Sarah Martinson, Michael P. Brenner, Martyna Plomecka, Brian P. Williams, Nicholas G. Reich, Zahra Shamsi
Probabilistic forecasting of infectious diseases is crucial for public health monitoring and preparedness but has historically relied on labor-intensive manual model curation by expert modeling teams. Bespoke development is an obstacle to scaling models to granular geographic resolutions or emerging pathogens. Here, we prospectively evaluate Empirical Research Assistance (ERA), an autonomous system that uses Large Language Model-guided tree search to iteratively generate, evaluate, and optimize executable forecasting software. ERA was primarily prompted with methodological approaches to implement, while in some instances it was tasked with developing forecasting approaches from scratch. In a fully prospective, real-time evaluation during the 2025-26 United States respiratory season, ERA generated methodologically diverse models for influenza, COVID-19, and respiratory syncytial virus (RSV). Aggregating a selection of these automatically-generated models yielded an ensemble that ranked first overall in official Centers for Disease Control and Prevention (CDC) post-season evaluations and outperformed the gold-standard CDC forecast ensembles across all three pathogens in a real-time prediction challenge. Additionally, ERA successfully navigated data-scarce scenarios for RSV. Controlled retrospective ablations allowed for additional explorations of the impact of optimization target and instruction-following on forecast accuracy. Preliminary experiments indicate that ERA's search optimization metric influences behavior of output forecasts, and using higher-tier language models in-search improves predictive performance. By autonomously generating interpretable code that creates epidemiologically relevant predictions, this framework provides a path toward rapidly scaling high-quality disease forecasting.
Eser Aygün, Anastasiya Belyaeva, Gheorghe Comanici, Marc Coram, Hao Cui, Jake Garrison, Renee Johnston Anton Kast, Cory Y. McLean 等 42 位作者
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments\cite{hannay2009how}. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS)\cite{silver2016mastering} to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish, and numerical solution of integrals, and a novel rule-based construction for time series forecasting. By devising and implementing novel solutions to diverse tasks, ERA represents a significant step towards accelerating scientific progress.
Hao Cui, Zahra Shamsi, Gowoon Cheon, Xuejian Ma, Shutong Li, Maria Tikhanovskaya, Peter Norgaard, Nayantara Mudur 等 34 位作者
Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding,Reasoning and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks with a total of 580 problems and solution pairs curated by experts in six disciplines - materials science, condensed matter physics, quantum computing, geospatial analysis, biodiversity, and proteins - covering both experimental and theoretical work-flows in science. We evaluate a range of closed and open LLMs on tasks in CURIE which requires domain expertise, comprehension of long in-context information,and multi-step reasoning. While Gemini Flash 2.0 and Claude-3 show consistent high comprehension across domains, the popular GPT-4o and command-R+ fail dramatically on protein sequencing tasks. With the best performance at 32% there is much room for improvement for all models. We hope that insights gained from CURIE can guide the future development of LLMs in sciences. Evaluation code and data are in https://github.com/google/curie