on heteroscedastic regression tasks | [
"machine-learning",
"uncertainty-quantification"
] | null | ARC-Bench 2026 | coverage_gap | minimize | false | 420 | Conformal prediction provides finite-sample coverage guarantees with minimal
assumptions, making it attractive for uncertainty quantification in practical
regression. However, standard split-conformal intervals are often globally
calibrated and can be inefficient when noise is heteroscedastic: they may
over-cover low-n... | 3 | [{"id": "H1", "statement": "On heteroscedastic datasets, at least one adaptive method (Mondrian or CQR-style) attains a lower absolute coverage gap to the 90% target than split-conformal by at least 0.02 on at least 2 of 3 datasets, averaged over ≥5 seeds.", "measurable": true}, {"id": "H2", "statement": "At matched ta... | {"research_question": "Do adaptive conformal procedures (Mondrian, CQR-style) improve coverage quality and efficiency over split-conformal for heteroscedastic regression under a fixed 90% target coverage?", "conditions": [{"name": "split_conformal_rf", "description": "Vanilla split-conformal regression using RandomFore... | {"id": "ml14-root", "requirements": "A credible experiment comparing split-conformal, Mondrian conformal, CQR-style, and optionally naive-quantile prediction intervals for ~90% target coverage on heteroscedastic regression: methods are implemented, executed on multiple datasets with multiple seeds, and results address ... | 10 | tasks/ml/manifests/ML14.yaml | tasks/ml/rubrics/ML14.json | |
ML15 | ml | Filter-based vs embedded L1 feature selection with injected noise features | Investigating filter-based feature selection (mutual_info_classif, chi2, f_classif) vs embedded L1-logistic selection on UCI classification datasets with injected irrelevant features | [
"machine-learning",
"feature-selection"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 420 | Feature selection on tabular classification problems is often presented as a
choice between simple univariate filters and embedded sparse models.
Univariate filters such as chi-squared, ANOVA F-score, and mutual
information are computationally cheap and easy to apply in a pipeline, but
they evaluate each feature indepe... | 3 | [{"id": "H1", "statement": "With 5x injected irrelevant features (relative to original feature count), L1-logistic embedded selection achieves higher mean test_accuracy than each filter method (mutual_info_classif, chi2, f_classif) on at least 2 of 3 datasets, averaged over >=5 seeds.", "measurable": true}, {"id": "H2"... | {"research_question": "Does embedded L1-logistic feature selection outperform univariate filter methods in robustness to injected irrelevant features on sklearn tabular classification datasets?", "conditions": [{"name": "filter_mutual_info_k20", "description": "SelectKBest(mutual_info_classif, k=20 or p if p<20) follow... | {"id": "ml15-root", "requirements": "A credible experiment studying filter-based feature selection (mutual information, chi-square, ANOVA F) versus embedded L1-logistic selection under injected irrelevant features: conditions are implemented, runs cover multiple datasets with multiple seeds and injection levels, and re... | 9 | tasks/ml/manifests/ML15.yaml | tasks/ml/rubrics/ML15.json | |
ML16 | ml | Bandit algorithm robustness under stationary and drifting reward regimes | Comparing multi-armed bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3) on synthetic Bernoulli and Gaussian arms under stationary and drift regimes | [
"reinforcement-learning",
"bandits"
] | null | ARC-Bench 2026 | cumulative_regret | minimize | false | 420 | Multi-armed bandit algorithms encode different assumptions about reward
generation and non-stationarity. Epsilon-greedy is simple and adaptable with
persistent exploration; UCB1 is optimism-driven and often strong in
stationary stochastic settings; Thompson sampling can be highly sample
efficient when model assumptions... | 3 | [{"id": "H1", "statement": "In stationary Bernoulli environments, either UCB1 or Thompson sampling achieves at least 10% lower mean cumulative regret than epsilon-greedy at horizon T on at least 2 of 3 datasets (averaged over >=20 seeds).", "measurable": true}, {"id": "H2", "statement": "In drifting environments, epsil... | {"research_question": "How do epsilon-greedy, UCB1, Thompson sampling, and Exp3 compare in cumulative regret across stationary versus drifting synthetic bandit tasks with Bernoulli and Gaussian rewards?", "conditions": [{"name": "epsilon_greedy_eps0.1", "description": "Epsilon-greedy with constant epsilon=0.1 and sampl... | {"id": "ml16-root", "requirements": "A credible experiment comparing bandit algorithms (epsilon-greedy, UCB1, Thompson sampling, Exp3, or equivalents) under stationary and drifting synthetic regimes: algorithms are implemented with a common interface, experiments cover multiple environments with repeated seeds, and res... | 9 | tasks/ml/manifests/ML16.yaml | tasks/ml/rubrics/ML16.json | |
ML17 | ml | Topic-model comparison on small 20newsgroups subsets: LDA vs NMF vs LSA | Comparing topic models (LDA, NMF, LSA) on a small subset of 20newsgroups by topic coherence (c_v) and document-cluster adjusted rand score | [
"natural-language-processing",
"topic-modeling"
] | null | ARC-Bench 2026 | coherence_cv | maximize | false | 720 | Topic modeling methods are often compared using either intrinsic quality
measures (such as topic coherence) or extrinsic utility (such as how well
document representations align with known labels), but the two views can
disagree. Latent Dirichlet Allocation (LDA), Non-negative Matrix
Factorization (NMF), and Latent Sem... | 3 | [{"id": "H1", "statement": "NMF achieves higher mean coherence_cv than LSA on at least 2 of 3 evaluated 20newsgroups subsets when using the same number of topics and preprocessing pipeline.", "measurable": true}, {"id": "H2", "statement": "Across the three methods (LDA, NMF, LSA), the method with the highest coherence_... | {"research_question": "How do LDA, NMF, and LSA compare on coherence_cv versus document-cluster ARI over small 20newsgroups subsets, and do intrinsic/extrinsic rankings diverge?", "conditions": [{"name": "lda_k4", "description": "LatentDirichletAllocation with n_components=4, max_iter=20, learning_method='batch', Count... | {"id": "ml17-root", "requirements": "A credible experiment comparing LDA, NMF, and LSA topic models on small 20newsgroups subsets: conditions are implemented, execution reports coherence and ARI on multiple subsets with repeated seeds, and results address H1/H2/H3 directionally.", "judging_note": "Score on scientific s... | 9 | tasks/ml/manifests/ML17.yaml | tasks/ml/rubrics/ML17.json | |
ML18 | ml | Post-hoc calibration methods for sklearn classifiers on tabular benchmarks | Evaluating probability calibration methods (Platt scaling, isotonic regression, temperature scaling) applied to scikit-learn classifiers (RandomForest, GBM, SVM-RBF) on UCI benchmarks | [
"machine-learning",
"calibration"
] | null | ARC-Bench 2026 | ece | minimize | false | 480 | Many widely used scikit-learn classifiers optimize discrimination rather
than probability quality. Random forests, gradient boosting models, and
RBF-kernel SVMs can achieve strong accuracy while output probabilities are
miscalibrated, especially on small or moderately imbalanced tabular data.
