Datasets:

AlgorithmicResearchGroup
/

aria-repo-benchmark

dataset string	model_name string	model_links list	paper_title string	paper_date timestamp[ns]	paper_url string	code_links list	metrics string	table_metrics list	prompts string	paper_text string	compute_hours float64	num_gpus int64	reasoning string	trainable_single_gpu_8h string	verified string	modality string	paper_title_drop string	paper_date_drop string	code_links_drop string	num_gpus_drop int64	dataset_link string	time_and_compute_verification string	link_to_colab_notebook string	run_possible string	notes string
PDBbind	BAPULM	[]	BAPULM: Binding Affinity Prediction using Language Models	2024-11-06T00:00:00	https://arxiv.org/abs/2411.04150v1	[ "https://github.com/radh55sh/BAPULM" ]	{'RMSE': '0.898±0.0172'}	[ "RMSE" ]	Given the following paper and codebase: Paper: BAPULM: Binding Affinity Prediction using Language Models Codebase: https://github.com/radh55sh/BAPULM Improve the BAPULM model on the PDBbind dataset. The result should improve on the following metrics: {'RMSE': '0.898±0.0172'}. You must use only the code...	BAPULM: Binding Affinity Prediction using Language Models Radheesh Sharma Meda†and Amir Barati Farimani∗,‡,¶,†,§ †Department of Chemical Engineering, Carnegie Mellon University, 15213, USA ‡Department of Mechanical Engineering, Carnegie Mellon University, 15213, USA ¶Department of Biomedical Engineering, Carnegie Mello...	1	1	The model uses ProtT5-XL-U50 and MolFormer architectures, which are large transformer-based models. Given that training on an Nvidia RTX 2080 Ti took approximately 4 minutes, and assuming training occurs over a reduced dataset with 100k sequences, with a complex architecture having a moderate number of parameters, a si...	yes	Yes	Bioinformatics	BAPULM: Binding Affinity Prediction using Language Models	2024-11-06 0:00:00	https://github.com/radh55sh/BAPULM	1	https://huggingface.co/datasets/radh25sh/BAPULM/resolve/main/prottrans_molformer_tensor_dataset100k.json?download=true	16sec * 60 epochs = 16 minutes	https://colab.research.google.com/drive/1--rNlCN01wUgN_6cTTuiVcusqSP9vGlG?usp=sharing	Yes	-- no pdbind dataset.Specifices to use prottrans malformer
Digital twin-supported deep learning for fault diagnosis	DANN	[]	A domain adaptation neural network for digital twin-supported fault diagnosis	2025-05-27T00:00:00	https://arxiv.org/abs/2505.21046v1	[ "https://github.com/JialingRichard/Digital-Twin-Fault-Diagnosis" ]	{'Accuray': '80.22'}	[ "Accuray" ]	Given the following paper and codebase: Paper: A domain adaptation neural network for digital twin-supported fault diagnosis Codebase: https://github.com/JialingRichard/Digital-Twin-Fault-Diagnosis Improve the DANN model on the Digital twin-supported deep learning for fault diagnosis dataset. The result ...	A domain adaptation neural network for digital twin-supported fault diagnosis Zhenling Chen CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, 91190, FranceHaiwei Fu CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, 91190, France Zhiguo Zeng Chair on Risk and Resilience of Complex Systems, Laboratoie Gen...	2	1	The DANN model employs a CNN architecture with two convolutional layers. Given the specified batch size of 32 and 250 training epochs on a dataset with 3,600 samples (360 samples per class for 9 distinct labels, plus a significantly smaller test set of 90 samples), the total iterations required for training would be (3...	yes	Yes	Time Series	A domain adaptation neural network for digital twin-supported fault diagnosis	2025-05-27T00:00:00.000Z	[https://github.com/JialingRichard/Digital-Twin-Fault-Diagnosis]	1	Included in Repo	3 Hours	Copy of train_ai_pytorch_DANN.ipynb	Yes	It starts and runs successfully
