{"id":"https://openalex.org/W7161774532","doi":"https://doi.org/10.48550/arxiv.2605.19457","title":"Generative Auto-Bidding with Unified Modeling and Exploration","display_name":"Generative Auto-Bidding with Unified Modeling and Exploration","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161774532","doi":"https://doi.org/10.48550/arxiv.2605.19457"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.19457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19457","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":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.2605.19457","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136569758","display_name":"Mingming Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Mingming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073092473","display_name":"Feiqing Zhuang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Feiqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136526572","display_name":"Na Li","orcid":"https://orcid.org/0000-0002-2852-8099"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Na","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065113355","display_name":"Shengjie Sun","orcid":"https://orcid.org/0009-0007-1519-6682"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Shengjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136518185","display_name":"Xiaowei Chen","orcid":"https://orcid.org/0000-0003-0906-6666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiaowei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108713708","display_name":"Junxiong Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Junxiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136577552","display_name":"Fei Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060902826","display_name":"Keping Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Keping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089307887","display_name":"Lixin Zou","orcid":"https://orcid.org/0000-0001-6755-871X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Lixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136545409","display_name":"Chenliang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenliang","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/T11182","display_name":"Auction Theory and Applications","score":0.5195000171661377,"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"}},"topics":[{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.5195000171661377,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05249999836087227,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.02630000002682209,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bidding","display_name":"Bidding","score":0.6586999893188477},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6159999966621399},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5787000060081482},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4875999987125397},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3962000012397766},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.38519999384880066},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.3797999918460846},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3361999988555908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7222999930381775},{"id":"https://openalex.org/C9233905","wikidata":"https://www.wikidata.org/wiki/Q3276328","display_name":"Bidding","level":2,"score":0.6586999893188477},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6159999966621399},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5787000060081482},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4733999967575073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41600000858306885},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3336000144481659},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32339999079704285},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C1525070","wikidata":"https://www.wikidata.org/wiki/Q2134714","display_name":"Real-time bidding","level":3,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.19457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19457","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":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.2605.19457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19457","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":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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"bidding":[1,19,105],"is":[2],"central":[3],"to":[4,40,101,131],"modern":[5],"digital":[6],"advertising.":[7],"Early":[8],"rule-based":[9],"methods":[10],"lacked":[11],"adaptability,":[12],"while":[13,121],"subsequent":[14],"Reinforcement":[15],"Learning":[16],"approaches":[17],"modeled":[18],"as":[20,137],"a":[21,54,83,91,97,138,190],"Markov":[22],"Decision":[23,98],"Process":[24],"but":[25],"struggled":[26],"with":[27,78,90],"long-term":[28],"dependencies.":[29],"Recent":[30],"generative":[31],"models":[32],"show":[33,196],"promise,":[34],"yet":[35],"they":[36],"lack":[37],"explicit":[38],"mechanisms":[39],"balance":[41],"exploration":[42,61,89,117,156],"and":[43,62,81,107,157,170,183,221,228],"safety,":[44],"relying":[45],"solely":[46],"on":[47,176,188],"action":[48,150],"perturbations":[49],"or":[50],"trajectory":[51],"guidance":[52],"without":[53],"safety":[55],"fallback.":[56,141],"This":[57],"results":[58],"in":[59,179],"inefficient":[60],"elevated":[63],"financial":[64],"risk":[65],"for":[66],"advertising":[67,193],"platforms.":[68],"To":[69],"address":[70],"this":[71],"gap,":[72],"we":[73],"propose":[74],"GUIDE":[75,95,197,208],"(Generative":[76],"Auto-Bidding":[77],"Unified":[79],"Modeling":[80],"Exploration),":[82],"framework":[84],"that":[85,167],"synergistically":[86],"integrates":[87],"directed":[88],"safe":[92,139],"fallback":[93],"mechanism.":[94],"employs":[96],"Transformer":[99],"(DT)":[100],"jointly":[102],"model":[103],"historical":[104],"actions":[106,136],"environmental":[108],"state":[109],"transitions.":[110],"A":[111],"Q-value":[112,143],"module":[113,144],"guides":[114],"the":[115,148],"DT's":[116],"via":[118],"regularization":[119],"constraints,":[120],"an":[122,163],"Inverse":[123],"Dynamics":[124],"Module":[125],"(IDM)":[126],"leverages":[127],"DT-predicted":[128],"future":[129],"states":[130],"infer":[132],"robust,":[133],"behaviorally":[134],"consistent":[135],"policy":[140],"The":[142],"then":[145],"adaptively":[146],"selects":[147],"final":[149],"between":[151],"these":[152,160],"two":[153],"options,":[154],"balancing":[155],"safety.":[158,171],"Together,":[159],"components":[161],"form":[162],"integrated":[164],"\"explore-safeguard-select\"":[165],"pipeline":[166],"unifies":[168],"efficiency":[169],"We":[172],"conduct":[173],"extensive":[174],"experiments":[175],"public":[177],"datasets,":[178],"simulated":[180],"auction":[181],"environments,":[182],"through":[184],"large-scale":[185],"online":[186],"deployment":[187],"Taobao,":[189],"leading":[191],"Chinese":[192],"platform.":[194],"Results":[195],"consistently":[198],"outperforms":[199],"state-of-the-art":[200],"baselines":[201],"across":[202],"all":[203],"scenarios.":[204],"In":[205],"real-world":[206],"deployment,":[207],"achieves":[209],"notable":[210],"gains:":[211],"+4.10%":[212],"ad":[213,216,219,223],"GMV,":[214],"+1.40%":[215],"clicks,":[217],"+1.66%":[218],"cost,":[220],"+3.52%":[222],"ROI,":[224],"demonstrating":[225],"its":[226],"effectiveness":[227],"strong":[229],"industrial":[230],"applicability.":[231]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-21T00:00:00"}
