{"id":"https://openalex.org/W7161791350","doi":"https://doi.org/10.48550/arxiv.2605.18808","title":"Compositional Literary Primitives in Instruction-Tuned LLMs: Cross-Architectural SAE Features for Self, Style, and Affect","display_name":"Compositional Literary Primitives in Instruction-Tuned LLMs: Cross-Architectural SAE Features for Self, Style, and Affect","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7161791350","doi":"https://doi.org/10.48550/arxiv.2605.18808"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18808","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.18808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116073235","display_name":"Jo\u00e3o Paulo Cavalcante Presa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Presa, Joao Paulo Cavalcante","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136510135","display_name":"Savio Salvarino Teles de Oliveira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Oliveira, Savio Salvarino Teles","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.32120001316070557,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.32120001316070557,"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/T12488","display_name":"Mental Health via Writing","score":0.14579999446868896,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.12630000710487366,"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/gemma","display_name":"Gemma","score":0.746399998664856},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4986000061035156},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4133000075817108},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3953999876976013},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.37220001220703125},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.35839998722076416},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.33000001311302185}],"concepts":[{"id":"https://openalex.org/C170806853","wikidata":"https://www.wikidata.org/wiki/Q2746099","display_name":"Gemma","level":2,"score":0.746399998664856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6229000091552734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5023999810218811},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4986000061035156},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47189998626708984},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.41909998655319214},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C152752567","wikidata":"https://www.wikidata.org/wiki/Q116877","display_name":"Code refactoring","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C22593422","wikidata":"https://www.wikidata.org/wiki/Q3242583","display_name":"Pedigree chart","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C2777515626","wikidata":"https://www.wikidata.org/wiki/Q496939","display_name":"Levenshtein distance","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18808","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.18808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18808","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":"article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7300494313240051}],"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],"characterize":[1],"a":[2,38,64,71,192,221],"compositional":[3,55],"architecture":[4,197],"of":[5,37,44,111,117],"literary":[6],"primitives":[7],"in":[8,139],"two":[9],"instruction-tuned":[10],"large":[11],"language":[12],"models":[13],"(Llama":[14],"3.1":[15],"8B-Instruct":[16],"and":[17,52,54,86,113,175,187,208,237],"Gemma":[18,90,157,169],"2":[19],"9B-IT)":[20],"via":[21],"sparse":[22],"autoencoders":[23],"on":[24,108,144,152,156],"mid-depth":[25],"residual":[26,98],"streams.":[27],"Four":[28],"feature":[29],"classes":[30],"emerge:":[31],"naming-gates":[32],"that":[33,57,181,198],"promote":[34],"lexical":[35],"tokens":[36],"target":[39,164],"affect,":[40],"an":[41],"eleven-self":[42],"cluster":[43],"first-person":[45],"register":[46,49,185],"features,":[47],"stylistic":[48],"modulators":[50],"(show-don't-tell":[51],"defamiliarization),":[53],"emotions":[56],"arise":[58],"only":[59],"from":[60],"multi-feature":[61,84],"steering.":[62],"Under":[63,100],"forced-choice":[65],"5-LLM":[66,227],"judge":[67,142],"panel":[68],"applied":[69],"to":[70],"27-category":[72],"emotion":[73,189],"taxonomy":[74],"(Cowen-Keltner),":[75],"Llama":[76,153,160],"reaches":[77,91],"full":[78],"27/27":[79],"coverage":[80,129],"by":[81],"combining":[82],"naming-gates,":[83],"recipes,":[85],"single":[87,97,193],"self-feature":[88,195],"steering;":[89],"23/27":[92],"with":[93,133,229],"adoration":[94],"as":[95,184,188],"the":[96,103,109,114,122,127,140,145,163,200,213,218,232],"strict-fail.":[99],"random":[101],"judging,":[102],"per-cell":[104],"pass":[105],"probability":[106],"is":[107,124,130,235],"order":[110],"$10^{-3}$":[112],"expected":[115],"number":[116],"two-seed":[118],"false-positive":[119],"cells":[120],"across":[121],"catalog":[123],"negligible,":[125],"so":[126],"observed":[128],"not":[131],"consistent":[132],"chance.":[134],"A":[135],"cross-architectural":[136],"asymmetry":[137],"sits":[138],"strict-versus-soft":[141],"contrast:":[143],"same":[146,214],"generations,":[147],"judges":[148],"agree":[149],"more":[150,166],"often":[151],"outputs":[154,158,161,170],"than":[155],"because":[159],"name":[162],"affect":[165],"directly":[167],"while":[168],"evoke":[171],"it":[172],"through":[173],"scene":[174],"imagery.":[176],"Both":[177],"architectures":[178],"contain":[179],"self-features":[180],"serve":[182],"simultaneously":[183],"markers":[186],"emitters,":[190],"including":[191],"most-RLHF-loaded":[194],"per":[196,241],"intensifies":[199],"institutional":[201],"Helper-AI":[202],"persona":[203],"at":[204,212],"one":[205],"operating":[206],"regime":[207],"produces":[209],"affect-categorizable":[210],"output":[211],"calibrated":[215],"coefficient.":[216],"Methodologically,":[217],"paper":[219],"presents":[220],"three-stage":[222],"validation":[223],"pipeline":[224],"(logit-lens,":[225],"LLM-rate,":[226],"judge)":[228],"documented":[230],"anti-patterns;":[231],"total":[233],"compute":[234],"single-GPU":[236],"about":[238],"15":[239],"minutes":[240],"emotion-feature":[242],"discovery":[243],"cycle.":[244]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-21T00:00:00"}