{"id":"https://openalex.org/W7155992259","doi":"https://doi.org/10.48550/arxiv.2604.22281","title":"DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning","display_name":"DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7155992259","doi":"https://doi.org/10.48550/arxiv.2604.22281"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22281","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.2604.22281","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041112881","display_name":"Joonmyung Choi","orcid":"https://orcid.org/0000-0003-3673-8069"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Joonmyung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134726312","display_name":"Sanghyeok Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Sanghyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134715154","display_name":"Jongha Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jongha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043210468","display_name":"Sehyung Kim","orcid":"https://orcid.org/0009-0003-6381-9291"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Sehyung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134660828","display_name":"Dohwan Ko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ko, Dohwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134683038","display_name":"Jihyung Kil","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kil, Jihyung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134677550","display_name":"Hyunwoo J. Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Hyunwoo J.","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9021999835968018,"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":0.9021999835968018,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.031199999153614044,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.016699999570846558,"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/security-token","display_name":"Security token","score":0.8263999819755554},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.8246999979019165},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7181000113487244},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6610000133514404},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5586000084877014},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46779999136924744},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3869999945163727},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.34220001101493835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8378000259399414},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8263999819755554},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8246999979019165},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6610000133514404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6136999726295471},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5586000084877014},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3440000116825104},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.34220001101493835},{"id":"https://openalex.org/C115067241","wikidata":"https://www.wikidata.org/wiki/Q1639854","display_name":"Token passing","level":3,"score":0.3395000100135803},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C2780479914","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Document classification","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26510000228881836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22281","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.2604.22281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22281","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,67,147],"vision-language":[3],"models":[4],"have":[5],"demonstrated":[6],"remarkable":[7],"performance":[8],"across":[9],"diverse":[10],"multi-modal":[11],"tasks,":[12],"including":[13],"document":[14,31,85],"question":[15],"answering":[16],"that":[17,56,139],"leverages":[18],"structured":[19],"visual":[20],"cues":[21],"from":[22],"text,":[23],"tables,":[24],"and":[25,36,63,83,145,150,164],"figures.":[26],"However,":[27],"unlike":[28],"natural":[29,61],"images,":[30],"images":[32,62],"contain":[33],"large":[34],"backgrounds":[35],"only":[37,98],"sparse":[38],"supporting":[39],"evidence,":[40],"leading":[41],"to":[42,73,122],"the":[43,69,99,103,119,128,136,148,155],"inefficient":[44],"consumption":[45],"of":[46,131],"substantial":[47],"computational":[48],"resources,":[49],"especially":[50],"for":[51,60,90,102],"long":[52],"documents.":[53,74],"We":[54],"observe":[55],"existing":[57],"token-reduction":[58],"methods":[59],"videos":[64],"fall":[65],"short":[66],"utilizing":[68],"structural":[70],"sparsity":[71],"unique":[72],"To":[75],"address":[76],"this,":[77],"we":[78],"propose":[79],"DocPrune,":[80],"a":[81],"training-free":[82],"progressive":[84],"token":[86,124],"pruning":[87,125],"framework":[88],"designed":[89],"efficient":[91],"long-document":[92],"understanding.":[93],"The":[94],"proposed":[95],"method":[96],"preserves":[97],"essential":[100],"tokens":[101],"task":[104],"while":[105,153],"removing":[106],"unnecessary":[107],"ones,":[108],"such":[109],"as":[110],"background":[111],"or":[112],"question-irrelevant":[113],"tokens.":[114],"Moreover,":[115],"it":[116],"automatically":[117],"selects":[118],"appropriate":[120],"layers":[121],"initiate":[123],"based":[126],"on":[127,135],"model's":[129],"level":[130],"comprehension.":[132],"Our":[133],"experiments":[134],"M3DocRAG":[137],"show":[138],"DocPrune":[140],"improves":[141],"throughput":[142],"by":[143,158],"3.0x":[144],"3.3x":[146],"encoder":[149],"decoder,":[151],"respectively,":[152],"boosting":[154],"F1":[156],"score":[157],"+1.0,":[159],"achieving":[160],"both":[161],"higher":[162],"accuracy":[163],"efficiency":[165],"without":[166],"any":[167],"additional":[168],"training.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-28T00:00:00"}