{"id":"https://openalex.org/W7156801236","doi":"https://doi.org/10.48550/arxiv.2604.23540","title":"Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization","display_name":"Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization","publication_year":2026,"publication_date":"2026-04-26","ids":{"openalex":"https://openalex.org/W7156801236","doi":"https://doi.org/10.48550/arxiv.2604.23540"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23540","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23540","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":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.2604.23540","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134765721","display_name":"Haosen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haosen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134820509","display_name":"Wenshuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wenshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134783829","display_name":"Lei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134772131","display_name":"Shaofeng Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Shaofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134795123","display_name":"Haozhe Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Haozhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134750629","display_name":"Yutao Yue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Yutao","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":0.9383000135421753,"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":0.9383000135421753,"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":0.010300000198185444,"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":0.007699999958276749,"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/oracle","display_name":"Oracle","score":0.546500027179718},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.49459999799728394},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.42590001225471497},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.39169999957084656},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.3750999867916107},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3725999891757965},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.3682999908924103},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.36739999055862427},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.36559998989105225}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.691100001335144},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49459999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4763000011444092},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3750999867916107},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.3571000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.251800000667572},{"id":"https://openalex.org/C92995354","wikidata":"https://www.wikidata.org/wiki/Q5165499","display_name":"Continuous optimization","level":4,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23540","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23540","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":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.2604.23540","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23540","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":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":{"Text-to-image":[0],"diffusion":[1],"models":[2],"have":[3],"achieved":[4],"remarkable":[5],"generative":[6],"capabilities,":[7],"yet":[8],"accurately":[9],"aligning":[10],"complex":[11,126],"textual":[12],"prompts":[13],"with":[14],"synthesized":[15],"layouts":[16],"remains":[17],"an":[18],"ongoing":[19],"challenge.":[20],"In":[21],"these":[22,79,100],"models,":[23],"the":[24,35,62,67,90,132,138,147,157],"initial":[25],"Gaussian":[26,69,159],"noise":[27,48,111,148],"acts":[28],"as":[29,113],"a":[30,107,119,151,216],"critical":[31],"structural":[32,135],"seed":[33],"dictating":[34],"macroscopic":[36],"layout.":[37],"Recent":[38],"online":[39],"optimization":[40,115,143,219],"and":[41,65,86,167,185,211],"search":[42],"methods":[43,80],"attempt":[44],"to":[45,49,118,140],"refine":[46],"this":[47],"enhance":[50],"text-image":[51],"alignment.":[52],"However,":[53],"relying":[54,124],"on":[55,125],"unconstrained":[56],"Euclidean":[57],"gradient":[58],"ascent":[59],"mathematically":[60,155],"inflates":[61],"latent":[63],"norm":[64,165],"destroys":[66],"standard":[68],"prior,":[70],"causing":[71],"severe":[72],"visual":[73],"artifacts":[74],"like":[75],"color":[76],"over-saturation.":[77],"Furthermore,":[78],"suffer":[81],"from":[82],"inefficient":[83],"semantic":[84,183,207],"routing":[85],"easily":[87],"fall":[88],"into":[89],"``reward":[91],"hacking''":[92],"trap":[93],"of":[94,123],"external":[95,127],"proxy":[96],"models.":[97,191],"To":[98],"address":[99],"intertwined":[101],"bottlenecks,":[102],"we":[103,129,154],"propose":[104],"Oracle":[105,179],"Noise,":[106],"zero-shot":[108],"framework":[109],"reframing":[110],"initialization":[112],"semantic-driven":[114],"strictly":[116,149],"confined":[117],"Riemannian":[120],"hypersphere.":[121],"Instead":[122],"parsers,":[128],"directly":[130],"identify":[131],"most":[133],"impactful":[134],"words":[136],"in":[137],"prompt":[139],"efficiently":[141],"route":[142],"energy.":[144],"By":[145],"updating":[146],"along":[150],"spherical":[152],"path,":[153],"preserve":[156],"original":[158],"distribution.":[160],"This":[161],"geometric":[162],"constraint":[163],"eliminates":[164],"inflation":[166],"unlocks":[168],"aggressive":[169],"step":[170],"sizes":[171],"for":[172],"rapid":[173],"convergence.":[174],"Extensive":[175],"experiments":[176],"demonstrate":[177],"that":[178],"Noise":[180],"significantly":[181],"accelerates":[182],"alignment":[184,208],"achieves":[186],"superior":[187],"aesthetics":[188],"without":[189],"black-box":[190],"It":[192],"completely":[193],"mitigates":[194],"Euclidean-induced":[195],"degradation,":[196],"establishing":[197],"state-of-the-art":[198],"performance":[199],"across":[200],"human":[201],"preference":[202],"metrics":[203],"(e.g.,":[204],"HPSv2,":[205],"ImageReward),":[206],"(CLIP":[209],"Score),":[210],"sample":[212],"diversity,":[213],"all":[214],"within":[215],"strict":[217],"2-second":[218],"budget.":[220]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}