google·May 19, 2026·2 min readCurated summary

Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery

Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics. ## How ERA Supports Scientific Coding - ERA starts with a scientific problem and a success metric. - It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions. - Its tree-search process evaluates thousands of alternatives to optimize the resulting model. - Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance. ## Applications to Open Scientific Problems - **Epidemiological forecasting** - Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV. - Forecasts ranked at or near the top of CDC leaderboards. - The techniques can potentially be adapted to other countries and diseases. - **California water-supply forecasting** - Produced seasonal runoff predictions for snow-fed river basins. - Delivered more accurate early forecasts than California’s official Bulletin 120 outlook. - Improved predictions could support water management and agriculture. - **Atmospheric carbon dioxide monitoring** - Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas. - Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles. - Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2. - **Solar-energy design** - Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries. - Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading. - **Retail forecasting** - Used economic indicators, Google Trends, historical patterns, and consumer sentiment. - Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast. ## Computational Discovery - Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs. - The system combines ERA with AlphaEvolve to support computational scientific investigation. - It complements other Gemini for Science experiments: - **Hypothesis Generation**, built with AI Co-Scientist, supports developing scientific hypotheses. - **Literature Insights** supports research and literature analysis. ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.

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Tree SearchAIMachine LearningGemini+4

google·Sep 8, 2025Original article

Accelerating scientific discovery with AI-powered empirical software (opens in new tab)

Google Research has introduced an AI-powered system designed to accelerate scientific discovery by automating the creation and optimization of "empirical software." By leveraging the Gemini model and tree search optimization, the system can propose, implement, and iteratively improve code for complex multidisciplinary challenges, achieving results that match or exceed human expert performance. This approach transforms scientific hypothesis evaluation from a months-long manual coding process into an automated search that can be completed in hours or days. ### The Concept of Empirical Software and Scorable Tasks * The system shifts focus from traditional functional correctness to "empirical software," where the primary objective is to maximize a predefined quality score. * It targets "scorable tasks," which are defined by a problem description, a specific scoring metric, and a dataset for training and validation. * This framework addresses the research bottleneck where scientists must manually test hundreds of models or parameters to achieve a breakthrough. ### System Architecture and Optimization Strategy * The engine takes a task description and optional context—such as ideas from scientific literature—as input to generate novel methodological concepts. * It utilizes a tree search strategy inspired by AlphaZero, employing an upper confidence bound to navigate and prioritize thousands of potential code variants. * The LLM acts as an iterative rewriter, refining executable code within a sandbox to continuously improve the performance score. * Outputs are designed to be fully verifiable, interpretable, and reproducible, providing scientists with the specific coded solutions used to reach a result. ### Demonstrated Performance Across Scientific Domains * The system was tested on six diverse benchmarks, including genomics, public health, geospatial analysis, neuroscience, and time-series forecasting. * In genomics, the system tackled the "batch integration" of single-cell RNA sequencing (scRNA-seq) data, a complex problem involving the removal of noise while preserving biological signals. * The AI discovered 40 novel methods that outperformed top expert-developed tools within the OpenProblems V2.0.0 batch integration benchmark. * Evaluation focused on advanced capabilities such as zero-shot generalization, high-dimensional signal processing, and uncertainty quantification. This system represents a significant shift toward "research engines" that participate actively in the scientific method through iterative experimentation. Scientists can utilize these tools to explore a much broader range of hypotheses than manual coding allows, potentially leading to faster breakthroughs in data-heavy fields like genomics and climate modeling.

Tree SearchAILarge Language ModelsGemini+4