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Por Stanford University & Princeton University →
Caduceus introduces bi-directional Mamba state space architectures with reverse-complement equivariance, outperforming transformers on long genomic sequence prediction while reducing compute footprint.
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Caduceus introduces bi-directional Mamba state space architectures with reverse-complement equivariance, outperforming transformers on long genomic sequence prediction while reducing compute footprint.
Información cotejada con una fuente oficial; no es un ensayo experimental directo. Fuente · Última revisión: 20/09/2026, 11:10:15 UTC
Use this citation format when referencing Caduceus in scientific publications and benchmark papers.
@software{caduceus_2026,
title = {{Caduceus}},
author = {{Stanford University & Princeton University}},
year = {2026},
url = {https://github.com/kuleshov-group/caduceus},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for Caduceus.
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Caduceus Scientific Architecture · Credit: Stanford University & Princeton University · Open original ↗
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