{"id":"https://openalex.org/W7162325143","doi":"https://doi.org/10.48550/arxiv.2605.23410","title":"What Linear Probes Miss: Multi-View Probing for Weight-Space Learning","display_name":"What Linear Probes Miss: Multi-View Probing for Weight-Space Learning","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162325143","doi":"https://doi.org/10.48550/arxiv.2605.23410"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23410","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.23410","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113189047","display_name":"Eunwoo Heo","orcid":"https://orcid.org/0009-0004-3886-1276"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heo, Eunwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136974264","display_name":"Kyeongkook Seo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Kyeongkook","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5089933293","display_name":"Jaejun Yoo","orcid":"https://orcid.org/0000-0001-5252-9668"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoo, Jaejun","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.21739999949932098,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.21739999949932098,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.09910000115633011,"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/T10028","display_name":"Topic Modeling","score":0.0949999988079071,"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/discriminative-model","display_name":"Discriminative model","score":0.7111999988555908},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4943999946117401},{"id":"https://openalex.org/keywords/standardization","display_name":"Standardization","score":0.478300005197525},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.387800008058548},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.37139999866485596},{"id":"https://openalex.org/keywords/documentation","display_name":"Documentation","score":0.3452000021934509},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3240000009536743},{"id":"https://openalex.org/keywords/scaling-law","display_name":"Scaling law","score":0.3005000054836273}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7111999988555908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7078999876976013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5551999807357788},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4943999946117401},{"id":"https://openalex.org/C188087704","wikidata":"https://www.wikidata.org/wiki/Q369577","display_name":"Standardization","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.387800008058548},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.37139999866485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37040001153945923},{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3122999966144562},{"id":"https://openalex.org/C2988430800","wikidata":"https://www.wikidata.org/wiki/Q428971","display_name":"Scaling law","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C178300618","wikidata":"https://www.wikidata.org/wiki/Q1898509","display_name":"Extant taxon","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23410","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.23410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23410","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7511948347091675}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"explosive":[1],"growth":[2],"of":[3,115],"open-source":[4],"model":[5],"repositories":[6],"has":[7],"created":[8],"a":[9,26,49,66,93,121],"Model":[10,136],"Jungle,":[11],"where":[12],"checkpoints":[13],"are":[14,63],"frequently":[15],"shared":[16],"without":[17],"adequate":[18],"documentation":[19],"or":[20],"metadata.":[21],"While":[22],"weight-space":[23],"learning":[24],"offers":[25],"pathway":[27],"to":[28,75,119],"identify":[29],"and":[30,124,155],"analyze":[31,111],"these":[32],"models":[33],"directly":[34],"from":[35,131],"their":[36],"parameters,":[37],"processing":[38],"full-scale":[39],"weights":[40],"is":[41,107],"computationally":[42],"prohibitive.":[43],"Probing-based":[44],"methods":[45,62],"have":[46],"emerged":[47],"as":[48],"lightweight":[50],"alternative,":[51],"extracting":[52],"permutation-equivariant":[53],"representations":[54],"via":[55],"learnable":[56],"probe":[57],"vectors.":[58],"However,":[59],"existing":[60],"probing":[61,95,117],"limited":[64],"by":[65],"single-view":[67],"design:":[68],"they":[69],"capture":[70],"first-order":[71,99],"structures":[72],"but":[73],"fail":[74],"encode":[76],"the":[77,112,135,142],"rich,":[78],"higher-order":[79],"correlation":[80],"patterns":[81],"inherent":[82],"in":[83],"row-column":[84],"interactions.":[85],"To":[86],"bridge":[87],"this":[88],"gap,":[89],"we":[90,110],"introduce":[91],"MVProbe,":[92],"multi-perspective":[94],"framework":[96],"that":[97,127],"synthesizes":[98],"signals":[100],"with":[101],"interaction-aware":[102],"(Gram-based)":[103],"views.":[104],"Our":[105],"approach":[106],"theoretically":[108],"grounded;":[109],"scaling":[113],"laws":[114],"different":[116],"orders":[118],"derive":[120],"principled":[122],"standardization":[123],"fusion":[125],"strategy":[126],"ensures":[128],"balanced":[129],"contributions":[130],"all":[132],"branches.":[133],"On":[134],"Jungle":[137],"benchmark,":[138],"MVProbe":[139],"consistently":[140],"outperforms":[141],"state-of-the-art":[143],"ProbeX":[144],"across":[145],"diverse":[146],"architectures,":[147],"including":[148],"discriminative":[149],"backbones":[150],"(ResNet,":[151],"SupViT,":[152],"MAE,":[153],"DINO)":[154],"large-scale":[156],"generative":[157],"LoRA":[158],"adapters":[159],"(Stable":[160],"Diffusion":[161],"LoRA).":[162]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}