{"id":"https://openalex.org/W7163987010","doi":"https://doi.org/10.48550/arxiv.2606.09441","title":"SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance","display_name":"SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7163987010","doi":"https://doi.org/10.48550/arxiv.2606.09441"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09441","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.2606.09441","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098717251","display_name":"Rya Sanovar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanovar, Rya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089179434","display_name":"Srikant Bharadwaj","orcid":"https://orcid.org/0000-0002-0422-5210"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bharadwaj, Srikant","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092005334","display_name":"Hritvik Taneja","orcid":"https://orcid.org/0000-0002-9870-7703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taneja, Hritvik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125404469","display_name":"Moinuddin Qureshi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qureshi, Moinuddin","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.20919999480247498,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.20919999480247498,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.13670000433921814,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.12870000302791595,"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":0.7771999835968018},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.7242000102996826},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6787999868392944},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.6455000042915344},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6218000054359436},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4779999852180481}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8147000074386597},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7771999835968018},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.7242000102996826},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6787999868392944},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.6455000042915344},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4075999855995178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3167000114917755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2962999939918518},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C2779089604","wikidata":"https://www.wikidata.org/wiki/Q7169333","display_name":"Permission","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C2778371909","wikidata":"https://www.wikidata.org/wiki/Q3771738","display_name":"Historical document","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09441","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.2606.09441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09441","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":{"Retrieval-Augmented":[0],"Generation":[1],"(RAG)":[2],"injects":[3],"LLM":[4],"queries":[5,29],"with":[6,189],"relevant":[7],"documents":[8,40,48,66,122],"to":[9,21,90,146,167,183,212,240],"improve":[10],"response":[11],"quality.":[12],"This":[13,170,199],"injection":[14],"increases":[15,57],"prompt":[16],"length":[17],"and":[18,56,68,124,221,261],"slows":[19],"time":[20],"first":[22],"token":[23],"(TTFT).":[24],"Unlike":[25],"standard":[26],"queries,":[27],"RAG":[28,51,65,114],"have":[30],"a":[31,96,163],"unique":[32],"property":[33],"of":[34,64,113,128,158,176,205,225,231,271],"context":[35],"reuse":[36,79],"where":[37,179,208],"the":[38,138,148,174,180,203,209,229,254,258],"same":[39],"recur":[41],"across":[42],"user":[43],"queries.":[44],"Thus,":[45],"fully":[46],"recomputing":[47],"for":[49,132,257],"every":[50],"query":[52],"does":[53],"redundant":[54],"compute":[55],"TTFT.":[58],"Prior":[59],"works":[60],"precompute":[61],"KV":[62,78,219,244],"tensors":[63],"offline":[67,123],"coarsely":[69],"recompute":[70],"some":[71],"tokens":[72],"during":[73,151],"online":[74],"prefill.":[75],"However,":[76],"such":[77,95],"is":[80,238],"often":[81],"slower":[82],"than":[83,243],"full":[84,272],"recomputation":[85,98],"on":[86],"modern":[87],"GPUs":[88],"due":[89],"high-latency":[91],"disk":[92,248],"transfers.":[93,249],"Further,":[94],"coarse-grained":[97],"degrades":[99],"accuracy.":[100],"To":[101],"address":[102],"these":[103],"limitations,":[104],"this":[105],"paper":[106],"proposes":[107],"SIFT:":[108],"Selective-Index":[109],"For":[110],"Fast":[111],"Compute":[112],"Prefill":[115],"by":[116,264],"Exploiting":[117],"Attention":[118],"Invariance.":[119],"SIFT":[120,216,252],"processes":[121],"extracts":[125],"fine-grained":[126],"locations":[127,150,224,260],"high":[129,159,177,190,206,226],"attention":[130,140,160,192,255],"scores":[131,161,178,207,227],"each":[133],"document.":[134],"Next,":[135],"we":[136],"identify":[137],"following":[139],"invariance":[141],"insights":[142],"that":[143],"enable":[144],"us":[145,172,201],"exploit":[147],"extracted":[149],"runtime:":[152],"(1)":[153],"Local-Attention":[154],"Invariance:":[155],"The":[156],"location":[157,175,204],"within":[162,269],"document":[164,181,210],"remain":[165],"invariant":[166],"surrounding":[168],"documents.":[169,198,214],"helps":[171,200],"predict":[173,202],"attends":[182,211],"itself.":[184],"(2)":[185],"Cross-Attention":[186],"Consistency:":[187],"Keys":[188],"intra-document":[191],"also":[193],"attract":[194],"cross-attention":[195],"from":[196],"subsequent":[197],"future":[213],"Critically,":[215],"stores":[217,223],"no":[218],"data":[220],"only":[222,256],"in":[228],"form":[230],"two":[232],"compact":[233],"bit":[234],"vectors.":[235],"SIFT's":[236],"storage":[237],"up":[239],"24,000x":[241],"smaller":[242],"tensors,":[245],"obviating":[246],"costly":[247],"During":[250],"prefill,":[251],"computes":[253],"marked":[259],"improves":[262],"TTFT":[263],"1.71x":[265],"while":[266],"holding":[267],"accuracy":[268],"1%":[270],"recompute.":[273]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
