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A multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory is described, designed to be generalizable to a wide range of scientific domains.
This work introduces a compositional sparse embedding (CoSE) for task conditioning that reduces learned task-memory parameters by over 90% while improving pass@2 in controlled ARC ablations.
The application of causal discovery to characterizing transcriptional responses to radiation as a function of dose rate in human cells is explored and inferred causal graphs reveal structured network features such as high in-degree housekeeping genes and high out-degree transcription factors.
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