{"id":"https://openalex.org/W7166837085","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2098","title":"Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning","display_name":"Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166837085","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2098"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.2098","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2098","pdf_url":"https://aclanthology.org/2026.findings-acl.2098.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.2098.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139837635","display_name":"Zichuan Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zichuan Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139782810","display_name":"Xian Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xian Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139778663","display_name":"Guojing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guojing Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139784917","display_name":"Yejing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yejing Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139824878","display_name":"Yijun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yijun Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139718936","display_name":"Zhao Zihao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao Zihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139818237","display_name":"Luo Yixuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo Yixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139734819","display_name":"Hanyu Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hanyu Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139774770","display_name":"Yefeng Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yefeng Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139815688","display_name":"Xiangyu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangyu Zhao","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82665709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"42286","last_page":"42302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.18050000071525574,"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":0.18050000071525574,"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":0.15549999475479126,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.05719999969005585,"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/action","display_name":"Action (physics)","score":0.3260999917984009},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.31450000405311584},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.313400000333786},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3100000023841858},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.2831999957561493},{"id":"https://openalex.org/keywords/non-monotonic-logic","display_name":"Non-monotonic logic","score":0.2824999988079071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6324999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.489300012588501},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.2098","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2098","pdf_url":"https://aclanthology.org/2026.findings-acl.2098.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.2098","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2098","pdf_url":"https://aclanthology.org/2026.findings-acl.2098.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1363456933","display_name":null,"funder_award_id":"62502404","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8299714314","display_name":null,"funder_award_id":"9229503","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"}],"funders":[{"id":"https://openalex.org/F4320307285","display_name":"Impact Fund","ror":"https://ror.org/00jb20j87"},{"id":"https://openalex.org/F4320309893","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23"},{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166837085.pdf","grobid_xml":"https://content.openalex.org/works/W7166837085.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,98],"large":[3,54],"language":[4,57],"models":[5,16,58],"(LLMs)":[6],"have":[7],"catalyzed":[8],"the":[9,39,71,100,106,133,163],"rise":[10],"of":[11,83,132],"reasoningintensive":[12],"inference":[13],"paradigms,":[14],"where":[15],"perform":[17],"explicit":[18],"step-by-step":[19],"reasoning":[20,65,85,102,124,138],"before":[21],"generating":[22,79],"final":[23,107],"answers.While":[24],"such":[25],"approaches":[26],"improve":[27],"answer":[28],"quality":[29],"and":[30,55,60,104,111,139],"interpretability,":[31],"they":[32],"incur":[33],"substantial":[34],"computational":[35,69,147],"overhead":[36],"due":[37],"to":[38,62,91,153,172],"prolonged":[40],"generation":[41,141],"sequences.In":[42],"this":[43],"paper,":[44],"we":[45],"propose":[46],"Tandem,":[47],"a":[48,75,80,93,115],"novel":[49],"collaborative":[50],"framework":[51],"that":[52,119,144],"synergizes":[53],"small":[56],"(LLMs":[59],"SLMs)":[61],"achieve":[63],"high-quality":[64],"with":[66],"significantly":[67],"reduced":[68],"cost.Specifically,":[70],"LLM":[72,155],"serves":[73],"as":[74],"strategic":[76],"coordinator,":[77],"efficiently":[78],"compact":[81],"set":[82],"critical":[84],"insights.These":[86],"insights":[87],"are":[88],"then":[89],"used":[90],"guide":[92],"smaller,":[94],"more":[95],"efficient":[96],"SLM":[97],"executing":[99],"full":[101],"process":[103],"delivering":[105],"response.To":[108],"balance":[109],"efficiency":[110],"reliability,":[112],"Tandem":[113,145],"introduces":[114],"cost-aware":[116],"termination":[117],"mechanism":[118],"adaptively":[120],"determines":[121],"when":[122],"sufficient":[123],"guidance":[125],"has":[126],"been":[127],"accumulated,":[128],"enabling":[129],"early":[130],"stopping":[131],"LLM's":[134],"generation.Experiments":[135],"on":[136,167],"mathematical":[137],"code":[140],"benchmarks":[142],"demonstrate":[143],"reduces":[146],"costs":[148],"by":[149],"approximately":[150],"40%":[151],"compared":[152],"standalone":[154],"reasoning,":[156],"while":[157],"achieving":[158],"superior":[159],"or":[160],"competitive":[161],"performance.Furthermore,":[162],"sufficiency":[164],"classifier":[165],"trained":[166],"one":[168],"domain":[169],"transfers":[170],"effectively":[171],"others":[173],"without":[174],"retraining.":[175]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
