{"id":"https://openalex.org/W7160629254","doi":"https://doi.org/10.48550/arxiv.2605.05524","title":"MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series","display_name":"MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160629254","doi":"https://doi.org/10.48550/arxiv.2605.05524"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05524","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":null,"license_id":null,"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.2605.05524","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124302543","display_name":"Shicheng Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Shicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135640628","display_name":"Nour Elhendawy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elhendawy, Nour","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126385630","display_name":"Jianle Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jianle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101369256","display_name":"Ke Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135679654","display_name":"Kun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135721058","display_name":"Yihang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yihang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135640579","display_name":"Lu Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Lu","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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.7228999733924866,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.7228999733924866,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.051899999380111694,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.03009999915957451,"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/identifiability","display_name":"Identifiability","score":0.796999990940094},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7800999879837036},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6675999760627747},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5174999833106995},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.398499995470047},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.38190001249313354},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.375},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3666999936103821},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.36640000343322754},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3504999876022339}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.796999990940094},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7800999879837036},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6675999760627747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5289999842643738},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5149999856948853},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4878999888896942},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.375},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3666999936103821},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2976999878883362},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27140000462532043},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.2700999975204468},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2538999915122986},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05524","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.05524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05524","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.8890153169631958}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Causal":[0,112],"representation":[1],"learning":[2],"(CRL)":[3],"seeks":[4],"to":[5,14,82,98],"recover":[6],"latent":[7,28,101,131,141,205],"variables":[8,132],"with":[9,37,123],"identifiability":[10,23,122],"guarantees,":[11],"typically":[12,31],"up":[13],"permutation":[15],"and":[16,55,137,168,196],"component-wise":[17],"reparameterization":[18],"under":[19,163],"appropriate":[20],"assumptions.":[21],"However,":[22],"does":[24],"not":[25],"imply":[26],"interpretability:":[27],"semantics":[29],"are":[30,53,76,161],"assigned":[32],"post":[33],"hoc":[34],"by":[35],"alignment":[36],"known":[38],"ground-truth":[39],"factors.":[40],"This":[41,86],"limitation":[42],"is":[43,59],"particularly":[44],"acute":[45],"in":[46,207],"scientific":[47,65,208],"time":[48,209],"series,":[49],"where":[50],"underlying":[51],"mechanisms":[52,206],"unknown":[54],"discovering":[56],"interpretable":[57,202],"structure":[58],"a":[60,88,114,142,174,197],"primary":[61],"goal.":[62],"In":[63],"contrast,":[64],"observations":[66,95,147],"(such":[67],"as":[68,79],"residue-pair":[69],"distances,":[70],"climate":[71],"indices,":[72],"or":[73],"process":[74],"sensors)":[75],"inherently":[77],"semantic,":[78],"they":[80],"correspond":[81],"named":[83],"physical":[84],"quantities.":[85],"raises":[87],"key":[89],"question:":[90],"can":[91],"the":[92,99,192],"interpretability":[93],"of":[94,145,204],"be":[96],"transferred":[97],"identifiable":[100,162],"space?":[102],"We":[103,155],"propose":[104],"MOSAIC":[105,129,179],"(Module":[106],"discovery":[107,203],"via":[108,133],"Sparse":[109],"Additive":[110],"Identifiable":[111],"learning),":[113],"sparse":[115,143],"temporal":[116,120,135],"VAE":[117],"that":[118,157],"integrates":[119],"CRL":[121],"support":[124],"recovery":[125,171],"over":[126],"observed":[127],"variables.":[128],"identifies":[130],"regime-conditioned":[134],"variation,":[136],"recovers":[138,180],"for":[139,173],"each":[140],"set":[144],"associated":[146],"through":[148],"an":[149],"additive":[150],"decoder,":[151],"yielding":[152],"module-level":[153],"interpretability.":[154],"show":[156],"ANOVA":[158],"main-effect":[159],"supports":[160],"general":[164],"smooth":[165],"mixing":[166],"functions,":[167],"provide":[169],"finite-sample":[170],"guarantees":[172],"tractable":[175],"sparse-additive":[176],"variant.":[177],"Empirically,":[178],"domain-consistent":[181],"variable":[182],"groups":[183],"across":[184],"RNA":[185],"molecular":[186],"dynamics,":[187],"solar":[188],"wind,":[189],"ENSO":[190],"climate,":[191],"Tennessee":[193],"Eastman":[194],"process,":[195],"synthetic":[198],"tokamak":[199],"benchmark,":[200],"enabling":[201],"series.":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}