{"id":"https://openalex.org/W7165845976","doi":"https://doi.org/10.48550/arxiv.2606.25191","title":"To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG","display_name":"To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165845976","doi":"https://doi.org/10.48550/arxiv.2606.25191"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25191","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.2606.25191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035555716","display_name":"Jungseob Lee","orcid":"https://orcid.org/0000-0002-9431-6342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jungseob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060151070","display_name":"C W Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Chanjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139362295","display_name":"Heuiseok Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Heuiseok","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":0.6474000215530396,"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":0.6474000215530396,"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":0.1965000033378601,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.044599998742341995,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6025999784469604},{"id":"https://openalex.org/keywords/quality-assessment","display_name":"Quality assessment","score":0.4788999855518341},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/isolation","display_name":"Isolation (microbiology)","score":0.4327999949455261},{"id":"https://openalex.org/keywords/multiple-models","display_name":"Multiple Models","score":0.3440000116825104},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.33329999446868896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029000282287598},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6025999784469604},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.4788999855518341},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4702000021934509},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4498000144958496},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2984538763","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring system","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C82157600","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic test","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C3019813237","wikidata":"https://www.wikidata.org/wiki/Q65089264","display_name":"Model validation","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25191","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.2606.25191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25191","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":{"Multi-agent":[0],"document":[1],"assessment":[2,16],"for":[3,83],"retrieval-augmented":[4],"generation":[5],"is":[6,53],"computationally":[7],"expensive,":[8],"driving":[9],"practitioners":[10],"toward":[11],"smaller,":[12],"deployable":[13],"models":[14,32,43],"whose":[15],"mechanisms":[17],"remain":[18],"poorly":[19],"understood.":[20],"We":[21],"conduct":[22],"a":[23,38,94,108,118,130],"controlled":[24],"study":[25],"of":[26,76],"training-free":[27],"interventions":[28],"on":[29],"7B-9B":[30],"instruction-tuned":[31],"across":[33],"diverse":[34],"QA":[35],"benchmarks,":[36],"revealing":[37],"sharp":[39],"dichotomy":[40],"in":[41],"how":[42],"benefit":[44],"from":[45,117],"assessment.":[46],"For":[47],"weaker":[48],"baselines,":[49],"the":[50],"dominant":[51],"mechanism":[52],"per-document":[54],"isolation.":[55],"Astoundingly,":[56],"assessment-free":[57],"isolation":[58],"matches":[59],"full":[60],"multi-agent":[61],"assessment,":[62],"demonstrating":[63],"that":[64,98],"resolving":[65],"multi-document":[66],"context":[67],"confusion,":[68],"rather":[69],"than":[70],"scoring":[71,87,100],"quality,":[72],"drives":[73],"outsized":[74],"gains":[75],"up":[77],"to":[78,124,134],"50":[79],"percentage":[80],"points.":[81],"Conversely,":[82],"strong":[84],"baselines":[85],"where":[86],"quality":[88],"matters,":[89],"we":[90,105],"introduce":[91],"Reasoning-Score":[92],"Coupling,":[93],"label-free":[95],"perturbation":[96],"probe":[97],"classifies":[99],"behavior.":[101],"Integrating":[102],"these":[103],"findings,":[104],"propose":[106],"MADARA,":[107],"model-adaptive":[109],"routing":[110],"architecture.":[111],"Crucially,":[112],"MADARA's":[113],"diagnostic":[114],"thresholds":[115],"derived":[116],"single":[119],"pilot":[120],"model":[121,127],"generalize":[122],"zero-shot":[123],"four":[125],"unseen":[126],"families,":[128],"providing":[129],"robust,":[131],"lightweight":[132],"pipeline":[133],"eliminate":[135],"computational":[136],"overhead.":[137]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-26T00:00:00"}