This matters in decision s... | 3 | [{"id": "H1", "statement": "At least one post-hoc calibrator (Platt, isotonic, or temperature) reduces ECE by at least 10% relative to the uncalibrated model for at least 2 of 3 classifiers on at least 2 of 3 datasets, averaged over >=3 seeds.", "measurable": true}, {"id": "H2", "statement": "Isotonic regression achiev... | {"research_question": "Which post-hoc calibration method (Platt, isotonic, temperature) most consistently improves probability calibration for RF/GBM/SVM-RBF on sklearn tabular datasets while preserving accuracy?", "conditions": [{"name": "uncalibrated", "description": "Base classifier probabilities/decision scores use... | {"id": "ml18-root", "requirements": "A credible experiment studying post-hoc probability calibration methods (Platt, isotonic, temperature, or equivalents) for sklearn classifiers: calibration conditions are implemented, execution covers multiple datasets/classifiers with repeated seeds, and results address H1/H2/H3 di... | 9 | tasks/ml/manifests/ML18.yaml | tasks/ml/rubrics/ML18.json | |
ML19 | ml | Comparing graph- and pseudo-label-based semi-supervised learning on small tabular datasets | Comparing semi-supervised learning strategies (LabelPropagation, LabelSpreading, SelfTrainingClassifier) on small UCI datasets with 10-20% labeled data | [
"machine-learning",
"semi-supervised-learning"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 420 | Semi-supervised learning is attractive when labels are scarce but unlabeled
examples are cheap. In sklearn, LabelPropagation and LabelSpreading provide
graph-based transductive approaches, while SelfTrainingClassifier offers a
pseudo-labeling wrapper around a standard supervised base learner. These
methods rely on diff... | 3 | [{"id": "H1", "statement": "At 10% labeled data, at least one semi-supervised method (LabelPropagation, LabelSpreading, or SelfTrainingClassifier) achieves test accuracy at least 3 percentage points higher than the supervised-only baseline on at least 2 of 3 datasets, averaged over ≥5 seeds.", "measurable": true}, {"id... | {"research_question": "How do graph-based (LabelPropagation/LabelSpreading) and pseudo-labeling (SelfTrainingClassifier) methods compare to supervised-only training under 10% and 20% labeled-data regimes on small tabular benchmarks?", "conditions": [{"name": "supervised_logreg_labeled_only", "description": "LogisticReg... | {"id": "ml19-root", "requirements": "A credible experiment comparing semi-supervised methods (LabelPropagation, LabelSpreading, SelfTrainingClassifier, or equivalents) against a supervised-only baseline under limited-label regimes: methods are implemented correctly, runs cover multiple datasets with repeated seeds, and... | 9 | tasks/ml/manifests/ML19.yaml | tasks/ml/rubrics/ML19.json | |
ML20 | ml | Classical forecaster robustness on synthetic seasonal time series | Benchmarking classical time-series forecasters (AR, ARIMA, SARIMAX, ETS, Theta) on synthetic seasonal series with varying noise, trend, and seasonality amplitudes | [
"time-series",
"forecasting"
] | null | ARC-Bench 2026 | smape | minimize | false | 720 | Classical univariate forecasting methods remain widely used because they are
interpretable and lightweight, yet their comparative behavior can flip under
different data-generating regimes. AR models often perform well when dynamics
are mostly autoregressive and weakly seasonal; ARIMA can absorb trend and
differencing s... | 3 | [{"id": "H1", "statement": "SARIMAX or ETS achieves the lowest mean sMAPE on at least 2 of 3 synthetic dataset families when seasonality amplitude is high (amplitude >= 8) and noise is low (sigma <= 1.0), averaged over >=5 seeds.", "measurable": true}, {"id": "H2", "statement": "Under high-noise settings (sigma >= 3.0)... | {"research_question": "Which classical forecasting method is most robust in sMAPE across controlled synthetic regimes of trend, seasonality amplitude, and noise?", "conditions": [{"name": "ar_lag12", "description": "Autoregressive model with lag order selected from {3,6,12} by AIC on training split."}, {"name": "arima_... | {"id": "ml20-root", "requirements": "A credible experiment benchmarking classical forecasters (AR, ARIMA, SARIMAX, ETS, Theta, or equivalents) on synthetic seasonal series: model conditions are implemented, execution covers multiple synthetic regimes with repeated seeds, and results address H1/H2/H3 directionally using... | 10 | tasks/ml/manifests/ML20.yaml | tasks/ml/rubrics/ML20.json | |
ML21 | ml | PC vs GES vs NOTEARS-linear for recovering small Gaussian linear-SEM DAGs | Comparing causal structure learning algorithms (PC, GES, NOTEARS-linear) on small synthetic linear-SEM DAGs of 5-15 nodes with Gaussian noise | [
"causal-inference",
"machine-learning"
] | null | ARC-Bench 2026 | structural_hamming_distance | minimize | false | 720 | Causal structure learning methods often claim asymptotic guarantees, but in
practical settings researchers usually face small graphs, finite samples, and
imperfect hyperparameter choices. For Gaussian linear structural equation
models (SEMs), three widely discussed paradigms are constraint-based (PC),
score-based (GES)... | 3 | [{"id": "H1", "statement": "At n_samples=1000, NOTEARS-linear achieves lower mean SHD than PC on at least 2 of 3 synthetic DAG regimes (averaged over >=5 random DAG/data seeds per regime).", "measurable": true}, {"id": "H2", "statement": "At n_samples=300, GES achieves mean SHD <= PC in all evaluated regimes, and stric... | {"research_question": "For 5-15 node Gaussian linear-SEM DAGs, how do PC, GES, and NOTEARS-linear compare in SHD and edge recovery across sparse/medium/dense synthetic regimes and finite sample sizes?", "conditions": [{"name": "pc_partial_corr", "description": "Constraint-based PC using Gaussian partial-correlation con... | {"id": "ml21-root", "requirements": "A credible experiment comparing causal structure learning methods (PC, GES, NOTEARS-linear, or equivalents) on small synthetic Gaussian linear-SEM DAGs: methods are implemented, runs cover multiple synthetic regimes with multiple seeds, and results address H1/H2/H3 directionally usi... | 9 | tasks/ml/manifests/ML21.yaml | tasks/ml/rubrics/ML21.json | |
ML22 | ml | Active learning query strategies for logistic regression on small tabular pools | Evaluating active learning query strategies (uncertainty sampling, margin sampling, query-by-committee, expected-error reduction) for logistic regression on small UCI pools | [
"machine-learning",
"active-learning"
] | null | ARC-Bench 2026 | accuracy_at_budget | maximize | false | 600 | Pool-based active learning is attractive when labels are expensive but a
modest unlabeled pool is available. For linear probabilistic models such as
logistic regression, classic query policies include uncertainty sampling,
margin sampling, query-by-committee (QBC), and expected-error reduction
style lookahead approxima... | 3 | [{"id": "H1", "statement": "At 30% labeling budget, at least 2 of {uncertainty_sampling, margin_sampling, qbc, expected_error_reduction} achieve higher mean test accuracy than random_sampling by ≥1.5 percentage points, averaged over ≥5 seeds.", "measurable": true}, {"id": "H2", "statement": "expected_error_reduction at... | {"research_question": "Which active learning query strategy provides the best label efficiency for logistic regression on small tabular pool-based classification tasks under fixed budgets?", "conditions": [{"name": "random_sampling", "description": "Baseline pool-based active learning that queries unlabeled points unif... | {"id": "ml22-root", "requirements": "A credible experiment studying active-learning query strategies (random, uncertainty, margin, QBC, expected error reduction, or equivalents) for logistic regression: strategies are implemented as distinct code paths, execution covers multiple datasets with repeated seeds under a fix... | 10 | tasks/ml/manifests/ML22.yaml | tasks/ml/rubrics/ML22.json | |
ML23 | ml | Pointwise vs pairwise vs listwise learning-to-rank on synthetic query-document benchmarks | Comparing learning-to-rank approaches (pointwise linear, pairwise RankNet-lite, listwise ListMLE-lite) on synthetic query-document pairs with known relevance grades | [
"information-retrieval",
"machine-learning"
] | null | ARC-Bench 2026 | ndcg_at_10 | maximize | false | 480 | Learning-to-rank (LTR) objectives are often grouped into pointwise, pairwise,
and listwise families, each optimizing a different surrogate of retrieval
quality. Pointwise methods reduce ranking to regression/classification on
individual query-document pairs; pairwise methods optimize relative order
between document pai... | 3 | [{"id": "H1", "statement": "ListMLE-lite achieves higher mean NDCG@10 than pointwise linear regression on at least 2 of 3 synthetic datasets, with an absolute improvement of at least 0.03 on each winning dataset (averaged over >=3 seeds).", "measurable": true}, {"id": "H2", "statement": "At least one of {RankNet-lite, ... | {"research_question": "Do pairwise/listwise lightweight ranking objectives provide consistent top-k retrieval gains over a pointwise linear baseline on synthetic grouped query-document data?", "conditions": [{"name": "pointwise_linear", "description": "Linear model trained pointwise on query-document feature vectors to... | {"id": "ml23-root", "requirements": "A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and ... | 9 | tasks/ml/manifests/ML23.yaml | tasks/ml/rubrics/ML23.json | |
