{"id":"https://openalex.org/W7163191529","doi":"https://doi.org/10.48550/arxiv.2606.01803","title":"OctoT2I: A Self-Evolving Agentic Text-to-Image Router","display_name":"OctoT2I: A Self-Evolving Agentic Text-to-Image Router","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163191529","doi":"https://doi.org/10.48550/arxiv.2606.01803"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01803","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":"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.2606.01803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137652326","display_name":"Xu Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137656885","display_name":"Bin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051231286","display_name":"Gehui Li","orcid":"https://orcid.org/0000-0003-4147-6876"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Gehui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033398332","display_name":"Yule Duan","orcid":"https://orcid.org/0000-0002-2505-8730"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Yule","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137633347","display_name":"Ronggang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ronggang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137702197","display_name":"Jian Zhang","orcid":"https://orcid.org/0009-0001-0725-8958"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jian","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.29440000653266907,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.29440000653266907,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.23600000143051147,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.22349999845027924,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6897000074386597},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5734000205993652},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5465999841690063},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.512499988079071},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.40849998593330383},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3968999981880188},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3479999899864197},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3352999985218048}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7491000294685364},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6897000074386597},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5465999841690063},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4731000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4625999927520752},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C2775896111","wikidata":"https://www.wikidata.org/wiki/Q642560","display_name":"Router","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C123197309","wikidata":"https://www.wikidata.org/wiki/Q2882343","display_name":"Multi-armed bandit","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01803","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":"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.2606.01803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01803","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":"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":[{"display_name":"Affordable and clean energy","score":0.5529563426971436,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,152],"explosive":[1],"growth":[2],"of":[3,59,82],"Text-to-Image":[4],"(T2I)":[5],"models,":[6],"from":[7,19,40,117],"large-scale":[8],"versions":[9],"to":[10,26],"lightweight,":[11],"real-time":[12],"ones,":[13],"now":[14],"faces":[15],"diminishing":[16],"marginal":[17],"returns":[18],"single-model":[20],"scaling.":[21],"Agentic":[22],"T2I":[23,37,76],"methods":[24,38],"emerged":[25],"alleviate":[27],"this":[28],"bottleneck":[29],"by":[30,112,119],"using":[31],"multiple":[32],"models.":[33],"However,":[34],"existing":[35],"agentic":[36,71],"suffer":[39],"three":[41],"key":[42],"challenges:":[43],"reliance":[44],"on":[45,103,176],"expensive":[46],"handcrafted":[47],"priors":[48],"or":[49],"human":[50,129],"annotations,":[51],"rigid":[52],"single-path":[53],"decision":[54],"mechanisms,":[55],"and":[56,85,106,141,184,200,203],"a":[57,69,79,90,113,180,185],"neglect":[58],"inference":[60,86,182],"efficiency.":[61,87,201],"To":[62],"address":[63],"these":[64],"challenges,":[65],"we":[66],"introduce":[67],"OctoT2I,":[68],"novel":[70,121],"framework":[72],"that":[73,95,170],"reformulates":[74],"the":[75,98,190],"task":[77],"as":[78],"joint":[80],"optimization":[81],"generation":[83],"quality":[84],"OctoT2I":[88,171],"implements":[89],"stateful,":[91],"multi-round":[92],"routing":[93],"strategy":[94,109],"adaptively":[96],"selects":[97],"most":[99],"suitable":[100],"tool":[101],"based":[102],"its":[104],"knowledge":[105,114],"memory.":[107],"This":[108,124],"is":[110],"enabled":[111],"base":[115],"built":[116],"scratch":[118],"our":[120],"Self-Evolving":[122],"Mechanism.":[123],"mechanism,":[125],"which":[126],"requires":[127],"no":[128],"supervision,":[130],"first":[131],"autonomously":[132],"defines":[133],"foundational":[134],"Conceptual":[135],"Dimensions":[136],"(eg,":[137],"style,":[138],"color,":[139],"count)":[140],"then":[142],"intelligently":[143],"explores":[144],"their":[145],"combinations":[146],"via":[147],"an":[148,195],"iterative\"":[149],"Propose--Solve--Evaluate--Learn\"(PSEL)":[150],"loop.":[151],"PSEL":[153],"loop":[154],"efficiently":[155],"discovers":[156],"each":[157],"tool's":[158],"capability":[159],"frontier,":[160],"driving":[161],"continuous":[162],"improvement":[163],"without":[164],"external":[165],"guidance.":[166],"Extensive":[167],"experiments":[168],"demonstrate":[169],"achieves":[172],"competitive":[173],"performance":[174,199],"(0.96)":[175],"GenEval":[177],"while":[178],"delivering":[179],"90.3%":[181],"speedup":[183],"56.6%":[186],"energy-efficiency":[187],"gain":[188],"over":[189],"leading":[191],"baseline":[192],"(Flow-GRPO),":[193],"striking":[194],"exceptional":[196],"balance":[197],"between":[198],"Code":[202],"models":[204],"will":[205],"be":[206],"made":[207],"available.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}