{"id":"https://openalex.org/W7159684199","doi":"https://doi.org/10.48550/arxiv.2604.27660","title":"From Context to Skills: Can Language Models Learn from Context Skillfully?","display_name":"From Context to Skills: Can Language Models Learn from Context Skillfully?","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159684199","doi":"https://doi.org/10.48550/arxiv.2604.27660"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27660","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.27660","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134994630","display_name":"Shuzheng Si","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Shuzheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134964339","display_name":"Haozhe Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Haozhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134951662","display_name":"Yu Lei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134991183","display_name":"Qingyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134962074","display_name":"Dingwei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Dingwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039766108","display_name":"Zhitong Wang","orcid":"https://orcid.org/0000-0001-9523-7163"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134987790","display_name":"Zhenhailong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhenhailong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076427086","display_name":"Kangyang Luo","orcid":"https://orcid.org/0009-0002-0328-2026"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Kangyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134929691","display_name":"Zheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134952349","display_name":"Gang Chen","orcid":"https://orcid.org/0000-0003-0780-5234"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Gang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030183493","display_name":"Fanchao Qi","orcid":"https://orcid.org/0000-0002-4400-4033"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Fanchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134995577","display_name":"Minjia Zhang (803691)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Minjia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134929153","display_name":"Maosong Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Maosong","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/T10028","display_name":"Topic Modeling","score":0.243599995970726,"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":0.243599995970726,"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":0.24169999361038208,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.0674000009894371,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/semantic-reasoner","display_name":"Semantic reasoner","score":0.7150999903678894},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6370000243186951},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.525600016117096},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47200000286102295},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.46480000019073486},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.3547999858856201},{"id":"https://openalex.org/keywords/dreyfus-model-of-skill-acquisition","display_name":"Dreyfus model of skill acquisition","score":0.3391000032424927},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.336899995803833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7792999744415283},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.7150999903678894},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5601999759674072},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3743000030517578},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27660","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.27660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27660","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":"Preprint"},"sustainable_development_goals":[{"score":0.8839015960693359,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many":[0],"real-world":[1],"tasks":[2,118,241],"require":[3],"language":[4,228],"models":[5],"(LMs)":[6],"to":[7,125,230],"reason":[8],"over":[9],"complex":[10],"contexts":[11],"that":[12,91,115,123,138,198],"exceed":[13],"their":[14],"parametric":[15],"knowledge.":[16],"This":[17],"calls":[18],"for":[19,53,67,78,168,210],"context":[20,45,54,233,239],"learning,":[21],"where":[22],"LMs":[23],"directly":[24],"learn":[25],"relevant":[26],"knowledge":[27],"from":[28,44,242],"the":[29,40,60,73,144,147,200,204,211],"given":[30],"context.":[31],"An":[32],"intuitive":[33],"solution":[34],"is":[35],"inference-time":[36],"skill":[37,65,80,132,166,173,189,201,218],"augmentation:":[38],"extracting":[39],"rules":[41],"and":[42,72,95,119,134,146,155,161,175,187,216],"procedures":[43],"into":[46,164,226],"natural-language":[47],"skills.":[48],"However,":[49],"constructing":[50],"such":[51],"skills":[52,98,222],"learning":[55,234,240],"scenarios":[56],"faces":[57],"two":[58],"challenges:":[59],"prohibitive":[61],"cost":[62],"of":[63,75],"manual":[64],"annotation":[66],"long,":[68],"technically":[69],"dense":[70],"contexts,":[71],"lack":[74],"external":[76,103],"feedback":[77],"automated":[79,172],"construction.":[81],"In":[82],"this":[83],"paper,":[84],"we":[85,191],"propose":[86],"Ctx2Skill,":[87],"a":[88,108,113,121,135,194],"self-evolving":[89],"framework":[90],"autonomously":[92],"discovers,":[93],"refines,":[94],"selects":[96],"context-specific":[97],"without":[99],"human":[100],"supervision":[101],"or":[102],"feedback.":[104,141],"At":[105],"its":[106],"core,":[107],"multi-agent":[109],"self-play":[110],"loop":[111],"has":[112],"Challenger":[114,145],"generates":[116],"probing":[117],"rubrics,":[120],"Reasoner":[122,148,212],"attempts":[124],"solve":[126],"them":[127,163],"guided":[128],"by":[129,182],"an":[130],"evolving":[131],"set,":[133],"neutral":[136],"Judge":[137],"provides":[139],"binary":[140],"Crucially,":[142],"both":[143,169],"evolve":[149],"through":[150],"accumulated":[151],"skills:":[152],"dedicated":[153],"Proposer":[154],"Generator":[156],"agents":[157],"analyze":[158],"failure":[159],"cases":[160,209],"synthesize":[162],"targeted":[165],"updates":[167],"sides,":[170],"enabling":[171],"discovery":[174],"refinement.":[176],"To":[177],"prevent":[178],"adversarial":[179],"collapse":[180],"caused":[181],"increasingly":[183],"extreme":[184],"task":[185],"generation":[186],"over-specialized":[188],"accumulation,":[190],"further":[192],"introduce":[193],"Cross-time":[195],"Replay":[196],"mechanism":[197],"identifies":[199],"set":[202],"achieving":[203],"best":[205],"balance":[206],"across":[207,249],"representative":[208],"side,":[213],"ensuring":[214],"robust":[215],"generalizable":[217],"evolution.":[219],"The":[220],"resulting":[221],"can":[223],"be":[224],"plugged":[225],"any":[227],"model":[229],"obtain":[231],"better":[232],"capability.":[235],"Evaluated":[236],"on":[237],"four":[238],"CL-bench,":[243],"Ctx2Skill":[244],"consistently":[245],"improves":[246],"solving":[247],"rates":[248],"backbone":[250],"models.":[251]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}
