{"id":"https://openalex.org/W7161565337","doi":"https://doi.org/10.48550/arxiv.2605.16046","title":"XSearch: Explainable Code Search via Concept-to-Code Alignment","display_name":"XSearch: Explainable Code Search via Concept-to-Code Alignment","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161565337","doi":"https://doi.org/10.48550/arxiv.2605.16046"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16046","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.2605.16046","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136382903","display_name":"Yiming Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102004483","display_name":"Ruofan Liu","orcid":"https://orcid.org/0000-0001-9421-7259"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ruofan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136431022","display_name":"Yun Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136435529","display_name":"Zicong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zicong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055558833","display_name":"Weiyu Kong","orcid":"https://orcid.org/0000-0002-7950-1806"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Weiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136411252","display_name":"Pengnian Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Pengnian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136422846","display_name":"Xiao Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136391375","display_name":"Weinan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weinan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012567911","display_name":"Qianxiang Wang","orcid":"https://orcid.org/0000-0002-6598-0041"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qianxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059624019","display_name":"Linpeng Huang","orcid":"https://orcid.org/0000-0002-1531-7962"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Linpeng","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":0.27230000495910645,"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":0.27230000495910645,"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":0.18930000066757202,"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/T10028","display_name":"Topic Modeling","score":0.1460999995470047,"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/embedding","display_name":"Embedding","score":0.6703000068664551},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6564000248908997},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.59579998254776},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5471000075340271},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5113999843597412},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.3912999927997589}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8187999725341797},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6703000068664551},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6564000248908997},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5471000075340271},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4693000018596649},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4196000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3986000120639801},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.26600000262260437},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16046","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.2605.16046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16046","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"code":[1,18,47,96,135,158,193],"search":[2,97,136],"has":[3],"been":[4],"widely":[5],"adopted":[6],"in":[7,148],"both":[8,223],"academia":[9],"and":[10,17,25,44,151,167,183,192,220,225,247],"industry.":[11],"These":[12],"approaches":[13],"embed":[14],"natural-language":[15],"queries":[16],"snippets":[19],"into":[20],"a":[21,138],"shared":[22],"embedding":[23,108],"space":[24],"retrieve":[26],"results":[27,245],"based":[28],"on":[29,35,106,199,208],"vector":[30],"similarity.":[31],"Despit":[32],"strong":[33],"performance":[34,207],"benchmark":[36],"datasets,":[37],"they":[38],"often":[39],"suffer":[40],"from":[41,211],"poor":[42],"explainability":[43],"generalization.":[45,174],"Retrieved":[46],"may":[48],"appear":[49],"semantically":[50],"similar":[51],"yet":[52],"miss":[53],"critical":[54],"functional":[55,127,146],"requirements":[56],"of":[57,64],"the":[58,66,125,149],"query,":[59],"while":[60],"providing":[61],"no":[62],"explanation":[63],"why":[65],"result":[67],"was":[68],"retrieved.":[69],"Moreover,":[70],"such":[71],"failures":[72],"become":[73],"more":[74,248],"severe":[75],"under":[76],"distribution":[77],"shift,":[78],"where":[79],"models":[80],"struggle":[81],"to":[82,84,213,230,242],"generalize":[83],"unseen":[85],"benchmarks.":[86],"In":[87],"this":[88,131],"work,":[89],"we":[90],"propose":[91],"XSearch,":[92],"an":[93,114,177],"intrinsically":[94],"explainable":[95],"framework.":[98],"Our":[99],"key":[100],"insight":[101],"is":[102],"that":[103,171,237],"by":[104,133],"relying":[105],"global":[107],"similarity,":[109],"existing":[110],"retrievers":[111],"inherently":[112],"take":[113],"inductive":[115],"view.":[116],"They":[117],"learn":[118],"statistical":[119],"patterns":[120],"rather":[121],"than":[122],"truly":[123],"understanding":[124],"query's":[126],"requirements.":[128],"We":[129,175],"address":[130],"problem":[132],"reformulating":[134],"as":[137],"deductive":[139],"concept":[140],"alignment":[141],"problem.":[142],"XSearch":[143,205],"(i)":[144],"identifies":[145],"concepts":[147,191],"query":[150,190],"(ii)":[152],"explicitly":[153],"aligns":[154],"them":[155],"with":[156,179,228],"corresponding":[157],"statements.":[159,194],"This":[160],"explain-then-predict":[161],"design":[162],"produces":[163],"inherent":[164],"concept-level":[165],"explanations":[166,239],"mitigates":[168],"shortcut":[169],"learning":[170],"harms":[172],"out-of-distribution":[173,209],"train":[176],"encoder":[178],"explicit":[180,187],"concept-alignment":[181,238],"objectives":[182],"perform":[184],"retrieval":[185],"through":[186],"matching":[188],"between":[189],"Experiments":[195],"show":[196],"that,":[197],"trained":[198],"CodeSearchNet":[200],"using":[201],"GraphCodeBERT":[202],"(125M":[203],"parameters),":[204],"improves":[206],"benchmarks":[210],"0.02":[212],"0.33":[214],"(15x)":[215],"over":[216],"eight":[217],"state-of-the-art":[218],"retrievers,":[219],"consistently":[221],"outperforms":[222],"encoder-":[224],"decoder-based":[226],"baselines":[227],"up":[229],"7B":[231],"parameters.":[232],"A":[233],"user":[234],"study":[235],"demonstrates":[236],"enable":[240],"users":[241],"evaluate":[243],"retrieved":[244],"faster":[246],"accurately.":[249]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-19T00:00:00"}
