{"id":"https://openalex.org/W7161715468","doi":"https://doi.org/10.48550/arxiv.2605.17104","title":"Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics","display_name":"Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics","publication_year":2026,"publication_date":"2026-05-16","ids":{"openalex":"https://openalex.org/W7161715468","doi":"https://doi.org/10.48550/arxiv.2605.17104"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17104","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17104","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":"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.2605.17104","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136479187","display_name":"Zhaoxin Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhaoxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136503082","display_name":"Nan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136467354","display_name":"Kun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136503981","display_name":"Jiahao Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136474115","display_name":"Lei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136501171","display_name":"Wenji Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Wenji","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":0.32170000672340393,"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":0.32170000672340393,"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":0.14790000021457672,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0835999995470047,"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/scientific-reasoning","display_name":"Scientific reasoning","score":0.7348999977111816},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.506600022315979},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.454800009727478},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3982999920845032},{"id":"https://openalex.org/keywords/logical-reasoning","display_name":"Logical reasoning","score":0.3808000087738037},{"id":"https://openalex.org/keywords/logical-conjunction","display_name":"Logical conjunction","score":0.37860000133514404}],"concepts":[{"id":"https://openalex.org/C2992562121","wikidata":"https://www.wikidata.org/wiki/Q3817808","display_name":"Scientific reasoning","level":2,"score":0.7348999977111816},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5181000232696533},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.4584999978542328},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C43971567","wikidata":"https://www.wikidata.org/wiki/Q3142865","display_name":"Logical reasoning","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C21847791","wikidata":"https://www.wikidata.org/wiki/Q191081","display_name":"Logical conjunction","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C2776289891","wikidata":"https://www.wikidata.org/wiki/Q1931511","display_name":"Neglect","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C124056412","wikidata":"https://www.wikidata.org/wiki/Q3320364","display_name":"Scientific evidence","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C97364631","wikidata":"https://www.wikidata.org/wiki/Q484284","display_name":"Deductive reasoning","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C195732255","wikidata":"https://www.wikidata.org/wiki/Q981008","display_name":"Sociology of scientific knowledge","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C55587333","wikidata":"https://www.wikidata.org/wiki/Q1133029","display_name":"Engineering ethics","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26840001344680786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17104","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17104","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":"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.2605.17104","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17104","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":"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":[{"id":"https://metadata.un.org/sdg/4","score":0.6000424027442932,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,49,52,60,65,79,84,112,138,169,177,185],"continuous":[2],"advancement":[3],"of":[4,51,67,100],"reasoning":[5,16,43,54,68],"abilities":[6],"in":[7,180,193],"Large":[8],"Language":[9],"Models":[10],"(LLMs),":[11],"their":[12],"application":[13],"to":[14,63,71,110,136],"scientific":[15,31,53,89,94,146,178,187,195],"tasks":[17],"has":[18],"gained":[19],"significant":[20],"research":[21,24],"attention.":[22],"Current":[23],"primarily":[25],"emphasizes":[26],"boosting":[27],"LLMs'":[28],"performance":[29],"on":[30,36,161],"QA":[32],"benchmarks":[33],"by":[34,125],"training":[35,170],"larger,":[37],"more":[38],"comprehensive":[39],"datasets":[40],"with":[41],"extended":[42],"chains.":[44],"However,":[45],"these":[46],"approaches":[47],"neglect":[48],"essence":[50],"process":[55],"--":[56],"logicality,":[57],"which":[58],"is":[59,198],"rational":[61],"foundation":[62],"ensure":[64],"validity":[66],"steps":[69],"leading":[70],"reliable":[72],"conclusions.":[73],"In":[74],"this":[75],"work,":[76],"we":[77,121,144,172],"make":[78],"first":[80],"systematic":[81],"investigation":[82],"into":[83],"internal":[85],"logicality":[86,179,188],"underlying":[87],"LLM":[88,181],"reasoning,":[90],"and":[91,103,130,151,183],"develop":[92],"a":[93,98,153,190],"logicality-enriched":[95],"methodology,":[96],"including":[97],"set":[99],"assessment":[101],"criteria":[102],"data":[104,142,171],"sampling":[105],"methods":[106],"for":[107],"logicality-guided":[108],"training,":[109],"improve":[111,176],"logical":[113,128],"faithfulness":[114],"as":[115,117,132],"well":[116],"task":[118],"performance.":[119],"Further,":[120],"take":[122],"physics,":[123],"characterized":[124],"its":[126],"diverse":[127],"structures":[129],"formalisms,":[131],"an":[133],"exemplar":[134],"discipline":[135],"practise":[137],"above":[139],"methodology.":[140],"For":[141],"construction,":[143],"extract":[145],"problems":[147],"from":[148],"academic":[149],"literature":[150],"sample":[152],"high-quality":[154],"dataset":[155],"exhibiting":[156],"strong":[157],"logicality.":[158],"Experiments":[159],"based":[160],"three":[162],"different":[163],"backbone":[164],"LLMs":[165],"reveal":[166],"that:":[167],"1)":[168],"constructed":[173],"can":[174],"effectively":[175],"reasoning;":[182],"2)":[184],"enriched":[186],"plays":[189],"critical":[191],"role":[192],"solving":[194],"problems.":[196],"Code":[197],"available":[199],"at":[200],"\\href{https://github.com/ScienceOne-AI/PhysLogic}{https://github.com/ScienceOne-AI/PhysLogic}.":[201]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
