A genome-conditioned 4B LLM agent predicts microbial life boundaries and matches larger frontier models via token fusion, tool use, and a counterfactual gene-grounding reward.
Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome.Bioinformatics, 37(15):2112–2120
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Wisteria unifies multi-scale feature learning in a Mamba-based DNA language model via gated convolutions, MLPs, and Fourier attention, showing strong benchmark performance on genomic tasks with short and long-range dependencies.
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GGBound: A Genome-Grounded Agent for Microbial Life-Boundary Prediction
A genome-conditioned 4B LLM agent predicts microbial life boundaries and matches larger frontier models via token fusion, tool use, and a counterfactual gene-grounding reward.
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Wisteria: A Unified Multi-Scale Feature Learning Framework for DNA Language Model
Wisteria unifies multi-scale feature learning in a Mamba-based DNA language model via gated convolutions, MLPs, and Fourier attention, showing strong benchmark performance on genomic tasks with short and long-range dependencies.