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  1. Home
  3. An Ai System To Help Scientists Write Expert-level Empirical Software.
  1. Home
  3. An Ai System To Help Scientists Write Expert-level Empirical Software.

Related Experiment Videos

An AI system to help scientists write expert-level empirical software.

Eser Aygün1, Anastasiya Belyaeva2, Gheorghe Comanici1

  • 1Google DeepMind, Montréal, Quebec, Canada.

Nature
|May 19, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

An AI system called Empirical Research Assistance (ERA) creates expert-level scientific software to speed up discovery. ERA uses AI to generate novel methods in fields like bioinformatics and epidemiology, outperforming human-developed solutions.

Related Experiment Videos

Area of Science:

  • Computational science
  • Artificial intelligence
  • Scientific discovery acceleration

Background:

  • Scientific discovery is often slowed by manual software development for computational experiments.
  • There is a need for automated systems to generate high-quality scientific software.

Purpose of the Study:

  • To introduce Empirical Research Assistance (ERA), an AI system designed to create expert-level scientific software.
  • To demonstrate ERA's capability in systematically improving quality metrics and navigating complex solution spaces.
  • To showcase ERA's effectiveness across diverse scientific domains.

Main Methods:

  • ERA utilizes a Large Language Model (LLM) integrated with Tree Search (TS).
  • The system explores and integrates external research ideas to enhance software quality.
  • Quality metrics are systematically improved through AI-driven exploration and optimization.

    • Main Results:

    • ERA discovered 40 novel bioinformatics methods for single-cell data analysis, surpassing human-developed methods.
    • ERA generated 14 epidemiological models for COVID-19 hospitalization forecasting, outperforming the CDC ensemble.
    • Expert-level software was produced for geospatial analysis, neural activity prediction, and numerical integration.

    Conclusions:

    • ERA demonstrates expert-level performance in generating scientific software across various disciplines.
    • The AI system significantly accelerates scientific progress by automating software creation.
    • ERA's approach represents a substantial advancement in computational research assistance.