A framework for translating mechanistic interpretability into concrete discovery outputs: biomarkers, regulatory hypotheses, cell-state taxonomies, drug target prioritization, compact algorithm extraction, artifact detection, and manifold-guided experimental design.
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An argument for treating biological foundation models as compressed maps of biological reality, and for using mechanistic interpretability to extract validated knowledge, causal structure, and compact biological algorithms from their internals.
Read articleFormal research outputs, including preprints and public reports from the Biodyn research pipeline.