{"id":"https://openalex.org/W7155096793","doi":"https://doi.org/10.48550/arxiv.2604.17243","title":"RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation","display_name":"RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation","publication_year":2026,"publication_date":"2026-04-19","ids":{"openalex":"https://openalex.org/W7155096793","doi":"https://doi.org/10.48550/arxiv.2604.17243"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17243","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.17243","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134111963","display_name":"Rui Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Min, Rui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134143227","display_name":"Liang Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Liang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008787798","display_name":"Shiyu Miao","orcid":"https://orcid.org/0009-0008-4237-7822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao, Shiyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134199512","display_name":"Shengxiang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Shengxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134181571","display_name":"Yuxuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuxuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134105276","display_name":"Chuanyi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chuanyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134209834","display_name":"Shimin Di","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di, Shimin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134141868","display_name":"Fan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Fan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5134111963"],"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":0.7185999751091003,"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":0.7185999751091003,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.05139999836683273,"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":0.033399999141693115,"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/robustness","display_name":"Robustness (evolution)","score":0.6359999775886536},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4812000095844269},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4684999883174896},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4401000142097473},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4235000014305115},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.39579999446868896},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.35199999809265137},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.34040001034736633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408999800682068},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6359999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4812000095844269},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.398499995470047},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17243","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.17243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17243","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"A":[0],"robust":[1,122],"Multimodal":[2],"Large":[3],"Language":[4],"Model":[5],"(MLLM)":[6],"for":[7],"Earth":[8,49,214],"Observation":[9,215],"should":[10],"maintain":[11,128],"consistent":[12,129],"interpretation":[13],"and":[14,80,167,180,208,224],"reasoning":[15,107],"under":[16,230],"realistic":[17,69,132,231],"input":[18,133],"variations.":[19,134],"However,":[20],"current":[21],"Remote":[22,123],"Sensing":[23,124],"MLLMs":[24],"fail":[25],"to":[26,44,92,127,147,178,187],"meet":[27],"this":[28,64,116],"requirement.":[29],"Trained":[30],"on":[31,201,212],"carefully":[32],"curated":[33],"clean":[34,138,166,179],"datasets,":[35],"they":[36],"learn":[37],"brittle":[38],"mappings":[39],"that":[40,100,218],"do":[41],"not":[42],"generalize":[43],"noisy":[45,157],"conditions":[46,169],"in":[47,60],"operational":[48],"Observation.":[50],"Consequently,":[51],"their":[52],"performance":[53],"degrades":[54],"when":[55],"confronted":[56],"with":[57,84,142],"imperfect":[58],"inputs":[59],"deployment.":[61],"To":[62,114],"quantify":[63],"vulnerability,":[65],"we":[66,118],"construct":[67],"a":[68,121,149],"set":[70],"of":[71,109],"multimodal":[72,232],"perturbations,":[73],"including":[74],"visual":[75,206],"degradations":[76,207],"such":[77],"as":[78],"cloud":[79],"fog":[81],"cover,":[82],"together":[83],"diverse":[85],"human-centric":[86],"textual":[87,209],"variations":[88],"ranging":[89],"from":[90],"colloquialisms":[91],"vague":[93],"or":[94],"omitted":[95],"instructions.":[96],"Empirical":[97],"evaluations":[98],"show":[99,217],"these":[101],"perturbations":[102],"significantly":[103],"impair":[104],"the":[105,171,183,198],"visual-semantic":[106],"capabilities":[108],"leading":[110],"RS":[111],"foundation":[112],"models.":[113],"address":[115],"limitation,":[117],"introduce":[119],"RemoteShield,":[120],"MLLM":[125],"trained":[126],"outputs":[130],"across":[131],"During":[135],"training,":[136],"each":[137],"sample":[139],"is":[140,160,185],"paired":[141],"its":[143],"image-text":[144],"perturbed":[145,168],"variants":[146],"form":[148],"semantic":[150],"equivalence":[151],"cluster.":[152,173],"Rather":[153],"than":[154,227],"directly":[155],"fitting":[156],"samples,":[158],"RemoteShield":[159,219],"optimized":[161],"through":[162],"preference":[163],"learning":[164],"over":[165,191],"within":[170],"same":[172],"By":[174],"comparing":[175],"model":[176,184,199],"responses":[177,190],"corrupted":[181],"inputs,":[182],"encouraged":[186],"favor":[188],"stable":[189],"perturbation-induced":[192],"failures.":[193],"This":[194],"cross-condition":[195,225],"alignment":[196],"helps":[197],"focus":[200],"underlying":[202],"task":[203],"semantics":[204],"despite":[205],"noise.":[210],"Experiments":[211],"three":[213],"tasks":[216],"consistently":[220],"delivers":[221],"stronger":[222],"robustness":[223],"consistency":[226],"representative":[228],"baselines":[229],"perturbations.":[233]},"counts_by_year":[],"updated_date":"2026-04-22T06:07:44.442478","created_date":"2026-04-22T00:00:00"}
