{"id":"https://openalex.org/W7164885886","doi":"https://doi.org/10.48550/arxiv.2606.14865","title":"GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness","display_name":"GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164885886","doi":"https://doi.org/10.48550/arxiv.2606.14865"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14865","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.2606.14865","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061351550","display_name":"Zhiyuan Ye","orcid":"https://orcid.org/0000-0001-5299-3145"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye, Zhiyuan","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043931204","display_name":"Xiangyu Zhou","orcid":"https://orcid.org/0000-0002-0217-5344"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou, Xiangyu","raw_affiliation_strings":["China Mobile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138752956","display_name":"Ji Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi, Ji","raw_affiliation_strings":["China Mobile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138703967","display_name":"Hao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Hao","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112113287","display_name":"Zhou Yi","orcid":"https://orcid.org/0009-0007-2997-3868"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou, Yi","raw_affiliation_strings":["China Mobile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9861999750137329,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9861999750137329,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0026000000070780516,"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":0.002099999925121665,"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/robustness","display_name":"Robustness (evolution)","score":0.8033000230789185},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.7085999846458435},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6804999709129333},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5586000084877014},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.5113999843597412},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45989999175071716},{"id":"https://openalex.org/keywords/model-parameter","display_name":"Model parameter","score":0.4115000069141388},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40149998664855957},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3856000006198883}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8033000230789185},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.7085999846458435},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6804999709129333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068999767303467},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5586000084877014},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4925000071525574},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43849998712539673},{"id":"https://openalex.org/C2983447341","wikidata":"https://www.wikidata.org/wiki/Q1413083","display_name":"Model parameter","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40059998631477356},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2800999879837036},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C31531917","wikidata":"https://www.wikidata.org/wiki/Q915157","display_name":"Robust control","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C157524613","wikidata":"https://www.wikidata.org/wiki/Q2828883","display_name":"Fine-tuning","level":2,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14865","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.2606.14865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14865","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"Training":[1],"(AT)":[2],"improves":[3,131],"neural":[4],"network":[5],"robustness,":[6],"but":[7,185],"most":[8],"methods":[9],"train":[10],"a":[11,51,106,127,140,146],"fixed":[12],"parameter":[13,153,204],"space":[14],"from":[15,135],"the":[16,22,31,37,71,115,163,175,188],"start.":[17],"This":[18],"paper":[19],"asks":[20],"whether":[21],"order":[23],"in":[24,70],"which":[25],"parameters":[26],"become":[27],"optimizable":[28,78],"can":[29,199],"affect":[30],"final":[32,38,165,182],"robust":[33,68,102,133,171,203],"solution,":[34],"even":[35],"when":[36],"architecture":[39,167,183],"or":[40],"computation":[41,143],"budget":[42,144],"is":[43,177],"controlled.":[44],"We":[45],"propose":[46],"GRAPE,":[47],"Guided":[48],"Parameter-Space":[49],"Evolution,":[50],"training":[52],"framework":[53],"for":[54],"compact":[55,201],"adversarial":[56,83],"robustness.":[57],"GRAPE":[58,100,130],"combines":[59],"parameter-space":[60,110,189,197],"stabilization":[61],"with":[62,122,145,162],"progressive":[63,109],"hidden":[64],"expansion:":[65],"it":[66],"stabilizes":[67],"optimization":[69],"currently":[72],"exposed":[73],"space,":[74],"gradually":[75],"releases":[76],"new":[77],"dimensions,":[79],"and":[80,112,202],"uses":[81],"an":[82],"spectral":[84],"utilization":[85],"score":[86],"to":[87,97,137,181,187],"guide":[88],"newly":[89],"released":[90],"capacity":[91],"toward":[92],"high-pressure":[93],"modules.":[94],"In":[95],"contrast":[96],"fixed-structure":[98,123],"AT,":[99],"treats":[101],"model":[103,119],"learning":[104],"as":[105,126],"process":[107],"of":[108,149],"exposure":[111,190],"evolution.":[113],"Under":[114],"standard":[116],"$\\ell_\\infty$":[117],"threat":[118],"on":[120],"CIFAR-10,":[121],"ResNet-18":[124,166],"AT":[125],"controlled":[128],"reference,":[129],"PGD-20":[132,170],"accuracy":[134],"51.70%":[136],"56.94%":[138],"at":[139],"nearly":[141],"matched":[142,207],"FLOPs":[147],"ratio":[148],"1.009x,":[150],"while":[151],"reducing":[152],"count":[154],"by":[155],"about":[156],"21.4%.":[157],"A":[158],"sequential":[159],"grow":[160],"variant":[161],"same":[164],"reaches":[168],"56.52%":[169],"accuracy,":[172],"indicating":[173],"that":[174,195],"gain":[176],"not":[178],"only":[179],"due":[180],"differences":[184],"also":[186],"path.":[191],"These":[192],"results":[193],"suggest":[194],"guided":[196],"evolution":[198],"yield":[200],"configurations":[205],"under":[206],"computation.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}
