In-context learning refers to the capacity of large language models to perform a new task based solely on a small number of examples or instructions provided within the prompt, without any retraining. This capability is particularly valuable in healthcare, where labeled data are scarce, difficult to obtain, and privacy constraints are substantial. Given only a few illustrative examples, a model can identify events (clinical trial endpoints) from medical histories, estimate risk for the occurrence of future events, or assess patient eligibility for clinical trials. A single model may therefore be adapted efficiently to a range of tasks and revised as clinical guidelines evolve, provided that its outputs are rigorously validated and remain subject to expert oversight.

The growth of network-structured data across domains like neuroscience and cybersecurity demands scalable graph analytics, but complex tasks like subgraph isomorphism remain accessible only to high-performance computing (HPC) specialists. Arachne is an open-source framework that democratizes high-performance graph analytics through a Python interface while abstracting parallelism complexities.