{"id":"https://openalex.org/W7128802388","doi":"https://doi.org/10.48550/arxiv.2602.11238","title":"SurveyLens: A Research Discipline-Aware Benchmark for Automatic Survey Generation","display_name":"SurveyLens: A Research Discipline-Aware Benchmark for Automatic Survey Generation","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128802388","doi":"https://doi.org/10.48550/arxiv.2602.11238"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.11238","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","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/A5064117263","display_name":"Bang Guo","orcid":"https://orcid.org/0009-0006-3755-5706"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guo, Beichen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048809651","display_name":"Zhiyuan Wen","orcid":"https://orcid.org/0000-0003-4106-1312"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Zhiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125928015","display_name":"Jia Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Jia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125938056","display_name":"Senzhang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Senzhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125904689","display_name":"Haochen Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Haochen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081781438","display_name":"Ruosong Yang","orcid":"https://orcid.org/0000-0002-9483-4000"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ruosong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Liu, Shuaiqi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Shuaiqi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5064117263"],"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.3628000020980835,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.3628000020980835,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.09269999712705612,"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/T11539","display_name":"Survey Methodology and Nonresponse","score":0.050999999046325684,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rubric","display_name":"Rubric","score":0.7437999844551086},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.710099995136261},{"id":"https://openalex.org/keywords/strengths-and-weaknesses","display_name":"Strengths and weaknesses","score":0.6287000179290771},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.41909998655319214},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.38929998874664307},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3824000060558319},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.3569999933242798}],"concepts":[{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.7437999844551086},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.710099995136261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621999740600586},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.6287000179290771},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5906999707221985},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3499000072479248},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.30640000104904175},{"id":"https://openalex.org/C8795937","wikidata":"https://www.wikidata.org/wiki/Q11862829","display_name":"Discipline","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C5395021","wikidata":"https://www.wikidata.org/wiki/Q1061410","display_name":"Program evaluation","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2632000148296356},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.11238","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.11238","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.11238","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.11238","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":0.8490539193153381,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"exponential":[1],"growth":[2],"of":[3,10,54,104,183],"scientific":[4],"literature":[5],"has":[6],"driven":[7],"the":[8,51,87,178],"evolution":[9],"Automatic":[11],"Survey":[12],"Generation":[13],"(ASG)":[14],"from":[15],"simple":[16],"pipelines":[17],"to":[18,44,50,71,130,133,142,195],"multi-agent":[19],"frameworks":[20],"and":[21,35,137,147,171,181],"commercial":[22],"Deep":[23,172],"Research":[24,173],"agents.":[25,174],"However,":[26],"current":[27],"ASG":[28,47,69,92,162,169],"evaluation":[29,117],"methods":[30,48,93,163],"rely":[31],"on":[32,67,164],"generic":[33],"metrics":[34],"are":[36],"heavily":[37],"biased":[38],"toward":[39],"Computer":[40],"Science":[41],"(CS),":[42],"failing":[43],"assess":[45,131],"whether":[46],"adhere":[49],"distinct":[52,179],"standards":[53],"various":[55],"academic":[56],"disciplines.":[57,97,111],"Consequently,":[58],"researchers,":[59],"especially":[60],"those":[61],"outside":[62],"CS,":[63],"lack":[64],"clear":[65],"guidance":[66,190],"using":[68],"systems":[70],"yield":[72],"high-quality":[73,106],"surveys":[74,108],"compliant":[75],"with":[76,126],"specific":[77,196],"discipline":[78],"standards.":[79],"To":[80],"bridge":[81],"this":[82],"gap,":[83],"we":[84,113],"introduce":[85],"SurveyLens,":[86,165],"first":[88],"discipline-aware":[89],"benchmark":[90],"evaluating":[91,159],"across":[94,186],"diverse":[95],"research":[96],"We":[98,154],"construct":[99],"SurveyLens-1k,":[100],"a":[101,115],"curated":[102],"dataset":[103],"1,000":[105],"human-written":[107,151],"spanning":[109],"10":[110],"Subsequently,":[112],"propose":[114],"dual-lens":[116],"framework:":[118],"(1)":[119],"Discipline-Aware":[120],"Rubric":[121],"Evaluation,":[122],"which":[123],"utilizes":[124],"LLMs":[125],"human":[127],"preference-aligned":[128],"weights":[129],"adherence":[132],"domain-specific":[134],"writing":[135],"standards;":[136],"(2)":[138],"Canonical":[139],"Alignment":[140],"Evaluation":[141],"rigorously":[143],"measure":[144],"content":[145],"coverage":[146],"synthesis":[148],"quality":[149],"against":[150],"survey":[152],"papers.":[153],"conduct":[155],"extensive":[156],"experiments":[157],"by":[158],"11":[160],"state-of-the-art":[161],"including":[166],"Vanilla":[167],"LLMs,":[168],"systems,":[170],"Our":[175],"analysis":[176],"reveals":[177],"strengths":[180],"weaknesses":[182],"each":[184],"paradigm":[185],"fields,":[187],"providing":[188],"essential":[189],"for":[191],"selecting":[192],"tools":[193],"tailored":[194],"disciplinary":[197],"requirements.":[198]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-14T00:00:00"}
