{"id":"https://openalex.org/W7164206780","doi":"https://doi.org/10.48550/arxiv.2606.10099","title":"Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion","display_name":"Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164206780","doi":"https://doi.org/10.48550/arxiv.2606.10099"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10099","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10099","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.2606.10099","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076950784","display_name":"Rafael Rivera Soto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soto, Rafael Rivera","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067651004","display_name":"Barry Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Barry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138322239","display_name":"Nicholas Andrews","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrews, Nicholas","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/T12380","display_name":"Authorship Attribution and Profiling","score":0.8903999924659729,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.8903999924659729,"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":0.043699998408555984,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.015300000086426735,"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/paraphrase","display_name":"Paraphrase","score":0.6535000205039978},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5934000015258789},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5758000016212463},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5314000248908997},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4708999991416931},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45590001344680786},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4429999887943268},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43209999799728394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7251999974250793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7131999731063843},{"id":"https://openalex.org/C2780922921","wikidata":"https://www.wikidata.org/wiki/Q255189","display_name":"Paraphrase","level":2,"score":0.6535000205039978},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5934000015258789},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5758000016212463},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5077999830245972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47749999165534973},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4429999887943268},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43209999799728394},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30979999899864197},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.28619998693466187},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10099","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10099","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.2606.10099","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10099","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":[{"display_name":"Reduced inequalities","score":0.7363833785057068,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"development":[2],"of":[3,34],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"has":[8,29],"raised":[9],"concerns":[10],"about":[11],"misuse":[12],"such":[13],"as":[14],"plagiarism,":[15],"misinformation,":[16],"and":[17,62,131,185],"automated":[18],"influence":[19],"operations,":[20],"motivating":[21],"the":[22,107,113,121,146,151,172],"need":[23],"for":[24,39,60,68,117],"robust":[25,43],"detectors.":[26,51],"Recent":[27],"work":[28],"shown":[30],"that":[31,47,73],"neural":[32],"representations":[33,123,174],"writing":[35],"style":[36,82,90,108,187],"are":[37,63],"effective":[38],"detection":[40,126],"and,":[41,149],"crucially,":[42],"to":[44,65,92,110,167,176],"adversarial":[45],"attacks":[46],"defeat":[48],"most":[49],"existing":[50],"However,":[52],"current":[53],"style-based":[54],"detectors":[55],"rely":[56],"on":[57,160,182,193],"authorship":[58,85,183],"labels":[59,86],"training,":[61],"limited":[64],"few-shot":[66,129,147],"inference":[67],"detection,":[69,171],"requiring":[70],"in-distribution":[71,161],"samples":[72],"may":[74],"not":[75],"always":[76],"be":[77],"available.":[78],"We":[79,119],"learn":[80],"discriminative":[81],"features":[83,115],"without":[84],"by":[87],"training":[88,105],"a":[89,101,128,132],"encoder":[91,103,109],"reconstruct":[93],"human-authored":[94],"text":[95],"from":[96],"its":[97],"machine-generated":[98],"paraphrase;":[99],"freezing":[100],"semantic":[102],"during":[104],"biases":[106],"capture":[111],"only":[112],"non-semantic":[114],"needed":[116],"reconstruction.":[118],"evaluate":[120],"learned":[122,173],"via":[124],"two":[125],"strategies:":[127],"detector":[130],"zero-shot":[133,152],"DeepSVDD-based":[134],"detector.":[135],"Across":[136],"benchmarks,":[137],"our":[138],"method":[139],"matches":[140],"or":[141],"outperforms":[142],"all":[143],"baselines":[144],"in":[145,150],"setting":[148],"regime,":[153],"is":[154],"competitive":[155,180],"with":[156],"fully":[157],"supervised":[158],"classifiers":[159],"test":[162],"data":[163],"while":[164],"generalizing":[165],"better":[166],"unseen":[168,177],"LLMs.":[169],"Beyond":[170],"generalize":[175],"tasks,":[178],"achieving":[179],"performance":[181],"verification":[184],"fine-grained":[186],"discrimination":[188],"despite":[189],"never":[190],"being":[191],"trained":[192],"either":[194],"objective.":[195]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}