ML24 | ml | Online binary classification under concept drift: SGD, PA, NB, and FTRL | Benchmarking online learning algorithms (SGD logistic, Passive-Aggressive, online Naive Bayes, Follow-the-Regularized-Leader) on streaming binary classification with concept drift | [
"online-learning",
"machine-learning"
] | null | ARC-Bench 2026 | prequential_accuracy | maximize | false | 420 | In streaming classification, data arrive sequentially and model updates must
be cheap and immediate. Under concept drift, the relationship between
features and labels changes over time, so static train/test evaluation can
be misleading. Prequential (test-then-train) evaluation better reflects
deployment: each incoming ... | 3 | [{"id": "H1", "statement": "On drifted streams, Passive-Aggressive or FTRL achieves higher prequential_accuracy than online Naive Bayes by at least 0.02 absolute on at least 2 of 3 datasets, averaged over >=5 seeds.", "measurable": true}, {"id": "H2", "statement": "For at least 2 of 3 drifted datasets, the best post-dr... | {"research_question": "Which online method among SGD logistic, Passive-Aggressive, online Naive Bayes, and FTRL-style logistic best balances prequential accuracy and post-drift recovery on binary data streams with concept drift?", "conditions": [{"name": "sgd_logistic", "description": "SGDClassifier with log_loss and p... | {"id": "ml24-root", "requirements": "A credible experiment studying online learners (SGD logistic, Passive-Aggressive, online Naive Bayes, FTRL-style logistic, or equivalents) under concept drift: streaming prequential setup is implemented, execution covers multiple datasets with multiple seeds, and results address H1/... | 10 | tasks/ml/manifests/ML24.yaml | tasks/ml/rubrics/ML24.json | |
ML25 | ml | Reservoir computing vs MLP vs GP for short-horizon Lorenz-63 forecasting | Testing whether a random-feature echo state network (reservoir computing) predicts Lorenz-63 short-horizon trajectories more accurately than a matched-parameter MLP and a Gaussian Process at small training-data sizes (N=200-2000 points) | [
"dynamical-systems",
"machine-learning",
"reservoir-computing"
] | null | ARC-Bench 2026 | valid_prediction_time | maximize | false | 720 | Reservoir computing (a.k.a. echo state networks, ESN) is a lightweight
approach for learning dynamical systems: a fixed random recurrent network
projects the input into a high-dimensional reservoir state, and only a
linear readout layer is trained. For chaotic systems, ESNs have repeatedly
been reported to match or out... | 3 | [{"id": "H1", "statement": "An echo-state network with spectral radius ≈ 0.9 and ≤ 500 reservoir units achieves a longer mean Valid Prediction Time (VPT ≥ 1.2×) than a parameter-matched MLP on Lorenz-63 at training-set sizes N ≤ 500.", "measurable": true}, {"id": "H2", "statement": "The Gaussian Process regressor is co... | {"research_question": "On Lorenz-63 short-horizon forecasting, does a small echo-state network achieve a longer valid prediction time than a parameter-matched MLP and a Gaussian Process regressor, and how does the ranking depend on training-set size?", "conditions": [{"name": "esn_N200", "description": "Echo-state netw... | {"id": "ml25-root", "requirements": "A credible CPU-scale study of reservoir computing (echo-state network), a matched-parameter MLP, and a Gaussian Process on Lorenz-63 short-horizon forecasting: Lorenz-63 is integrated, the three model families are implemented, multiple training-set sizes are swept, valid-prediction-... | 11 | tasks/ml/manifests/ML25.yaml | tasks/ml/rubrics/ML25.json | |
P01 | physics | Reproducing the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \to \mu^+\mu^-$ | Reproducing warped extra dimension Kaluza-Klein graviton resonance from arXiv:hep-ph/9909255 | [
"high-energy-physics",
"bsm-phenomenology",
"extra-dimensions"
] | hep-ph/9909255 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the cross-section vs $\sqrt{s}$ scan of $e^+e^- \to \mu^+\mu^-$
in the Randall-Sundrum warped extra dimension scenario as a benchmark of
the agent's ability to assemble a full Lagrangian -> MC -> analysis
pipeline. The model adds a tower of massive spin-2 KK gravitons
$h^{(n)}_{\alpha\beta}$ ($n=1,\ldots,5$... | 3 | [{"id": "H1", "statement": "The first KK resonance $m_1 = 600$ GeV produces a peak in $\\sigma(e^+e^- \\to \\mu^+\\mu^-)$ at $\\sqrt{s} = 600$ GeV that lies within 5% of the input mass.", "measurable": true}, {"id": "H2", "statement": "Off-resonance cross sections at $\\sqrt{s}=200$ and $\\sqrt{s}=400$ GeV agree with t... | {"research_question": "Does the agent reproduce the Randall-Sundrum KK-graviton resonance pattern in $e^+e^- \\to \\mu^+\\mu^-$ across the $\\sqrt{s}=200$-$1200$ GeV scan, matching the published positions and continuum heights of Figure 2 of arXiv:hep-ph/9909255?", "conditions": [{"name": "sqrt_s_scan", "description": ... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p01-root", "requirements": "A credible HEP collider study reproducing the published reference figure for RS warped extra dimension KK-graviton resonance in $e^+e^- \\to \\mu^+\\mu^-$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs... | 15 | tasks/physics/manifests/P01.yaml | tasks/physics/rubrics/P01.json |
P02 | physics | Reproducing $pp \to \mu^\pm N$ cross section vs heavy-Majorana-neutrino mass at $\sqrt{s} = 7, 8, 14$ TeV | Reproducing heavy Majorana neutrino production $pp \to \mu^\pm N$ at LHC 7/8/14 TeV from arXiv:1308.2209 | [
"high-energy-physics",
"bsm-phenomenology",
"neutrino-physics"
] | 1308.2209 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the LHC sensitivity to a single heavy Majorana neutrino $N$
in the muon channel as a benchmark of the agent's ability to assemble a
hadron-collider Lagrangian -> MC -> analysis pipeline. The BSM extension
adds a right-handed singlet $N$ mixing with $\nu_\mu$ via the
dimensionless real parameter $V_{\mu N}$,... | 3 | [{"id": "H1", "statement": "At $m_N = 200$ GeV and $\\sqrt{s}=14$ TeV, $\\sigma(pp \\to \\mu^\\pm N)$ falls within $\\pm 30\\%$ of the published reference value.", "measurable": true}, {"id": "H2", "statement": "Cross-section ordering $\\sigma(14\\,\\mathrm{TeV}) > \\sigma(8\\,\\mathrm{TeV}) > \\sigma(7\\,\\mathrm{TeV}... | {"research_question": "Does the agent reproduce the cross-section curves for $pp \\to \\mu^\\pm N$ as a function of the heavy-Majorana-neutrino mass at the 7, 8, and 14 TeV LHC, matching Figure 3 of arXiv:1308.2209?", "conditions": [{"name": "lhc_7tev", "description": "$pp$ collisions at $\\sqrt{s}=7$ TeV with CTEQ6L P... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p02-root", "requirements": "A credible HEP collider study reproducing the published reference figure for Heavy Majorana neutrino $pp \\to \\mu^\\pm N$ at LHC 7/8/14 TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generatio... | 15 | tasks/physics/manifests/P02.yaml | tasks/physics/rubrics/P02.json |
P03 | physics | Reproducing 8 TeV LHC dilepton exclusion contours for a B-L Z' boson with kinetic mixing | Reproducing 8 TeV LHC dilepton exclusion contours for B-L Z' with kinetic mixing from arXiv:1605.02910 | [
"high-energy-physics",
"bsm-phenomenology",
"z-prime",
"statistical-recast"
] | 1605.02910 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the LHC Run 1 sensitivity to a B-L gauge boson Z' with
kinetic mixing as a benchmark of the agent's ability to combine MC
parameter scans with an analytic statistical recast. The model has a
Z' coupling to SM fermions via a left/right combination
$C_{f,L/R}(\tilde g, g_1', M_{Z'})$ where $g_1'$ is the B-L g... | 3 | [{"id": "H1", "statement": "For the $M_{Z'}=2$ TeV panel, the exclusion contour crosses the $\\tilde g = 0$ axis at $g_1' \\in [0.10, 0.20]$ (matching the published value within 30%).", "measurable": true}, {"id": "H2", "statement": "The $(A, B, C)$ quadratic coefficients extracted at $M_{Z'}=2$ TeV satisfy $A > 0$ and... | {"research_question": "Does the agent reproduce the three-panel $(\\tilde g, g_1')$ 2$\\sigma$ exclusion contours at $M_{Z'}=2.0, 2.5, 3.0$ TeV from the LHC Run 1 dilepton search at $\\sqrt{s}=8$ TeV with $\\mathcal{L}=20$ fb$^{-1}$ (Figure 1 of arXiv:1605.02910)?", "conditions": [{"name": "mzp_2tev", "description": "$... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p03-root", "requirements": "A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing, three-panel exclusion in $(\\tilde g, g_1')$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and gen... | 15 | tasks/physics/manifests/P03.yaml | tasks/physics/rubrics/P03.json |
P04 | physics | Reproducing $\sigma(pp \to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at $\sqrt{s}=13$ TeV | Reproducing $\sigma(pp \to Z')$ vs $g_1'$ slices for kinetic-mixing B-L Z' at 13 TeV from arXiv:1605.02910 | [
"high-energy-physics",
"bsm-phenomenology",
"z-prime"
] | 1605.02910 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the $\sqrt{s}=13$ TeV LHC production cross section
$\sigma(pp \to Z')$ as a function of $g_1'$ along three constant-
$\tilde g$ slices, for the same kinetic-mixing B-L $Z'$ model as P03.
