{"id":"https://openalex.org/W7162567355","doi":"https://doi.org/10.48550/arxiv.2605.27105","title":"Lost in the Evidence? Reproducing Document Position and Context Size Effects in RAG","display_name":"Lost in the Evidence? Reproducing Document Position and Context Size Effects in RAG","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162567355","doi":"https://doi.org/10.48550/arxiv.2605.27105"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27105","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.2605.27105","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002096079","display_name":"Jorge Gab\u00edn","orcid":"https://orcid.org/0000-0002-5494-0765"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gab\u00edn, Jorge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068564161","display_name":"Anxo P\u00e9rez","orcid":"https://orcid.org/0000-0002-0480-006X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perez, Anxo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137154739","display_name":"Javier Parapar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parapar, Javier","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":0.9100000262260437,"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":0.9100000262260437,"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/T10028","display_name":"Topic Modeling","score":0.051600001752376556,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.005799999926239252,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7177000045776367},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6140000224113464},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.557200014591217},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.5364000201225281},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.47690001130104065},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.4691999852657318},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4401000142097473},{"id":"https://openalex.org/keywords/sampling-bias","display_name":"Sampling bias","score":0.36489999294281006}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7177000045776367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6924999952316284},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6140000224113464},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.5364000201225281},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.46369999647140503},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4401000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3743000030517578},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.36489999294281006},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32690000534057617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C78780964","wikidata":"https://www.wikidata.org/wiki/Q7233193","display_name":"Position paper","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27105","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.2605.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27105","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5643965601921082}],"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],"systems":[3],"rely":[4],"on":[5,105,140,200,263],"retrieved":[6],"documents":[7],"being":[8],"concatenated":[9],"into":[10],"a":[11,62,79,90,115,158,169,183],"model's":[12],"input":[13],"context,":[14],"making":[15],"both":[16,226],"document":[17],"ordering":[18,102],"and":[19,38,47,54,70,137,148,187,198,212,233,236,255,260],"context":[20,213],"size":[21,214],"critical":[22],"yet":[23],"controversial":[24],"design":[25],"choices.":[26],"Prior":[27],"work":[28,262],"reports":[29],"position-based":[30],"effects":[31],"such":[32,193],"as":[33,194],"lost":[34,144],"in":[35,145,151,163],"the":[36,146],"middle":[37,147],"related":[39],"long-context":[40],"phenomena.":[41],"However,":[42],"empirical":[43],"findings":[44],"remain":[45],"inconsistent":[46],"hard":[48],"to":[49,175,190,247,257],"reproduce":[50,136],"across":[51,109],"models,":[52],"datasets,":[53],"evaluation":[55,81,191],"protocols.":[56],"In":[57,178],"this":[58,179],"paper,":[59],"we":[60,113,134,155,181,204],"present":[61],"systematic":[63],"reproducibility":[64,259],"study":[65,157,186],"that":[66,86,119,225,237],"revisits":[67],"these":[68,130],"claims":[69],"examines":[71],"how":[72,209],"they":[73],"evolve":[74],"with":[75,230],"contemporary":[76],"LLMs":[77],"under":[78,219],"controlled":[80],"framework.":[82],"We":[83,251],"first":[84],"show":[85],"topic":[87,96,111,121,132,196],"sampling":[88,108],"is":[89,166],"major":[91],"source":[92],"of":[93,208],"variance:":[94],"small":[95],"sets":[97],"can":[98],"mask":[99],"or":[100],"exaggerate":[101],"effects.":[103],"Based":[104],"repeated":[106],"subset":[107],"multiple":[110],"budgets,":[112],"provide":[114],"practical":[116],"calibration":[117],"procedure":[118],"identifies":[120],"counts":[122],"yielding":[123],"stable":[124],"trends":[125],"at":[126],"feasible":[127],"cost.":[128],"Using":[129],"fixed":[131],"sets,":[133],"then":[135],"extend":[138],"results":[139,223],"position":[141],"sensitivity,":[142],"re-evaluating":[143],"positional":[149],"biases":[150],"modern":[152],"LLMs.":[153],"Then,":[154],"also":[156],"more":[159],"realistic":[160],"RAG":[161,249,265],"scenario":[162],"which":[164],"relevance":[165],"mediated":[167],"by":[168],"retriever":[170],"rather":[171],"than":[172],"oracle":[173],"access":[174],"ground-truth":[176],"documents.":[177],"setting,":[180],"re-examine":[182],"recent":[184],"industry":[185],"identify":[188],"discrepancies":[189],"choices":[192],"limited":[195],"coverage":[197],"reliance":[199],"LLM-based":[201],"judges.":[202],"Finally,":[203],"conduct":[205],"an":[206],"analysis":[207],"retrieval":[210,231],"order":[211],"affect":[215],"downstream":[216],"LLM":[217],"performance":[218],"imperfect":[220],"retrieval.":[221],"Our":[222],"demonstrate":[224],"factors":[227],"interact":[228],"strongly":[229],"quality":[232],"model":[234],"choice,":[235],"conclusions":[238],"drawn":[239],"from":[240],"idealised":[241],"setups":[242],"do":[243],"not":[244],"always":[245],"transfer":[246],"real-world":[248],"pipelines.":[250],"release":[252],"all":[253],"code":[254],"configurations":[256],"support":[258],"future":[261],"robust":[264],"evaluation.":[266]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}