{"id":"https://openalex.org/W7148308326","doi":"https://doi.org/10.48550/arxiv.2604.01220","title":"Universal YOCO for Efficient Depth Scaling","display_name":"Universal YOCO for Efficient Depth Scaling","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148308326","doi":"https://doi.org/10.48550/arxiv.2604.01220"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01220","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.2604.01220","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132801922","display_name":"Yutao Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yutao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132824881","display_name":"Li Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132825697","display_name":"Tianzhu Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Tianzhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132827889","display_name":"Shaohan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Shaohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132800052","display_name":"Jianyong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132815845","display_name":"Furu Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Furu","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/T10028","display_name":"Topic Modeling","score":0.4339999854564667,"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/T10028","display_name":"Topic Modeling","score":0.4339999854564667,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.2167000025510788,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.043699998408555984,"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/scalability","display_name":"Scalability","score":0.7064999938011169},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6675000190734863},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.6348999738693237},{"id":"https://openalex.org/keywords/recursion","display_name":"Recursion (computer science)","score":0.5910000205039978},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.4691999852657318},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.45669999718666077}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7064999938011169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6902999877929688},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6675000190734863},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.6348999738693237},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.5910000205039978},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.490200012922287},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.487199991941452},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4185999929904938},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C189783530","wikidata":"https://www.wikidata.org/wiki/Q352090","display_name":"CPU cache","level":3,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01220","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.2604.01220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01220","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":{"The":[0,108],"rise":[1],"of":[2,13,158],"test-time":[3],"scaling":[4,135],"has":[5],"remarkably":[6],"boosted":[7],"the":[8,52,70,87,156],"reasoning":[9],"and":[10,36,117,134,151,161],"agentic":[11],"proficiency":[12],"Large":[14],"Language":[15],"Models":[16],"(LLMs).":[17],"Yet,":[18],"standard":[19],"Transformers":[20],"struggle":[21],"to":[22,59,90],"scale":[23],"inference-time":[24],"compute":[25],"efficiently,":[26],"as":[27],"conventional":[28],"looping":[29],"strategies":[30],"suffer":[31],"from":[32],"high":[33],"computational":[34],"overhead":[35],"a":[37,61,75,97,112,165],"KV":[38,115],"cache":[39,116],"that":[40,78,101,144,155],"inflates":[41],"alongside":[42],"model":[43],"depth.":[44],"We":[45],"present":[46],"Universal":[47,76],"YOCO":[48,53,71,103,109],"(YOCO-U),":[49],"which":[50],"combines":[51],"decoder-decoder":[54],"architecture":[55,110],"with":[56,126],"recursive":[57,162],"computation":[58,163],"achieve":[60],"synergistic":[62],"effect":[63],"greater":[64],"than":[65],"either":[66],"alone.":[67],"Built":[68],"on":[69],"framework,":[72],"YOCO-U":[73,130,145],"implements":[74],"Self-Decoder":[77],"performs":[79],"multiple":[80],"iterations":[81],"via":[82],"parameter":[83],"sharing,":[84],"while":[85,120,137],"confining":[86],"iterative":[88],"process":[89],"shallow,":[91],"efficient-attention":[92,159],"layers.":[93],"This":[94],"combination":[95],"yields":[96],"favorable":[98],"capability-efficiency":[99],"tradeoff":[100],"neither":[102],"nor":[104],"recursion":[105,122],"achieves":[106],"independently.":[107],"provides":[111],"constant":[113],"global":[114],"linear":[118],"pre-filling,":[119],"partial":[121],"enhances":[123],"representational":[124],"depth":[125],"limited":[127],"overhead.":[128],"Together,":[129],"improves":[131],"token":[132],"utility":[133],"behavior":[136],"maintaining":[138],"efficient":[139],"inference.":[140],"Empirical":[141],"results":[142],"confirm":[143],"remains":[146],"highly":[147],"competitive":[148],"in":[149],"general":[150],"long-context":[152],"benchmarks,":[153],"demonstrating":[154],"integration":[157],"architectures":[160],"is":[164],"promising":[166],"direction":[167],"for":[168],"scalable":[169],"LLMs.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}
