Discover, analyze, and implement ML research with intelligent agents. ScoutML scouts the entire ArXiv landscape to bring you actionable insights and step-by-step implementation guides.
Powerful API and CLI for discovering, analyzing, and implementing ML research.
Scout Every Paper
Access ArXiv CS papers, each fully parsed with structured metadata, abstracts, methods, and results - all queryable via our API.
"Find papers using transformers for time series forecasting"
147 papers • Ranked by relevance • Full content access
Your AI Research Scout
Intelligent agents that scout ML research to help you implement papers, solve limitations, and design experiments.
Beyond Simple Search
Use powerful analytics to compare methods, analyze trends, explore citation networks, and synthesize findings across thousands of papers.
Real examples of what you can do with ScoutML
{
"count": 3,
"method": "diffusion models",
"papers": [
{
"arxiv_id": "2112.10752",
"title": "High-Resolution Image Synthesis with Latent Diffusion Models",
"authors": ["Rombach, R.", "Blattmann, A.", "Lorenz, D.", "Esser, P.", "Ommer, B."],
"year": 2022,
"citation_count": 9823,
"methods": ["diffusion models", "latent diffusion", "cross-attention", "perceptual compression"],
"code_url": "https://github.com/CompVis/stable-diffusion"
}
]
}
{
"comparison": {
"dataset": "ImageNet",
"architectures": {
"vision_transformer": {
"best_accuracy": 90.45,
"avg_accuracy": 87.3,
"paper_count": 156,
"common_variants": ["ViT-L/16", "ViT-B/32", "ViT-H/14"]
},
"convolutional_neural_network": {
"best_accuracy": 91.2,
"avg_accuracy": 85.7,
"paper_count": 892,
"common_variants": ["ResNet", "EfficientNet", "ConvNeXt"]
}
}
}
}
Three simple steps to access ML research
Get started in seconds with our Python CLI or integrate directly with our REST API.
pip install scoutml
Use natural language to search papers, analyze trends, compare methods, or explore citation networks across the database.
Receive parsed, structured data ready for analysis - papers, methods, datasets, results, and insights.
Accelerate research across all domains
Literature reviews, method surveys, trend analysis, citation tracking for thesis and publications.
State-of-the-art tracking, competitive analysis, method selection, benchmark comparisons.
Patent research, technology scouting, feasibility studies, implementation guidance.
Got questions? We've got answers.
ScoutML is a powerful API and CLI tool that scouts ML research papers for you. It provides intelligent agents that can analyze papers, generate implementation guides, critique research, solve limitations, and help design experiments - all through a simple command-line interface or REST API.
We have ArXiv CS papers fully parsed and indexed, covering everything from foundational papers in 1991 to the latest submissions. Our database updates daily as new papers are published on ArXiv.
We parse and structure: abstracts, methods, datasets used, experimental results, benchmarks, citations, author information, and key contributions. This allows for deep semantic search and analysis beyond simple keyword matching.
Simply install our Python package with 'pip install scoutml', set your API key, and start scouting! You can search papers, get implementation guides, compare methods, and more. Our CLI provides rich terminal output and our API returns structured JSON for integration.
You can search papers by topic, compare methods across papers, analyze research trends over time, explore citation networks, find papers using specific datasets or techniques, and much more. Our intelligent agents can even help you implement papers and design experiments.
Our free tier includes a generous 10,000 API calls per month (1,000/day). The Pro tier offers 100,000 API calls per month (10,000/day) for just $10/month. These limits are designed to be more than enough for serious research work while helping us cover infrastructure costs.