Abstract:In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus operators, inducing a piecewise-linear structure aligned with dynamic programming formulations. From multi-species sequence alignments, our model learns all possible pairwise distances and is trained using a combination of $\ell_1$ and tropical symmetric distance metric losses with an ultrametric violation penalty. We leverage the well known isomorphic relationship between the space of all phylogenetic trees with $n$ species and tropical Grassmannian to show that tropical attention provides a natural geometric framework for phylogenetic inference.
On empirical $DS1-DS11$ alignments, where true trees are unknown, the tropical model produces distance matrices that are substantially closer to their BME-induced tree metrics than the baseline models. These results suggest that tropical attention is a useful geometric inductive bias for neural phylogenetic inference, especially under distribution shift and when tree-metric consistency is important.
| Subjects: | Populations and Evolution (q-bio.PE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13894 [q-bio.PE] |
| (or arXiv:2605.13894v1 [q-bio.PE] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13894 arXiv-issued DOI via DataCite (pending registration) |
From: Ruriko Yoshida [view email]
[v1]
Tue, 12 May 2026 10:54:37 UTC (554 KB)
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