{"id":"https://openalex.org/W7131425621","doi":"https://doi.org/10.48550/arxiv.2602.19157","title":"Facet-Level Persona Control by Trait-Activated Routing with Contrastive SAE for Role-Playing LLMs","display_name":"Facet-Level Persona Control by Trait-Activated Routing with Contrastive SAE for Role-Playing LLMs","publication_year":2026,"publication_date":"2026-02-22","ids":{"openalex":"https://openalex.org/W7131425621","doi":"https://doi.org/10.48550/arxiv.2602.19157"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.19157","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19157","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.2602.19157","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031751106","display_name":"Wenqiu Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Wenqiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102530845","display_name":"Zhen Wan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047823141","display_name":"Takahiro Komamizu","orcid":"https://orcid.org/0000-0002-3041-4330"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Komamizu, Takahiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126680354","display_name":"Ichiro Ide","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ide, Ichiro","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/T14074","display_name":"Persona Design and Applications","score":0.8529999852180481,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T14074","display_name":"Persona Design and Applications","score":0.8529999852180481,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.01360000018030405,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.011699999682605267,"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/persona","display_name":"Persona","score":0.8012999892234802},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6769999861717224},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5314000248908997},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.429500013589859},{"id":"https://openalex.org/keywords/personality","display_name":"Personality","score":0.40470001101493835},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.38499999046325684}],"concepts":[{"id":"https://openalex.org/C313442","wikidata":"https://www.wikidata.org/wiki/Q778556","display_name":"Persona","level":2,"score":0.8012999892234802},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6769999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514000296592712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5924999713897705},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C187288502","wikidata":"https://www.wikidata.org/wiki/Q641118","display_name":"Personality","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3982999920845032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39100000262260437},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.31130000948905945},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.19157","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19157","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.2602.19157","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19157","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":{"Personality":[0],"control":[1,87,181],"in":[2,61],"Role-Playing":[3],"Agents":[4],"(RPAs)":[5],"is":[6,101,187],"commonly":[7],"achieved":[8],"via":[9,24],"training-free":[10],"methods":[11],"that":[12,83,141,173],"inject":[13],"persona":[14,70,180],"descriptions":[15],"and":[16,40,50,67,120,130,149,160],"memory":[17],"through":[18],"prompts":[19],"or":[20,23],"retrieval-augmented":[21],"generation,":[22],"supervised":[25],"fine-tuning":[26],"(SFT)":[27],"on":[28,135],"persona-specific":[29],"corpora.":[30],"While":[31],"SFT":[32],"can":[33,58,178],"be":[34,59],"effective,":[35],"it":[36],"requires":[37],"persona-labeled":[38],"data":[39],"retraining":[41],"for":[42,107],"new":[43,97],"roles,":[44],"limiting":[45],"flexibility.":[46],"In":[47],"contrast,":[48],"prompt-":[49],"RAG-based":[51],"signals":[52],"are":[53,113],"easy":[54],"to":[55,65,103],"apply":[56],"but":[57],"diluted":[60],"long":[62],"dialogues,":[63],"leading":[64],"drifting":[66],"sometimes":[68],"inconsistent":[69],"behavior.":[71],"To":[72],"address":[73],"this,":[74],"we":[75],"propose":[76],"a":[77,124],"contrastive":[78],"Sparse":[79],"AutoEncoder":[80],"(SAE)":[81],"framework":[82],"learns":[84],"facet-level":[85],"personality":[86,132],"vectors":[88,112,177],"aligned":[89],"with":[90],"the":[91,116,142,168],"Big":[92],"Five":[93],"30-facet":[94],"model.":[95],"A":[96],"15,000-sample":[98],"leakage-controlled":[99],"corpus":[100],"constructed":[102],"provide":[104],"balanced":[105],"supervision":[106],"each":[108],"facet.":[109],"The":[110,163],"learned":[111],"integrated":[114],"into":[115],"model's":[117],"residual":[118],"space":[119],"dynamically":[121],"selected":[122],"by":[123],"trait-activated":[125],"routing":[126],"module,":[127],"enabling":[128],"precise":[129],"interpretable":[131],"steering.":[133],"Experiments":[134],"Large":[136],"Language":[137],"Models":[138],"(LLMs)":[139],"show":[140],"proposed":[143],"method":[144],"maintains":[145],"stable":[146],"character":[147],"fidelity":[148],"output":[150],"quality":[151],"across":[152],"contextualized":[153],"settings,":[154],"outperforming":[155],"Contrastive":[156],"Activation":[157],"Addition":[158],"(CAA)":[159],"prompt-only":[161],"baselines.":[162],"combined":[164],"SAE+Prompt":[165],"configuration":[166],"achieves":[167],"best":[169],"overall":[170],"performance,":[171],"confirming":[172],"contrastively":[174],"trained":[175],"latent":[176],"enhance":[179],"while":[182],"preserving":[183],"dialogue":[184],"coherence.":[185],"Dataset":[186],"available":[188],"at:":[189],"https://github.com/lunat5078/BigFive-Personality-Facets-Dataset":[190]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-02-26T00:00:00"}