MNIST	GatedGCN+	[]	Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence	2025-02-13T00:00:00	https://arxiv.org/abs/2502.09263v1	[ "https://github.com/LUOyk1999/GNNPlus" ]	{'Accuracy': '98.712 ± 0.137'}	[ "Accuracy" ]	Given the following paper and codebase: Paper: Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence Codebase: https://github.com/LUOyk1999/GNNPlus Improve the GatedGCN+ model on the MNIST dataset. The result should improve on the following metrics: {'Accur...	Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence Yuankai Luo1 2Lei Shi1Xiao-Ming Wu2 Abstract Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expres- siveness, issues like over-smoothing and over- squashing, and challenges in captu...	4	1	The GNN models (GCN, GIN, and GatedGCN) enhanced with GNN+ have approximately 500K parameters each, which is moderate for graph neural networks. The datasets used involve a variety of sizes, but the mentioned ones have a maximum of around 500K graphs (like the OGB datasets). Given the average training time of these mod...	yes	Yes	Graph	Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence	2025-02-13T00:00:00.000Z	[https://github.com/LUOyk1999/GNNPlus]	1	https://data.pyg.org/datasets/benchmarking-gnns/MNIST_v2.zip	9 hour approx - ( 200 epochs * avg 157.2 sec)	https://drive.google.com/file/d/1Y7jMNhNybbdgrUJa_MxcOrbwpJNkDPav/view?usp=sharing	Yes	null
ogbg-molhiv	GatedGCN+	[]	"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence(...TRUNCATED)	2025-02-13T00:00:00	https://arxiv.org/abs/2502.09263v1	[ "https://github.com/LUOyk1999/GNNPlus" ]	"{'Test ROC-AUC': '0.8040 ± 0.0164', 'Validation ROC-AUC': '0.8329 ± 0.0158', 'Number of params': (...TRUNCATED)	[ "Test ROC-AUC", "Ext. data", "Validation ROC-AUC", "Number of params" ]	"Given the following paper and codebase:\n Paper: Unlocking the Potential of Classic GNNs for Gra(...TRUNCATED)	"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence(...TRUNCATED)	4	1	"The paper describes training across 14 well-known graph-level datasets with a mean parameter count (...TRUNCATED)	yes	Yes	Graph	"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence(...TRUNCATED)	2025-02-13T00:00:00.000Z	[https://github.com/LUOyk1999/GNNPlus]	1	http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/hiv.zip	approx 40 min - ( 100 epochs * 22.8s)	https://drive.google.com/file/d/1Y7jMNhNybbdgrUJa_MxcOrbwpJNkDPav/view?usp=sharing	Yes	null
Fashion-MNIST	Continued fraction of straight lines	[]	Real-valued continued fraction of straight lines	2024-12-16T00:00:00	https://arxiv.org/abs/2412.16191v1	["https://github.com/grasshopper14/Continued-fraction-of-straight-lines/blob/main/continued_fraction(...TRUNCATED)	{'Accuracy': '84.12', 'Trainable Parameters': '7870', 'NMI': '74.4'}	[ "Percentage error", "Accuracy", "Trainable Parameters", "NMI", "Power consumption" ]	"Given the following paper and codebase:\n Paper: Real-valued continued fraction of straight line(...TRUNCATED)	"Real-valued continued fraction of straight lines Vijay Prakash S Alappuzha, Kerala, India. prakash.(...TRUNCATED)	4	1	"The model is trained on the Fashion-MNIST dataset, which consists of 60,000 training images and 10,(...TRUNCATED)	yes	Yes	CV	Real-valued continued fraction of straight lines	2024-12-16T00:00:00.000Z	[https://github.com/grasshopper14/Continued-fraction-of-straight-lines/blob/main/continued_fraction_reg.py]	1	https://github.com/zalandoresearch/fashion-mnist	20 min	https://colab.research.google.com/drive/1LNMCRLMIWN5U_9WDeRxYmcbnAgaNadSd?usp=sharing	Yes	Yes Everythng is running successfully
Traffic	GLinear	[]	"Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series(...TRUNCATED)	2025-01-02T00:00:00	https://arxiv.org/abs/2501.01087v3	[ "https://github.com/t-rizvi/GLinear" ]	{'MSE ': '0.3222'}	[ "MSE " ]	"Given the following paper and codebase:\n Paper: Bridging Simplicity and Sophistication using GL(...TRUNCATED)	"IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE JOURNAL, 2025 1 Bridging Simplic(...TRUNCATED)	4	1	"The GLinear model, being a simplified architecture without complex components like Transformers, sh(...TRUNCATED)	yes	Yes	Time Series	"Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series(...TRUNCATED)	2025-01-02 0:00:00	https://github.com/t-rizvi/GLinear	1	Inside the repo in dataset folder	193 sec * 4 = 12.9 minutes	https://colab.research.google.com/drive/1sI72VSxjN4cyQR7UrueWfBXwoFi9Y9Qr?usp=sharing	Yes	"-- Training on all data set is included inside the scripts/EXP-LookBackWindow_\\&_LongForecasting/(...TRUNCATED)
