{"id":"https://openalex.org/W7166123336","doi":"https://doi.org/10.48550/arxiv.2606.27047","title":"NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models","display_name":"NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166123336","doi":"https://doi.org/10.48550/arxiv.2606.27047"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27047","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.27047","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045683097","display_name":"Henry Shaowu Yuchi","orcid":"https://orcid.org/0000-0002-8827-1328"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuchi, Henry Shaowu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058477567","display_name":"Michal Kucer","orcid":"https://orcid.org/0000-0002-1438-4442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kucer, Michal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139397075","display_name":"Benjamin H. Sims","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sims, Benjamin H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021838180","display_name":"S N Peterson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peterson, Selma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125522984","display_name":"Emily Taylor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taylor, Emily","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":0.5047000050544739,"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":0.5047000050544739,"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":0.08290000259876251,"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/T13629","display_name":"Text Readability and Simplification","score":0.08009999990463257,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7817000150680542},{"id":"https://openalex.org/keywords/competence","display_name":"Competence (human resources)","score":0.5418999791145325},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49070000648498535},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4767000079154968},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.439300000667572},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.38839998841285706}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7817000150680542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7087000012397766},{"id":"https://openalex.org/C100521375","wikidata":"https://www.wikidata.org/wiki/Q2015382","display_name":"Competence (human resources)","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.462799996137619},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.361299991607666},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C14224292","wikidata":"https://www.wikidata.org/wiki/Q13600188","display_name":"Conceptual framework","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.260699987411499},{"id":"https://openalex.org/C168725872","wikidata":"https://www.wikidata.org/wiki/Q991663","display_name":"Sophistication","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27047","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.27047","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27047","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7725038528442383}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,140],"(LLMs)":[3],"have":[4],"demonstrated":[5],"strong":[6],"performance":[7,133],"across":[8,135],"a":[9,23,57,84,123,169],"wide":[10],"range":[11],"of":[12,126,161],"tasks,":[13],"but":[14,37],"ensuring":[15],"their":[16],"reliability":[17],"in":[18,51,176],"highly":[19],"technical":[20,98,177],"domains":[21],"remains":[22],"significant":[24],"challenge.":[25],"In":[26],"nuclear":[27,63],"engineering,":[28],"problem":[29],"solving":[30],"often":[31],"requires":[32],"not":[33],"only":[34],"factual":[35,145],"knowledge":[36],"also":[38],"quantitative":[39,147],"reasoning":[40,148],"and":[41,78,93,109,119,130,149,165],"conceptual":[42,150],"understanding.":[43],"To":[44],"address":[45],"the":[46,112,139,159],"need":[47],"for":[48,59,104,172],"systematic":[49],"evaluation":[50,163],"this":[52],"domain,":[53],"we":[54],"introduce":[55],"NuclearQAv2,":[56],"benchmark":[58,67,117,171],"assessing":[60,173],"LLMs":[61,127],"on":[62,144],"engineering":[64],"knowledge.":[65],"The":[66],"comprises":[68],"approximately":[69],"1,240":[70],"question-answer":[71],"pairs":[72],"spanning":[73],"three":[74],"categories:":[75],"boolean,":[76],"numeric,":[77],"verbal.":[79],"NuclearQAv2":[80,129,167],"is":[81],"constructed":[82],"using":[83,128],"hybrid":[85],"pipeline":[86],"that":[87],"combines":[88],"expert-authored":[89],"questions,":[90,146],"existing":[91],"datasets,":[92],"LLM-assisted":[94],"generation":[95,108],"from":[96],"domain-specific":[97],"corpora.":[99],"By":[100],"leveraging":[101],"structured":[102],"prompting":[103],"both":[105],"automated":[106],"question":[107],"response":[110],"evaluation,":[111],"proposed":[113],"framework":[114],"enables":[115],"scalable":[116,170],"construction":[118],"evaluation.":[120],"We":[121],"evaluate":[122],"diverse":[124],"set":[125],"observe":[131],"substantial":[132],"differences":[134],"task":[136],"types.":[137],"While":[138],"generally":[141],"perform":[142],"well":[143],"understanding":[151],"remain":[152],"considerably":[153],"more":[154],"challenging.":[155],"These":[156],"results":[157],"highlight":[158],"importance":[160],"multi-faceted":[162],"frameworks":[164],"establish":[166],"as":[168],"LLM":[174],"capabilities":[175],"domains.":[178]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-27T00:00:00"}