{"id":"https://openalex.org/W7128007153","doi":"https://doi.org/10.48550/arxiv.2602.03849","title":"HybridQuestion: Human-AI Collaboration for Identifying High-Impact Research Questions","display_name":"HybridQuestion: Human-AI Collaboration for Identifying High-Impact Research Questions","publication_year":2025,"publication_date":"2025-12-18","ids":{"openalex":"https://openalex.org/W7128007153","doi":"https://doi.org/10.48550/arxiv.2602.03849"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.03849","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125170503","display_name":"Keyu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Keyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125112719","display_name":"Fengli Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Fengli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125099861","display_name":"Yong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125192861","display_name":"Tie-Yan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tie-Yan","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77079668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T13274","display_name":"Expert finding and Q&A systems","score":0.5519999861717224,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.5519999861717224,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.08950000256299973,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.08330000191926956,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.6309000253677368},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5357999801635742},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4991999864578247},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4641999900341034},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.396699994802475},{"id":"https://openalex.org/keywords/scientific-discovery","display_name":"Scientific discovery","score":0.36880001425743103}],"concepts":[{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.701200008392334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.640999972820282},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.6309000253677368},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4641999900341034},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.396699994802475},{"id":"https://openalex.org/C2984917352","wikidata":"https://www.wikidata.org/wiki/Q12772819","display_name":"Scientific discovery","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3384000062942505},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C87868495","wikidata":"https://www.wikidata.org/wiki/Q750843","display_name":"Information processing","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C23213167","wikidata":"https://www.wikidata.org/wiki/Q2351730","display_name":"Scientific progress","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.03849","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.03849","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.03849","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.03849","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7164973020553589,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,110,131,161],"\"AI":[1],"Scientist\"":[2],"paradigm":[3],"is":[4,24,105],"transforming":[5],"scientific":[6,34],"research":[7,14,47],"by":[8],"automating":[9],"key":[10,38],"stages":[11],"of":[12,33,68,94,100,123,145,196],"the":[13,31,89,97,191,199],"process,":[15],"from":[16],"idea":[17],"generation":[18],"to":[19,26,58,63,125,141,149,189],"scholarly":[20],"writing.":[21],"This":[22],"shift":[23],"expected":[25],"accelerate":[27],"discovery":[28],"and":[29,71,198],"expand":[30],"scope":[32],"inquiry.":[35],"However,":[36],"a":[37,83,127,157,171],"question":[39],"remains":[40,74,239],"unclear:":[41],"can":[42],"AI":[43,95,215],"scientists":[44],"identify":[45,190],"meaningful":[46],"questions?":[48],"While":[49],"Large":[50],"Language":[51],"Models":[52],"(LLMs)":[53],"have":[54],"been":[55],"applied":[56],"successfully":[57],"task-specific":[59],"ideation,":[60],"their":[61],"potential":[62],"conduct":[64],"strategic,":[65],"long-term":[66],"assessments":[67],"past":[69],"breakthroughs":[70],"future":[72],"questions":[73],"largely":[75],"unexplored.":[76],"To":[77,180],"address":[78],"this":[79,138,168,182],"gap,":[80],"we":[81,184],"explore":[82],"human-AI":[84],"hybrid":[85,128],"solution":[86],"that":[87,175,213,236],"integrates":[88],"scalable":[90],"data":[91,140],"processing":[92,120],"capabilities":[93],"with":[96,220],"value":[98],"judgment":[99,238],"human":[101,178,221,237],"experts.":[102],"Our":[103,210],"methodology":[104],"structured":[106],"in":[107,119,223,231],"three":[108],"phases.":[109],"first":[111],"phase,":[112,133,163],"AI-Accelerated":[113],"Information":[114],"Gathering,":[115],"leverages":[116],"AI's":[117],"advantage":[118],"vast":[121],"amounts":[122],"literature":[124],"generate":[126],"information":[129],"base.":[130],"second":[132],"Candidate":[134],"Question":[135,165],"Proposing,":[136],"utilizes":[137],"synthesized":[139],"prompt":[142],"an":[143,151,186],"ensemble":[144],"six":[146],"diverse":[147],"LLMs":[148],"propose":[150],"initial":[152],"candidate":[153],"pool,":[154],"filtered":[155],"via":[156],"cross-model":[158],"voting":[159],"mechanism.":[160],"third":[162],"Hybrid":[164],"Selection,":[166],"refines":[167],"pool":[169],"through":[170],"multi-stage":[172],"filtering":[173],"process":[174],"progressively":[176],"increases":[177],"oversight.":[179],"validate":[181],"system,":[183],"conducted":[185],"experiment":[187],"aiming":[188],"Top":[192,200],"10":[193,201],"Scientific":[194,202],"Breakthroughs":[195],"2025":[197],"Questions":[203],"for":[204,241],"2026":[205],"across":[206],"five":[207],"major":[208],"disciplines.":[209],"analysis":[211],"reveals":[212],"while":[214],"agents":[216],"demonstrate":[217],"high":[218],"alignment":[219],"experts":[222],"recognizing":[224],"established":[225],"breakthroughs,":[226],"they":[227],"exhibit":[228],"greater":[229],"divergence":[230],"forecasting":[232],"prospective":[233],"questions,":[234],"suggesting":[235],"crucial":[240],"evaluating":[242],"subjective,":[243],"forward-looking":[244],"challenges.":[245]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-07T00:00:00"}
