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Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted \"long branch attraction\" (LBA) in which long non-sister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees.","url_abs":"https://arxiv.org/abs/2201.04663v3","url_pdf":"https://arxiv.org/pdf/2201.04663v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accurate-phylogenetic-inference-with-a","repo_url":"https://github.com/forgotten/nn-phylogenetics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"accurate-phylogenetic-inference-with-a","repo_url":"https://github.com/crsl4/nn-phylogenetics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.04663","atlas_url":"https://app.syntology.ai/?focus=2201.04663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.04663"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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