{"id":"https://openalex.org/W7165393398","doi":"https://doi.org/10.48550/arxiv.2606.19625","title":"Where Does Social Reasoning Come From? Capability Provenance in Language Models","display_name":"Where Does Social Reasoning Come From? Capability Provenance in Language Models","publication_year":2026,"publication_date":"2026-06-17","ids":{"openalex":"https://openalex.org/W7165393398","doi":"https://doi.org/10.48550/arxiv.2606.19625"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19625","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.2606.19625","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119181254","display_name":"Glenn Matlin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matlin, Glenn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139012257","display_name":"Chandreyi Chakraborty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Chandreyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007329393","display_name":"Saehee Eom","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eom, Saehee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124887593","display_name":"Mika Okamoto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okamoto, Mika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122916028","display_name":"Rayan Castilla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Castilla, Rayan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119823551","display_name":"Louis Jaburi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaburi, Louis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126302357","display_name":"Alvin Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Alvin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139006936","display_name":"Taywon Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Taywon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092912338","display_name":"Lucia Quirke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quirke, Lucia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138980312","display_name":"Stella Biderman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Biderman, Stella","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5094027963","display_name":"Mark Riedl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riedl, Mark","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/T13910","display_name":"Computational and Text Analysis Methods","score":0.4268999993801117,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.4268999993801117,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.15410000085830688,"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/T13629","display_name":"Text Readability and Simplification","score":0.053599998354911804,"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/benchmark","display_name":"Benchmark (surveying)","score":0.5597000122070312},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.539900004863739},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.5327000021934509},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5289000272750854},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.4982999861240387},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.46720001101493835},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.4230000078678131},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.40720000863075256},{"id":"https://openalex.org/keywords/pragmatics","display_name":"Pragmatics","score":0.3743000030517578}],"concepts":[{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6079000234603882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5871999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5837000012397766},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5597000122070312},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.46720001101493835},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.4230000078678131},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C11693617","wikidata":"https://www.wikidata.org/wiki/Q181839","display_name":"Pragmatics","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.36329999566078186},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.36070001125335693},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3125},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C2780922921","wikidata":"https://www.wikidata.org/wiki/Q255189","display_name":"Paraphrase","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.29319998621940613},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28049999475479126},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2474386","wikidata":"https://www.wikidata.org/wiki/Q461183","display_name":"Text corpus","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19625","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.2606.19625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19625","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":0.5567649006843567,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,63],"use":[1],"training-data":[2],"attribution":[3,25,66],"as":[4],"an":[5],"interpretable":[6],"tool":[7],"for":[8,157],"capability":[9,105],"discovery,":[10],"mapping":[11],"which":[12,47,51],"regions":[13,49],"of":[14],"the":[15,76,131,136,141,160,175],"pretraining":[16],"corpus":[17,48,128],"support":[18,50],"social-reasoning":[19],"versus":[20],"STEM-reasoning":[21],"in":[22,94],"OLMo3-7B.":[23],"Training-data":[24],"measures":[26],"how":[27],"strongly":[28],"each":[29],"training":[30],"document":[31],"influences":[32],"a":[33,37,71,95],"model's":[34],"predictions":[35],"on":[36,125],"benchmark,":[38],"but":[39],"document-level":[40],"scores":[41],"are":[42],"too":[43],"noisy":[44],"to":[45],"identify":[46],"capabilities,":[52],"and":[53,90,104,111,117,121,130,168,179],"prior":[54],"work":[55],"has":[56],"emphasized":[57],"factual":[58],"knowledge":[59,142],"rather":[60],"than":[61,139,164],"reasoning.":[62],"compute":[64],"gradient-based":[65],"(TrackStar":[67],"via":[68],"Bergson)":[69],"over":[70],"working":[72],"set":[73],"drawn":[74],"from":[75],"de-duplicated":[77],"Dolma3":[78],"mix,":[79],"aggregate":[80],"influence":[81,177],"across":[82],"WebOrganizer's":[83],"24-format":[84],"x":[85],"24-topic":[86],"taxonomy":[87],"(576":[88],"bins),":[89],"contrast":[91,132],"benchmark":[92,162],"pairs":[93],"2x2":[96],"design":[97],"that":[98],"varies":[99],"domain":[100],"(social":[101],"vs.":[102,108],"STEM)":[103],"type":[106],"(reasoning":[107],"knowledge):":[109],"SocialIQA":[110],"MMLU":[112,118],"Social":[113,120],"Sciences":[114],"against":[115],"ARC-Challenge":[116],"STEM.":[119],"STEM":[122],"reasoning":[123,137],"draw":[124],"qualitatively":[126],"distinct":[127],"regions,":[129],"is":[133],"sharper":[134],"at":[135,140],"level":[138],"level.":[143],"Targeted":[144],"machine":[145],"unlearning":[146,180],"provides":[147],"partial":[148],"causal":[149],"validation:":[150],"forgetting":[151],"high-attribution":[152],"topic":[153],"bins":[154],"(e.g.,":[155],"Literature":[156],"SocialIQA)":[158],"degrades":[159],"aligned":[161],"more":[163],"within-bin":[165],"random":[166],"baselines,":[167],"we":[169],"open-source":[170],"all":[171],"code,":[172],"sampling":[173],"manifests,":[174],"bin-level":[176],"matrix,":[178],"checkpoints.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-20T00:00:00"}
