{"id":"https://openalex.org/W7161697739","doi":"https://doi.org/10.48550/arxiv.2605.16786","title":"Lever: Speculative LLM Inference on Smartphones","display_name":"Lever: Speculative LLM Inference on Smartphones","publication_year":2026,"publication_date":"2026-05-16","ids":{"openalex":"https://openalex.org/W7161697739","doi":"https://doi.org/10.48550/arxiv.2605.16786"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.16786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067496010","display_name":"Tuowei Wang","orcid":"https://orcid.org/0009-0006-8272-8151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tuowei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136482777","display_name":"Fengzu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Fengzu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069456711","display_name":"\u5b59\u5ef6\u51e1","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yanfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136479878","display_name":"Wei Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136469561","display_name":"Ju Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Ju","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/T14347","display_name":"Big Data and Digital Economy","score":0.11599999666213989,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14347","display_name":"Big Data and Digital Economy","score":0.11599999666213989,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11181","display_name":"Advanced Data Storage Technologies","score":0.06419999897480011,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.05869999900460243,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/inference","display_name":"Inference","score":0.7160000205039978},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6116999983787537},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5296000242233276},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4916999936103821},{"id":"https://openalex.org/keywords/speculation","display_name":"Speculation","score":0.4000999927520752},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.3862999975681305},{"id":"https://openalex.org/keywords/dram","display_name":"Dram","score":0.3831000030040741},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.36309999227523804},{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.34060001373291016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8353000283241272},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7160000205039978},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6116999983787537},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4916999936103821},{"id":"https://openalex.org/C47941915","wikidata":"https://www.wikidata.org/wiki/Q107885","display_name":"Speculation","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C7366592","wikidata":"https://www.wikidata.org/wiki/Q1255620","display_name":"Dram","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34790000319480896},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2858999967575073},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C91317487","wikidata":"https://www.wikidata.org/wiki/Q1530412","display_name":"Simultaneity","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C156884757","wikidata":"https://www.wikidata.org/wiki/Q798554","display_name":"Backtracking","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C2779194965","wikidata":"https://www.wikidata.org/wiki/Q11711607","display_name":"Ixabepilone","level":5,"score":0.2606000006198883},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C107524782","wikidata":"https://www.wikidata.org/wiki/Q40164","display_name":"Lever","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.16786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"Large":[0],"language":[1],"models":[2,13],"(LLMs)":[3],"are":[4],"increasingly":[5],"needed":[6],"for":[7,53,86,103],"interactive":[8],"mobile":[9,120,155],"applications,":[10],"but":[11],"high-quality":[12],"exceed":[14],"the":[15,37,113,185],"limited":[16,90],"DRAM":[17],"available":[18],"on":[19,108],"smartphones.":[20,109],"Flash":[21],"storage":[22],"can":[23,60],"hold":[24],"larger":[25,66],"models,":[26],"yet":[27],"flash-backed":[28,105,189],"inference":[29,107,167,177],"is":[30,49],"slow":[31],"because":[32],"autoregressive":[33],"decoding":[34,48,118],"repeatedly":[35],"invokes":[36],"target":[38,68],"model":[39,59,69],"and":[40,82,93,131,178,190],"incurs":[41],"costly":[42],"I/O.":[43],"We":[44,97],"observe":[45],"that":[46,164],"speculative":[47,117,182],"a":[50,56,65],"natural":[51],"fit":[52],"this":[54],"setting:":[55],"small":[57],"draft":[58],"remain":[61],"in":[62],"DRAM,":[63],"while":[64],"flash-resident":[67],"verifies":[70],"multiple":[71],"candidate":[72],"tokens":[73],"per":[74],"invocation.":[75],"However,":[76],"existing":[77],"methods":[78],"assume":[79],"server-class":[80],"accelerators":[81],"fail":[83],"to":[84,144,158],"account":[85],"prolonged":[87],"I/O":[88],"latency,":[89],"computation":[91],"parallelism,":[92],"irregular":[94],"speculation":[95,152],"execution.":[96],"present":[98],"Lever,":[99],"an":[100,129,170],"end-to-end":[101],"system":[102],"efficient":[104],"LLM":[106,192],"Lever":[110,165],"jointly":[111],"optimizes":[112],"three":[114],"stages":[115],"of":[116,172],"under":[119],"constraints.":[121],"For":[122,135,148],"drafting,":[123],"it":[124,137,150],"builds":[125],"token":[126],"trees":[127],"using":[128],"I/O-":[130],"compute-aware":[132],"gain-cost":[133],"objective.":[134],"verification,":[136],"prunes":[138],"low-value":[139],"branches":[140],"through":[141],"early-exit":[142],"prediction":[143],"reduce":[145],"target-model":[146],"computation.":[147],"execution,":[149],"maps":[151],"efficiently":[153],"across":[154],"CPU-NPU":[156],"hardware":[157],"improve":[159],"utilization.":[160],"Comprehensive":[161],"evaluations":[162],"show":[163],"reduces":[166],"latency":[168,186],"by":[169],"average":[171],"2.93x":[173],"over":[174,180],"baseline":[175],"flash-offloaded":[176],"1.50x":[179],"conventional":[181],"decoding,":[183],"narrowing":[184],"gap":[187],"between":[188],"memory-resident":[191],"inference.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}