{"id":"https://openalex.org/W7138426857","doi":"https://doi.org/10.48550/arxiv.2603.15397","title":"SFCoT: Safer Chain-of-Thought via Active Safety Evaluation and Calibration","display_name":"SFCoT: Safer Chain-of-Thought via Active Safety Evaluation and Calibration","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138426857","doi":"https://doi.org/10.48550/arxiv.2603.15397"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15397","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.2603.15397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129747975","display_name":"Yu Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122081805","display_name":"Wenlong Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Wenlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102731573","display_name":"Tiejun Wu","orcid":"https://orcid.org/0000-0001-7748-964X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Tiejun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129676143","display_name":"Xiaohu Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Xiaohu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129734499","display_name":"Qiannan Si","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Qiannan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680172","display_name":"Guangquan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Guangquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129713363","display_name":"Bin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Bin","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9455999732017517,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9455999732017517,"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/T10028","display_name":"Topic Modeling","score":0.010499999858438969,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.010499999858438969,"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/safer","display_name":"SAFER","score":0.8600999712944031},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.7368999719619751},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5008999705314636},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.47279998660087585},{"id":"https://openalex.org/keywords/active-safety","display_name":"Active safety","score":0.47119998931884766},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4643999934196472},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41179999709129333}],"concepts":[{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.8600999712944031},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7368999719619751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6517999768257141},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C127757376","wikidata":"https://www.wikidata.org/wiki/Q2056514","display_name":"Active safety","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.31790000200271606},{"id":"https://openalex.org/C132835097","wikidata":"https://www.wikidata.org/wiki/Q7663745","display_name":"System safety","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2986542766","wikidata":"https://www.wikidata.org/wiki/Q2090494","display_name":"Vehicle safety","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C2992886853","wikidata":"https://www.wikidata.org/wiki/Q18381816","display_name":"Post hoc","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26260000467300415},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15397","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.2603.15397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15397","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":[{"id":"https://metadata.un.org/sdg/16","score":0.4497857689857483,"display_name":"Peace, Justice and strong institutions"}],"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],"(LLMs)":[3],"have":[4],"demonstrated":[5],"remarkable":[6],"capabilities":[7],"in":[8,71,140],"complex":[9],"reasoning":[10,42,69,94,106],"tasks.":[11],"However,":[12],"they":[13],"remain":[14],"highly":[15],"susceptible":[16],"to":[17,36,47,88,104,123],"jailbreak":[18],"attacks":[19],"that":[20,114],"undermine":[21],"their":[22],"safety":[23,78,133],"alignment.":[24],"Existing":[25],"defense":[26],"mechanisms":[27],"typically":[28],"rely":[29],"on":[30],"post":[31],"hoc":[32],"filtering":[33],"applied":[34],"only":[35],"the":[37,93,117],"final":[38],"output,":[39],"leaving":[40],"intermediate":[41],"steps":[43,70],"unmonitored":[44],"and":[45,65,130],"vulnerable":[46],"adversarial":[48],"manipulation.":[49],"To":[50],"address":[51],"this":[52,54],"gap,":[53],"paper":[55],"proposes":[56],"a":[57,76,82,137],"SaFer":[58],"Chain-of-Thought":[59],"(SFCoT)":[60],"framework,":[61],"which":[62],"proactively":[63],"evaluates":[64],"calibrates":[66],"potentially":[67],"unsafe":[68],"real":[72],"time.":[73],"SFCoT":[74,115],"incorporates":[75],"three-tier":[77],"scoring":[79],"system":[80],"alongside":[81],"multi-perspective":[83],"consistency":[84],"verification":[85],"mechanism,":[86],"designed":[87],"detect":[89],"potential":[90],"risks":[91],"throughout":[92],"process.":[95],"A":[96],"dynamic":[97],"intervention":[98],"module":[99],"subsequently":[100],"performs":[101],"targeted":[102],"calibration":[103],"redirect":[105],"trajectories":[107],"toward":[108],"safe":[109],"outcomes.":[110],"Experimental":[111],"results":[112],"demonstrate":[113],"reduces":[116],"attack":[118],"success":[119],"rate":[120],"from":[121],"$58.97\\%$":[122],"$12.31\\%$,":[124],"demonstrating":[125],"it":[126],"as":[127],"an":[128],"effective":[129],"efficient":[131],"LLM":[132],"enhancement":[134],"method":[135],"without":[136],"significant":[138],"decline":[139],"general":[141],"performance.":[142]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}