GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis

Scientific research increasingly depends on complex computational analysis, yet the design and execution of such analysis remain labor-intensive, error-prone, and difficult to scale. From genomics [23, 103, 125], materials discovery [25], drug development [49, 106], and epidemiology [69, 9], to climate modeling [60] and personalized medicine [36], the analytical backbone of modern science involves highly structured, multi-step workflows written in code. These workflows must integrate raw or semi-structured data, apply statistical or mechanistic models, and yield interpretable results--all under the constraints of evolving domain knowledge, platform variation, and methodological rigor. Recent advances in large language models (LLMs) have led to rapid progress in general-purpose agents capable of decomposing tasks [118, 113, 79, 18], interacting with tools [86, 138, 147, 49], and producing executable code [30, 126, 167, 114, 140]. These developments have raised the prospect that LLM-based agents might soon contribute meaningfully to scientific discovery by automating analysis pipelines, exploring hypotheses, or refining computational models [8, 60]. However, realizing this promise requires bridging a fundamental gap between general reasoning ability and the structured, precision-driven nature of scientific computation. While many LLM-based agents have shown competence in retrieving documents, calling APIs, or planning abstract tasks [165, 154, 46, 124], these capabilities fall short in domains where scientific progress depends on code. In fields such as transcriptomics [90, 69, 9], protein engineering [106], and statistical genetics [36, 76], research workflows are encoded as sequences of programmatic transformations, each tailored to the idiosyncrasies of a specific dataset, model assumption, or experimental design.