Mamba author highlights SSMs for long-sequence genomic prediction, open-sourcing Cerberus models
StanfordAILab · x · 2026-10-11
Mamba co-author Albert Gu amplified a new preprint applying state space models (built on Hydra, bidirectional Mamba) to long-sequence genomic prediction. His earlier small-scale DNA language modeling suggested Mamba beats Transformers and convolutions; the bio team behind the new work shows SSMs enable modeling at higher nucleotide resolution and improve performance across multiple genomic prediction tasks. They also released Cerberus, a set of open PyTorch models based on this research.
Related event: Calico Open-Sources Cerberus: SSM-Based Genomic Models Outpace Borzoi(7 posts)→
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