{"id":"https://openalex.org/W7168149333","doi":"https://doi.org/10.48550/arxiv.2607.08839","title":"Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing","display_name":"Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing","publication_year":2026,"publication_date":"2026-07-09","ids":{"openalex":"https://openalex.org/W7168149333","doi":"https://doi.org/10.48550/arxiv.2607.08839"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08839","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":"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.2607.08839","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059789621","display_name":"Dominick Reilly","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reilly, Dominick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019206078","display_name":"Qile Wu","orcid":"https://orcid.org/0009-0003-1096-1892"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Qiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014478861","display_name":"Hiromi Wakaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wakaki, Hiromi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005981376","display_name":"S R Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Srijan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140549332","display_name":"Yuki Mistufuji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mistufuji, Yuki","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/T10028","display_name":"Topic Modeling","score":0.4569999873638153,"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/T10028","display_name":"Topic Modeling","score":0.4569999873638153,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17880000174045563,"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":0.06449999660253525,"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/modalities","display_name":"Modalities","score":0.9169999957084656},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.815500020980835},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7663999795913696},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5600000023841858},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.3716000020503998}],"concepts":[{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.9169999957084656},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.815500020980835},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7663999795913696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523000001907349},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5600000023841858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5533000230789185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4320000112056732},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3027999997138977},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08839","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":"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.2607.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08839","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":"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":{"Multimodal":[0],"Large":[1],"Language":[2],"Models":[3],"(MLLMs)":[4],"are":[5,42],"typically":[6],"designed":[7],"under":[8,34,183],"the":[9,90,93,190,201],"assumption":[10],"that":[11,83,116,163,220],"all":[12],"modalities":[13,41,49,64,182],"available":[14,43,246],"during":[15,45,235],"training":[16,153],"will":[17,243],"also":[18],"be":[19,244],"accessible":[20],"at":[21,204,226,247],"inference.":[22],"However,":[23],"many":[24],"real-world":[25],"settings":[26],"violate":[27],"this":[28,110,143],"assumption,":[29],"requiring":[30],"models":[31],"to":[32,57,95,189,215],"operate":[33],"a":[35,80,112,125,152,159,184],"privileged":[36,191],"modality":[37,127,192,196],"setting,":[38,193],"where":[39,194],"auxiliary":[40,221],"only":[44,132],"training.":[46,236],"While":[47],"these":[48],"contain":[50],"valuable":[51],"information,":[52],"existing":[53,139],"MLLMs":[54],"largely":[55],"fail":[56],"leverage":[58],"them":[59],"effectively,":[60],"as":[61,65,136,200],"they":[62],"treat":[63],"interchangeable":[66],"inputs":[67],"rather":[68,129],"than":[69,130],"sources":[70],"of":[71,77,124],"complementary":[72],"supervision.":[73],"We":[74,171],"propose":[75],"Mixture":[76],"Probes":[78],"(MoP),":[79],"novel":[81],"framework":[82],"disentangles":[84],"modality-specific":[85],"and":[86,118,167,180,240],"modality-general":[87],"signals":[88],"within":[89],"MLLM,":[91],"allowing":[92],"model":[94,238],"preserve":[96],"modality-dependent":[97],"structure":[98],"while":[99],"learning":[100],"transferable":[101],"representations":[102,123],"across":[103,174],"modalities.":[104],"At":[105],"its":[106],"core,":[107],"MoP":[108,148,156,173,207],"achieves":[109],"through":[111],"structured":[113],"probing":[114],"mechanism":[115],"extracts":[117],"organizes":[119],"information":[120],"from":[121],"intermediate":[122],"shared":[126],"encoder,":[128],"relying":[131],"on":[133],"final-layer":[134],"alignment":[135],"done":[137],"in":[138],"MLLMs.":[140],"To":[141],"support":[142],"disentanglement,":[144],"we":[145],"further":[146],"introduce":[147],"Cross-modal":[149],"Training":[150],"(MoP-X),":[151],"strategy":[154],"for":[155],"centered":[157],"around":[158],"probe":[160,165],"disentanglement":[161],"loss":[162],"prevents":[164],"collapse":[166],"encourages":[168],"cross-modal":[169],"learning.":[170],"evaluate":[172],"two":[175],"domains":[176],"spanning":[177],"eight":[178],"tasks":[179],"four":[181],"comprehensive":[185],"evaluation":[186,241],"protocol":[187],"tailored":[188],"each":[195],"is":[197],"independently":[198],"treated":[199],"sole":[202],"input":[203],"inference":[205],"time.":[206],"consistently":[208],"outperforms":[209],"strong":[210],"MLLM":[211],"baselines,":[212],"achieving":[213],"up":[214],"65%":[216],"relative":[217],"improvement,":[218],"demonstrating":[219],"modalities,":[222],"even":[223],"when":[224,232],"unavailable":[225],"inference,":[227],"can":[228],"provide":[229],"substantial":[230],"gains":[231],"effectively":[233],"leveraged":[234],"Code,":[237],"checkpoints,":[239],"protocols":[242],"made":[245],"https://github.com/Sony/MoP.":[248]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-14T00:00:00"}
