{"id":"https://openalex.org/W7147681597","doi":"https://doi.org/10.48550/arxiv.2603.27665","title":"Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling","display_name":"Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling","publication_year":2026,"publication_date":"2026-03-29","ids":{"openalex":"https://openalex.org/W7147681597","doi":"https://doi.org/10.48550/arxiv.2603.27665"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27665","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27665","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.2603.27665","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132718776","display_name":"Minh-Tuan Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tran, Minh-Tuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132675802","display_name":"Xuan-May Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Xuan-May","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101460496","display_name":"Quan Tran","orcid":"https://orcid.org/0009-0005-7360-9639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Quan Hung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021790939","display_name":"Mehrtash Harandi","orcid":"https://orcid.org/0000-0002-6937-6300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harandi, Mehrtash","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036447132","display_name":"Dinh Phung","orcid":"https://orcid.org/0000-0002-9977-8247"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phung, Dinh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132673926","display_name":"Trung Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Trung","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5132718776"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8342999815940857,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8342999815940857,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12290","display_name":"Human Motion and Animation","score":0.01940000057220459,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11349","display_name":"Music Technology and Sound Studies","score":0.016200000420212746,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/generative-grammar","display_name":"Generative grammar","score":0.8041999936103821},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6585999727249146},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6093999743461609},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5713000297546387},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4668999910354614}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.8041999936103821},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73580002784729},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6585999727249146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6276999711990356},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5713000297546387},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4124000072479248},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.376800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27665","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27665","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.2603.27665","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27665","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"generative":[1,31,51,108,131],"models,":[2],"such":[3],"as":[4],"diffusion":[5],"and":[6,96,110,116],"auto-regressive":[7],"networks,":[8],"are":[9,68],"inherently":[10],"static,":[11],"relying":[12],"on":[13,54],"a":[14,46,126],"fixed":[15],"set":[16],"of":[17],"pretrained":[18,72],"parameters":[19],"to":[20,33,86,136],"handle":[21],"all":[22],"inputs.":[23],"In":[24],"contrast,":[25],"humans":[26],"flexibly":[27],"adapt":[28,135],"their":[29],"internal":[30],"representations":[32],"each":[34,137],"perceptual":[35],"or":[36,80],"imaginative":[37],"context.":[38],"Inspired":[39],"by":[40],"this":[41],"capability,":[42],"we":[43],"introduce":[44],"Composer,":[45],"new":[47,127],"paradigm":[48,128],"for":[49,129],"adaptive":[50],"modeling":[52],"based":[53],"test-time":[55,117],"instance-specific":[56],"parameter":[57,62,122],"composition.":[58],"Composer":[59,102,124],"generates":[60],"input-conditioned":[61],"adaptations":[63],"at":[64],"inference":[65],"time,":[66],"which":[67],"injected":[69],"into":[70],"the":[71],"model's":[73],"weights,":[74],"enabling":[75],"per-input":[76],"specialization":[77],"without":[78],"fine-tuning":[79],"retraining.":[81],"Adaptation":[82],"occurs":[83],"once":[84],"prior":[85],"multi-step":[87],"generation,":[88],"yielding":[89],"higher-quality,":[90],"context-aware":[91],"outputs":[92],"with":[93],"minimal":[94],"computational":[95],"memory":[97],"overhead.":[98],"Experiments":[99],"show":[100],"that":[101,133],"substantially":[103],"improves":[104],"performance":[105],"across":[106],"diverse":[107],"models":[109,115,132],"use":[111],"cases,":[112],"including":[113],"lightweight/quantized":[114],"scaling.":[118],"By":[119],"leveraging":[120],"input-aware":[121],"composition,":[123],"establishes":[125],"designing":[130],"dynamically":[134],"input,":[138],"moving":[139],"beyond":[140],"static":[141],"parameterization.":[142]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}