{"id":"https://openalex.org/W7134067749","doi":"https://doi.org/10.48550/arxiv.2603.04647","title":"Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models","display_name":"Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7134067749","doi":"https://doi.org/10.48550/arxiv.2603.04647"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04647","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","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/A5128243059","display_name":"Xin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076434580","display_name":"S. Gadgil","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gadgil, Saili Uday","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112152799","display_name":"Jiarong Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Jiarong","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.168016,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.4350000023841858,"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"}},"topics":[{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.4350000023841858,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.24420000612735748,"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":0.08129999786615372,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6570000052452087},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5764999985694885},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.536300003528595},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5275999903678894},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.4821000099182129},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4812999963760376},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.41600000858306885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8352000117301941},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6570000052452087},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5275999903678894},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42899999022483826},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.41600000858306885},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.39410001039505005},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3774000108242035},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.32440000772476196},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.2980000078678131},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.27320000529289246}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04647","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04647","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":"pmh:doi:10.48550/arxiv.2603.04647","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.7904835343360901,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval":[0],"augmented":[1,48,191],"generation":[2,31,49,64,91,172,192],"mitigates":[3],"limitations":[4],"of":[5,61,132,181],"large":[6],"language":[7,163],"models":[8],"in":[9,125,189],"factual":[10,156],"consistency":[11,144],"and":[12,30,63,74,93,99,135,145,158,179,185],"knowledge":[13],"updating":[14],"by":[15],"introducing":[16],"external":[17],"knowledge.":[18],"However,":[19],"practical":[20],"applications":[21],"still":[22],"suffer":[23],"from":[24,96,116],"semantic":[25,53,80,100,143,183],"misalignment":[26],"between":[27,72],"retrieved":[28,85],"results":[29,86,166],"objectives,":[32],"as":[33,35],"well":[34],"insufficient":[36],"evidence":[37,56,76,98,107,113,146,186],"utilization.":[38],"To":[39],"address":[40],"these":[41],"challenges,":[42],"this":[43,103],"paper":[44],"proposes":[45],"a":[46,78,121,149],"retrieval":[47,62,190],"method":[50,67],"that":[51,84],"integrates":[52],"alignment":[54,184],"with":[55,90],"constraints":[57,147],"through":[58],"coordinated":[59,182],"modeling":[60,142,188],"stages.":[65],"The":[66],"first":[68],"represents":[69],"the":[70,129,152,177],"relevance":[71],"queries":[73],"candidate":[75],"within":[77,148],"unified":[79,150],"space.":[81],"This":[82,127,175],"ensures":[83],"remain":[87],"semantically":[88],"consistent":[89],"goals":[92],"reduces":[94],"interference":[95],"noisy":[97],"drift.":[101],"On":[102],"basis,":[104],"an":[105,117],"explicit":[106],"constraint":[108,187],"mechanism":[109],"is":[110,114],"introduced.":[111],"Retrieved":[112],"transformed":[115],"implicit":[118],"context":[119],"into":[120],"core":[122],"control":[123],"factor":[124],"generation.":[126],"restricts":[128],"expression":[130],"scope":[131],"generated":[133],"content":[134],"strengthens":[136],"dependence":[137],"on":[138],"evidence.":[139],"By":[140],"jointly":[141],"framework,":[151],"proposed":[153],"approach":[154],"improves":[155],"reliability":[157],"verifiability":[159],"while":[160],"preserving":[161],"natural":[162],"fluency.":[164],"Comparative":[165],"show":[167],"stable":[168],"improvements":[169],"across":[170],"multiple":[171],"quality":[173],"metrics.":[174],"confirms":[176],"effectiveness":[178],"necessity":[180],"tasks.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-07T00:00:00"}