{"id":"https://openalex.org/W7154615311","doi":"https://doi.org/10.48550/arxiv.2604.13073","title":"OmniTrace: A Unified Framework for Generation-Time Attribution in Omni-Modal LLMs","display_name":"OmniTrace: A Unified Framework for Generation-Time Attribution in Omni-Modal LLMs","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7154615311","doi":"https://doi.org/10.48550/arxiv.2604.13073"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13073","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,"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.2604.13073","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113392281","display_name":"Qianqi Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Qianqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693807","display_name":"Yichen Guo","orcid":"https://orcid.org/0009-0000-0721-4763"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006379171","display_name":"Ching-Chen Kuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuo, Ching-Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133789733","display_name":"Shan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Shan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133766200","display_name":"Hang Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133774591","display_name":"Yang Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133811898","display_name":"Xin Eric Wang","orcid":"https://orcid.org/0009-0001-0734-7993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xin Eric","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.616100013256073,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.616100013256073,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.17949999868869781,"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":0.053300000727176666,"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/attribution","display_name":"Attribution","score":0.8303999900817871},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.44589999318122864},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.41749998927116394},{"id":"https://openalex.org/keywords/authorship-attribution","display_name":"Authorship attribution","score":0.4066999852657318},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.3937000036239624},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.37389999628067017},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.37040001153945923},{"id":"https://openalex.org/keywords/affordance","display_name":"Affordance","score":0.35920000076293945}],"concepts":[{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.8303999900817871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6371999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4480000138282776},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C3020202489","wikidata":"https://www.wikidata.org/wiki/Q2032038","display_name":"Authorship attribution","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.37950000166893005},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3319000005722046},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C2777026412","wikidata":"https://www.wikidata.org/wiki/Q2684591","display_name":"Statement (logic)","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.29100000858306885},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2554999887943268},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13073","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,"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.2604.13073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13073","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,"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":0.7858871221542358,"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":{"Modern":[0],"multimodal":[1,56,109],"large":[2],"language":[3,188],"models":[4,53],"(MLLMs)":[5],"generate":[6],"fluent":[7],"responses":[8],"from":[9],"interleaved":[10],"text,":[11],"image,":[12],"audio,":[13,139],"and":[14,45,63,117,124,135,140,151,157],"video":[15,141],"inputs.":[16],"However,":[17],"identifying":[18],"which":[19],"input":[20],"sources":[21,121],"support":[22],"each":[23,105],"generated":[24,106],"statement":[25],"remains":[26],"an":[27],"open":[28],"challenge.":[29],"Existing":[30],"attribution":[31,68,147,166,173],"methods":[32],"are":[33],"primarily":[34],"designed":[35],"for":[36,184],"classification":[37],"settings,":[38],"fixed":[39],"prediction":[40],"targets,":[41],"or":[42,93,130],"single-modality":[43],"architectures,":[44],"do":[46],"not":[47],"naturally":[48],"extend":[49],"to":[50,108],"autoregressive,":[51],"decoder-only":[52],"performing":[54],"open-ended":[55],"generation.":[57],"We":[58],"introduce":[59],"OmniTrace,":[60],"a":[61,70,81,175,181],"lightweight":[62],"model-agnostic":[64],"framework":[65],"that":[66,84,144,171],"formalizes":[67],"as":[69,90,174],"generation-time":[71,177],"tracing":[72,178],"problem":[73,179],"over":[74],"the":[75],"causal":[76],"decoding":[77],"process.":[78],"OmniTrace":[79],"provides":[80,180],"unified":[82],"protocol":[83],"converts":[85],"arbitrary":[86],"token-level":[87],"signals":[88,112],"such":[89],"attention":[91],"weights":[92],"gradient-based":[94],"scores":[95],"into":[96,113],"coherent":[97,126],"span-level,":[98],"cross-modal":[99],"explanations":[100,153],"during":[101],"decoding.":[102],"It":[103],"traces":[104],"token":[107],"inputs,":[110],"aggregates":[111],"semantically":[114],"meaningful":[115],"spans,":[116],"selects":[118],"concise":[119],"supporting":[120],"through":[122],"confidence-weighted":[123],"temporally":[125],"aggregation,":[127],"without":[128],"retraining":[129],"supervision.":[131],"Evaluations":[132],"on":[133],"Qwen2.5-Omni":[134],"MiniCPM-o-4.5":[136],"across":[137,163],"visual,":[138],"tasks":[142],"demonstrate":[143],"generation-aware":[145],"span-level":[146],"produces":[148],"more":[149],"stable":[150],"interpretable":[152],"than":[154],"naive":[155],"self-attribution":[156],"embedding-based":[158],"baselines,":[159],"while":[160],"remaining":[161],"robust":[162],"multiple":[164],"underlying":[165],"signals.":[167],"Our":[168],"results":[169],"suggest":[170],"treating":[172],"structured":[176],"scalable":[182],"foundation":[183],"transparency":[185],"in":[186],"omni-modal":[187],"models.":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-17T00:00:00"}