{"id":"https://openalex.org/W7136866070","doi":"https://doi.org/10.48550/arxiv.2603.12942","title":"ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries","display_name":"ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136866070","doi":"https://doi.org/10.48550/arxiv.2603.12942"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12942","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.2603.12942","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129471592","display_name":"Hang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052009653","display_name":"Fengyi Shen","orcid":"https://orcid.org/0000-0001-7621-9779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Fengyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129430739","display_name":"Dong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Dong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101493085","display_name":"Liudi Yang","orcid":"https://orcid.org/0000-0002-3625-1069"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Liudi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129400436","display_name":"Xudong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xudong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129462060","display_name":"Jinkui Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Jinkui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060444894","display_name":"Zhenshan Bing","orcid":"https://orcid.org/0000-0002-0896-2517"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bing, Zhenshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129493485","display_name":"Ziyuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129449159","display_name":"Alois Knoll","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Knoll, Alois","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":0.8718000054359436,"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":0.8718000054359436,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.017500000074505806,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6223000288009644},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5884000062942505},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5620999932289124},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5224000215530396},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4250999987125397},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.38839998841285706},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.37540000677108765},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3693000078201294},{"id":"https://openalex.org/keywords/auxiliary-memory","display_name":"Auxiliary memory","score":0.3671000003814697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786300003528595},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6223000288009644},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5884000062942505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.583899974822998},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37549999356269836},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C178278151","wikidata":"https://www.wikidata.org/wiki/Q7936607","display_name":"Visual memory","level":3,"score":0.36640000343322754},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.335099995136261},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.30709999799728394},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C159176650","wikidata":"https://www.wikidata.org/wiki/Q43261","display_name":"Horizon","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C2908950501","wikidata":"https://www.wikidata.org/wiki/Q11072","display_name":"Memory problems","level":4,"score":0.29339998960494995},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12942","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.2603.12942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12942","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language-action":[0],"(VLA)":[1],"models":[2],"for":[3,75,90,96],"closed-loop":[4],"robot":[5,144],"control":[6],"are":[7,101],"typically":[8],"cast":[9],"under":[10],"the":[11,43,114],"Markov":[12],"assumption,":[13],"making":[14],"them":[15],"prone":[16],"to":[17,81,104,124],"errors":[18],"on":[19,177],"tasks":[20,179],"requiring":[21],"historical":[22],"context.":[23],"To":[24],"incorporate":[25],"memory,":[26,84,127],"existing":[27],"VLAs":[28],"either":[29],"retrieve":[30],"from":[31],"a":[32,59,181],"memory":[33,73,88,152],"bank,":[34],"which":[35],"can":[36],"be":[37],"misled":[38],"by":[39,180],"distractors,":[40],"or":[41,120],"extend":[42],"frame":[44],"window,":[45],"whose":[46],"fixed":[47],"horizon":[48],"still":[49],"limits":[50],"long-term":[51,97],"retention.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56,128,146],"introduce":[57,129],"ReMem-VLA,":[58],"Recurrent":[60],"Memory":[61],"VLA":[62,169],"model":[63],"equipped":[64],"with":[65],"two":[66],"sets":[67],"of":[68],"learnable":[69],"queries:":[70],"frame-level":[71],"recurrent":[72,87],"queries":[74,89,100],"propagating":[76],"information":[77],"across":[78,93,154],"consecutive":[79],"frames":[80],"support":[82],"short-term":[83],"and":[85,106,142,162,172,174],"chunk-level":[86],"carrying":[91],"context":[92,109],"temporal":[94],"chunks":[95],"memory.":[98,164],"These":[99],"trained":[102],"end-to-end":[103],"aggregate":[105],"maintain":[107],"relevant":[108],"over":[110],"time,":[111],"implicitly":[112],"guiding":[113],"model's":[115],"decisions":[116],"without":[117],"additional":[118],"training":[119,136],"inference":[121],"cost.":[122],"Furthermore,":[123],"enhance":[125],"visual":[126,163],"Past":[130],"Observation":[131],"Prediction":[132],"as":[133],"an":[134],"auxiliary":[135],"objective.":[137],"Through":[138],"extensive":[139],"memory-centric":[140],"simulation":[141],"real-world":[143],"experiments,":[145],"demonstrate":[147],"that":[148],"ReMem-VLA":[149,165],"exhibits":[150],"strong":[151],"capabilities":[153],"multiple":[155],"dimensions,":[156],"including":[157],"spatial,":[158],"sequential,":[159],"episodic,":[160],"temporal,":[161],"significantly":[166],"outperforms":[167],"memory-free":[168],"baselines":[170],"$\u03c0$0.5":[171],"OpenVLA-OFT":[173],"surpasses":[175],"MemoryVLA":[176],"memory-dependent":[178],"large":[182],"margin.":[183]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-17T00:00:00"}
