Large Language Model Agents for Autonomous Scientific Discovery and Experiment Design
Keywords:
large language model agents, autonomous scientific discovery, experiment design, research infrastructure, reproducibility, governanceAbstract
Large language model agents are increasingly proposed as autonomous collaborators in scientific discovery, not merely as interfaces for literature search or code generation but as systems capable of planning experiments, calling domain tools, interpreting results, and revising hypotheses. This paper examines the system-level conditions under which such agents can support credible autonomous scientific discovery and experiment design. It argues that the central challenge is not model capability alone but the integration of reasoning, memory, tool use, provenance, laboratory execution, governance, and long-term maintenance into robust socio-technical infrastructures. The paper reviews the evolution from assistive language models to agentic discovery systems, analyzes layered architectures that connect foundation models to instruments and data repositories, and discusses closed-loop experiment design in chemistry, materials science, biology, and computational domains. It then evaluates infrastructure and sustainability constraints, including compute intensity, energy demand, and technical debt, before addressing robustness, security, reproducibility, fairness, and policy. The analysis suggests that autonomous discovery agents must be evaluated not only by novelty or predictive accuracy but also by reproducibility, auditability, safety, access equity, and contribution to public knowledge. A governance model combining provenance standards, model and dataset documentation, human oversight, and domain-specific validation is essential. The paper concludes that large language model agents can meaningfully expand scientific capacity only when they are embedded in accountable infrastructures that treat discovery as a collective, cumulative, and socially situated process rather than as an isolated optimization problem.
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