BTAD	URD	[]	Unlocking the Potential of Reverse Distillation for Anomaly Detection	2024-12-10T00:00:00	https://arxiv.org/abs/2412.07579v1	[ "https://github.com/hito2448/urd" ]	"{'Segmentation AUROC': '98.1', 'Detection AUROC': '93.9', 'Segmentation AUPRO': '78.5', 'Segmentati(...TRUNCATED)	[ "Detection AUROC", "Segmentation AUROC", "Segmentation AP", "Segmentation AUPRO" ]	"Given the following paper and codebase:\n Paper: Unlocking the Potential of Reverse Distillation(...TRUNCATED)	"Unlocking the Potential of Reverse Distillation for Anomaly Detection Xinyue Liu1, Jianyuan Wang2*,(...TRUNCATED)	4	1	"The proposed method utilizes a WideResNet50 architecture as a teacher network which typically has a(...TRUNCATED)	yes	Yes	CV	Unlocking the Potential of Reverse Distillation for Anomaly Detection	2024-12-10 0:00:00	https://github.com/hito2448/urd	1	https://www.mydrive.ch/shares/38536/3830184030e49fe74747669442f0f282/download/420938113-1629952094/mvtec_anomaly_detection.tar.xz; https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz	8 hours for one folder. There are 11 folders.	https://drive.google.com/file/d/1OLbo3FifM1a7-wbCtfpjZrZLr0K5bS87/view?usp=sharing	Yes	-- Just need to change the num_workers in train.py according to system
York Urban Dataset	DT-LSD	[]	DT-LSD: Deformable Transformer-based Line Segment Detection	2024-11-20T00:00:00	https://arxiv.org/abs/2411.13005v1	[ "https://github.com/SebastianJanampa/DT-LSD" ]	{'sAP5': '30.2', 'sAP10': '33.2', 'sAP15': '35.1'}	[ "sAP5", "sAP10", "sAP15", "FH" ]	"Given the following paper and codebase:\n Paper: DT-LSD: Deformable Transformer-based Line Segme(...TRUNCATED)	"DT-LSD: Deformable Transformer-based Line Segment Detection Sebastian Janampa The University of New(...TRUNCATED)	4	1	"The proposed DT-LSD model has a relatively small batch size of 2 and uses a single Nvidia RTX A5500(...TRUNCATED)	yes	Yes	CV	DT-LSD: Deformable Transformer-based Line Segment Detection	2024-11-20 0:00:00	https://github.com/SebastianJanampa/DT-LSD	1	script to download is provided in colab file.	uses cpu to trainf or some reason 8hr per epoch	https://colab.research.google.com/drive/1XPiW-hDq6q8HNZ4yVP0oAn-3a1_ay5rG?usp=sharing	Yes	-- Trains but uses cpu for some reason
UCR Anomaly Archive	KAN	[]	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks	2024-11-01T00:00:00	https://arxiv.org/abs/2411.00278v1	[ "https://github.com/issaccv/KAN-AD" ]	{'AUC ROC ': '0.7489'}	[ "Average F1", "AUC ROC " ]	"Given the following paper and codebase:\n Paper: KAN-AD: Time Series Anomaly Detection with Kolm(...TRUNCATED)	"KAN-AD: Time Series Anomaly Detection with Kolmogorov–Arnold Networks Quan Zhou*, Changhua Pei, H(...TRUNCATED)	4	1	"The KAN-AD model is based on a novel architecture that leverages Fourier series for anomaly detecti(...TRUNCATED)	yes	Yes	Time Series	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks	2024-11-01 0:00:00	https://github.com/issaccv/KAN-AD	1	Downloaded when running prepeare_env.sh from repository & uses UTS dataset, https://github.com/CSTCloudOps/datasets	"There are 5 folders. May take around 2 hours or more no idea as time was not specified and traing w(...TRUNCATED)	https://colab.research.google.com/drive/1sE1mKwy3n9yameE-JG27Oa_HI-q8lFn9?usp=sharing	Yes	"-- After the installation of environment.sh. I changed a line of code to run matplot lib on colab (...TRUNCATED)
Chameleon	CoED	[]	Improving Graph Neural Networks by Learning Continuous Edge Directions	2024-10-18T00:00:00	https://arxiv.org/abs/2410.14109v1	[ "https://github.com/hormoz-lab/coed-gnn" ]	{'Accuracy': '79.69±1.35'}	[ "Accuracy" ]	"Given the following paper and codebase:\n Paper: Improving Graph Neural Networks by Learning Con(...TRUNCATED)	"Preprint IMPROVING GRAPH NEURAL NETWORKS BY LEARN - INGCONTINUOUS EDGE DIRECTIONS Seong Ho Pahng1, (...TRUNCATED)	4	1	"The proposed CoED GNN is a graph neural network architecture that utilizes a complex-valued Laplaci(...TRUNCATED)	yes	Yes	Graph	Improving Graph Neural Networks by Learning Continuous Edge Directions	2024-10-18 0:00:00	https://github.com/hormoz-lab/coed-gnn	1	specify on the classification.py and it handles itself	2 min	https://colab.research.google.com/drive/1FiCFbVmQhjIqcCdViYynfEb9mWtJkB09?usp=sharing	Yes	-- I have put the best parameter with advice of "Gemini" Can change accordingly.

