Unifying AI-assisted scientific discovery around exploration, hypothesis generation, and testing

Large language models (LLMs) and agent systems increasingly support scientific discovery across domains. Yet the literature remains fragmented around isolated tasks, with limited attention to how components form integrated workflows. Here, we introduce exploration, hypothesis generation, and testing (EXHYTE) as an empirical workflow abstraction for reviewing AI-assisted scientific discovery. We mapped a corpus of recent studies to EXHYTE stages and substages to identify recurring strategies, workflow connections, and capability gaps. The analysis shows strong progress in exploration, especially retrieval, knowledge assembly, and representation learning as well as growing capacity for hypothesis and idea generation. The central bottleneck remains the transition from hypothesis or idea generation to executable experimental or computational testing, followed by feedback that can refine subsequent workflow steps. Because EXHYTE organizes systems by workflow function rather than agent architecture, it applies to both multi-agent scaffolds and emerging generalist tool-using agents. It also shows that integrated workflows do not imply full automation: current systems still depend on human judgment for problem formulation, contextualization, interpretation, risk assessment, and oversight. We discuss evaluation, reproducibility, hypothesis-space narrowing, external validation, and governance considerations.