The cross section is exactly quadratic in the couplings,
$\sigma(g_1', \tilde g) = A g_1'^2 + B g_1' \tilde g + C \tilde ... | 3 | [{"id": "H1", "statement": "At Panel A ($M_{Z'}=2$ TeV, $\\tilde g = 0$, $g_1' = 0.10$), the reconstructed $\\sigma(pp \\to Z')$ lies within $\\pm 30\\%$ of the published value.", "measurable": true}, {"id": "H2", "statement": "The curve at $\\tilde g = -0.10$ in Panel A lies above the $\\tilde g = 0$ curve at $g_1' = ... | {"research_question": "Does the agent reproduce $\\sigma(pp \\to Z')$ vs $g_1'$ for fixed-$\\tilde g$ slices at $M_{Z'}=2$ and 3 TeV at $\\sqrt{s}=13$ TeV (Figure 10 of arXiv:1605.02910)?", "conditions": [{"name": "mzp_2tev_3runs", "description": "$M_{Z'}=2$ TeV at $\\sqrt{s}=13$ TeV: 3 signal runs at $(g_1', \\tilde g... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p04-root", "requirements": "A credible HEP collider study reproducing the published reference figure for B-L $Z'$ kinetic mixing $\\sigma(g_1')$ slices at $\\sqrt{s}=13$ TeV. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and gene... | 15 | tasks/physics/manifests/P04.yaml | tasks/physics/rubrics/P04.json |
P05 | physics | Reproducing the normalized $E_T^\mathrm{miss}$ shape in $pp \to a W^\pm \gamma$ from a photophobic ALP EFT | Reproducing photophobic ALP EFT $E_T^\mathrm{miss}$ shape in $pp \to a W^\pm \gamma$ from arXiv:1701.05379 | [
"high-energy-physics",
"bsm-phenomenology",
"axion-like-particles",
"effective-field-theory"
] | 1701.05379 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the normalized $E_T^\mathrm{miss}$ distribution for the
process $pp \to a W^\pm (\to \ell^\pm \nu)\, \gamma$ at $\sqrt{s}=13$
TeV in the bosonic ALP EFT, as a benchmark of the agent's ability to
handle a higher-dimensional EFT operator and parton-level missing-energy
reconstruction. The Lagrangian is
$\delt... | 3 | [{"id": "H1", "statement": "The peak of the normalized $E_T^\\mathrm{miss}$ distribution lies in the bin range $[100, 250]$ GeV, consistent with the published shape.", "measurable": true}, {"id": "H2", "statement": "The integrated normalized fraction of events with $E_T^\\mathrm{miss} > 500$ GeV is in $[0.05, 0.30]$ at... | {"research_question": "Does the agent reproduce the parton-level $E_T^\\mathrm{miss}$ shape for $pp \\to a W^\\pm \\gamma$, $W \\to \\ell\\nu$ at $\\sqrt{s}=13$ TeV under the photophobic ALP EFT (Figure 8 of arXiv:1701.05379)?", "conditions": [{"name": "alp_cw1_fa1tev", "description": "Bosonic ALP EFT at $c_{\\tilde W}... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p05-root", "requirements": "A credible HEP collider study reproducing the published reference figure for Photophobic ALP EFT $E_T^\\mathrm{miss}$ in $pp \\to a W^\\pm \\gamma$. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and ge... | 15 | tasks/physics/manifests/P05.yaml | tasks/physics/rubrics/P05.json |
P06 | physics | Reproducing $U_1$ vector-leptoquark $\sqrt{|g_c^* g_b|}$ vs $M_{U_1}$ exclusion from ATLAS+CMS $pp \to \tau\nu$ | Reproducing $U_1$ vector leptoquark exclusion from combined ATLAS+CMS $pp \to \tau\nu$ recast from arXiv:1811.07920 | [
"high-energy-physics",
"bsm-phenomenology",
"leptoquark",
"flavor-anomaly",
"statistical-recast"
] | 1811.07920 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the LHC sensitivity to a $U_1$ vector leptoquark from the
$pp \to \tau\nu$ channel via $bc \to U_1 \to \tau\nu$ exchange,
combining ATLAS + CMS searches in a binned profile-likelihood, as a
benchmark of the agent's ability to (i) generate LQ events with
bottom-quark and charm-quark initial-state partons, (i... | 3 | [{"id": "H1", "statement": "The combined ATLAS+CMS 2$\\sigma$ exclusion at $M_{U_1} = 1$ TeV gives $\\sqrt{|g_c^* g_b|} \\in [0.3, 1.5]$, matching the published value within 30%.", "measurable": true}, {"id": "H2", "statement": "The reconstructed exclusion contour intersects the LH $R_{D^{(*)}}$ band ($\\epsilon_L = 0.... | {"research_question": "Does the agent reproduce the 2$\\sigma$ exclusion contour for the $U_1$ vector leptoquark in the $(\\sqrt{|g_c^* g_b|}, M_{U_1})$ plane from combined ATLAS+CMS $pp \\to \\tau\\nu$ recast (Figure 3 of arXiv:1811.07920)?", "conditions": [{"name": "atlas_recast", "description": "Mass scan $M_{U_1} \... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p06-root", "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ vector-leptoquark recast of LHC $pp \\to \\tau\\nu$ (ATLAS+CMS). The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and gen... | 15 | tasks/physics/manifests/P06.yaml | tasks/physics/rubrics/P06.json |
P07 | physics | Reproducing the $m_{ej}$ resonance in $pp \to \mathrm{LQ} \to ej$ via the LUXlep proton lepton PDF | Reproducing scalar leptoquark $m_{ej}$ resonance via LUXlep proton lepton PDF from arXiv:2005.06475 | [
"high-energy-physics",
"bsm-phenomenology",
"leptoquark",
"lepton-pdf"
] | 2005.06475 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the LHC sensitivity to a scalar leptoquark in the
$pp \to \mathrm{LQ} \to ej$ channel as a benchmark of the agent's
ability to assemble a full Lagrangian -> MC -> shower -> detector ->
analysis pipeline using a non-trivial PDF (LUXlep, with electrons inside
the proton). The Lagrangian
$\mathcal{L} = \lambda... | 3 | [{"id": "H1", "statement": "Reconstructed $m_{ej}$ peaks within $\\pm 5\\%$ of the input $M_\\mathrm{LQ}=3000$ GeV after Pythia8+Delphes smearing (i.e., peak in [2850, 3150] GeV).", "measurable": true}, {"id": "H2", "statement": "The generation-level $p_T(\\ell, j) > 500$ GeV cut yields $\\geq 50\\%$ acceptance for the... | {"research_question": "Does the agent reproduce the $m_{ej}$ invariant-mass distribution for $pp \\to \\mathrm{LQ} \\to ej$ at $\\sqrt{s}=13$ TeV with LUXlep PDF and $M_\\mathrm{LQ}=3$ TeV (Figure 2 of arXiv:2005.06475)?", "conditions": [{"name": "lq_3tev_lambda1", "description": "Scalar LQ at $M_\\mathrm{LQ}=3000$ GeV... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p07-root", "requirements": "A credible HEP collider study reproducing the published reference figure for Scalar leptoquark resonance $m_{ej}$ via LUXlep proton lepton PDF. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and generat... | 15 | tasks/physics/manifests/P07.yaml | tasks/physics/rubrics/P07.json |
P08 | physics | Reproducing $\sigma(pp \to Z' \to \mu^+\mu^-)$ vs $M_{Z'}$ for SSM and E6-$\psi$ scenarios at $\sqrt{s}=13$ TeV | Reproducing SSM and $E_6$-$\psi$ Z' dimuon cross sections at 13 TeV LHC from arXiv:2103.02708 | [
"high-energy-physics",
"bsm-phenomenology",
"z-prime"
] | 2103.02708 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the dimuon production cross section $\sigma(pp \to Z' \to
\mu^+\mu^-)$ as a function of $M_{Z'}$ for two canonical $Z'$ benchmarks
- the Sequential Standard Model (SSM) where the $Z'$ inherits SM
electroweak couplings, and the $E_6$-derived $Z'_\psi$ with universal
coupling $g_\psi = 0.0942$. The general La... | 3 | [{"id": "H1", "statement": "$\\sigma_\\mathrm{SSM}(M_{Z'}=2\\,\\mathrm{TeV}) > \\sigma_\\psi(M_{Z'}=2\\,\\mathrm{TeV})$ by a factor in $[3, 30]$ at $\\sqrt{s}=13$ TeV (SSM has larger couplings).", "measurable": true}, {"id": "H2", "statement": "$\\sigma_\\mathrm{SSM}(M_{Z'}=1\\,\\mathrm{TeV})$ falls within $\\pm 30\\%$... | {"research_question": "Does the agent reproduce $\\sigma(pp \\to Z' \\to \\mu^+\\mu^-)$ vs $M_{Z'}$ for the SSM ($Z'_\\mathrm{SSM}$) and $E_6$-derived $Z'_\\psi$ benchmarks at the 13 TeV LHC (Figure 4 of arXiv:2103.02708)?", "conditions": [{"name": "ssm_mass_scan", "description": "$Z'_\\mathrm{SSM}$ with electroweak co... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p08-root", "requirements": "A credible HEP collider study reproducing the published reference figure for General $Z'$: SSM vs $E_6$-$\\psi$ dimuon cross section vs mass at LHC. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropriate PDFs and ge... | 15 | tasks/physics/manifests/P08.yaml | tasks/physics/rubrics/P08.json |
P09 | physics | Reproducing the normalized $|\eta|$ shape in $\mu^+\mu^- \to b\bar b$ from a $U_1$ vector leptoquark at $\sqrt{s}=3$ TeV | Reproducing $U_1$ leptoquark $|\eta|$ shape in $\mu^+\mu^- \to b\bar b$ at 3 TeV muon collider from arXiv:2104.05720 | [
"high-energy-physics",
"bsm-phenomenology",
"leptoquark",
"muon-collider"
] | 2104.05720 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the normalized pseudorapidity shape of the $b$ quark in
$\mu^+\mu^- \to b\bar b$ at a 3 TeV muon collider, comparing SM
($s$-channel $\gamma^*/Z^*$) against $U_1$ vector-leptoquark
$t$-channel exchange. The Lagrangian has the standard vector-LQ
kinetic term plus
$\mathcal{L} \supset (g_U/\sqrt{2})\,U_1^\mu ... | 3 | [{"id": "H1", "statement": "At $\\beta_L^{32} = 1.0$, $m_\\mathrm{LQ} = 1$ TeV, the central-bin ($|\\eta| < 0.1$) normalized fraction exceeds the SM by $\\geq 50\\%$ (LQ $t$-channel pulls events forward+central).", "measurable": true}, {"id": "H2", "statement": "At $\\beta_L^{32}=0.1$, the normalized $|\\eta|$ shape di... | {"research_question": "Does the agent reproduce the normalized $|\\eta|$ distributions of $\\mu^+\\mu^- \\to b\\bar b$ for SM and $U_1$ leptoquark signal at $\\sqrt{s}=3$ TeV muon collider, in the two coupling regimes $\\beta_L^{32} \\in \\{1.0, 0.1\\}$ (Figure 11 of arXiv:2104.05720)?", "conditions": [{"name": "sm_bas... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p09-root", "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ vector-LQ angular shape in $\\mu^+\\mu^- \\to b\\bar b$ at 3 TeV muon collider. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the specified process with appropria... | 15 | tasks/physics/manifests/P09.yaml | tasks/physics/rubrics/P09.json |
