{"id":"https://openalex.org/W7161554937","doi":"https://doi.org/10.48550/arxiv.2605.16107","title":"Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection","display_name":"Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161554937","doi":"https://doi.org/10.48550/arxiv.2605.16107"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16107","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.2605.16107","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136436427","display_name":"Chenwang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Chenwang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136359706","display_name":"Yiuming Cheung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheung, Yiuming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136358365","display_name":"Bo Ram Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133688958","display_name":"Shuhai Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuhai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136404234","display_name":"Defu Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Defu","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/T11147","display_name":"Misinformation and Its Impacts","score":0.6606000065803528,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.6606000065803528,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.07699999958276749,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.06889999657869339,"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/inference","display_name":"Inference","score":0.6403999924659729},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6158999800682068},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5952000021934509},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.4943999946117401},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3971000015735626},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.37959998846054077},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.3686000108718872},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3179999887943268},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3077000081539154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483999729156494},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6403999924659729},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6158999800682068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6011999845504761},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5952000021934509},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.4943999946117401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4715999960899353},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3377000093460083},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C2776552730","wikidata":"https://www.wikidata.org/wiki/Q189656","display_name":"Disinformation","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C2776240099","wikidata":"https://www.wikidata.org/wiki/Q327018","display_name":"Interrogation","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16107","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.2605.16107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16107","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine-generated":[0],"texts":[1],"(MGTs)":[2],"pose":[3],"risks":[4],"such":[5],"as":[6],"disinformation":[7],"and":[8,59,98,102,166,182,191],"phishing,":[9],"underscoring":[10],"the":[11,70,78,82,90,94,162,167],"need":[12],"for":[13,118,122],"reliable":[14],"detection.":[15,120],"Metric-based":[16],"methods,":[17],"which":[18],"extract":[19],"statistically":[20],"distinguishable":[21],"features":[22],"of":[23,56,81,93],"MGTs,":[24],"are":[25,34],"often":[26],"more":[27],"practical":[28],"than":[29],"complex":[30],"model-based":[31],"methods":[32,47],"that":[33,134,149],"prone":[35],"to":[36],"overfitting.":[37],"Given":[38],"their":[39,57,100],"diverse":[40],"designs,":[41],"we":[42,87,109,125,143,160],"first":[43],"place":[44],"representative":[45],"metric-based":[46],"within":[48],"a":[49,53,64,111,129,145,173],"unified":[50],"framework,":[51],"enabling":[52],"clear":[54],"assessment":[55],"advantages":[58],"limitations.":[60],"Our":[61],"analysis":[62],"identifies":[63],"core":[65],"challenge":[66],"across":[67,185],"these":[68,107],"methods:":[69],"token-level":[71,95,136],"detection":[72,96],"score":[73,97,157,165],"is":[74],"easily":[75],"biased":[76],"by":[77],"inherent":[79],"randomness":[80],"MGTs":[83],"generation":[84],"process.":[85],"Then,":[86],"theoretically":[88],"derive":[89],"multi-hop":[91],"transitions":[92],"explore":[99],"local":[101,123,163],"global":[103,141,168],"relations.":[104],"Based":[105],"on":[106],"findings,":[108],"propose":[110],"multi-level":[112,175],"contextual":[113,156],"token":[114],"relation":[115],"modeling":[116],"framework":[117],"MGT":[119],"Specifically,":[121],"relations,":[124,142],"model":[126],"them":[127],"through":[128],"lightweight":[130],"Markov-informed":[131],"calibration":[132],"module":[133,148],"refines":[135],"evidence":[137],"before":[138],"aggregation.":[139],"For":[140],"introduce":[144],"rule-support":[146,169],"reasoning":[147,170],"uses":[150],"explicit":[151],"logical":[152],"rules":[153],"derived":[154],"from":[155],"statistics.":[158],"Finally,":[159],"combine":[161],"calibrated":[164],"signal":[171],"in":[172],"joint":[174],"inference":[176],"framework.":[177],"Extensive":[178],"experiments":[179],"show":[180],"broad":[181],"substantial":[183],"improvements":[184],"various":[186],"real-world":[187],"scenarios,":[188],"including":[189],"cross-LLM":[190],"cross-domain":[192],"settings,":[193],"with":[194],"low":[195],"computational":[196],"overhead.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-19T00:00:00"}
