{"id":"https://openalex.org/W7127986004","doi":"https://doi.org/10.48550/arxiv.2602.06022","title":"Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering","display_name":"Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7127986004","doi":"https://doi.org/10.48550/arxiv.2602.06022"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.06022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119181466","display_name":"Miranda Muqing Miao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Miao, Miranda Muqing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123305275","display_name":"Young-Min Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Young-Min","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125198380","display_name":"Lyle Ungar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ungar, Lyle","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5119181466"],"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":0.30979999899864197,"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":0.30979999899864197,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.13830000162124634,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0640999972820282,"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/correctness","display_name":"Correctness","score":0.8636999726295471},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7731999754905701},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5238000154495239},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.520799994468689},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.38119998574256897},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.31839999556541443},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3125}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.8636999726295471},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7731999754905701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7246999740600586},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43860000371932983},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3736000061035156},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3125},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C8854915","wikidata":"https://www.wikidata.org/wiki/Q4350200","display_name":"Information transfer","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C108913964","wikidata":"https://www.wikidata.org/wiki/Q2376856","display_name":"System under test","level":4,"score":0.25870001316070557},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.06022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.06022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06022","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":"pmh:doi:10.48550/arxiv.2602.06022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.5767390131950378,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,73],"(LLMs)":[3],"exhibit":[4],"persistent":[5],"miscalibration,":[6],"especially":[7],"after":[8],"instruction":[9],"tuning":[10],"and":[11,74,83,119,151],"preference":[12],"alignment.":[13],"Modified":[14],"training":[15],"objectives":[16],"can":[17,134],"improve":[18,155],"calibration,":[19],"but":[20],"retraining":[21,100],"is":[22],"expensive.":[23],"Inference-time":[24],"steering":[25,52],"offers":[26],"a":[27,49,148],"lightweight":[28],"alternative,":[29],"yet":[30],"most":[31],"existing":[32],"methods":[33],"optimize":[34],"proxies":[35],"for":[36],"correctness":[37,40,57],"rather":[38],"than":[39],"itself.":[41],"We":[42,67,92],"introduce":[43],"CORAL":[44,69,145],"(Correctness-Optimized":[45],"Residual":[46],"Activation":[47],"Lens),":[48],"regularized":[50,138],"inference-time":[51],"method":[53],"that":[54,76,95,128],"captures":[55],"distributed":[56,129],"signals":[58],"from":[59],"model":[60,132],"internal":[61],"activations":[62],"using":[63,137],"weight-decay":[64],"MLP":[65],"probes.":[66],"evaluate":[68],"across":[70],"three":[71],"7B-parameter":[72],"find":[75],"it":[77],"consistently":[78],"improves":[79],"accuracy":[80,117],"by":[81,88],"10\\%":[82],"expected":[84],"calibration":[85],"error":[86],"(ECE)":[87],"50\\%":[89],"on":[90],"average.":[91],"additionally":[93],"demonstrate":[94],"these":[96],"gains":[97],"transfer":[98],"without":[99],"to":[101,154],"the":[102,126],"complete":[103],"published":[104],"test":[105],"sets":[106],"of":[107],"four":[108],"held-out":[109],"benchmarks":[110],"(ARC-Challenge,":[111],"HellaSwag,":[112],"Math-MC,":[113],"OpenBookQA),":[114],"averaging":[115],"14\\%":[116],"improvements":[118],"49\\%":[120],"ECE":[121],"improvements.":[122],"Our":[123],"results":[124],"support":[125],"hypothesis":[127],"information":[130],"in":[131],"internals":[133],"be":[135],"extracted":[136],"probes":[139],"when":[140],"individual":[141],"neurons":[142],"are":[143],"insufficient.":[144],"thus":[146],"provides":[147],"compute-efficient,":[149],"transferable,":[150],"calibration-aware":[152],"approach":[153],"MCQA":[156],"performance":[157],"during":[158],"inference.":[159]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-07T00:00:00"}