P10 | physics | Reproducing $\beta_L^{32}$-vs-$m_\mathrm{LQ}$ exclusion + 5$\sigma$ discovery contours for $U_1$ at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders | Reproducing $U_1$ exclusion + 5$\sigma$ discovery contours at 3 and 14 TeV muon colliders from arXiv:2104.05720 | [
"high-energy-physics",
"bsm-phenomenology",
"leptoquark",
"muon-collider",
"statistical-recast"
] | 2104.05720 | ARC-Bench Physics 2026 | primary_metric | maximize | false | 3,600 | Reproducing the muon-collider sensitivity to a $U_1$ vector
leptoquark in the coupling-vs-mass plane via a binned-likelihood
recast of $\mu^+\mu^- \to b\bar b$, as a benchmark of the agent's
ability to combine MC parameter scans with statistical recasting
spanning two orders of magnitude in mass and four orders in coup... | 3 | [{"id": "H1", "statement": "At $\\sqrt{s}=14$ TeV with 20 ab$^{-1}$, the 95% CL exclusion contour reaches $\\beta_L^{32} \\leq 0.01$ at $m_\\mathrm{LQ} = 10$ TeV (matching the published value within a factor of 2).", "measurable": true}, {"id": "H2", "statement": "The 14 TeV exclusion contour extends to higher $m_\\mat... | {"research_question": "Does the agent reproduce the 95% CL exclusion and 5$\\sigma$ discovery contours for the $U_1$ leptoquark in the $(m_\\mathrm{LQ}, \\beta_L^{32})$ plane at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders (Figure 12 of arXiv:2104.05720)?", "conditions": [{"name": "sm_baselines", "descr... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "p10-root", "requirements": "A credible HEP collider study reproducing the published reference figure for $U_1$ exclusion + 5$\\sigma$ discovery contours at 3 TeV (1 ab$^{-1}$) and 14 TeV (20 ab$^{-1}$) muon colliders. The agent must (a) implement the BSM Lagrangian, (b) generate Monte Carlo events for the speci... | 15 | tasks/physics/manifests/P10.yaml | tasks/physics/rubrics/P10.json |
Q01 | quantum | Comparing quantum data encoding strategies for variational classifiers | Comparing quantum data encoding strategies (angle, amplitude, IQP, ZZ feature map) for variational quantum classifiers on small low-dimensional binary classification tasks | [
"quantum-machine-learning",
"data-encoding",
"feature-maps"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 1,800 | Quantum data encoding is widely regarded as the bottleneck of variational
quantum machine learning. The way classical features are loaded into a
quantum state determines what functions the subsequent variational ansatz
can express. Three encoding families are compared here: angle encoding
(single-qubit rotations only, ... | 3 | [{"id": "H1", "statement": "The best of the three quantum encodings (5-seed mean) achieves test_accuracy within 5 absolute percentage points of the BETTER of the two classical baselines (logistic_regression and mlp_classifier, 5-seed means) on synthetic_classification_6d.", "measurable": true}, {"id": "H2", "statement"... | {"research_question": "On a moderately difficult 6D binary classification task, does the choice of quantum data encoding (angle, amplitude, ZZ feature map) measurably change variational classifier accuracy, and does at least one encoding approach the classical baseline?", "conditions": [{"name": "angle_encoding", "desc... | {"id": "q01-root", "requirements": "A minimal pilot study comparing three quantum data encoding strategies (angle, amplitude, ZZ feature map) for a variational quantum classifier on ONE moderately difficult 6D binary classification dataset (synthetic_classification_6d with class_sep=0.4). The agent must (a) implement a... | 9 | tasks/quantum/manifests/Q01.yaml | tasks/quantum/rubrics/Q01.json | |
Q02 | quantum | Entangling-gate ablation in a variational quantum classifier | Ablating entangling gates in a variational quantum classifier: how much classification accuracy is actually attributable to CNOTs versus single-qubit rotations | [
"quantum-machine-learning",
"entanglement",
"ablation-study"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 1,200 | Quantum machine learning models routinely use parameterized circuits
that mix single-qubit rotations with entangling two-qubit gates,
typically CNOTs. The common narrative is that entanglement is the
source of quantum advantage. If a variational classifier with no
entangling gates whatsoever can match the accuracy of o... | 3 | [{"id": "H1", "statement": "full_entanglement achieves mean test_accuracy (averaged over 5 seeds) at least 5 absolute percentage points higher than no_entanglement on at least 2 of 3 datasets.", "measurable": true}, {"id": "H2", "statement": "half_entanglement mean test_accuracy lies strictly between full_entanglement ... | {"research_question": "In a variational quantum classifier on low dimensional binary classification, how much of the model's accuracy is actually due to ansatz entangling gates, and does the accuracy degrade monotonically as CNOTs are removed?", "conditions": [{"name": "full_entanglement", "description": "Feature map: ... | {"id": "q02-root", "requirements": "A controlled ablation study of entangling gates in a variational quantum classifier. Holding encoding (angle), ansatz template (EfficientSU2 reps=2), optimizer (COBYLA), and datasets fixed (matching Q01), the agent must implement full / half / no entanglement variants by selectively ... | 9 | tasks/quantum/manifests/Q02.yaml | tasks/quantum/rubrics/Q02.json | |
Q03 | quantum | Classical optimizer comparison for VQE on H2 under finite shot noise | Benchmarking classical optimizers (SPSA, COBYLA, L-BFGS-B, ADAM) for VQE convergence on H2 under finite shot noise across multiple shot budgets and bond lengths | [
"variational-quantum-eigensolver",
"quantum-chemistry",
"classical-optimization"
] | null | ARC-Bench 2026 | shots_to_chemical_accuracy | minimize | false | 1,200 | The Variational Quantum Eigensolver (VQE) is the canonical algorithm
for near-term quantum chemistry. The quality of a VQE result depends
on the ansatz, but it also critically depends on the classical
optimizer that updates variational parameters using noisy energy
estimates from shot-based measurement. Different optim... | 3 | [{"id": "H1", "statement": "At 1024 shots per energy evaluation, SPSA reaches chemical accuracy (|E_running_mean - E_FCI| < 1.6 mHa for 5 consecutive 5-eval windows) using fewer cumulative shots than L-BFGS-B (3-seed median), on at least 1 of 2 H2 geometries.", "measurable": true}, {"id": "H2", "statement": "At 1024 sh... | {"research_question": "Under finite shot noise, which classical optimizer reaches VQE chemical accuracy in the fewest cumulative shots, and how does the answer depend on shot budget per evaluation and on Hamiltonian difficulty?", "conditions": [{"name": "spsa", "description": "qiskit_algorithms.optimizers.SPSA(maxiter=... | {"id": "q03-root", "requirements": "A credible VQE optimizer comparison on H2 in STO-3G basis at two bond lengths (0.74 A equilibrium, 1.5 A stretched), at a fixed shot budget of 1024 shots per energy evaluation. The agent must (a) construct the 4-qubit H2 Hamiltonian at each geometry via qiskit_nature.PySCFDriver, (b)... | 9 | tasks/quantum/manifests/Q03.yaml | tasks/quantum/rubrics/Q03.json | |
Q04 | quantum | Data re-uploading depth vs Fourier expressivity for variational quantum regression | Data re-uploading depth vs Fourier expressivity for variational quantum regression: characterizing how the trainable bandwidth of a parameterized re-uploading circuit scales with re-uploading layers L on synthetic 1D regression targets | [
"quantum-machine-learning",
"data-reuploading",
"expressivity"
] | null | ARC-Bench 2026 | test_mse | minimize | false | 900 | Data re-uploading is a variational quantum circuit pattern in which the
classical input x is loaded into the same qubits multiple times,
interleaved with trainable rotation layers: the circuit
W(theta_L) U(x) W(theta_{L-1}) U(x) ... W(theta_1) U(x) |0> applies L
encoding blocks U(x) and L trainable blocks W(theta). Per... | 3 | [{"id": "H1", "statement": "On the sinusoid-mixture target, the L=5 re-uploading model achieves test MSE at least 4 times lower than the L=1 re-uploading model (2-seed mean), demonstrating that increasing re-uploading depth improves regression accuracy on bandwidth-rich targets.", "measurable": true}, {"id": "H2", "sta... | {"research_question": "On 1D regression with known target bandwidth, does increasing data re-uploading depth L produce monotonically lower test MSE on bandwidth-rich targets, and does the recovered Fourier spectrum overlap with the target spectrum scale predictably with L?", "conditions": [{"name": "reuploading_L1", "d... | {"id": "q04-root", "requirements": "A credible empirical study of data re-uploading depth vs Fourier expressivity in variational quantum regression. The agent must (a) implement 4 re-uploading circuits at L in {1, 3, 5, 7} using qiskit ParameterVector + RY/RZ rotation gates, (b) train each with parameter-shift Adam on ... | 9 | tasks/quantum/manifests/Q04.yaml | tasks/quantum/rubrics/Q04.json | |
Q05 | quantum | Barren plateau onset under cost-function locality | Barren plateau onset under cost-function locality: empirically measuring gradient variance of hardware-efficient ansatze under global vs local cost functions across depth, testing the Cerezo 2021 prediction that local costs avoid exponential gradient suppression | [
"quantum-machine-learning",
"barren-plateaus",
"trainability"
] | null | ARC-Bench 2026 | log_gradient_variance | maximize | false | 600 | A barren plateau is the phenomenon, first identified by McClean et al.
