{"id":"https://openalex.org/W7147690584","doi":"https://doi.org/10.48550/arxiv.2603.26891","title":"Strategic Candidacy in Generative AI Arenas","display_name":"Strategic Candidacy in Generative AI Arenas","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7147690584","doi":"https://doi.org/10.48550/arxiv.2603.26891"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26891","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.2603.26891","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121830582","display_name":"Chris Hays","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hays, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428089","display_name":"Ran Li","orcid":"https://orcid.org/0000-0002-8787-352X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Rachel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099447651","display_name":"Bailey Flanigan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Flanigan, Bailey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052541789","display_name":"Manish Raghavan","orcid":"https://orcid.org/0000-0002-4155-8145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghavan, Manish","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.17649999260902405,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.17649999260902405,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.1703999936580658,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.08990000188350677,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7491999864578247},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7429999709129333},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6833999752998352},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5562000274658203},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5447999835014343},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5382999777793884},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5210999846458435},{"id":"https://openalex.org/keywords/mean-reciprocal-rank","display_name":"Mean reciprocal rank","score":0.5145000219345093}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7491999864578247},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7429999709129333},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6833999752998352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6152999997138977},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5562000274658203},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5447999835014343},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5151000022888184},{"id":"https://openalex.org/C44083865","wikidata":"https://www.wikidata.org/wiki/Q3853443","display_name":"Mean reciprocal rank","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4648999869823456},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C2779172887","wikidata":"https://www.wikidata.org/wiki/Q184316","display_name":"PageRank","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.3100999891757965},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29159998893737793},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2743000090122223},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2506999969482422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26891","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.2603.26891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26891","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"AI":[0],"arenas,":[1],"which":[2,109],"rank":[3,64,177,203],"generative":[4],"models":[5,21,50,132,148,189],"from":[6,33,99,113,133],"pairwise":[7,134],"preferences":[8],"of":[9,20,25,54,65,81,157,186],"users,":[10],"are":[11,31,199],"a":[12,38,127,172],"popular":[13],"method":[14],"for":[15,130],"measuring":[16],"the":[17,23,56,63,75,79,82,100,169,194,219],"relative":[18],"performance":[19],"in":[22,74,94,168],"course":[24],"their":[26,66,117,146,176,187,204],"organic":[27],"use.":[28],"Because":[29],"rankings":[30,144,152],"computed":[32],"noisy":[34],"preferences,":[35],"there":[36],"is":[37,119,165,221],"concern":[39],"that":[40,141,162,171,196,217],"model":[41,158,197],"producers":[42,110,142,198],"can":[43,70,111],"exploit":[44],"this":[45,85,163],"randomness":[46],"by":[47,89,179],"submitting":[48,114,184],"many":[49],"(e.g.,":[51],"multiple":[52],"variants":[53],"essentially":[55],"same":[57],"model)":[58],"and":[59,77,93,149,224],"thereby":[60],"artificially":[61],"improve":[62,175],"top":[67],"models.":[68],"This":[69],"lead":[71],"to":[72,97,120,153,193,201,227],"degradations":[73],"quality,":[76],"therefore":[78],"usefulness,":[80],"ranking.":[83],"In":[84,212],"paper,":[86],"we":[87,215],"begin":[88],"establishing,":[90],"both":[91],"theoretically":[92],"simulations":[95],"calibrated":[96],"data":[98],"platform":[101],"Arena":[102],"(formerly":[103],"LMArena,":[104],"Chatbot":[105],"Arena),":[106],"conditions":[107],"under":[108,231],"benefit":[112],"clones":[115],"when":[116],"goal":[118],"be":[121],"ranked":[122],"highly.":[123],"We":[124,160],"then":[125],"propose":[126],"new":[128],"mechanism":[129,164,220],"ranking":[131,210,228],"comparisons,":[135],"called":[136],"You-Rank-We-Rank":[137],"(YRWR).":[138],"It":[139],"requires":[140],"submit":[143],"over":[145],"own":[147,205],"uses":[150],"these":[151],"correct":[154],"statistical":[155],"estimates":[156],"quality.":[159],"prove":[161],"approximately":[166,222],"clone-robust,":[167],"sense":[170],"producer":[173,232],"cannot":[174],"much":[178],"doing":[180],"anything":[181],"other":[182],"than":[183],"each":[185],"unique":[188],"exactly":[190],"once.":[191],"Moreover,":[192],"extent":[195],"able":[200],"correctly":[202],"models,":[206],"YRWR":[207],"improves":[208],"overall":[209],"accuracy.":[211],"further":[213],"simulations,":[214],"show":[216],"indeed":[218],"clone-robust":[223],"quantify":[225],"improvements":[226],"accuracy,":[229],"even":[230],"misranking.":[233]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-02T00:00:00"}
