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Weco
The AI Research Engineer Turning Benchmarks into Breakthroughs

What is Weco?

Weco introduces AIDE, an AI Research Engineer designed to transform development workflows by automating experimentation and optimization. It functions as a self-improving system that evaluates code performance against defined benchmarks, running numerous experiments to identify and implement enhancements. This process allows Weco to compound performance gains systematically, moving beyond manual tuning and one-shot code generation.

Trusted by AI labs, Weco focuses on a metric-first engineering approach. Its core engine, AIDE, iteratively refines solutions based on measurable results, demonstrating superior performance in challenges like OpenAI's MLE-Bench and METR's RE-Bench compared to other methods and even human researchers. The platform aims to automate the research and development process itself, systematically trading computational resources for significant improvements in code quality and efficiency.

Features

  • Automated Experimentation: Runs hundreds of experiments automatically to find optimal solutions.
  • Benchmark-Driven Optimization: Uses specified metrics and benchmarks to guide the optimization process.
  • Evaluation-First Engineering: Iterates on solutions until evaluation metrics show improvement.
  • AI-Powered Code Iteration: Modifies and refines code based on performance data.
  • Performance Comparison: Demonstrates effectiveness against baselines and other agents.
  • Support for Complex Tasks: Applicable to GPU kernel optimization, model development, and prompt engineering.

Use Cases

  • Optimizing GPU kernel performance for machine learning tasks.
  • Accelerating AI model development cycles through automated refinement.
  • Improving prompt engineering results via iterative testing.
  • Enhancing performance in data science competitions.
  • Automating research and experimentation for ML engineers and AI scientists.

FAQs

  • What tasks can AIDE solve?
    AIDE can tackle various tasks, including GPU Kernel Optimization, AI Model Development, Prompt Engineering, and has shown strong performance in Data Science Competitions by automating experimentation and optimization based on specific metrics.

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