in 2018, that the gradient of a variational quantum circuit's loss
becomes exponentially small in the number of qubits when the ansatz is
drawn from a sufficiently random circuit ensemble. Concretely, for a
loss L(theta) = <psi(theta)|H|psi(theta)> ... | 3 | [{"id": "H1", "statement": "At ansatz depth L=10, the empirical variance of the gradient under local cost is at least 20 times larger than under global cost (3-seed mean of variance per condition), confirming Cerezo et al. 2021's prediction that locality changes the barren-plateau scaling.", "measurable": true}, {"id":... | {"research_question": "Does the local-cost trick avoid the barren plateau on a 6-qubit EfficientSU2 ansatz at L=10 and L=20, as predicted by Cerezo et al. 2021, when compared against the global-cost baseline and a shallow local-cost control?", "conditions": [{"name": "local_cost_L10", "description": "Proposed-method co... | {"id": "q05-root", "requirements": "An empirical study of barren plateau onset on a 6-qubit EfficientSU2 ansatz under local vs global cost functions and shallow vs deep ansatz. The agent must (a) build the EfficientSU2 ansatz with reps in {2, 10, 20} via qiskit.circuit.library, (b) for each (cost, depth) condition samp... | 9 | tasks/quantum/manifests/Q05.yaml | tasks/quantum/rubrics/Q05.json | |
Q06 | quantum | Neural-network warm-start for QAOA-MaxCut initialization | Neural-network warm-start for QAOA-MaxCut: comparing MLP-predicted initial parameters against random and fixed initialization across in-distribution and out-of-distribution graph families | [
"ai-for-quantum",
"qaoa",
"meta-learning"
] | null | ARC-Bench 2026 | iterations_to_target_ratio | minimize | false | 1,200 | The Quantum Approximate Optimization Algorithm (QAOA) prepares a
parameterized quantum state to approximate the maximum cut of a graph.
At depth p=1 the state depends on two real parameters (beta, gamma),
and the algorithm proceeds by classical optimization of those two
parameters against the average cut size sampled f... | 3 | [{"id": "H1", "statement": "On in-distribution Erdős-Rényi graphs at n=6, the MLP-predicted initialization reaches approximation ratio 0.9 in at most 10 COBYLA iterations on average (3-graph mean), while random initialization requires at least 18 iterations to reach the same target.", "measurable": true}, {"id": "H2", ... | {"research_question": "Does an MLP trained on small Erdős-Rényi graph features predict QAOA-MaxCut initial parameters that reduce COBYLA iteration count compared to random and fixed init, and does the advantage transfer to graphs of a different distribution than the MLP was trained on?", "conditions": [{"name": "mlp_in... | {"id": "q06-root", "requirements": "An empirical study of neural-network warm-start for QAOA-MaxCut at p=1. The agent must (a) precompute 200 Erdős-Rényi G(n=6, p=0.5) training graphs with their optimal (beta, gamma) via grid search to label the MLP training set, (b) train sklearn.MLPRegressor to predict (beta, gamma) ... | 9 | tasks/quantum/manifests/Q06.yaml | tasks/quantum/rubrics/Q06.json | |
Q07 | quantum | Matrix Product State classifier vs neural network at matched parameter count | Matrix Product State classifier vs neural network at matched parameter count on downsampled image classification, characterizing the bond-dimension scaling of tensor-network classifiers as a quantum-inspired classical baseline for QML | [
"quantum-inspired-classical",
"tensor-networks",
"supervised-learning"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 1,500 | A Matrix Product State (MPS) is a tensor network in which a target
tensor of N indices is decomposed into a 1D chain of N small tensors,
each connected to its neighbors by "bond" indices of dimension chi.
MPS originated in condensed-matter physics as the representation
underlying the Density Matrix Renormalization Grou... | 3 | [{"id": "H1", "statement": "On 8x8 downsampled MNIST, the MPS classifier at bond dimension chi=16 reaches at least 92 percent test accuracy (3-seed mean), matching the Stoudenmire & Schwab 2016 result scaled to this image size.", "measurable": true}, {"id": "H2", "statement": "On 8x8 downsampled Fashion-MNIST, the para... | {"research_question": "On 8x8 downsampled MNIST and Fashion-MNIST, do MPS classifiers at moderate bond dimension chi reach the test accuracy of a parameter-matched CNN, and how does accuracy scale with chi?", "conditions": [{"name": "mps_chi4", "description": "Matrix Product State classifier with bond dimension chi=4. ... | {"id": "q07-root", "requirements": "An empirical study of Matrix Product State (MPS) classifiers vs neural network baselines at matched parameter count on 8x8 downsampled image classification. The agent must (a) build an MPS classifier following Stoudenmire and Schwab 2016 with cos/sin per-pixel feature embedding and b... | 9 | tasks/quantum/manifests/Q07.yaml | tasks/quantum/rubrics/Q07.json | |
Q08 | quantum | Layerwise learning vs end-to-end training for variational quantum classifiers | Layerwise learning vs end-to-end training for variational quantum classifiers: testing the Skolik 2021 claim that incremental ansatz growth avoids barren plateaus on small UCI binary classification tasks | [
"quantum-machine-learning",
"training-strategies",
"barren-plateaus"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 1,500 | Variational quantum classifiers (VQCs) at moderate depth (reps >= 5)
suffer from barren plateaus: the gradient of the loss with respect to
any single parameter is exponentially suppressed when the ansatz is
initialized randomly across all layers. Skolik et al. (Quantum 2021,
"Layerwise learning for quantum neural netwo... | 3 | [{"id": "H1", "statement": "Layerwise training reaches higher test accuracy than end-to-end training on at least 1 of 2 datasets, with a 3-seed-mean gap of at least 3 absolute percentage points.", "measurable": true}, {"id": "H2", "statement": "The mean gradient norm during the first 10 optimization steps of the layerw... | {"research_question": "Does layerwise incremental training of a 4-qubit reps=5 VQC reach higher test accuracy or faster convergence than end-to-end training of the same ansatz on small UCI binary classification?", "conditions": [{"name": "layerwise_growth", "description": "EfficientSU2(num_qubits=4, reps=5, entanglemen... | {"id": "q08-root", "requirements": "An empirical study of layerwise vs end-to-end training of a 4-qubit reps=5 EfficientSU2 VQC on UCI binary classification (iris-binary and breast-cancer). The agent must (a) implement the layerwise training schedule manually (train reps=1, freeze, add reps=1, train, ...), (b) implemen... | 9 | tasks/quantum/manifests/Q08.yaml | tasks/quantum/rubrics/Q08.json | |
Q09 | quantum | Noise-aware variational quantum classifier training | Noise-aware variational quantum classifier training: comparing training under simulated depolarizing noise vs ideal-statevector training, evaluating both robustness to test-time noise and clean-test transfer | [
"ai-for-quantum",
"noisy-quantum-training",
"robustness"
] | null | ARC-Bench 2026 | test_accuracy | maximize | false | 1,500 | Real quantum hardware exhibits gate errors, readout errors, and
decoherence at rates that vary across devices. A VQC trained on a
noiseless statevector simulator may converge to parameters that work
on the ideal training distribution but degrade sharply when deployed
on a noisy device. The noise-aware training strategy... | 3 | [{"id": "H1", "statement": "On the noisy test set at p=0.01, the noisy-trained-mid VQC (training noise p=0.005) achieves test accuracy at least 5 absolute percentage points higher than the ideal-trained VQC (3-seed mean), demonstrating that moderate noise-aware training improves test-time noise robustness.", "measurabl... | {"research_question": "Does noise-aware VQC training at a moderate training noise rate (p=0.005) produce a model more robust to test-time depolarizing noise than ideal training, and does high training noise cause negative transfer on clean test data?", "conditions": [{"name": "train_ideal", "description": "VQC trained ... | {"id": "q09-root", "requirements": "An empirical study of noise-aware VQC training. Four quantum training conditions vary the training-time depolarizing noise rate (ideal, p=0.001, p=0.005, p=0.01), evaluated on both an ideal test set and a noisy test set at p=0.01. The agent must (a) implement the noise model via qisk... | 9 | tasks/quantum/manifests/Q09.yaml | tasks/quantum/rubrics/Q09.json | |
Q10 | quantum | Quantum autoencoder for pure-state compression | Quantum autoencoder for pure-state compression: training a parameterized circuit to compress n-qubit Haar-random states into m<n latent qubits via SWAP-test loss, measuring reconstruction fidelity vs compression ratio | [
"quantum-machine-learning",
"generative-models",
"autoencoders"
] | null | ARC-Bench 2026 | reconstruction_fidelity | maximize | false | 1,200 | A quantum autoencoder takes an n-qubit quantum state, applies a
parameterized circuit that disentangles the information onto a
smaller subset of m < n "latent" qubits, and traces out the
remaining n - m "trash" qubits. The latent qubits are then expanded
back to n qubits via a fresh ancilla register and a decoder circu... | 3 | [{"id": "H1", "statement": "The trained autoencoder achieves reconstruction fidelity at least 0.2 absolute higher than a random-unitary encoder at the same compression ratio (3-seed mean across the 20 Haar test states), on at least 2 of 3 compression-ratio conditions, confirming that training extracts non-trivial struc... | {"research_question": "On an ensemble of Haar-random pure states, what reconstruction fidelity does a trained quantum autoencoder achieve at compression ratios 4->2, 4->3, 6->3, and how much better is it than an untrained encoder at the same compression?", "conditions": [{"name": "compress_4_to_2", "description": "Enco... | {"id": "q10-root", "requirements": "An empirical study of a quantum autoencoder on Haar-random pure states. The agent must (a) construct encoder + decoder circuits as EfficientSU2(reps=3) from qiskit.circuit.library, (b) implement the SWAP-test loss via auxiliary qubits + Hadamard-controlled-SWAP-Hadamard or compute tr... | 9 | tasks/quantum/manifests/Q10.yaml | tasks/quantum/rubrics/Q10.json | |
