{"id":"https://openalex.org/W7162751963","doi":"https://doi.org/10.48550/arxiv.2605.30284","title":"ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure","display_name":"ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162751963","doi":"https://doi.org/10.48550/arxiv.2605.30284"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30284","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.2605.30284","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137389046","display_name":"A. J. Lew","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130485","display_name":"Notable Labs (United States)","ror":"https://ror.org/03b9h7664","country_code":"US","type":"company","lineage":["https://openalex.org/I4210130485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lew, A. J.","raw_affiliation_strings":["Unreasonable Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Unreasonable Labs","institution_ids":["https://openalex.org/I4210130485"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137349129","display_name":"Y. Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130485","display_name":"Notable Labs (United States)","ror":"https://ror.org/03b9h7664","country_code":"US","type":"company","lineage":["https://openalex.org/I4210130485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cao, Y.","raw_affiliation_strings":["Unreasonable Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Unreasonable Labs","institution_ids":["https://openalex.org/I4210130485"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137399707","display_name":"M. J. Buehler","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130485","display_name":"Notable Labs (United States)","ror":"https://ror.org/03b9h7664","country_code":"US","type":"company","lineage":["https://openalex.org/I4210130485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Buehler, M. J.","raw_affiliation_strings":["Unreasonable Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Unreasonable Labs","institution_ids":["https://openalex.org/I4210130485"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210130485"],"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/T11948","display_name":"Machine Learning in Materials Science","score":0.6039999723434448,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.6039999723434448,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.16380000114440918,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.04569999873638153,"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/benchmark","display_name":"Benchmark (surveying)","score":0.5450000166893005},{"id":"https://openalex.org/keywords/scientific-reasoning","display_name":"Scientific reasoning","score":0.4821999967098236},{"id":"https://openalex.org/keywords/scientific-discovery","display_name":"Scientific discovery","score":0.4724999964237213},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4684999883174896},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4636000096797943},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.3776000142097473},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.3625999987125397},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.31859999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5468000173568726},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5357000231742859},{"id":"https://openalex.org/C2992562121","wikidata":"https://www.wikidata.org/wiki/Q3817808","display_name":"Scientific reasoning","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C2984917352","wikidata":"https://www.wikidata.org/wiki/Q12772819","display_name":"Scientific discovery","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4636000096797943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3955000042915344},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.31139999628067017},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C138379479","wikidata":"https://www.wikidata.org/wiki/Q1116876","display_name":"Scientific modelling","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30284","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.2605.30284","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30284","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":[{"score":0.4965569078922272,"id":"https://metadata.un.org/sdg/4","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":{"Scientific":[0],"discovery":[1,45,60,167,180],"is":[2,104,115],"an":[3],"inherently":[4],"creative":[5],"and":[6,61,84,124,179,203,216,222,233],"uncertain":[7],"process,":[8],"requiring":[9],"reasoning":[10,39,154,178],"beyond":[11],"the":[12,70,82,102,111,118,121,187],"recall":[13],"of":[14,99,130,137,145,189],"known":[15],"knowledge.":[16],"While":[17],"many":[18],"benchmarks":[19],"have":[20],"been":[21],"proposed":[22],"to":[23,69,152],"evaluate":[24,197],"large":[25],"language":[26],"model":[27,56,103],"(LLM)":[28],"performance":[29,57],"on":[30],"deep":[31],"research":[32,85,112],"tasks":[33],"via":[34,126],"multi-hop":[35],"retrieval,":[36],"their":[37,227],"innovative":[38],"abilities":[40],"essential":[41],"for":[42,54,162,165,174,185],"true":[43],"scientific":[44,59,166,177],"remain":[46],"largely":[47],"untested.":[48],"We":[49,218],"introduce":[50],"a":[51,66,88,146,172],"benchmark":[52],"framework":[53,170],"evaluating":[55,176],"in":[58,182,235],"reasoning,":[62],"building":[63],"up":[64],"from":[65,87,120,140],"raw":[67],"problem":[68],"classical":[71],"null":[72],"hypothesis":[73],"test.":[74],"In":[75],"our":[76],"framework,":[77],"models":[78],"initially":[79],"receive":[80],"only":[81],"topic":[83],"question":[86],"recent":[89],"paper,":[90],"with":[91,106,117,242],"technical":[92],"details":[93],"progressively":[94],"revealed.":[95],"At":[96],"each":[97],"stage":[98],"information":[100],"disclosure,":[101],"tasked":[105],"generating":[107],"hypotheses":[108],"that":[109,220],"address":[110],"question,":[113],"which":[114],"compared":[116],"conclusions":[119,142,245],"original":[122],"paper":[123],"evaluated":[125],"automated":[127],"semantic":[128,138],"similarity":[129],"constituent":[131],"atomic":[132],"claims.":[133],"This":[134],"progressive":[135],"evaluation":[136],"divergence":[139],"ground-truth":[141],"enables":[143],"assessment":[144],"model's":[147],"innovativeness":[148],"(under":[149,156],"minimal":[150,248],"information)":[151],"grounded":[153],"capabilities":[155,181],"full":[157],"experimental":[158],"details),":[159],"both":[160],"critical":[161],"using":[163],"LLMs":[164],"purposes.":[168],"Our":[169],"provides":[171],"foundation":[173],"systematically":[175],"LLMs,":[183],"crucial":[184],"advancing":[186],"development":[188],"next-generation":[190],"AI":[191],"scientist/co-scientist":[192],"systems.":[193],"Specifically,":[194],"here":[195],"we":[196],"GPT-5,":[198],"GPT-5.4,":[199],"Gemini":[200,204,223],"2.5":[201],"pro,":[202],"3.1":[205,224],"pro":[206,225],"preview":[207],"across":[208],"45":[209],"papers":[210],"spanning":[211],"bioactive":[212],"materials,":[213,215],"mechanical":[214],"nanomaterials.":[217],"find":[219],"GPT-5.4":[221,234],"outperform":[226],"previous":[228],"generation":[229],"counterparts":[230],"as":[231],"expected,":[232],"particular":[236],"maintains":[237],"0.7":[238],"F1":[239],"score":[240],"alignment":[241],"ground":[243],"truth":[244],"even":[246],"under":[247],"context.":[249]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