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ARIA Repo Benchmark

The ARIA Repo Benchmark is part of the ARIA benchmark suite, a collection of closed-book benchmarks probing the ML knowledge that frontier models have internalized during training. This dataset contains 58 curated research paper implementations with metadata for evaluating whether ML experiments described in papers can be reproduced.

Dataset Summary

Size: 58 entries
Coverage: Computer Vision, NLP, Time Series, Graph, Bioinformatics
Purpose: Evaluate AI agents on their ability to locate, understand, and reproduce ML research experiments

Each entry links a research paper to its code repository, dataset, metrics, and compute requirements, along with verification of whether the experiment is reproducible on constrained hardware.

Dataset Structure

Key Fields

Field	Type	Description
`paper_title`	string	Title of the research paper
`paper_url`	string	ArXiv URL
`paper_date`	timestamp	Publication date
`paper_text`	string	Full paper text
`dataset`	string	Dataset used in the paper
`dataset_link`	string	Link to the dataset
`model_name`	string	Model name
`code_links`	list[string]	GitHub repository links
`metrics`	string	Performance metrics reported
`table_metrics`	list[string]	Detailed metrics from tables
`prompts`	string	Evaluation prompts
`modality`	string	Data modality (CV, NLP, Time Series, Graph, etc.)

Compute & Reproducibility Fields

Field	Type	Description
`compute_hours`	float64	Estimated training compute hours
`num_gpus`	int64	Number of GPUs required
`reasoning`	string	Reasoning about compute estimates
`trainable_single_gpu_8h`	string	Trainable on a single GPU in 8 hours
`verified`	string	Verification status
`time_and_compute_verification`	string	Compute verification notes
`link_to_colab_notebook`	string	Google Colab notebook link
`run_possible`	string	Whether the code runs successfully
`notes`	string	Additional notes

Usage

from datasets import load_dataset

ds = load_dataset("AlgorithmicResearchGroup/aria-repo-benchmark", split="train")

for entry in ds:
    print(f"{entry['paper_title']} - {entry['modality']} - Reproducible: {entry['run_possible']}")

Related Resources

Citation

@misc{aria_repo_benchmark,
    title={ARIA Repo Benchmark},
    author={Algorithmic Research Group},
    year={2024},
    publisher={Hugging Face},
    url={https://huggingface.co/datasets/AlgorithmicResearchGroup/aria-repo-benchmark}
}

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