{"id":"https://openalex.org/W7160853806","doi":"https://doi.org/10.48550/arxiv.2605.07182","title":"Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control","display_name":"Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160853806","doi":"https://doi.org/10.48550/arxiv.2605.07182"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07182","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.07182","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089955412","display_name":"Ali Taghibakhshi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taghibakhshi, Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135836383","display_name":"Ruisi Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Ruisi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135876803","display_name":"Saurav Muralidharan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muralidharan, Saurav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084832562","display_name":"Sharath Turuvekere Sreenivas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sreenivas, Sharath Turuvekere","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135855414","display_name":"Aditya Vavre","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vavre, Aditya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063510148","display_name":"Ameya Sunil Mahabaleshwarkar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahabaleshwarkar, Ameya Sunil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135851126","display_name":"Bilal Kartal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kartal, Bilal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135831866","display_name":"Sheldon Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Sheldon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135885372","display_name":"Marcin Chochowski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chochowski, Marcin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135848716","display_name":"Zijia Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135901078","display_name":"Akhiad Bercovich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bercovich, Akhiad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135862269","display_name":"Ran Zilberstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zilberstein, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135831600","display_name":"Ran El-Yaniv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El-Yaniv, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061409282","display_name":"Yonatan Geifman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geifman, Yonatan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135900315","display_name":"Daniel Korzekwa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Korzekwa, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135846261","display_name":"Yoshi Suhara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suhara, Yoshi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124283176","display_name":"Oluwatobi Olabiyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olabiyi, Oluwatobi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135890633","display_name":"Ashwath Aithal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aithal, Ashwath","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011019569","display_name":"Nima Tajbakhsh","orcid":"https://orcid.org/0000-0001-8614-4811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tajbakhsh, Nima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135840905","display_name":"Pavlo Molchanov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Molchanov, Pavlo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":20,"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":0.446399986743927,"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":0.446399986743927,"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":0.09709999710321426,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.08529999852180481,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.7735999822616577},{"id":"https://openalex.org/keywords/star","display_name":"Star (game theory)","score":0.5734000205993652},{"id":"https://openalex.org/keywords/elasticity","display_name":"Elasticity (physics)","score":0.4927999973297119},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45159998536109924},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4032000005245209},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.38749998807907104},{"id":"https://openalex.org/keywords/lens","display_name":"Lens (geology)","score":0.3276999890804291},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.3127000033855438}],"concepts":[{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.7735999822616577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5924999713897705},{"id":"https://openalex.org/C2780897414","wikidata":"https://www.wikidata.org/wiki/Q7600592","display_name":"Star (game theory)","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C121854251","wikidata":"https://www.wikidata.org/wiki/Q62932","display_name":"Elasticity (physics)","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4090999960899353},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C15336307","wikidata":"https://www.wikidata.org/wiki/Q1766051","display_name":"Lens (geology)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C157486923","wikidata":"https://www.wikidata.org/wiki/Q1376436","display_name":"String (physics)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C160343418","wikidata":"https://www.wikidata.org/wiki/Q185256","display_name":"Rigidity (electromagnetism)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C1306188","wikidata":"https://www.wikidata.org/wiki/Q4060687","display_name":"Nested loop join","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07182","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.07182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07182","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":"article"},"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":{"Training":[0],"a":[1,37,48,62,74,104,164,203,211],"family":[2],"of":[3,56,77,82,89,93,198],"large":[4],"language":[5],"models":[6,191],"(LLMs),":[7],"either":[8],"from":[9,172,208],"scratch":[10,209],"or":[11,193],"via":[12,61,137,236,249],"iterative":[13],"compression,":[14],"is":[15],"prohibitively":[16],"expensive":[17],"and":[18,117,130,142,181,201,210,232,256],"inefficient,":[19],"requiring":[20],"separate":[21],"training":[22,68,187],"runs":[23],"for":[24,112],"each":[25,113],"model":[26,52,239],"in":[27],"the":[28,54,80,87,125,149,158,222],"family.":[29],"In":[30],"this":[31],"paper,":[32],"we":[33,153,177],"introduce":[34],"Star":[35,70,101,119,155,244],"Elastic,":[36],"novel":[38,105],"LLM":[39],"post-training":[40,64],"method":[41],"that":[42,108,260],"adds":[43],"N":[44],"nested":[45,135,190,254],"submodels":[46,111,136],"to":[47,157,228,246],"given":[49],"parent":[50],"reasoning":[51,114],"using":[53],"compute":[55],"one":[57],"run":[58],"(N-fold":[59],"savings)":[60],"single":[63],"job.":[65],"Beyond":[66],"reducing":[67],"costs,":[69],"Elastic":[71,102,120,151,156,245],"also":[72],"addresses":[73],"fundamental":[75],"limitation":[76],"efficient":[78],"reasoning:":[79],"rigidity":[81],"static":[83],"architectures,":[84],"which":[85],"forces":[86],"allocation":[88],"constant":[90],"resources":[91],"regardless":[92],"token":[94],"difficulty.":[95],"By":[96],"unlocking":[97],"elastic":[98,218,258],"budget":[99,219],"control,":[100],"enables":[103],"inference":[106],"scheme":[107],"uses":[109],"different":[110],"phase":[115],"(thinking":[116],"answering).":[118],"supports":[121],"(1)":[122],"nesting":[123],"along":[124],"SSM,":[126],"embedding":[127],"channel,":[128],"MoE,":[129],"FFN":[131],"axes,":[132],"(2)":[133],"learning":[134],"an":[138],"end-to-end":[139],"trainable":[140],"router,":[141],"(3)":[143],"curriculum-based":[144],"knowledge":[145],"distillation.":[146],"Building":[147],"on":[148,167],"Nemotron":[150,160,173],"framework,":[152],"apply":[154],"NVIDIA":[159],"Nano":[161,174],"models,":[162],"with":[163,185],"particular":[165],"focus":[166],"hybrid":[168],"Mixture-of-Experts":[169],"(MoE)":[170],"architectures:":[171],"v3":[175],"(30B/3.6A),":[176],"generate":[178],"23B":[179],"(2.8A)":[180],"12B":[182],"(2.0A)":[183],"variants":[184],"160B":[186],"tokens.":[188],"All":[189],"match":[192],"outperform":[194],"independently":[195],"trained":[196],"baselines":[197],"comparable":[199],"size":[200],"achieve":[202],"360x":[204],"reduction":[205,213],"versus":[206],"pretraining":[207],"7x":[212],"over":[214],"state-of-the-art":[215],"compression.":[216],"Crucially,":[217],"control":[220],"advances":[221],"accuracy-latency":[223],"Pareto":[224],"frontier,":[225],"achieving":[226],"up":[227],"16%":[229],"higher":[230],"accuracy":[231],"1.9x":[233],"lower":[234],"latency":[235],"dynamic":[237],"per-phase":[238],"selection.":[240],"We":[241],"further":[242],"extend":[243],"quantized":[247],"regimes":[248],"Quantization-Aware":[250],"Distillation":[251],"(QAD),":[252],"producing":[253],"NVFP4":[255],"FP8":[257],"checkpoints":[259],"preserve":[261],"zero-shot":[262],"slicing":[263],"while":[264],"delivering":[265],"smaller":[266],"deployment":[267],"footprints.":[268]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-12T00:00:00"}