B01 | biology | E. coli succinate-production strain optimization via constraint-based knockout screen | E. coli succinate-production strain optimization via constraint-based knockout screen on iAF1260 | [
"systems-biology",
"metabolic-engineering",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 1,800 | Succinate is a top-12 platform chemical and a recurring case study in
metabolic engineering. The standard genome-scale model E. coli iAF1260
(2382 reactions, 1668 metabolites, 1261 genes) provides a well-validated
testbed for in-silico strain design: from a fixed glucose-minimal medium
one can predict (a) the maximum a... | 3 | [{"id": "H1", "statement": "Wild-type aerobic E. coli iAF1260 on glucose-minimal medium predicts a biomass growth rate within ±10% of 0.736 1/h (the published BIGG iAF1260 reference value).", "measurable": true}, {"id": "H2", "statement": "The biomass-vs-succinate production envelope, computed via FVA at biomass fracti... | {"research_question": "Which single-gene knockouts of central carbon metabolism in E. coli iAF1260 produce the largest predicted succinate secretion flux while preserving at least 50% of wild-type biomass growth, and what is the mechanistic role of each in shifting flux toward succinate?", "conditions": [{"name": "wt_a... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metrics (object of numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).", "must... | {"id": "B01", "requirements": "A credible constraint-based experiment on E. coli iAF1260 that (a) loads the BIGG model and validates wild-type growth, (b) computes a flux distribution under the standard objective, (c) characterises the biomass-vs-succinate production envelope, (d) runs a single-gene knockout screen tar... | 15 | tasks/biology/manifests/B01.yaml | tasks/biology/rubrics/B01.json |
B02 | biology | E. coli acetate overflow metabolism phase map under glucose and oxygen limitation | E. coli acetate overflow metabolism phase map under glucose and oxygen limitation | [
"systems-biology",
"constraint-based-modelling",
"phase-plane-analysis"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 1,800 | Overflow acetate secretion in E. coli is a canonical constraint-based
modelling phenotype. A credible CPU-scale study loads a validated E. coli
genome-scale model, fixes a glucose-minimal medium, sweeps glucose and oxygen
uptake bounds, computes growth and major secretion products, and identifies
the transition from re... | 3 | [{"id": "H1", "statement": "The selected E. coli BIGG model grows on aerobic glucose-minimal medium with a positive biomass objective and no infeasible solver status.", "measurable": true}, {"id": "H2", "statement": "A two-dimensional glucose-vs-oxygen phase plane contains distinct regimes with high oxygen/low acetate ... | {"research_question": "Under which glucose and oxygen uptake regimes does E. coli switch from respiratory growth to acetate overflow?", "conditions": [{"name": "aerobic_glucose_reference", "description": "Wild-type E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen available."}, {"name": "glucose_oxygen_p... | [{"id": "req_results_json", "type": "artifact", "description": "A results.json file exists with primary_metric, metrics, hypotheses, summary, and structured_results.", "must_pass": true}, {"id": "req_phase_plane_table", "type": "artifact", "description": "A machine-readable phase-plane table exists with glucose_bound, ... | {"id": "B02", "requirements": "A credible constraint-based E. coli acetate-overflow phase-plane study that loads a validated BIGG model, sets glucose and oxygen medium bounds, sweeps a two-dimensional nutrient grid, reports growth and by-product secretion, and interprets the acetate overflow boundary.", "judging_note":... | 15 | tasks/biology/manifests/B02.yaml | tasks/biology/rubrics/B02.json |
B03 | biology | Anaerobic E. coli lactate-overproduction knockout screen | Anaerobic E. coli lactate-overproduction knockout screen | [
"systems-biology",
"metabolic-engineering",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 1,800 | Lactate production under anaerobic glucose conditions is a tractable
metabolic-engineering task for constraint-based modelling. A credible study
loads an E. coli BIGG model, validates anaerobic growth, computes WT lactate
secretion, screens central-carbon and fermentation knockouts, and ranks
interventions that increas... | 3 | [{"id": "H1", "statement": "The model supports positive anaerobic glucose growth after oxygen uptake is closed.", "measurable": true}, {"id": "H2", "statement": "The lactate production envelope shows a tradeoff between biomass growth and maximum lactate secretion.", "measurable": true}, {"id": "H3", "statement": "Top l... | {"research_question": "Which single-gene knockouts increase anaerobic lactate secretion in E. coli while preserving at least 30-50% of WT growth?", "conditions": [{"name": "wt_anaerobic_glucose", "description": "E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen uptake closed."}, {"name": "lactate_product... | [{"id": "req_results_json", "type": "artifact", "description": "results.json contains primary_metric, metrics, hypotheses, summary, and top KO structured results.", "must_pass": true}, {"id": "req_anaerobic_medium", "type": "numeric", "description": "Oxygen uptake is explicitly closed and anaerobic WT growth is reporte... | {"id": "B03", "requirements": "A credible anaerobic E. coli lactate-overproduction study using COBRApy that validates anaerobic growth, computes a lactate production envelope, screens a focused central-carbon/fermentation knockout set, and explains top candidates mechanistically.", "judging_note": "Accept iJO1366, iAF1... | 15 | tasks/biology/manifests/B03.yaml | tasks/biology/rubrics/B03.json |
B04 | biology | S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes | S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes | [
"systems-biology",
"metabolic-engineering",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 1,800 | Yeast ethanol fermentation is a natural extension beyond E. coli while still
staying inside BIGG/COBRApy constraint-based modelling. A credible study
loads iMM904 or a comparable yeast GSMM, validates growth on glucose, sweeps
oxygen availability, compares glucose and alternative carbon sources, and
quantifies ethanol ... | 3 | [{"id": "H1", "statement": "The yeast model grows on glucose-minimal medium and produces a feasible FBA solution.", "measurable": true}, {"id": "H2", "statement": "Ethanol secretion/yield increases as oxygen uptake is restricted relative to fully aerobic growth.", "measurable": true}, {"id": "H3", "statement": "Carbon-... | {"research_question": "How do oxygen limitation and carbon source alter predicted ethanol secretion and yield in S. cerevisiae?", "conditions": [{"name": "yeast_glucose_reference", "description": "S. cerevisiae iMM904 or comparable model on glucose-minimal medium."}, {"name": "oxygen_sweep", "description": "Sweep oxyge... | [{"id": "req_results_json", "type": "artifact", "description": "results.json contains yeast model ID, metrics, hypotheses, and structured oxygen/carbon-source results.", "must_pass": true}, {"id": "req_oxygen_sweep", "type": "artifact", "description": "A machine-readable oxygen-sweep table exists with oxygen bound, gro... | {"id": "B04", "requirements": "A credible S. cerevisiae ethanol-yield study that loads a yeast GSMM, validates growth, sweeps oxygen uptake, swaps carbon sources, and reports ethanol secretion/yield with interpretable figures.", "judging_note": "The main challenge is adapting the pipeline beyond E. coli while remaining... | 15 | tasks/biology/manifests/B04.yaml | tasks/biology/rubrics/B04.json |
B05 | biology | M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation | M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation | [
"systems-biology",
"drug-target-prioritisation",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 2,400 | Constraint-based essentiality analysis can prioritise metabolic drug-target
hypotheses in pathogen models. A credible study loads an M. tuberculosis GSMM
such as iNJ661 when available, validates biomass production under a documented
medium, performs single-gene and/or single-reaction deletion, and ranks
essential targe... | 3 | [{"id": "H1", "statement": "The selected M. tuberculosis model produces positive biomass under the documented reference medium.", "measurable": true}, {"id": "H2", "statement": "Single-gene or single-reaction deletion identifies a non-empty set of essential metabolic targets under the reference condition.", "measurable... | {"research_question": "Which condition-specific metabolic genes or reactions are predicted essential in M. tuberculosis and are plausible drug-target hypotheses?", "conditions": [{"name": "mtb_reference_medium", "description": "M. tuberculosis iNJ661 or comparable model under a documented reference medium."}, {"name": ... | [{"id": "req_results_json", "type": "artifact", "description": "results.json contains model ID, WT growth, essential target counts, top targets, and hypothesis verdicts.", "must_pass": true}, {"id": "req_deletion_table", "type": "artifact", "description": "A gene or reaction deletion table exists with target ID, status... | {"id": "B05", "requirements": "A credible M. tuberculosis essentiality study using COBRApy that validates a pathogen GSMM, runs single-gene and/or single-reaction deletions, identifies essential targets, and prioritises interpretable drug-target hypotheses.", "judging_note": "Accept gene deletions, reaction deletions, ... | 15 | tasks/biology/manifests/B05.yaml | tasks/biology/rubrics/B05.json |
