Phylogenetic Inference

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Algorithmically Aligned Neural Agglomerative Tree Construction
Oct 5, 2026Robert R Nerem, Pranav Singh, Cheyenne Ward +1Clustering Hierarchical Clustering
University of California San Diego · University of Washington
Approximating SPR Distance Between Phylogenetic Trees with Graph Neural Networks
Jul 17, 2026Renata Martins Castanheira, Miguel Bugalho, Cátia VazGraph Neural Networks Phylogenetic Inference
ISEL – Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Portugal · ALGORITMI Research Center/CCPM, Portugal · INESC-ID Lisboa, Portugal
Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction
Jun 20, 2026Zixuan Liu, Kaijie Yu, Chun He +5Cross-Modal Alignment Phylogenetic Inference
Zhejiang Key Lab of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou 310027, China · State Key Laboratory of Rice Biology and Breeding & Ministry of Agricultural and Rural Affairs Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China · Rural Development Academy, Zhejiang University, Hangzhou 310058, China +1
MalTree: Tracing Malware Evolution from Embeddings at Scale
Jun 4, 2026Akash Amalan, Georgios Smaragdakis, Tom J. VieringPhylogenetic Inference Malware Analysis
Delft University of Technology, Delft, The Netherlands.
PhylaFlow: Hybrid Flow Matching in Billera-Holmes-Vogtmann Tree Space for Phylogenetic Inference
May 21, 2026Yasha Ektefaie, Leo Cui, Shrey Jain +2Flow Matching Tree Search
Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard · Department of Biomedical Informatics, Harvard Medical School · Centennial High School +1
Phylogenetic Tree Inference with Tropical Axial Attention
May 12, 2026Chris Teska, Kurt Pasque, Ruriko Yoshida +1Phylogenetic Inference Attention Mechanisms
In this work, we introduce a Tropical Axial Attention neural reasoning architecture...
Tree-Conditioned Edit Flows for Ancestral Sequence Reconstruction
May 5, 2026Emil Sharafutdinov, Ingemar AndréFlow Matching Phylogenetic Inference
Center for Molecular Protein Science Lund University Box 124, 221 00 Lund
PhyloGFN: Phylogenetic inference with generative flow networks
Oct 12, 2023Mingyang Zhou, Zichao Yan, Elliot Layne +5Bayesian Inference Posterior Sampling
McGill University · McGill University, Mila · Universit´e de Montr´eal, Mila

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Algorithmically Aligned Neural Agglomerative Tree Construction

Robert R Nerem, Pranav Singh, Cheyenne Ward, Yusu Wang

University of California San Diego · University of Washington

Clustering Hierarchical Clustering Phylogenetic Inference

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Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear which merge rule best suits a given dataset or task. In contrast, neural approaches can learn from data, but often fail to retain the efficiency and size generalization of classical algorithms. We introduce NN-linkage, a neural network (NN) model that can learn task-specific and locally dependent merge rules while retaining the recursive structure and efficient inference of classical linkage algorithms. In particular, our model is algorithmically aligned with the Lance-Williams (LW) recurrence, a parameterized framework for defining a broad, continuous family of linkage rules for agglomerative HC. Classical methods such as single linkage (SL), complete linkage (CL), and average linkage arise as discrete choices within this broader family. We show that NN-linkage is a universal approximator for continuous linkage functions, including LW recurrences, and, when paired with a transformer encoding, can also approximate globally dependent rules such as robust single-linkage. We further show that NN-linkage can exactly implement any symmetric constant-coefficient LW recurrence across all input sizes. On the empirical front, we evaluate NN-linkage in real-world applications, clock-tree routing and phylogenetic reconstruction, using both synthetic and real datasets, demonstrating its effectiveness over both classical algorithms and other neural approaches. By learning merge rules directly from target trees, NN-linkage extends efficient HC to scientific and engineering objectives not adequately captured by existing hand-designed linkage rules.

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