{"id":"https://openalex.org/W7167614987","doi":"https://doi.org/10.48550/arxiv.2607.04389","title":"Decentralized Aggregation of LLM Predictions via Wagering Mechanisms","display_name":"Decentralized Aggregation of LLM Predictions via Wagering Mechanisms","publication_year":2026,"publication_date":"2026-07-05","ids":{"openalex":"https://openalex.org/W7167614987","doi":"https://doi.org/10.48550/arxiv.2607.04389"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.04389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04389","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.2607.04389","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140177649","display_name":"Yuhong Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yuhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140211940","display_name":"David M. Pennock","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pennock, David M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100783583","display_name":"Xintong Wang","orcid":"https://orcid.org/0000-0002-3049-2910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xintong","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":0.22050000727176666,"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":0.22050000727176666,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.10620000213384628,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.09279999881982803,"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/information-aggregation","display_name":"Information aggregation","score":0.6496000289916992},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.6047999858856201},{"id":"https://openalex.org/keywords/incentive-compatibility","display_name":"Incentive compatibility","score":0.5651999711990356},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5317999720573425},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5260000228881836},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.5009999871253967},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.4975000023841858},{"id":"https://openalex.org/keywords/private-information-retrieval","display_name":"Private information retrieval","score":0.40799999237060547}],"concepts":[{"id":"https://openalex.org/C2986046992","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Information aggregation","level":2,"score":0.6496000289916992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.647599995136261},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.6047999858856201},{"id":"https://openalex.org/C91810955","wikidata":"https://www.wikidata.org/wiki/Q7731670","display_name":"Incentive compatibility","level":3,"score":0.5651999711990356},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5260000228881836},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.5009999871253967},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.4975000023841858},{"id":"https://openalex.org/C99221444","wikidata":"https://www.wikidata.org/wiki/Q1532069","display_name":"Private information retrieval","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37700000405311584},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32580000162124634},{"id":"https://openalex.org/C2778207910","wikidata":"https://www.wikidata.org/wiki/Q397610","display_name":"Agr\u00e9gation","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29739999771118164},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.29490000009536743},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.04389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04389","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.2607.04389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04389","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":[{"id":"https://metadata.un.org/sdg/8","score":0.649785578250885,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"It":[0],"is":[1,110],"increasingly":[2],"common":[3],"to":[4,16,21,32,37,45,112],"aggregate":[5],"predictions":[6,72],"from":[7],"multiple":[8],"LLMs,":[9],"each":[10,62],"with":[11],"domain":[12],"expertise":[13],"or":[14],"access":[15,36],"private":[17,39],"tools":[18],"and":[19,41,67,71,118,143,157,163,186],"data,":[20],"improve":[22],"collective":[23],"prediction":[24,66,98],"performance.":[25],"In":[26],"decentralized":[27,124,179],"settings,":[28],"aggregation":[29,58,171,182],"weights":[30],"need":[31],"be":[33],"determined":[34],"without":[35,129],"models'":[38],"information":[40],"should":[42],"remain":[43],"robust":[44],"strategic":[46],"reporting.":[47],"We":[48,133],"propose":[49],"a":[50,65,68,81,147],"family":[51],"of":[52,97,126],"advantage-aligned":[53,181],"wagering":[54],"mechanisms":[55],"for":[56,151],"LLM":[57],"(WALLA),":[59],"in":[60,173],"which":[61],"model":[63],"reports":[64],"learned":[69],"wager,":[70],"are":[73],"aggregated":[74],"using":[75],"wagers":[76],"as":[77],"weights.":[78],"WALLA":[79,168],"introduces":[80],"leave-one-out":[82],"baseline":[83],"into":[84],"the":[85,107,113,152],"net":[86],"payout":[87],"function,":[88],"yielding":[89],"three":[90],"desirable":[91],"properties:":[92],"(1)":[93],"dominant-strategy":[94],"incentive":[95],"compatibility":[96],"under":[99],"arbitrary":[100],"belief":[101],"structure,":[102],"(2)":[103],"advantage--wager":[104],"alignment,":[105],"where":[106],"optimal":[108,131],"wager":[109,121,127],"proportional":[111],"model's":[114],"expected":[115],"score":[116],"advantage,":[117],"(3)":[119],"prediction-agnostic":[120],"optimization,":[122],"enabling":[123],"learning":[125],"policies":[128],"requiring":[130],"predictions.":[132],"further":[134],"instantiate":[135],"two":[136],"mechanism":[137],"variants":[138],"that":[139,167],"trade":[140],"off":[141],"normality":[142],"no-arbitrage":[144],"while":[145,176],"maintaining":[146],"bounded":[148],"worst-case":[149],"deficit":[150],"mechanism.":[153],"Experiments":[154],"on":[155],"question-answering":[156],"forecasting":[158],"benchmarks":[159],"across":[160],"heterogeneous":[161],"models":[162],"private-information":[164],"settings":[165],"show":[166],"matches":[169],"centralized":[170],"methods":[172],"predictive":[174],"performance,":[175],"simultaneously":[177],"achieving":[178],"learning,":[180],"weights,":[183],"uncertainty":[184],"awareness,":[185],"incentive-compatible":[187],"prediction.":[188]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}
