{"id":"https://openalex.org/W7166837621","doi":"https://doi.org/10.48550/arxiv.2606.30919","title":"Budget-Adaptive Routing: Skipping the Weak When the Strong Answers Anyway","display_name":"Budget-Adaptive Routing: Skipping the Weak When the Strong Answers Anyway","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166837621","doi":"https://doi.org/10.48550/arxiv.2606.30919"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.30919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30919","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.2606.30919","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139743800","display_name":"Wei Geng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078639938","display_name":"Nitinder Mohan","orcid":"https://orcid.org/0000-0001-6198-016X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohan, Nitinder","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027075213","display_name":"J\u00f6rg Ott","orcid":"https://orcid.org/0000-0001-8311-8036"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ott, J\u00f6rg","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/T10036","display_name":"Advanced Neural Network Applications","score":0.22110000252723694,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.22110000252723694,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.14110000431537628,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.11879999935626984,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/estimator","display_name":"Estimator","score":0.755299985408783},{"id":"https://openalex.org/keywords/router","display_name":"Router","score":0.6218000054359436},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5230000019073486},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.48829999566078186},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.482699990272522},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.4458000063896179},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.41269999742507935},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.390500009059906}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.755299985408783},{"id":"https://openalex.org/C2775896111","wikidata":"https://www.wikidata.org/wiki/Q642560","display_name":"Router","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5250999927520752},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5230000019073486},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.482699990272522},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3601999878883362},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C102408133","wikidata":"https://www.wikidata.org/wiki/Q5156350","display_name":"Competitive analysis","level":3,"score":0.3010999858379364},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2976999878883362},{"id":"https://openalex.org/C2983435990","wikidata":"https://www.wikidata.org/wiki/Q22725","display_name":"Network routing","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.30919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30919","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.2606.30919","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30919","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":{"Edge-cloud":[0],"inference":[1],"collaborations":[2],"are":[3,50,199],"often":[4],"designed":[5],"with":[6,194],"a":[7,73],"routing":[8,33,91],"estimator":[9,34,76],"that":[10,49,58,89,106],"decides":[11],"whether":[12],"to":[13,23,160],"offload":[14,67,128],"each":[15],"frame":[16],"from":[17,93],"weak":[18,37,41,84],"models":[19,25],"at":[20,86,166,190,201],"the":[21,27,32,36,40,66,83,97,114,141,150,179,185],"edge":[22],"stronger":[24,177],"in":[26],"cloud.":[28],"Existing":[29],"systems":[30],"place":[31],"after":[35],"detector,":[38],"so":[39],"forward":[42],"pass":[43],"still":[44],"runs":[45],"even":[46],"on":[47],"frames":[48],"later":[51],"offloaded.":[52],"In":[53],"this":[54,59],"paper,":[55],"we":[56,71,104,119],"argue":[57],"weak-conditioned":[60,101,110],"design":[61],"can":[62],"be":[63],"suboptimal":[64],"when":[65],"budget":[68,129],"varies.":[69],"First,":[70],"present":[72],"competitive":[74],"weak-skipping":[75,108],"(0.153":[77],"GFLOPs,":[78],"about":[79],"29x":[80],"lighter":[81],"than":[82,178],"detector":[85],"4.49":[87],"GFLOPs)":[88],"extracts":[90],"signal":[92],"raw":[94],"pixels,":[95],"outperforming":[96,171],"common":[98],"after-weak":[99],"placement":[100,111],"baselines.":[102],"Second,":[103],"show":[105],"neither":[107],"nor":[109],"dominates":[112],"across":[113,149],"full":[115],"operating":[116,151,192],"curve,":[117],"and":[118],"propose":[120],"budget-adaptive":[121,138],"routing,":[122],"which":[123],"selects":[124],"between":[125],"them":[126],"by":[127,158],"via":[130],"two":[131],"offline-tuned":[132],"thresholds.":[133],"On":[134],"PASCAL":[135],"VOC,":[136],"our":[137],"router":[139],"traces":[140],"upper":[142],"accuracy":[143],"envelope":[144],"of":[145],"both":[146],"fixed":[147],"placements":[148],"range.":[152],"Our":[153],"method":[154],"reduces":[155],"per-frame":[156],"latency":[157],"up":[159],"19.1":[161],"ms":[162],"(about":[163],"30%":[164],"lower":[165],"rho":[167],"=":[168],"0.9).":[169],"Besides":[170],"SOTA":[172],"methods,":[173],"it":[174],"is":[175],"surprisingly":[176],"strong":[180,186],"model":[181],"(+1.7":[182],"pp":[183],"over":[184],"model's":[187],"peak":[188],"mAP)":[189],"some":[191],"points":[193],"far":[195],"less":[196],"compute.":[197],"Artifacts":[198],"available":[200],"https://github.com/ViGeng/bgt-ada":[202]},"counts_by_year":[],"updated_date":"2026-07-02T06:18:51.028212","created_date":"2026-07-02T00:00:00"}
