{"id":"https://openalex.org/W7161296277","doi":"https://doi.org/10.48550/arxiv.2605.13894","title":"Phylogenetic Tree Inference with Tropical Axial Attention","display_name":"Phylogenetic Tree Inference with Tropical Axial Attention","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161296277","doi":"https://doi.org/10.48550/arxiv.2605.13894"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13894","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.13894","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136253002","display_name":"Chris Teska","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teska, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093854302","display_name":"Kurt Pasque","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pasque, Kurt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079338715","display_name":"Ruriko Yoshida","orcid":"https://orcid.org/0000-0003-0995-2553"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoshida, Ruriko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124040981","display_name":"Baran Hashemi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hashemi, Baran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.19550000131130219,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.19550000131130219,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.06949999928474426,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.06780000030994415,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/phylogenetic-tree","display_name":"Phylogenetic tree","score":0.6862999796867371},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4912000000476837},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.47760000824928284},{"id":"https://openalex.org/keywords/tropical-geometry","display_name":"Tropical geometry","score":0.4625999927520752},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.44369998574256897},{"id":"https://openalex.org/keywords/phylogenetic-network","display_name":"Phylogenetic network","score":0.420199990272522},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.375900000333786},{"id":"https://openalex.org/keywords/distance-matrices-in-phylogeny","display_name":"Distance matrices in phylogeny","score":0.3675000071525574}],"concepts":[{"id":"https://openalex.org/C193252679","wikidata":"https://www.wikidata.org/wiki/Q242125","display_name":"Phylogenetic tree","level":3,"score":0.6862999796867371},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5146999955177307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5008999705314636},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C202652594","wikidata":"https://www.wikidata.org/wiki/Q2982669","display_name":"Tropical geometry","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C26619641","wikidata":"https://www.wikidata.org/wiki/Q3142246","display_name":"Phylogenetic network","level":4,"score":0.420199990272522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C5349765","wikidata":"https://www.wikidata.org/wiki/Q5282866","display_name":"Distance matrices in phylogeny","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C61067352","wikidata":"https://www.wikidata.org/wiki/Q1897429","display_name":"Ultrametric space","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C41168302","wikidata":"https://www.wikidata.org/wiki/Q3772859","display_name":"Computational phylogenetics","level":5,"score":0.34470000863075256},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C2874115","wikidata":"https://www.wikidata.org/wiki/Q17099562","display_name":"Persistent homology","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.26179999113082886},{"id":"https://openalex.org/C198043062","wikidata":"https://www.wikidata.org/wiki/Q180953","display_name":"Metric space","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13894","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.13894","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13894","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"introduce":[4],"a":[5,22,45,86,128],"Tropical":[6],"Axial":[7],"Attention":[8],"neural":[9,134],"reasoning":[10],"architecture":[11],"that":[12,82,108,124],"replaces":[13],"vanilla":[14],"softmax":[15],"dot-product":[16],"attention":[17,84,126],"with":[18,26,55,74],"max-plus":[19],"operators,":[20],"inducing":[21],"piecewise-linear":[23],"structure":[24],"aligned":[25],"dynamic":[27],"programming":[28],"formulations.":[29],"From":[30],"multi-species":[31],"sequence":[32],"alignments,":[33,96],"our":[34],"model":[35,104],"learns":[36],"all":[37,71],"possible":[38],"pairwise":[39],"distances":[40],"and":[41,49,77,141],"is":[42,127,145],"trained":[43],"using":[44],"combination":[46],"of":[47,70],"$\\ell_1$":[48],"tropical":[50,78,83,103,125],"symmetric":[51],"distance":[52,106],"metric":[53],"losses":[54],"an":[56],"ultrametric":[57],"violation":[58],"penalty.":[59],"We":[60],"leverage":[61],"the":[62,68,102,118],"well":[63],"known":[64],"isomorphic":[65],"relationship":[66],"between":[67],"space":[69],"phylogenetic":[72,91,135],"trees":[73,99],"$n$":[75],"species":[76],"Grassmannian":[79],"to":[80,112],"show":[81],"provides":[85],"natural":[87],"geometric":[88,130],"framework":[89],"for":[90,133],"inference.":[92],"On":[93],"empirical":[94],"$DS1-DS11$":[95],"where":[97],"true":[98],"are":[100,109],"unknown,":[101],"produces":[105],"matrices":[107],"substantially":[110],"closer":[111],"their":[113],"BME-induced":[114],"tree":[115],"metrics":[116],"than":[117],"baseline":[119],"models.":[120],"These":[121],"results":[122],"suggest":[123],"useful":[129],"inductive":[131],"bias":[132],"inference,":[136],"especially":[137],"under":[138],"distribution":[139],"shift":[140],"when":[142],"tree-metric":[143],"consistency":[144],"important.":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-16T00:00:00"}
