{"id":"https://openalex.org/W7147015785","doi":"https://doi.org/10.48550/arxiv.2603.29085","title":"PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering","display_name":"PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147015785","doi":"https://doi.org/10.48550/arxiv.2603.29085"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29085","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.2603.29085","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132547635","display_name":"Xingyu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132687753","display_name":"Rongguang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Rongguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132651119","display_name":"Yuying Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132716520","display_name":"Mengqing Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Mengqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132623474","display_name":"Chenyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132635365","display_name":"Tao Sheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132712704","display_name":"Sujith Ravi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ravi, Sujith","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132681589","display_name":"Dan Roth","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roth, Dan","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":0.8388000130653381,"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":0.8388000130653381,"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":0.08160000294446945,"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":0.01889999955892563,"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/question-answering","display_name":"Question answering","score":0.8914999961853027},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.4733000099658966},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42890000343322754},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.33489999175071716},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.3257000148296356},{"id":"https://openalex.org/keywords/iterative-refinement","display_name":"Iterative refinement","score":0.32109999656677246},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.31060001254081726}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8914999961853027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786300003528595},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.551800012588501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49410000443458557},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C21025794","wikidata":"https://www.wikidata.org/wiki/Q5141219","display_name":"Cognitive models of information retrieval","level":4,"score":0.2662999927997589},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C90288658","wikidata":"https://www.wikidata.org/wiki/Q3318149","display_name":"Human\u2013computer information retrieval","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29085","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.2603.29085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29085","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":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"remain":[4],"brittle":[5],"on":[6],"multi-hop":[7],"question":[8],"answering":[9,12],"(MHQA),":[10],"where":[11],"requires":[13],"combining":[14],"evidence":[15,51,79,86],"across":[16],"documents":[17],"through":[18],"retrieval":[19,23,114],"and":[20,34,58],"reasoning.":[21],"Iterative":[22],"systems":[24],"can":[25],"fail":[26],"by":[27],"locking":[28],"onto":[29],"an":[30,90],"early":[31],"low-recall":[32],"trajectory":[33],"amplifying":[35],"downstream":[36],"errors,":[37],"while":[38],"planning-only":[39],"approaches":[40],"may":[41],"produce":[42],"static":[43],"query":[44],"sets":[45],"that":[46,65],"cannot":[47],"adapt":[48],"when":[49],"intermediate":[50],"changes.":[52],"We":[53],"propose":[54],"\\textbf{Planned":[55],"Active":[56],"Retrieval":[57],"Reasoning":[59],"RAG":[60],"(PAR$^2$-RAG)},":[61],"a":[62,77],"two-stage":[63],"framework":[64],"separates":[66],"\\emph{coverage}":[67],"from":[68],"\\emph{commitment}.":[69],"PAR$^2$-RAG":[70,97,106],"first":[71],"performs":[72],"breadth-first":[73],"anchoring":[74],"to":[75,109,118],"build":[76],"high-recall":[78],"frontier,":[80],"then":[81],"applies":[82],"depth-first":[83],"refinement":[84],"with":[85,104,113],"sufficiency":[87],"control":[88],"in":[89,120],"iterative":[91],"loop.":[92],"Across":[93],"four":[94],"MHQA":[95],"benchmarks,":[96],"consistently":[98],"outperforms":[99],"existing":[100],"state-of-the-art":[101],"baselines,":[102],"compared":[103],"IRCoT,":[105],"achieves":[107],"up":[108,117],"\\textbf{23.5\\%}":[110],"higher":[111],"accuracy,":[112],"gains":[115],"of":[116],"\\textbf{10.5\\%}":[119],"NDCG.":[121]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
