{"id":"https://openalex.org/W7160840973","doi":"https://doi.org/10.48550/arxiv.2605.07307","title":"Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts","display_name":"Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160840973","doi":"https://doi.org/10.48550/arxiv.2605.07307"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07307","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.2605.07307","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135870023","display_name":"Yi-Chang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yi-Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067740015","display_name":"Feng-Ting Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Feng-Ting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135878041","display_name":"Da-shan Shiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiu, Da-shan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135847442","display_name":"Hung-yi Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hung-yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":0.7820000052452087,"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":0.7820000052452087,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.0812000036239624,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0203000009059906,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/counterintuitive","display_name":"Counterintuitive","score":0.7544000148773193},{"id":"https://openalex.org/keywords/shuffling","display_name":"Shuffling","score":0.5587999820709229},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5497999787330627},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.5252000093460083},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4449000060558319},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41659998893737793},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41519999504089355},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4090000092983246},{"id":"https://openalex.org/keywords/chain","display_name":"Chain (unit)","score":0.3953000009059906},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.3675999939441681}],"concepts":[{"id":"https://openalex.org/C101097943","wikidata":"https://www.wikidata.org/wiki/Q5176983","display_name":"Counterintuitive","level":2,"score":0.7544000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6541000008583069},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.593999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5909000039100647},{"id":"https://openalex.org/C167927819","wikidata":"https://www.wikidata.org/wiki/Q1930567","display_name":"Shuffling","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5497999787330627},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.5252000093460083},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39649999141693115},{"id":"https://openalex.org/C199185054","wikidata":"https://www.wikidata.org/wiki/Q552299","display_name":"Chain (unit)","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3237000107765198},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30550000071525574},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2892000079154968},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C140843580","wikidata":"https://www.wikidata.org/wiki/Q840067","display_name":"Defeasible reasoning","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C62360110","wikidata":"https://www.wikidata.org/wiki/Q96777007","display_name":"Circumscription","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07307","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.2605.07307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07307","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":[{"score":0.4341457784175873,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"reasoning":[1,39,61,117,143,208],"language":[2,160],"models":[3,43],"generate":[4],"dense,":[5],"sequential":[6,57],"chain-of-thought":[7],"traces":[8],"implicitly":[9],"assuming":[10],"that":[11,16,145,189],"every":[12],"token":[13],"contributes":[14],"and":[15,34,44,89,149,169,197,206],"steps":[17],"must":[18],"be":[19],"consumed":[20],"in":[21,115],"order.":[22],"We":[23],"challenge":[24],"both":[25,147],"assumptions":[26],"through":[27],"a":[28,60,116,142,182,194],"systematic":[29],"intervention":[30],"pipeline--removal,":[31],"masking,":[32],"shuffling,":[33],"noise":[35],"injection--applied":[36],"to":[37,85,128],"model-generated":[38],"chains":[40],"across":[41],"three":[42,45,52],"benchmarks.":[46],"Our":[47],"findings":[48],"are":[49],"counterintuitive":[50],"on":[51,193],"dimensions.":[53],"Order:":[54],"Does":[55],"the":[56,113],"order":[58],"of":[59],"chain":[62,118,144],"matter":[63],"for":[64,120],"answer":[65,121,190],"extraction?":[66,122],"No--line-level":[67],"shuffling":[68,77,83],"reduces":[69],"accuracy":[70,127,136,178],"by":[71,137],"less":[72],"than":[73,107],"0.5":[74],"pp;":[75],"word-level":[76],"retains":[78],"62%-89%":[79],"accuracy;":[80],"only":[81],"token-level":[82],"collapses":[84,126],"near":[86],"zero.":[87],"Pretrained-only":[88],"instruction-tuned":[90],"variants":[91],"exhibit":[92],"near-identical":[93],"tolerance":[94],"(78.67%":[95],"vs.":[96],"78.00%":[97],"under":[98],"line":[99],"shuffling),":[100],"indicating":[101],"order-independence":[102],"originates":[103],"from":[104],"pretraining":[105],"rather":[106],"reasoning-specific":[108],"fine-tuning.":[109],"Dense:":[110],"Is":[111,141],"all":[112],"information":[114],"important":[119],"No--masking":[123],"numeric":[124],"digits":[125],"exactly":[129],"0%,":[130],"while":[131],"masking":[132],"alphabetic":[133],"prose":[134],"improves":[135],"4.7":[138],"pp.":[139],"Robustness:":[140],"is":[146],"order-shuffling":[148],"non-dense":[150],"still":[151,165],"robust?":[152],"Yes--the":[153],"most":[154],"aggressively":[155],"reduced":[156],"representation":[157],"(all":[158],"natural":[159],"removed,":[161],"lines":[162],"arbitrarily":[163],"shuffled)":[164],"achieves":[166],"83%":[167],"accuracy,":[168],"injecting":[170],"false":[171],"answers":[172],"at":[173],"3x":[174],"true-answer":[175],"frequency":[176],"leaves":[177],"unchanged":[179],"(83.3%-&gt;83.3%),":[180],"falsifying":[181],"frequency-based":[183],"extraction":[184,191],"account.":[185],"These":[186],"results":[187],"establish":[188],"operates":[192],"sparse,":[195],"order-insensitive,":[196],"structurally":[198],"robust":[199],"informational":[200],"substrate,":[201],"opening":[202],"paths":[203],"toward":[204],"parallelized":[205],"token-efficient":[207],"generation.":[209]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-12T00:00:00"}
