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arXiv:2610.19053v1 Announce Type: new Abstract: Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from trackin...
arXiv:2310.08774v3 Announce Type: replace-cross Abstract: Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history an...
The paper introduces NN‑linkage, a neural network model that learns task‑specific merge rules for hierarchical clustering while preserving the recursive structure and efficiency of classical linkage algorithms. It aligns with the Lance‑Williams recurrence, enabling it to approximate any continuous linkage function and even implement symmetric constant‑coefficient LW recurrences exactly. Experiments on clock‑tree routing and phylogenetic reconstruction show that NN‑linkage outperforms both traditional linkage methods and other neural approaches, extending efficient hierarchical clustering to new scientific and engineering tasks.
arXiv:2607. 18311v1 Announce Type: cross Abstract: Comparing phylogenetic tree topologies is essential for understanding epidemic dynamics, yet biologically meaningful distances such as the Subtree Prune and Regraft (SPR) distance are NP-hard to compute and intractable on large datasets.
arXiv:2606. 27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making.
arXiv:2605. 13894v2 Announce Type: replace-cross 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.