AstroAgents: A Multi-Agent AI for Hypothesis Generation from Mass Spectrometry Data

With upcoming sample return missions across the solar system and the increasing availability of mass spectrometry data, there is an urgent need for methods that analyze such data within the context of existing astrobiology literature and generate plausible hypotheses regarding the emergence of life on Earth. Hypothesis generation from mass spectrometry data is challenging due to factors such as environmental contaminants, the complexity of spectral peaks, and difficulties in cross-matching these peaks with prior studies. To address these challenges, we introduce AstroAgents, a large language model-based, multi-agent AI system for hypothesis generation from mass spectrometry data. AstroAgents is structured around eight collaborative agents: a data analyst, a planner, three domain scientists, an accumulator, a literature reviewer, and a critic. The system processes mass spectrometry data alongside user-provided research papers. The data analyst interprets the data, and the planner delegates specific segments to the scientist agents for in-depth exploration. The accumulator then collects and deduplicates the generated hypotheses, and the literature reviewer identifies relevant literature using Semantic Scholar. Finally, the critic evaluates the hypotheses, offering rigorous suggestions for improvement. To assess AstroAgents, an astrobiology expert evaluated the novelty and validity of more than a hundred hypotheses generated from data obtained from eight meteorites and ten soil samples. Of these hypotheses, surprisingly, 36% were identified as plausible, and among those, 66% were novel.