{"id":"https://openalex.org/W7166685848","doi":"https://doi.org/10.48550/arxiv.2606.28397","title":"CLOSER-VLN: Closed-Loop Self-Verified Retrieval-Augmented Reasoning for Aerial Vision-Language Navigation","display_name":"CLOSER-VLN: Closed-Loop Self-Verified Retrieval-Augmented Reasoning for Aerial Vision-Language Navigation","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7166685848","doi":"https://doi.org/10.48550/arxiv.2606.28397"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28397","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.2606.28397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130976927","display_name":"Shaoxuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shaoxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139685178","display_name":"Xiangyu Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139654277","display_name":"Xiaoguang Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xiaoguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139671399","display_name":"Junfeng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Junfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003608372","display_name":"Hongyun Zhao","orcid":"https://orcid.org/0000-0002-1251-3888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139644228","display_name":"Yaoming Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yaoming","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9818000197410583,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9818000197410583,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.003599999938160181,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0017999999690800905,"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/action","display_name":"Action (physics)","score":0.705299973487854},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6748999953269958},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5889000296592712},{"id":"https://openalex.org/keywords/semantic-reasoner","display_name":"Semantic reasoner","score":0.5200999975204468},{"id":"https://openalex.org/keywords/conjunction","display_name":"Conjunction (astronomy)","score":0.36800000071525574},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3418999910354614}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728999853134155},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.705299973487854},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6748999953269958},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5889000296592712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5877000093460083},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3303999900817871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3086000084877014},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2831999957561493},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28397","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.2606.28397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28397","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6223624348640442}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"navigation":[1,25],"(VLN)":[2],"has":[3],"recently":[4],"advanced":[5],"with":[6],"large":[7,34,76],"language":[8],"and":[9,49,79,107,126,162,192],"multimodal":[10,165],"models,":[11],"enabling":[12],"agents":[13],"to":[14,81],"follow":[15],"natural-language":[16],"instructions":[17,48],"in":[18,63,69,110,122],"unseen":[19],"environments":[20],"without":[21],"training":[22],"a":[23,95,111,128,137,148,163,172],"task-specific":[24],"policy.":[26],"However,":[27],"most":[28],"existing":[29],"VLN":[30,124],"methods":[31],"relying":[32],"on":[33,145,183,195],"models":[35],"still":[36],"adopt":[37],"an":[38],"open-loop":[39],"decision-execution":[40],"approach,":[41],"where":[42,66,187],"candidate":[43,142],"actions":[44,71,143,157],"are":[45,52],"generated":[46,158],"from":[47,171],"observations":[50],"but":[51],"rarely":[53],"verified":[54],"or":[55],"corrected":[56],"before":[57,114],"execution.":[58],"This":[59],"causes":[60],"critical":[61],"issues":[62],"aerial":[64,123],"VLN,":[65],"minor":[67],"errors":[68],"intermediate":[70],"may":[72],"quickly":[73],"accumulate":[74],"into":[75],"trajectory":[77],"deviations":[78],"lead":[80],"target":[82],"loss.":[83],"To":[84],"address":[85],"this":[86],"issue,":[87],"we":[88],"propose":[89],"Closed-loop":[90],"Self-verified":[91],"Retrieval-augmented":[92],"Reasoning":[93],"(CLOSER),":[94],"training-policy-free":[96],"method":[97],"that":[98],"sequentially":[99],"performs":[100],"action":[101,108,150],"reasoning,":[102],"reliability":[103,155],"verification,":[104],"targeted":[105,169],"retrieval,":[106],"correction":[109],"closed-loop":[112,203],"manner":[113],"executing":[115],"concrete":[116],"actions.":[117],"We":[118,179],"instantiate":[119],"the":[120,154,160,184,196,200],"CLOSER":[121],"tasks":[125],"develop":[127],"CLOSER-VLN":[129,188],"framework,":[130],"which":[131],"is":[132],"composed":[133],"of":[134,156,202],"three":[135],"components:":[136],"hierarchical":[138],"reasoner":[139],"for":[140,152,167],"generating":[141],"based":[144],"available":[146],"information,":[147],"multidimensional":[149],"verifier":[151],"assessing":[153],"by":[159],"reasoner,":[161],"verification-triggered":[164],"retriever":[166],"retrieving":[168],"exemplars":[170],"memory":[173],"bank":[174],"only":[175],"when":[176],"verification":[177],"fails.":[178],"conduct":[180],"experimental":[181],"evaluations":[182],"CityNav":[185],"benchmark,":[186],"achieves":[189],"32.01%":[190],"SR":[191],"21.28%":[193],"SPL":[194],"test-unseen":[197],"split,":[198],"confirming":[199],"effectiveness":[201],"reasoning.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
