{"id":"https://openalex.org/W7165362279","doi":"https://doi.org/10.48550/arxiv.2606.20333","title":"SoftSkill: Behavioral Compression for Contextual Adaptation","display_name":"SoftSkill: Behavioral Compression for Contextual Adaptation","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165362279","doi":"https://doi.org/10.48550/arxiv.2606.20333"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.20333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20333","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.2606.20333","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101212446","display_name":"Xijia Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Xijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102573328","display_name":"Yihua Teng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teng, Yihua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139003136","display_name":"Xinyu Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139001219","display_name":"Ziru Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138971502","display_name":"Kecheng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139016532","display_name":"Yuzhi Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yuzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074987017","display_name":"Suiyun Zhang","orcid":"https://orcid.org/0000-0002-3205-5896"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Suiyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138987457","display_name":"Rui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138987271","display_name":"Lingpeng Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Lingpeng","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.29170000553131104,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.29170000553131104,"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":0.21639999747276306,"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/T12090","display_name":"Language and cultural evolution","score":0.07249999791383743,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6452000141143799},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.640500009059906},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6049000024795532},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5885999798774719},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4878000020980835},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.4553000032901764},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.41850000619888306},{"id":"https://openalex.org/keywords/reset","display_name":"Reset (finance)","score":0.37450000643730164},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.36070001125335693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7434999942779541},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6452000141143799},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6049000024795532},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5885999798774719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5152999758720398},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4097999930381775},{"id":"https://openalex.org/C2779795794","wikidata":"https://www.wikidata.org/wiki/Q7315343","display_name":"Reset (finance)","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.34060001373291016},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31220000982284546},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2842999994754791},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2549000084400177},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.20333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20333","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.2606.20333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20333","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":[{"score":0.8193384408950806,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Agent":[0],"skills":[1,83,190],"are":[2,20,26,191],"commonly":[3],"deployed":[4],"as":[5,90,161,195,205],"natural-language":[6,51],"Markdown":[7,148,197],"files":[8,19],"that":[9,79,187],"encode":[10],"answer":[11],"policies,":[12],"evidence-use":[13],"habits,":[14],"and":[15,22,87,121,137],"task":[16,31,189],"procedures.":[17],"These":[18],"readable":[21],"portable,":[23],"but":[24,173,204],"they":[25],"consumed":[27],"indirectly:":[28],"for":[29],"each":[30],"instance,":[32],"a":[33,39,50,56,63,76,102,152,162,211],"frozen":[34,212],"language":[35],"model":[36,70,213],"must":[37],"translate":[38],"long":[40],"textual":[41],"artifact":[42],"into":[43],"generation-time":[44],"behavior.":[45,181],"This":[46],"paper":[47],"asks":[48],"whether":[49],"skill":[52,149],"can":[53],"instead":[54],"initialize":[55],"compact":[57,206],"continuous":[58],"context":[59],"object,":[60],"refined":[61],"by":[62,112,132],"trainable":[64],"soft":[65,82],"delta":[66],"while":[67,142],"the":[68,184,215],"base":[69],"remains":[71],"frozen.":[72],"We":[73,156],"propose":[74],"SoftSkill,":[75],"frozen-backbone":[77],"method":[78],"tunes":[80],"such":[81],"with":[84,151],"next-token":[85],"prediction":[86],"deploys":[88],"them":[89],"latent":[91,207],"behavioral":[92],"priors":[93],"at":[94,201],"inference":[95,202],"time.":[96],"In":[97],"our":[98],"main":[99],"single-round":[100],"setting,":[101],"length-32":[103],"SoftSkill":[104,129],"prefix":[105],"on":[106,115,119,124,135,140],"Qwen3.5-4B":[107],"improves":[108,130],"over":[109,209],"no-skill":[110],"prompting":[111],"8.3":[113],"points":[114,118,123,134,139],"SearchQA,":[116],"42.1":[117],"LiveMath,":[120,141],"1.3":[122],"DocVQA.":[125],"Relative":[126],"to":[127,145,198],"SkillOpt,":[128],"accuracy":[131],"5.2":[133],"SearchQA":[136],"12.5":[138],"replacing":[143],"hundreds":[144],"thousands":[146],"of":[147],"tokens":[150],"few":[153],"virtual":[154],"tokens.":[155],"further":[157],"study":[158],"agentic":[159],"execution":[160],"harder":[163],"boundary":[164],"case,":[165],"where":[166],"sparse":[167],"trajectory":[168],"imitation":[169],"provides":[170],"useful":[171],"signal":[172],"does":[174],"not":[175,194],"yet":[176],"robustly":[177],"compress":[178],"long-horizon":[179],"procedural":[180],"More":[182],"broadly,":[183],"results":[185],"suggest":[186],"some":[188],"better":[192],"treated":[193],"additional":[196],"be":[199],"reinterpreted":[200],"time,":[203],"controls":[208],"how":[210],"enters":[214],"task.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-20T00:00:00"}