{"id":"https://openalex.org/W7136882874","doi":"https://doi.org/10.48550/arxiv.2603.12760","title":"HIFICL: High-Fidelity In-Context Learning for Multimodal Tasks","display_name":"HIFICL: High-Fidelity In-Context Learning for Multimodal Tasks","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136882874","doi":"https://doi.org/10.48550/arxiv.2603.12760"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12760","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12760","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.2603.12760","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129484149","display_name":"Xiaoyu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129496267","display_name":"Yuhang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kang, Xuanshuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Xuanshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Luo, Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129399693","display_name":"Fangqi Lou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lou, Fangqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101705096","display_name":"Xiaohua Wu","orcid":"https://orcid.org/0000-0002-2420-9568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Xiaohua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129422090","display_name":"Zihan Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Zihan","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.38260000944137573,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.38260000944137573,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.12099999934434891,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.057999998331069946,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.580299973487854},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.542900025844574},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5051000118255615},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.47690001130104065},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4715999960899353},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.44859999418258667},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4169999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7944999933242798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.597000002861023},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.580299973487854},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.542900025844574},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.47690001130104065},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43779999017715454},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4169999957084656},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12760","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12760","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.2603.12760","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12760","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In-Context":[0,74],"Learning":[1,75],"(ICL)":[2],"is":[3,25,148],"a":[4,13,43,63,91,100,104,114,125],"significant":[5],"paradigm":[6],"for":[7,18,107],"Large":[8],"Multimodal":[9],"Models":[10],"(LMMs),":[11],"using":[12],"few":[14],"in-context":[15],"demonstrations":[16,38],"(ICDs)":[17],"new":[19],"task":[20],"adaptation.":[21],"However,":[22],"its":[23],"performance":[24],"sensitive":[26],"to":[27,77,97],"demonstration":[28],"configurations":[29],"and":[30,51,109,112],"computationally":[31],"expensive.":[32],"Mathematically,":[33],"the":[34,47,52,68,81],"influence":[35],"of":[36,46,86,93,127],"these":[37],"can":[39],"be":[40],"decomposed":[41],"into":[42],"dynamic":[44],"mixture":[45],"standard":[48],"attention":[49],"output":[50],"context":[53],"values.":[54],"Current":[55],"approximation":[56,140],"methods":[57,141],"simplify":[58],"this":[59,122],"process":[60],"by":[61,67],"learning":[62],"\"shift":[64],"vector\".":[65],"Inspired":[66],"exact":[69],"decomposition,":[70],"we":[71],"introduce":[72],"High-Fidelity":[73],"(HIFICL)":[76],"more":[78],"faithfully":[79],"model":[80],"ICL":[82],"mechanism.":[83],"HIFICL":[84],"consists":[85],"three":[87],"key":[88],"components:":[89],"1)":[90],"set":[92],"\"virtual":[94],"key-value":[95],"pairs\"":[96],"act":[98],"as":[99],"learnable":[101],"context,":[102],"2)":[103],"low-rank":[105],"factorization":[106],"stable":[108],"regularized":[110],"training,":[111],"3)":[113],"simple":[115],"end-to-end":[116],"training":[117],"objective.":[118],"From":[119],"another":[120],"perspective,":[121],"mechanism":[123],"constitutes":[124],"form":[126],"context-aware":[128],"Parameter-Efficient":[129],"Fine-Tuning":[130],"(PEFT).":[131],"Extensive":[132],"experiments":[133],"show":[134],"that":[135],"HiFICL":[136],"consistently":[137],"outperforms":[138],"existing":[139],"on":[142],"several":[143],"multimodal":[144],"benchmarks.":[145],"The":[146],"code":[147],"available":[149],"at":[150],"https://github.com/bbbandari/HiFICL.":[151]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-17T00:00:00"}