{"id":"https://openalex.org/W7129462048","doi":"https://doi.org/10.48550/arxiv.2602.13647","title":"SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering","display_name":"SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering","publication_year":2026,"publication_date":"2026-02-14","ids":{"openalex":"https://openalex.org/W7129462048","doi":"https://doi.org/10.48550/arxiv.2602.13647"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.13647","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126237329","display_name":"Rui Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yu, Rui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126268855","display_name":"Tianyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tianyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101979547","display_name":"Ruixia Liu","orcid":"https://orcid.org/0000-0002-4044-5384"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ruixia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126199383","display_name":"Yinglong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yinglong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5126237329"],"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":0.44530001282691956,"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":0.44530001282691956,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.24819999933242798,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.18449999392032623,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/security-token","display_name":"Security token","score":0.5753999948501587},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4848000109195709},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/fragmentation","display_name":"Fragmentation (computing)","score":0.4065999984741211},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.3203999996185303},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.310699999332428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462000250816345},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5753999948501587},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4848000109195709},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4277999997138977},{"id":"https://openalex.org/C191015642","wikidata":"https://www.wikidata.org/wiki/Q1132459","display_name":"Fragmentation (computing)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3896999955177307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3765000104904175},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31060001254081726},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.29420000314712524},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2777615720","wikidata":"https://www.wikidata.org/wiki/Q11888847","display_name":"Prioritization","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.13647","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.13647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13647","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.13647","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"question-answering":[1,20],"(QA)":[2],"over":[3,21],"extensive":[4],"scientific":[5],"literature":[6],"is":[7,16,34],"essential":[8],"for":[9,72,167,210],"evidence-based":[10],"engineering":[11,212],"decision-making.":[12],"Retrieval-augmented":[13],"generation":[14],"(RAG)":[15],"increasingly":[17],"applied":[18],"to":[19,52,64,104,126,148,176],"long":[22],"academic":[23,92],"papers,":[24],"where":[25],"accurate":[26],"evidence":[27,181,197],"allocation":[28,182],"under":[29,135],"a":[30,55,95,106,118,136,162,207],"fixed":[31,137],"token":[32,138],"budget":[33],"critical.":[35],"However,":[36],"existing":[37,149],"approaches":[38],"flatten":[39],"papers":[40,93],"into":[41],"unstructured":[42],"chunks,":[43],"destroying":[44],"the":[45,69,87,101,114],"native":[46,88,102,163],"hierarchical":[47,89],"structure":[48,90],"and":[49,67,129,143,160,180,195,215],"forcing":[50],"retrieval":[51,97,120,145,178,193],"operate":[53],"in":[54,218],"disordered":[56],"space.":[57],"This":[58],"produces":[59],"fragmented":[60],"contexts,":[61],"misallocates":[62],"tokens":[63],"non-evidential":[65],"regions,":[66],"increases":[68],"reasoning":[70],"burden":[71],"downstream":[73],"language":[74],"models.To":[75],"address":[76],"these":[77],"issues,":[78],"we":[79],"propose":[80],"SF-RAG,":[81],"an":[82],"RAG":[83,150],"framework":[84],"that":[85,122,189],"treats":[86],"of":[91],"as":[94],"low-entropy":[96,144,164],"prior.SF-RAG":[98],"first":[99],"inherits":[100],"hierarchy":[103],"construct":[105],"structure-fidelity":[107],"index,":[108],"which":[109],"prevents":[110],"entropy":[111,154],"increase":[112,155],"at":[113],"source.It":[115],"then":[116],"designs":[117],"path-guided":[119],"mechanism":[121],"aligns":[123],"query":[124],"semantics":[125],"relevant":[127],"sections":[128],"selects":[130],"high":[131],"relevance":[132],"root-to-leaf":[133],"paths":[134],"budget,":[139],"yielding":[140],"compact,":[141],"coherent,":[142],"contexts.In":[146],"contrast":[147],"approaches,":[151],"SF-RAG":[152,190],"avoids":[153],"caused":[156],"by":[157],"destructive":[158],"preprocessing":[159],"provides":[161],"structural":[165,174,200],"basis":[166],"subsequent":[168],"retrieval.":[169],"We":[170],"further":[171],"introduce":[172],"entropy-based":[173],"diagnostics":[175],"quantify":[177],"fragmentation":[179,194],"accuracy.Evaluations":[183],"across":[184],"three":[185],"QA":[186],"benchmarks":[187],"show":[188],"significantly":[191],"reduces":[192],"improves":[196],"allocation.":[198],"These":[199],"benefits":[201],"drive":[202],"superior":[203],"answer":[204],"quality,":[205],"establishing":[206],"scalable":[208],"foundation":[209],"intelligent":[211],"document":[213],"systems":[214],"future":[216],"applications":[217],"technical":[219],"specifications.":[220]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-18T00:00:00"}
