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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AlphaevolveAIMachine LearningGemini+4

google·Sep 29, 2025Original article

AI as a research partner: Advancing theoretical computer science with AlphaEvolve (opens in new tab)

AlphaEvolve, an LLM-powered coding agent developed by Google DeepMind, facilitates mathematical discovery by evolving code to find complex combinatorial structures that are difficult to design manually. By utilizing a "lifting" technique, the system discovers finite structures that can be plugged into existing proof frameworks to establish new universal theorems in complexity theory. This methodology has successfully produced state-of-the-art results for the MAX-4-CUT problem and tightened bounds on the hardness of certifying properties in random graphs. ## The Role of AlphaEvolve in Mathematical Research * The system uses an iterative feedback loop to morph code snippets, evaluating the resulting mathematical structures and refining the code toward more optimal solutions. * AlphaEvolve operates as a tool-based assistant that generates specific proof elements, which can then be automatically verified by computer programs to ensure absolute mathematical correctness. * By focusing on verifiable finite structures, the agent overcomes the common "hallucination" issues of LLMs, as the final output is a computationally certified object rather than a speculative text-based proof. ## Bridging Finite Discovery and Universal Statements through Lifting * Theoretical computer science often requires proofs that hold true for all problem sizes ($\forall n$), a scale that AI systems typically struggle to address directly. * The "lifting" technique treats a proof as a modular structure where a specific finite component—such as a combinatorial gadget—can be replaced with a more efficient version while keeping the rest of the proof intact. * When AlphaEvolve finds a superior finite structure, the improvement is "lifted" through the existing mathematical framework to yield a stronger universal theorem without requiring a human to redesign the entire logical architecture. ## Optimizing Gadget Reductions and MAX-k-CUT * Researchers applied the agent to "gadget reductions," which are recipes used to map known intractable problems to new ones to prove computational hardness (NP-hardness). * AlphaEvolve discovered complex gadgets that were previously unknown because they were too intricate for researchers to construct by hand. * These discoveries led to a new state-of-the-art inapproximability result for the MAX-4-CUT problem, defining more precise limits on how accurately the problem can be solved by any efficient algorithm. ## Advancing Average-Case Hardness in Random Graphs * The agent was tasked with uncovering structures related to the average-case hardness of certifying properties within random graphs. * By evolving better combinatorial structures for these specific instances, the team was able to tighten existing mathematical bounds, providing a clearer picture of when certain graph properties become computationally intractable to verify. This research demonstrates that LLM-based agents can serve as genuine research partners by focusing on the discovery of verifiable, finite components within broader theoretical frameworks. For researchers in mathematics and computer science, this "lifting" approach provides a practical roadmap for using AI to solve bottleneck problems that were previously restricted by the limits of manual construction.

AlphaevolveAILarge Language ModelsGemini+5