{"id":"https://openalex.org/W7154394364","doi":"https://doi.org/10.48550/arxiv.2604.11229","title":"RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering","display_name":"RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154394364","doi":"https://doi.org/10.48550/arxiv.2604.11229"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11229","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.2604.11229","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091627905","display_name":"Zhuoyu Wu","orcid":"https://orcid.org/0000-0002-9075-7626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhuoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133560515","display_name":"Wenhui Ou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ou, Wenhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133561801","display_name":"Pei-Sze Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Pei-Sze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133563208","display_name":"Wenqi Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Wenqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039283151","display_name":"Sailaja Rajanala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajanala, Sailaja","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073449839","display_name":"Rapha\u00ebl C. -W. Phan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phan, Rapha\u00ebl C. -W.","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/T11948","display_name":"Machine Learning in Materials Science","score":0.8098999857902527,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.8098999857902527,"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/T10028","display_name":"Topic Modeling","score":0.05950000137090683,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.02160000056028366,"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/pipeline","display_name":"Pipeline (software)","score":0.798799991607666},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6953999996185303},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.607699990272522},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5575000047683716},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.4625000059604645},{"id":"https://openalex.org/keywords/data-retrieval","display_name":"Data retrieval","score":0.46070000529289246},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3547999858856201}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8040000200271606},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.798799991607666},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.7170000076293945},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6953999996185303},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3677000105381012},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3395000100135803},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29280000925064087},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C90288658","wikidata":"https://www.wikidata.org/wiki/Q3318149","display_name":"Human\u2013computer information retrieval","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11229","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.2604.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11229","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":[{"score":0.5715801119804382,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieving":[0],"procedure-oriented":[1,123],"evidence":[2],"from":[3],"materials":[4,124],"science":[5],"papers":[6],"is":[7],"difficult":[8],"because":[9],"key":[10],"synthesis":[11],"details":[12],"are":[13,21,130],"often":[14],"scattered":[15],"across":[16],"long,":[17],"context-heavy":[18],"documents":[19],"and":[20,41,82,92,97,128],"not":[22],"well":[23],"captured":[24],"by":[25],"paragraph-only":[26,69],"dense":[27,60,70],"retrieval.":[28],"We":[29,100],"present":[30],"RECIPER,":[31],"a":[32,117],"dual-view":[33],"retrieval":[34,61,67,120],"pipeline":[35],"that":[36,111],"indexes":[37],"both":[38],"paragraph-level":[39],"context":[40],"compact":[42],"large":[43],"language":[44],"model-extracted":[45],"procedural":[46,112],"summaries,":[47],"then":[48],"combines":[49],"the":[50],"two":[51],"candidate":[52],"streams":[53],"with":[54],"lightweight":[55],"lexical":[56],"reranking.":[57],"Across":[58],"four":[59],"backbones,":[62],"RECIPER":[63],"consistently":[64],"improves":[65],"early-rank":[66],"over":[68],"retrieval,":[71],"achieving":[72],"average":[73],"gains":[74],"of":[75],"+3.73":[76],"in":[77,80,84],"Recall@1,":[78,95],"+2.85":[79],"nDCG@10,":[81],"+3.13":[83],"MRR.":[85],"With":[86],"BGE-large-en-v1.5,":[87],"it":[88],"reaches":[89],"86.82%,":[90],"97.07%,":[91],"97.85%":[93],"on":[94],"Recall@5,":[96],"Recall@10,":[98],"respectively.":[99],"further":[101],"observe":[102],"improved":[103],"downstream":[104],"question":[105,125],"answering":[106],"under":[107],"automatic":[108],"metrics,":[109],"suggesting":[110],"summaries":[113],"can":[114],"serve":[115],"as":[116],"useful":[118],"complementary":[119],"signal":[121],"for":[122],"answering.":[126],"Code":[127],"data":[129],"available":[131],"at":[132],"https://github.com/ReaganWu/RECIPER.":[133]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}