B06 | biology | E. coli carbon-source robustness and condition-dependent essential genes | E. coli carbon-source robustness and condition-dependent essential genes | [
"systems-biology",
"essentiality-analysis",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 2,400 | Carbon-source swaps are a direct mfa-agent capability and produce interpretable
condition-dependent phenotypes. A credible study loads an E. coli GSMM, defines
a base minimal medium, tests multiple single-carbon sources, runs essentiality
analysis on a focused gene set under each feasible source, and identifies genes
w... | 3 | [{"id": "H1", "statement": "The model predicts positive growth on glucose and at least two additional supported carbon sources.", "measurable": true}, {"id": "H2", "statement": "Growth rates and secretion profiles differ substantially across carbon sources.", "measurable": true}, {"id": "H3", "statement": "A focused ce... | {"research_question": "Which E. coli metabolic vulnerabilities are carbon-source dependent across glucose, glycerol, acetate, succinate, and fructose-like conditions?", "conditions": [{"name": "carbon_source_panel", "description": "Close background carbon uptake, open one carbon source at a time, and run FBA/pFBA."}, {... | [{"id": "req_results_json", "type": "artifact", "description": "results.json contains carbon-source growth table summary, essentiality counts, hypothesis verdicts, and summary.", "must_pass": true}, {"id": "req_carbon_table", "type": "artifact", "description": "A carbon-source comparison table exists with source, excha... | {"id": "B06", "requirements": "A credible E. coli carbon-source robustness study that loads a validated GSMM, swaps carbon sources, reports growth/secretion profiles, and identifies condition-dependent essential genes from a focused gene set.", "judging_note": "Score explicit medium handling and condition-dependent int... | 15 | tasks/biology/manifests/B06.yaml | tasks/biology/rubrics/B06.json |
B07 | biology | Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli | Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli | [
"systems-biology",
"method-benchmarking",
"constraint-based-modelling"
] | null | ARC-Bench Biology 2026 | primary_metric | maximize | false | 1,800 | A method benchmark is well matched to mfa-agent because it tests robust use of
COBRApy APIs rather than biological novelty alone. A credible study loads a
validated E. coli GSMM, runs standard FBA, pFBA, loopless FBA when available,
and FVA at a fixed fraction of optimum, then compares growth, flux sparsity,
runtime, a... | 3 | [{"id": "H1", "statement": "FBA, pFBA, and loopless FBA preserve the same biomass optimum within solver tolerance under the same medium.", "measurable": true}, {"id": "H2", "statement": "pFBA produces a lower total absolute flux norm and/or fewer active reactions than unconstrained FBA.", "measurable": true}, {"id": "H... | {"research_question": "How do standard FBA, pFBA, loopless FBA, and FVA differ in flux parsimony and variability while preserving E. coli growth predictions?", "conditions": [{"name": "standard_fba", "description": "Maximize biomass on aerobic glucose-minimal medium."}, {"name": "pfba", "description": "Maximize biomass... | [{"id": "req_results_json", "type": "artifact", "description": "results.json contains method-level metrics, hypotheses, summary, and solver/version metadata.", "must_pass": true}, {"id": "req_method_table", "type": "artifact", "description": "A method-comparison table exists with method, status, growth, total_abs_flux,... | {"id": "B07", "requirements": "A reproducible FBA-method benchmark on E. coli that compares standard FBA, pFBA, loopless FBA when available, and FVA near optimum using consistent medium, metrics, runtime logging, and interpretable figures.", "judging_note": "This is a protocol-quality task. Reward robust implementation... | 15 | tasks/biology/manifests/B07.yaml | tasks/biology/rubrics/B07.json |
S01 | statistics | Bootstrap confidence intervals under non-Gaussian data | Bootstrap confidence interval coverage under light-tailed, skewed, and heavy-tailed data | [
"statistics",
"resampling-methods",
"simulation-study"
] | null | ARC-Bench Statistics 2026 | primary_metric | maximize | false | 300 | Bootstrap confidence intervals are widely used when analytic standard errors
are inconvenient, but their finite-sample coverage can change substantially
with skewness, heavy tails, contamination, and the choice of statistic. A
credible study of this topic should simulate data-generating processes with
known estimands, ... | 3 | [{"id": "H1", "statement": "Percentile bootstrap intervals for the sample mean achieve near-nominal coverage under light-tailed Gaussian data, especially at moderate sample sizes.", "measurable": true}, {"id": "H2", "statement": "Percentile bootstrap intervals for the sample mean under-cover under heavy-tailed or conta... | {"research_question": "How do bootstrap confidence interval methods behave across light-tailed, skewed, and heavy-tailed distributions?", "estimands": [{"name": "population_mean", "description": "The true mean of each simulated data-generating process when it exists."}, {"name": "robust_location", "description": "A rob... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "s01-root", "requirements": "A credible simulation study evaluating whether bootstrap confidence interval choice affects empirical coverage under light-tailed, skewed, and heavy-tailed data. The submission should implement multiple CI methods, simulate several data-generating distributions and sample sizes, repo... | 12 | tasks/statistics/manifests/S01.yaml | tasks/statistics/rubrics/S01.json |
S02 | statistics | Double machine learning for average treatment effect estimation | Double machine learning for average treatment effect estimation under nonlinear confounding | [
"statistics",
"causal-inference",
"semiparametric-estimation"
] | null | ARC-Bench Statistics 2026 | primary_metric | maximize | false | 300 | Semiparametric causal inference separates a low-dimensional target parameter,
such as the average treatment effect, from infinite-dimensional nuisance
functions such as the propensity score e(X) and outcome regressions m0(X) and
m1(X). Double machine learning studies how the target parameter can remain
estimable when t... | 4 | [{"id": "H1", "statement": "A naive difference-in-means estimator is biased under confounded treatment assignment.", "measurable": true}, {"id": "H2", "statement": "A doubly robust / orthogonalized estimator has lower absolute bias than simple plug-in regression or IPW when nuisance functions are nonlinear.", "measurab... | {"research_question": "How does double machine learning estimate a finite-dimensional ATE while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding?", "estimand": {"name": "average_treatment_effect", "description": "ATE = E[Y(1) - Y(0)] in a simulated observational study wi... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3/h4 entries each carrying a `supported` boolean), summary (... | {"id": "s02-root", "requirements": "A credible semiparametric simulation study evaluating how double machine learning / orthogonalized AIPW estimates a finite-dimensional average treatment effect while using flexible estimators for infinite-dimensional nuisance functions under nonlinear confounding. The submission shou... | 15 | tasks/statistics/manifests/S02.yaml | tasks/statistics/rubrics/S02.json |
S03 | statistics | Reliability of LLM-assisted statistical model selection | Reliability of LLM-assisted statistical model selection under assumption violations | [
"statistics",
"model-selection",
"llm-evaluation"
] | null | ARC-Bench Statistics 2026 | primary_metric | maximize | false | 300 | Large language models are increasingly used to assist with statistical data
analysis, including model selection, test choice, and interpretation. However,
statistical model selection requires matching assumptions to data-generating
conditions, not merely producing plausible-sounding analysis text. An LLM or
LLM-like ru... | 3 | [{"id": "H1", "statement": "A simple prompt-only LLM-style recommendation system is more likely to choose standard parametric tests even when assumptions are violated.", "measurable": true}, {"id": "H2", "statement": "Adding computed diagnostic statistics, such as skewness, variance ratio, normality tests, and sample-s... | {"research_question": "Can LLM-assisted statistical model selection reliably choose appropriate procedures when assumptions are violated?", "task_type": {"name": "statistical_test_selection", "description": "Given dataset summaries and analysis goals, choose an appropriate statistical test or model."}, "conditions": [{... | [{"id": "req_results_json", "type": "artifact", "description": "A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metric_key (string), metrics (object with numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non... | {"id": "s03-root", "requirements": "A credible controlled simulation study evaluating whether LLM-assisted or LLM-like statistical model selection chooses appropriate procedures under assumption violations. The submission should generate statistical-analysis tasks with known reference decisions, compare text-only and d... | 14 | tasks/statistics/manifests/S03.yaml | tasks/statistics/rubrics/S03.json |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.