{"id":"https://openalex.org/W7223268308","doi":"https://doi.org/10.48550/arxiv.2610.31192","title":"Large Language Models Meet Bayesian Optimization: Towards Self-evolving Wireless Digital Twins","display_name":"Large Language Models Meet Bayesian Optimization: Towards Self-evolving Wireless Digital Twins","publication_year":2026,"publication_date":"2026-10-22","ids":{"openalex":"https://openalex.org/W7223268308","doi":"https://doi.org/10.48550/arxiv.2610.31192"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.31192","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.31192","pdf_url":"https://arxiv.org/pdf/2610.31192","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.31192","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072724858","display_name":"Yingzhuo Sun","orcid":"https://orcid.org/0000-0003-4428-6939","observed_orcids":["https://orcid.org/0000-0003-4428-6939"]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Sun, Yingzhuo","raw_affiliation_strings":["Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010577437","display_name":"Haoyun Li","orcid":"https://orcid.org/0000-0002-6318-587X","observed_orcids":["https://orcid.org/0000-0002-6318-587X"]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Li, Haoyun","raw_affiliation_strings":["Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159355029","display_name":"Ming Xiao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114469986","display_name":"Francisco J. Ferr\u00e3o","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ferrao, Francisco J.","raw_affiliation_strings":["Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052927830","display_name":"Berna \u00d6zbek","orcid":"https://orcid.org/0000-0003-4359-7874","observed_orcids":["https://orcid.org/0000-0003-4359-7874"]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ozbek, Berna","raw_affiliation_strings":["Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering , KTH Royal Institute of Technology , 100 44 Stockholm , Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.6812000274658203,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.6812000274658203,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.03350000083446503,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11409","display_name":"Advanced Wireless Network Optimization","score":0.03269999846816063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9700000286102295},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":1.0},{"id":"https://openalex.org/keywords/multi-user-beamforming","display_name":"multi-user beamforming","score":0.6779999732971191},{"id":"https://openalex.org/keywords/online-adaptation","display_name":"online adaptation","score":0.7620000243186951}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7017999887466431},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.5784000158309937},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5741999745368958},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5436000227928162},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.49880000948905945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49880000948905945},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2669999897480011},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.31192","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.31192","pdf_url":"https://arxiv.org/pdf/2610.31192","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.31192","doi":"https://doi.org/10.48550/arxiv.2610.31192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.31192","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.31192","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.31192","pdf_url":"https://arxiv.org/pdf/2610.31192","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6773999929428101,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223268308.pdf","grobid_xml":"https://content.openalex.org/works/W7223268308.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W7172441153","https://openalex.org/W7203826422"],"related_works":["https://openalex.org/W7127129856","https://openalex.org/W4410226386","https://openalex.org/W7127343200","https://openalex.org/W7159762450","https://openalex.org/W4417354648","https://openalex.org/W7171281610","https://openalex.org/W4414532505","https://openalex.org/W4406457468","https://openalex.org/W7139911988","https://openalex.org/W4401307128"],"abstract_inverted_index":{"Wireless":[0],"digital":[1],"twins":[2],"(DTs)":[3],"are":[4],"increasingly":[5],"integrated":[6],"with":[7,142,157],"artificial":[8],"intelligence":[9],"to":[10,57,111],"support":[11],"online":[12,62],"modeling,":[13],"prediction,":[14],"and":[15,37,53,79,101],"network":[16],"optimization.":[17],"However,":[18],"most":[19],"existing":[20],"approaches":[21],"perform":[22],"adaptation":[23,63],"within":[24],"predefined":[25],"learning":[26],"or":[27,94],"optimization":[28,55,81,103],"structures,":[29],"which":[30,74],"may":[31],"become":[32],"unsuitable":[33],"when":[34],"wireless":[35,59,120,162],"tasks":[36,140],"operating":[38],"conditions":[39],"change.":[40],"In":[41,105],"this":[42],"article,":[43],"we":[44,107],"investigate":[45],"the":[46,84,98,109,127,131,138,143,151],"integration":[47],"of":[48,153],"large":[49],"language":[50],"models":[51],"(LLMs)":[52],"Bayesian":[54],"(BO)":[56],"extend":[58],"DTs":[60],"from":[61],"towards":[64],"algorithm-level":[65],"self-evolution.":[66],"We":[67],"introduce":[68],"an":[69],"LLM-assisted":[70],"BO":[71,75,89,133,159],"framework":[72,110],"in":[73],"provides":[76],"a":[77,118],"stable":[78],"sample-efficient":[80],"backbone,":[82],"while":[83],"LLM":[85,155],"determines":[86],"whether":[87],"selected":[88],"components":[90],"should":[91],"be":[92],"retained":[93],"adapted":[95],"based":[96],"on":[97,137],"current":[99],"task":[100],"accumulated":[102],"experience.":[104],"simulations,":[106],"apply":[108],"Gaussian":[112],"process":[113],"(GP)":[114],"kernel":[115,129,146],"discovery":[116],"for":[117,160],"multi-user":[119],"beam-selection":[121],"problem.":[122],"Simulation":[123],"results":[124,149],"show":[125],"that":[126],"LLM-discovered":[128],"improves":[130],"final":[132],"performance":[134],"by":[135],"19.7%":[136],"training":[139],"compared":[141],"strongest":[144],"standard":[145],"baseline.":[147],"The":[148],"demonstrate":[150],"potential":[152],"combining":[154],"flexibility":[156],"structured":[158],"self-evolving":[161],"DTs.":[163]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-24T00:00:00"}