{"id":"https://openalex.org/W7141114648","doi":"https://doi.org/10.48550/arxiv.2603.25249","title":"Semantic-Aware Prefix Learning for Token-Efficient Image Generation","display_name":"Semantic-Aware Prefix Learning for Token-Efficient Image Generation","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141114648","doi":"https://doi.org/10.48550/arxiv.2603.25249"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25249","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.2603.25249","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130802234","display_name":"Qingfeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qingfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130742756","display_name":"Haoxian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haoxian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130785605","display_name":"Xu He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130796072","display_name":"Songlin Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Songlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101322892","display_name":"Zhixue Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Zhixue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130783293","display_name":"Xiaoqiang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130736347","display_name":"Pengfei Wan Guoqi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Pengfei Wan Guoqi","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":0.9503999948501587,"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":0.9503999948501587,"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/T11448","display_name":"Face recognition and analysis","score":0.007899999618530273,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0032999999821186066,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.7534999847412109},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.6151999831199646},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5698000192642212},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4611999988555908},{"id":"https://openalex.org/keywords/lexical-analysis","display_name":"Lexical analysis","score":0.4284999966621399},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4108999967575073},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.3684999942779541},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.3603000044822693}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7534999847412109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7384999990463257},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.6151999831199646},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5698000192642212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5435000061988831},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38510000705718994},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3560999929904938},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25249","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.2603.25249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25249","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visual":[0],"tokenizers":[1,21],"play":[2],"a":[3,66,76,89,130],"central":[4],"role":[5],"in":[6,38],"latent":[7,31,100,116,157],"image":[8],"generation":[9,121,162],"by":[10],"bridging":[11],"high-dimensional":[12],"images":[13],"and":[14,98,148,152],"tractable":[15],"generative":[16],"modeling.":[17],"However,":[18],"most":[19],"existing":[20],"are":[22,34],"still":[23],"trained":[24],"with":[25],"reconstruction-dominated":[26],"objectives,":[27],"which":[28,94],"often":[29],"yield":[30],"representations":[32],"that":[33,70,113,140,153],"only":[35],"weakly":[36],"grounded":[37,156],"high-level":[39],"semantics.":[40],"Recent":[41],"approaches":[42],"improve":[43],"semantic":[44,49,73,96],"alignment,":[45],"but":[46],"typically":[47],"treat":[48],"signals":[50],"as":[51],"auxiliary":[52],"regularization":[53],"rather":[54,122],"than":[55,123],"making":[56],"them":[57],"functionally":[58],"necessary":[59],"for":[60,120],"representation":[61],"learning.":[62],"We":[63],"propose":[64],"SMAP,":[65],"SeMantic-Aware":[67],"Prefix":[68],"tokenizer":[69],"injects":[71],"class-level":[72],"conditions":[74,97],"into":[75],"query-based":[77],"1D":[78],"tokenization":[79,150],"framework.":[80],"To":[81,111],"make":[82],"semantics":[83],"indispensable":[84],"during":[85],"training,":[86],"SMAP":[87,141],"introduces":[88],"tail":[90],"token":[91,109,166],"dropping":[92],"strategy,":[93],"forces":[95],"early":[99],"prefixes":[101],"to":[102],"bear":[103],"increasing":[104],"responsibility":[105],"under":[106,164],"progressively":[107],"reduced":[108],"budgets.":[110,167],"verify":[112],"the":[114],"resulting":[115],"space":[117,158],"is":[118],"useful":[119],"reconstruction":[124,144],"alone,":[125],"we":[126],"further":[127],"introduce":[128],"CARD,":[129],"hybrid":[131],"Causal":[132],"AutoRegressive--Diffusion":[133],"generator.":[134],"Extensive":[135],"experiments":[136],"on":[137],"ImageNet":[138],"show":[139],"consistently":[142],"improves":[143],"quality":[145],"across":[146],"discrete":[147],"continuous":[149],"settings,":[151],"its":[154],"semantically":[155],"yields":[159],"strong":[160],"downstream":[161],"performance":[163],"compact":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-28T00:00:00"}