{"id":"https://openalex.org/W7141121141","doi":"https://doi.org/10.48550/arxiv.2603.25035","title":"Mechanistically Interpreting Compression in Vision-Language Models","display_name":"Mechanistically Interpreting Compression in Vision-Language Models","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141121141","doi":"https://doi.org/10.48550/arxiv.2603.25035"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25035","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25035","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116085865","display_name":"Vyshakha Elluru","orcid":"https://orcid.org/0009-0000-0148-7162"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Elluru, Veeraraju","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018244556","display_name":"Arth Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Arth","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130766386","display_name":"Roberto Aguero","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aguero, Roberto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078083467","display_name":"Ajay Agarwal","orcid":"https://orcid.org/0000-0003-0499-5511"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agarwal, Ajay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130787911","display_name":"Debojyoti Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Debojyoti","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130747068","display_name":"Hreetam Paul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paul, Hreetam","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5116085865"],"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":0.32910001277923584,"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":0.32910001277923584,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.23829999566078186,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09549999982118607,"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/quantization","display_name":"Quantization (signal processing)","score":0.6603999733924866},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.621399998664856},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5979999899864197},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.498199999332428},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.47279998660087585},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4226999878883362},{"id":"https://openalex.org/keywords/counterfactual-conditional","display_name":"Counterfactual conditional","score":0.39629998803138733}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682000160217285},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6603999733924866},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.621399998664856},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5979999899864197},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.39629998803138733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39480000734329224},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.36399999260902405},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25035","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25035","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":"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":{"Compressed":[0],"vision-language":[1],"models":[2,24],"(VLMs)":[3],"are":[4,34],"widely":[5],"used":[6],"to":[7,48],"reduce":[8],"memory":[9],"and":[10,31,44,52,72],"compute":[11],"costs,":[12],"making":[13],"them":[14],"a":[15,82,99,120],"suitable":[16],"choice":[17,130],"for":[18],"real-world":[19],"deployment.":[20],"However,":[21],"compressing":[22],"these":[23],"raises":[25],"concerns":[26],"about":[27],"whether":[28],"internal":[29,74],"computations":[30],"safety":[32,112,134],"behaviors":[33],"preserved.":[35],"In":[36],"this":[37,93],"work,":[38],"we":[39,95],"use":[40],"causal":[41],"circuit":[42,67],"analysis":[43],"crosscoder-based":[45],"feature":[46],"comparisons":[47],"examine":[49],"how":[50],"pruning":[51,64,118],"quantization":[53,77],"fundamentally":[54],"change":[55],"the":[56,79,87,129],"internals":[57],"across":[58,110],"representative":[59],"VLMs.":[60],"We":[61],"observe":[62],"that":[63,102,117,128],"generally":[65],"keeps":[66],"structure":[68],"intact":[69],"but":[70],"rotates":[71],"attenuates":[73],"features,":[75],"while":[76],"modifies":[78],"circuits":[80],"at":[81],"higher":[83],"level":[84],"yet":[85],"leaves":[86],"surviving":[88],"features":[89],"better":[90],"aligned.":[91],"Leveraging":[92],"insight,":[94],"also":[96],"introduce":[97],"VLMSafe-420,":[98],"novel":[100],"benchmark":[101],"pairs":[103],"harmful":[104],"inputs":[105],"with":[106],"matched":[107],"benign":[108],"counterfactuals":[109],"various":[111],"categories.":[113],"Our":[114],"findings":[115],"show":[116],"causes":[119],"sharp":[121],"drop":[122],"in":[123],"genuine":[124],"refusal":[125],"behavior,":[126],"suggesting":[127],"of":[131],"compression":[132],"has":[133],"implications.":[135]},"counts_by_year":[],"updated_date":"2026-03-28T06:16:51.555046","created_date":"2026-03-28T00:00:00"}