{"id":"https://openalex.org/W7162794643","doi":"https://doi.org/10.48550/arxiv.2605.29829","title":"OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation","display_name":"OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162794643","doi":"https://doi.org/10.48550/arxiv.2605.29829"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29829","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.29829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137340464","display_name":"Haochen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137367498","display_name":"\u8d75\u53ef Zhao Ke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014046253","display_name":"Mengyuan Ma","orcid":"https://orcid.org/0000-0002-4441-7663"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Mengyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137395174","display_name":"Xingyu Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137329962","display_name":"Xiangfeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiangfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137331008","display_name":"Hong Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.2076999992132187,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.2076999992132187,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.16220000386238098,"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/T10028","display_name":"Topic Modeling","score":0.06700000166893005,"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/reuse","display_name":"Reuse","score":0.6837999820709229},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.4909999966621399},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.38989999890327454},{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.37540000677108765},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.37139999866485596},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3702999949455261},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3596999943256378},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3549000024795532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7562999725341797},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6065999865531921},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.37139999866485596},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3702999949455261},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.35350000858306885},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2944999933242798},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C3019612716","wikidata":"https://www.wikidata.org/wiki/Q730920","display_name":"Problem solver","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C49848784","wikidata":"https://www.wikidata.org/wiki/Q131714","display_name":"Archetype","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29829","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.29829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29829","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8427198529243469}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Leveraging":[0],"Large":[1],"Language":[2],"Models":[3],"(LLMs)":[4],"to":[5,34,47,49],"automatically":[6],"formulate":[7],"and":[8,45,62,69,96,142,152,177],"solve":[9],"optimization":[10,67],"problems":[11,78],"from":[12],"natural":[13],"language":[14],"has":[15],"emerged":[16],"as":[17],"an":[18,58],"efficient":[19],"paradigm":[20],"for":[21,66],"automated":[22],"optimization.":[23],"However,":[24],"existing":[25,116],"methods":[26],"still":[27],"exhibit":[28],"limited":[29],"generalization:":[30],"they":[31],"are":[32,179],"sensitive":[33],"superficial":[35],"narrative":[36],"variations,":[37],"reuse":[38],"experience":[39],"mainly":[40],"at":[41,181],"the":[42,120,172],"case":[43],"level,":[44],"struggle":[46],"adapt":[48],"shifted":[50],"or":[51,118],"emerging":[52],"problem":[53,140],"types.":[54],"We":[55],"propose":[56],"OptSkills,":[57],"archetype-centric":[59],"skill":[60,121,164],"learning":[61,165],"reasoning":[63],"agent":[64],"system":[65,76,128],"modeling":[68,94],"solving.":[70],"To":[71,87,110],"improve":[72,88,111],"robust":[73],"generalization,":[74,90,113],"our":[75],"clusters":[77],"by":[79,161],"their":[80],"underlying":[81],"archetypes":[82],"rather":[83],"than":[84],"surface":[85],"narratives.":[86],"in-distribution":[89],"it":[91,114,155,168],"explores":[92],"diverse":[93,139],"paradigms":[95],"solver":[97],"configurations":[98],"within":[99],"each":[100],"cluster,":[101],"then":[102],"distills":[103],"successful":[104],"trajectories":[105],"into":[106],"reusable":[107],"workflow-level":[108],"skills.":[109],"out-of-distribution":[112],"refines":[115],"skills":[117,178],"expands":[119],"library":[122],"using":[123],"newly":[124],"obtained":[125],"trajectories.":[126],"Our":[127],"achieves":[129,156],"a":[130,148],"state-of-the-art":[131],"micro-averaged":[132],"accuracy":[133],"of":[134],"68.27%":[135],"on":[136,146,166,171],"datasets":[137],"encompassing":[138],"types":[141],"scenarios.":[143],"In":[144],"addition,":[145],"MIPLIB-NL,":[147],"highly":[149],"challenging":[150],"large-scale":[151],"high-dimensional":[153],"benchmark,":[154],"26.91%":[157],"accuracy,":[158],"outperforming":[159],"DeepSeek-V3.2-Thinking":[160],"4.53%.":[162],"After":[163],"Nano-CO,":[167],"reaches":[169],"72.79%":[170],"OOD":[173],"NLCO":[174],"benchmark.":[175],"Code":[176],"available":[180],"https://github.com/fujiwaranoM0kou/OptSkills.":[182]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-30T00:00:00"}
