Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
Molecular Biology and Evolution4(4)
3 Pith papers cite this work. Polarity classification is still indexing.
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VeloTree infers differentiation trees from RNA velocity fields by defining cell dissimilarity as the squared varifold distance between integral curves of the velocity field.
HyperEvoGen uses hyperbolic variational inference to learn phylogenetic representations from protein alignments that preserve hierarchy and scale with evolutionary divergence, outperforming baselines in ancestral reconstruction on simulated data.
citing papers explorer
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Analysis and Explainability of LLMs Via Evolutionary Methods
Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
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VeloTree: Inferring single-cell trajectories from RNA velocity fields with varifold distances
VeloTree infers differentiation trees from RNA velocity fields by defining cell dissimilarity as the squared varifold distance between integral curves of the velocity field.
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HyperEvoGen: Exploring deep phylogeny using non-Euclidean variational inference
HyperEvoGen uses hyperbolic variational inference to learn phylogenetic representations from protein alignments that preserve hierarchy and scale with evolutionary divergence, outperforming baselines in ancestral reconstruction on simulated data.