{"id":"https://openalex.org/W7162414395","doi":"https://doi.org/10.48550/arxiv.2605.24960","title":"Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization","display_name":"Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W7162414395","doi":"https://doi.org/10.48550/arxiv.2605.24960"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24960","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.2605.24960","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137058751","display_name":"Jingyi Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137070521","display_name":"Qianli Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qianli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001646936","display_name":"Pepa Atanasova","orcid":"https://orcid.org/0000-0002-0023-2616"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atanasova, Pepa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027823774","display_name":"Nils Feldhus","orcid":"https://orcid.org/0009-0008-7408-7483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feldhus, Nils","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136993183","display_name":"Isabelle Augenstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Augenstein, Isabelle","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/T10028","display_name":"Topic Modeling","score":0.20340000092983246,"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":0.20340000092983246,"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":0.1264999955892563,"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"}},{"id":"https://openalex.org/T11883","display_name":"Embodied and Extended Cognition","score":0.06870000064373016,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6496000289916992},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5950000286102295},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5598999857902527},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.4553999900817871},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.37700000405311584},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.3571999967098236}],"concepts":[{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6496000289916992},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5950000286102295},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5598999857902527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586000084877014},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.4553999900817871},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3571999967098236},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35530000925064087},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3433000147342682},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3172999918460846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C76188268","wikidata":"https://www.wikidata.org/wiki/Q1783165","display_name":"Context effect","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.2556999921798706},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24960","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.2605.24960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24960","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Chain-of-Thought":[0],"(CoT)":[1],"faithfulness,":[2,22,33],"i.e.,":[3],"whether":[4,79],"CoTs":[5],"genuinely":[6],"reflect":[7],"large":[8],"language":[9],"models'":[10],"(LLM)":[11],"underlying":[12],"behavior,":[13],"is":[14,164],"typically":[15],"evaluated":[16],"under":[17],"two":[18,76,94,104],"disjoint":[19,150],"paradigms:":[20],"contextual":[21,123,131,147],"measured":[23],"by":[24,35,53],"perturbing":[25],"the":[26,72,75,103,122,130],"input":[27],"or":[28],"CoT":[29,162],"trace,":[30],"and":[31,80,88,96,154,169,174],"parametric":[32,40,113],"assessed":[34],"intervening":[36],"on":[37,135],"a":[38,56,166],"model's":[39],"knowledge.":[41],"Yet":[42],"prior":[43],"work":[44],"compares":[45],"them":[46],"only":[47],"descriptively.":[48],"We":[49],"fill":[50],"this":[51],"gap":[52],"proposing":[54],"FaithMate,":[55],"unified":[57],"preference-alignment":[58],"interface":[59],"for":[60],"optimizing":[61,111],"models":[62],"towards":[63,112],"either":[64],"faithfulness":[65,84,98,114,133,153,163],"paradigm.":[66],"It":[67],"enables":[68],"us":[69],"to":[70,81,142],"investigate":[71],"interplay":[73],"between":[74],"paradigms,":[77,120],"examining":[78],"what":[82],"extent":[83],"gains":[85,117,134],"generalize":[86],"within":[87],"across":[89,118],"paradigms.":[90],"Across":[91],"three":[92],"models,":[93],"datasets,":[95],"six":[97],"metrics,":[99],"we":[100],"find":[101],"that":[102,145,161],"paradigms":[105],"are":[106],"positively":[107],"coupled,":[108],"yet":[109],"asymmetric:":[110],"yields":[115],"consistent":[116],"both":[119],"whereas":[121],"counterpart":[124],"delivers":[125],"more":[126],"variable":[127],"gains.":[128],"Within":[129],"paradigm,":[132],"one":[136],"metric":[137],"do":[138],"not":[139,165],"consistently":[140],"transfer":[141],"others,":[143],"implying":[144],"existing":[146],"metrics":[148],"capture":[149],"facets":[151],"of":[152],"exposing":[155],"inherent":[156],"trade-offs.":[157],"These":[158],"findings":[159],"imply":[160],"monolithic":[167],"objective":[168],"therefore":[170],"requires":[171],"multifaceted":[172],"optimization":[173],"evaluation.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
