The cycle of scientific discovery is slowed by manual, time-consuming creation of software needed for computational experiments. Empirical Research Assistance (ERA) is an AI system designed to generate expert-level scientific software. ERA aims to maximize a quality metric that evaluates the software it produces. The system uses a Large Language Model (LLM) together with Tree Search (TS) to systematically improve the quality metric. It also uses the search process to intelligently navigate toward better software outputs. The approach targets the software bottleneck by automating parts of the development workflow while focusing on measurable quality improvements.
"The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments1. 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)2 to systematically improve the quality metric and intelligently navigate t"
"Empirical Research Assistance (ERA) is 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)2 to systematically improve the quality metric and intelligently navigate t"
#ai-for-scientific-software #large-language-models #tree-search #computational-experiments #software-quality-metrics
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