{"id":"https://openalex.org/W7166701042","doi":"https://doi.org/10.48550/arxiv.2606.29971","title":"NeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries","display_name":"NeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166701042","doi":"https://doi.org/10.48550/arxiv.2606.29971"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.29971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29971","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.2606.29971","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075754900","display_name":"Aydin Javadov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Javadov, Aydin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139655829","display_name":"Shyngys Aitkazinov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aitkazinov, Shyngys","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120604870","display_name":"Tobias Hoesli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoesli, Tobias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139680264","display_name":"Florian von Wangenheim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"von Wangenheim, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139703010","display_name":"Bjoern Schuller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schuller, Bjoern","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5037854125","display_name":"James Ollier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ollier, Joseph","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":0.2937999963760376,"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":0.2937999963760376,"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":0.1437000036239624,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.05469999834895134,"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/inference","display_name":"Inference","score":0.6247000098228455},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.6008999943733215},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.5670999884605408},{"id":"https://openalex.org/keywords/preference-elicitation","display_name":"Preference elicitation","score":0.5382000207901001},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.47510001063346863},{"id":"https://openalex.org/keywords/expert-elicitation","display_name":"Expert elicitation","score":0.4652999937534332},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43479999899864197},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4077000021934509},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3628999888896942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6590999960899353},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6247000098228455},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.6008999943733215},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.5670999884605408},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.5382000207901001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5020999908447266},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.47510001063346863},{"id":"https://openalex.org/C72161134","wikidata":"https://www.wikidata.org/wiki/Q5421219","display_name":"Expert elicitation","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4077000021934509},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.33660000562667847},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C170494330","wikidata":"https://www.wikidata.org/wiki/Q1778434","display_name":"Cognitive map","level":3,"score":0.3151000142097473},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29989999532699585},{"id":"https://openalex.org/C45384764","wikidata":"https://www.wikidata.org/wiki/Q838667","display_name":"Requirements elicitation","level":4,"score":0.299699991941452},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C88482812","wikidata":"https://www.wikidata.org/wiki/Q6453666","display_name":"Modular programming","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.29971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29971","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.2606.29971","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29971","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":0.7135071754455566,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"growing":[1],"body":[2],"of":[3,10,43,94,103,117],"work":[4],"suggests":[5],"that":[6,44,59],"the":[7,40,91,241],"reasoning":[8,235],"capabilities":[9],"large":[11],"language":[12],"models":[13],"are":[14],"largely":[15,46],"latent":[16],"in":[17,206],"their":[18,98],"base":[19],"form,":[20],"with":[21,85,204],"post-training":[22],"primarily":[23],"amplifying":[24],"rather":[25],"than":[26],"introducing":[27],"them.":[28],"However,":[29],"this":[30],"evidence":[31],"comes":[32],"mainly":[33],"from":[34,90,120],"mathematical":[35,125,242],"and":[36,57,96,126,136,162,170,188,200,238,243],"coding":[37,127,244],"benchmarks,":[38,128],"leaving":[39],"boundary":[41],"conditions":[42],"claim":[45],"unexplored,":[47],"namely":[48],"which":[49],"cognitive":[50,121,214],"tasks":[51,119],"can":[52],"be":[53],"recovered":[54],"through":[55,100,197,222],"elicitation":[56,70,198,205,229,236],"where":[58,234,246],"recovery":[60],"fails.":[61],"To":[62],"investigate":[63],"this,":[64],"we":[65,231],"introduce":[66],"NeuReasoner,":[67],"a":[68,78,86,104,115,141,225],"theory-grounded":[69,228],"instrument.":[71],"At":[72,146],"each":[73],"step,":[74],"an":[75],"orchestrator":[76],"pairs":[77],"Neuro":[79],"Lens,":[80,88],"inspired":[81],"by":[82],"functional":[83],"specificity,":[84],"Cognitive":[87],"drawn":[89],"Erotetic":[92],"Theory":[93],"Reasoning,":[95],"integrates":[97],"outputs":[99],"internal":[101],"modularization":[102],"single":[105],"model,":[106],"without":[107],"external":[108],"tools.":[109],"We":[110],"evaluate":[111],"NeuReasoner":[112,149,180,223],"on":[113,155,212,219],"CogBench,":[114],"suite":[116],"behavioral":[118],"psychology,":[122],"alongside":[123],"standard":[124],"measuring":[129],"both":[130,207],"its":[131,137,210],"improvement":[132],"over":[133],"vanilla":[134],"inference":[135],"ability":[138],"to":[139,174,183,195],"match":[140],"model's":[142],"post-trained":[143],"thinking":[144],"mode.":[145],"sufficient":[147],"scale,":[148],"matches":[150],"or":[151],"exceeds":[152],"thinking-mode":[153],"baselines":[154,172],"arithmetic":[156],"reasoning,":[157,161],"code":[158],"generation,":[159],"Bayesian":[160],"reward":[163],"learning;":[164],"these":[165],"gains":[166],"persist":[167],"against":[168],"self-consistency":[169],"iterative-refinement":[171],"matched":[173],"NeuReasoner's":[175],"per-decision":[176],"call":[177],"budget.":[178],"Using":[179],"allows":[181],"us":[182],"find":[184],"clear":[185],"boundaries:":[186],"risk-taking":[187],"decision":[189],"making":[190],"under":[191],"uncertainty":[192],"remains":[193],"hard":[194],"recover":[196],"alone,":[199],"model":[201],"scale":[202],"interacts":[203],"directions:":[208],"widening":[209],"advantage":[211],"some":[213],"signatures":[215],"while":[216],"erasing":[217],"it":[218],"others.":[220],"Overall,":[221],"as":[224],"modular,":[226],"interpretable,":[227],"instrument,":[230],"empirically":[232],"map":[233],"succeeds":[237],"fails,":[239],"beyond":[240],"benchmarks":[245],"prior":[247],"claims":[248],"have":[249],"rested.":[250]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
