{"id":"https://openalex.org/W7164123910","doi":"https://doi.org/10.48550/arxiv.2606.09907","title":"LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts","display_name":"LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164123910","doi":"https://doi.org/10.48550/arxiv.2606.09907"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09907","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09907","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.09907","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052219753","display_name":"Maxx Richard Rahman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Maxx Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138294566","display_name":"Prakhar Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Prakhar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138377689","display_name":"Wolfgang Maass","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maass, Wolfgang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9789999723434448,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9789999723434448,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.003599999938160181,"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/T10028","display_name":"Topic Modeling","score":0.0031999999191612005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.6216999888420105},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4659000039100647},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44760000705718994},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.40630000829696655},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3993000090122223},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.35989999771118164},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.3540000021457672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.707099974155426},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.6216999888420105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5569000244140625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.498199999332428},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4659000039100647},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2913999855518341},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09907","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09907","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.09907","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09907","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":"Preprint"},"sustainable_development_goals":[{"score":0.4046933352947235,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"clinical":[1,172],"learning":[2],"is":[3],"increasingly":[4],"important":[5],"for":[6,129,138,169],"integrating":[7],"diverse":[8],"patient":[9,37],"data,":[10],"including":[11],"imaging,":[12],"text,":[13],"and":[14,75,87,133,146,159],"personalised":[15],"health":[16],"records.":[17],"However,":[18],"it":[19],"faces":[20],"two":[21],"fundamental":[22],"challenges:":[23],"i)":[24],"modality":[25,85,91],"missingness,":[26],"where":[27,42],"arbitrary":[28],"subsets":[29],"of":[30,46],"modalities":[31,158],"are":[32],"unavailable":[33],"at":[34],"a":[35,108,126,166],"given":[36],"visit,":[38],"ii)":[39],"longitudinal":[40,80],"dynamics,":[41],"the":[43,51,98],"diagnostic":[44],"significance":[45],"an":[47,71,113],"observation":[48],"depends":[49],"on":[50,143],"patient's":[52],"evolving":[53],"disease":[54,131],"trajectory":[55],"over":[56],"time.":[57],"Existing":[58],"methods":[59],"address":[60,103],"these":[61],"challenges":[62],"in":[63,162],"isolation:":[64],"missing-modality":[65],"frameworks":[66],"treat":[67],"each":[68],"visit":[69,124],"as":[70],"independent":[72],"static":[73],"snapshot":[74],"discard":[76],"temporal":[77,120],"context,":[78],"while":[79],"models":[81],"often":[82],"assume":[83],"complete":[84],"availability":[86],"degrade":[88],"under":[89,153],"systematic":[90],"incompleteness.":[92],"We":[93],"propose":[94],"LongMoE":[95,106,150],"(Longitudinal":[96],"Mixture-of-Experts),":[97],"unified":[99],"framework":[100],"to":[101],"jointly":[102],"both":[104],"challenges.":[105],"combines":[107],"context-aware":[109],"imputation":[110],"module":[111,116],"with":[112],"attentional":[114],"tokenization":[115],"that":[117,149],"captures":[118],"frequency-domain":[119],"patterns":[121],"across":[122],"irregular":[123],"sequences,":[125],"trajectory-aware":[127],"encoder":[128],"modeling":[130],"progression,":[132],"context-conditioned":[134],"Sparse":[135],"MoE":[136],"routing":[137],"patient-specific":[139],"expert":[140],"selection.":[141],"Experiments":[142],"ADNI,":[144],"OASIS-3,":[145],"MIMIC-IV":[147],"show":[148],"improves":[151],"robustness":[152],"missing":[154],"or":[155],"weak":[156],"contemporaneous":[157],"remains":[160],"competitive":[161],"full-modality":[163],"settings,":[164],"establishing":[165],"strong":[167],"foundation":[168],"longitudinally-aware":[170],"multimodal":[171],"learning.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}