{"id":"https://openalex.org/W7163059941","doi":"https://doi.org/10.48550/arxiv.2605.31100","title":"Vector Linking via Cross-Model Local Isometric Consistency","display_name":"Vector Linking via Cross-Model Local Isometric Consistency","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163059941","doi":"https://doi.org/10.48550/arxiv.2605.31100"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31100","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":"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.2605.31100","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085176870","display_name":"Ziying Chen","orcid":"https://orcid.org/0009-0005-8931-6662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ziying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137560410","display_name":"Yang Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137585747","display_name":"He Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137608428","display_name":"Beining Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Beining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043376740","display_name":"Tianjian Yang","orcid":"https://orcid.org/0009-0000-4699-9874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Tianjian","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.26409998536109924,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.26409998536109924,"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"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.13379999995231628,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.10920000076293945,"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":0.7099999785423279},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.5947999954223633},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.529699981212616},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5218999981880188},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4796999990940094},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.415800005197525},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.40220001339912415},{"id":"https://openalex.org/keywords/hamming-distance","display_name":"Hamming distance","score":0.39570000767707825},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.382999986410141}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7099999785423279},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5947999954223633},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.529699981212616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5224000215530396},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5218999981880188},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4796999990940094},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46399998664855957},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39089998602867126},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.382999986410141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34619998931884766},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C84316537","wikidata":"https://www.wikidata.org/wiki/Q36255","display_name":"Unit vector","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C177292542","wikidata":"https://www.wikidata.org/wiki/Q440959","display_name":"Vector map","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C150807984","wikidata":"https://www.wikidata.org/wiki/Q1992074","display_name":"Bit array","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2770000100135803},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C82668687","wikidata":"https://www.wikidata.org/wiki/Q3046456","display_name":"Earth mover's distance","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C118965365","wikidata":"https://www.wikidata.org/wiki/Q44528","display_name":"Euclidean vector","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31100","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":"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.2605.31100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31100","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":"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"study":[1],"Vector":[2],"Linking:":[3],"given":[4],"two":[5],"embedding":[6,66,117],"clouds":[7],"produced":[8],"by":[9,84],"different":[10],"black-box":[11],"encoders":[12,33],"over":[13],"partially":[14],"overlapping":[15],"datasets,":[16],"recover":[17],"cross-model":[18,140],"object":[19],"correspondences":[20],"using":[21],"only":[22],"vectors.":[23],"Empirically":[24],"and":[25,96,116,122,130,139],"theoretically,":[26],"we":[27,60],"show":[28],"that":[29,68],"independently":[30],"trained":[31],"contrastive":[32],"exhibit":[34],"local":[35],"geometric":[36,65],"consistency:":[37],"short-range":[38],"distances":[39,50,85],"are":[40,51],"approximately":[41],"preserved":[42],"up":[43],"to":[44,54,86,105,135],"a":[45,73,102],"scale":[46],"factor,":[47],"while":[48],"long-range":[49],"not":[52],"due":[53],"model-specific":[55],"distortion.":[56],"Building":[57],"on":[58],"this,":[59],"propose":[61],"an":[62],"iterative,":[63],"reference-based":[64],"hashing":[67],"recovers":[69],"vector":[70,83,136],"links":[71,92,108],"from":[72],"tiny":[74],"seed":[75,128],"set":[76],"of":[77],"paired":[78,88],"anchors.":[79,111],"It":[80],"represents":[81],"each":[82],"sampled":[87],"anchors,":[89,132],"proposes":[90],"candidate":[91],"via":[93],"hash-space":[94],"matching,":[95],"aggregates":[97],"evidence":[98],"across":[99,113],"views":[100],"in":[101],"Beta-Bernoulli":[103],"posterior":[104],"bootstrap":[106],"high-confidence":[107],"as":[109],"new":[110],"Experiments":[112],"multiple":[114],"benchmarks":[115],"model":[118],"pairs":[119],"demonstrate":[120],"accurate":[121],"robust":[123],"linking":[124],"under":[125],"varying":[126],"overlap,":[127],"budgets,":[129],"out-of-domain":[131],"with":[133],"applications":[134],"database":[137],"integration":[138],"clustering.":[141],"Code":[142],"is":[143],"available":[144],"at":[145],"https://github.com/DBgroup-Edinburgh/VecLinking.":[146]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
