{"id":"https://openalex.org/W7161987842","doi":"https://doi.org/10.48550/arxiv.2605.20408","title":"Spectral Souping: A Unified Framework for Online Preference Alignment","display_name":"Spectral Souping: A Unified Framework for Online Preference Alignment","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161987842","doi":"https://doi.org/10.48550/arxiv.2605.20408"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20408","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20408","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127866363","display_name":"Yinlam Chow","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chow, Yinlam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049376842","display_name":"Guy Tennenholtz","orcid":"https://orcid.org/0000-0002-2727-2351"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tennenholtz, Guy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001274018","display_name":"Ted Yun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun, Ted","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136707771","display_name":"James Harrison","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harrison, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136707719","display_name":"Arthur Gretton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gretton, Arthur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029374742","display_name":"Andr\u00e9 Barreto","orcid":"https://orcid.org/0000-0001-8533-1754"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barreto, Andre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136617372","display_name":"Bo Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.32589998841285706,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.32589998841285706,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.16599999368190765,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.09929999709129333,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/preference","display_name":"Preference","score":0.6413999795913696},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6036999821662903},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5834000110626221},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5482000112533569},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5407000184059143},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5027999877929688},{"id":"https://openalex.org/keywords/preference-elicitation","display_name":"Preference elicitation","score":0.4277999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7598999738693237},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6413999795913696},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6036999821662903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5875999927520752},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5834000110626221},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5450999736785889},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5407000184059143},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.4277999997138977},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20408","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.20408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20408","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"Learning":[1],"from":[2],"Human":[3],"Feedback":[4],"(RLHF)":[5],"effectively":[6],"aligns":[7],"Large":[8],"Language":[9],"Models":[10],"(LLMs)":[11],"with":[12],"aggregate":[13],"human":[14],"preferences":[15],"but":[16],"often":[17],"fails":[18],"to":[19,60,64,154],"address":[20],"the":[21,48,115],"diverse":[22],"and":[23,146],"conflicting":[24],"needs":[25],"of":[26,50,79],"individual":[27,155],"users.":[28],"To":[29],"overcome":[30],"this":[31],"issue,":[32],"we":[33,74],"introduce":[34],"Spectral":[35],"Souping,":[36],"a":[37,51,71,77,86,144],"unified":[38],"framework":[39],"for":[40,117,150],"efficient,":[41],"online":[42,92,119,127],"preference":[43,89,123,128],"alignment.":[44],"Our":[45],"contribution":[46],"is":[47,58],"discovery":[49],"universal":[52],"spectral":[53],"representation":[54],"within":[55],"LLMs,":[56],"which":[57],"proven":[59],"be":[61],"highly":[62],"amenable":[63],"model":[65,112],"merging.":[66],"This":[67],"theoretical":[68],"insight":[69],"enables":[70],"two-phase":[72],"methodology:":[73],"first":[75],"learn":[76],"basis":[78],"specialized":[80],"policies":[81,99],"offline,":[82],"each":[83],"focused":[84],"on":[85,126],"distinct,":[87],"fine-grained":[88],"dimension.":[90],"An":[91],"adaptation":[93,113],"algorithm":[94],"then":[95],"efficiently":[96],"``soups''":[97],"these":[98],"at":[100],"inference":[101],"time,":[102],"either":[103],"by":[104],"merging":[105],"their":[106],"outputs":[107],"or":[108],"parameters,":[109],"enabling":[110],"rapid":[111],"without":[114],"need":[116],"costly":[118],"retraining":[120],"w.r.t.":[121],"tailored":[122],"rewards.":[124],"Experiments":[125],"alignment":[129],"benchmarks":[130],"demonstrate":[131],"that":[132],"our":[133],"method":[134],"achieves":[135],"significant":[136],"performance":[137],"improvements":[138],"over":[139],"existing":[140],"state-of-the-art":[141],"approaches,":[142],"presenting":[143],"scalable":[145],"computationally":[147],"efficient":[148],"solution":[149],"dynamically":[151],"adapting":[152],"LLMs":[153],"user":[156],"preferences.":[157]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-22T00:00:00"}
