{"id":"https://openalex.org/W7160164732","doi":"https://doi.org/10.48550/arxiv.2605.00557","title":"Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output","display_name":"Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160164732","doi":"https://doi.org/10.48550/arxiv.2605.00557"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00557","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.2605.00557","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121598601","display_name":"James Mooney","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mooney, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135264956","display_name":"Zae Myung Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Zae Myung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135177450","display_name":"Young-Jun Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Young-Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135269555","display_name":"Dongyeop Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Dongyeop","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/T11986","display_name":"Scientific Computing and Data Management","score":0.3156999945640564,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.3156999945640564,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.11680000275373459,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.0494999997317791,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sensemaking","display_name":"Sensemaking","score":0.8001999855041504},{"id":"https://openalex.org/keywords/ideation","display_name":"Ideation","score":0.5669000148773193},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5175999999046326},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5023000240325928},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.49320000410079956},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.4797999858856201},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.4652000069618225},{"id":"https://openalex.org/keywords/expediting","display_name":"Expediting","score":0.4350999891757965},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4072999954223633}],"concepts":[{"id":"https://openalex.org/C2780554381","wikidata":"https://www.wikidata.org/wiki/Q2063340","display_name":"Sensemaking","level":2,"score":0.8001999855041504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657800018787384},{"id":"https://openalex.org/C170477896","wikidata":"https://www.wikidata.org/wiki/Q17039022","display_name":"Ideation","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.4797999858856201},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C134448949","wikidata":"https://www.wikidata.org/wiki/Q1384274","display_name":"Expediting","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4072999954223633},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39100000262260437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3862000107765198},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C2780691134","wikidata":"https://www.wikidata.org/wiki/Q1129448","display_name":"Preamble","level":3,"score":0.34869998693466187},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33480000495910645},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C2776240099","wikidata":"https://www.wikidata.org/wiki/Q327018","display_name":"Interrogation","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.2985000014305115},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.2768999934196472},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C96851999","wikidata":"https://www.wikidata.org/wiki/Q848369","display_name":"Subconscious","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00557","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.2605.00557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00557","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":{"Scientific":[0],"discovery":[1],"is":[2],"an":[3,66],"extended":[4],"process":[5],"of":[6,43,57,100],"ideation--surveying":[7],"prior":[8],"work,":[9],"forming":[10],"hypotheses,":[11],"and":[12,81,135,154,190],"refining":[13],"reasoning--yet":[14],"existing":[15],"approaches":[16],"treat":[17],"this":[18],"phase":[19],"as":[20,39],"a":[21,33,40,54,74,98,121,182,191],"brief":[22],"preamble":[23],"despite":[24],"its":[25,78],"central":[26],"role":[27],"in":[28,61,124],"research.":[29],"We":[30,51,93],"introduce":[31],"SCISENSE,":[32],"sensemaking-grounded":[34],"framework":[35],"that":[36,112,164],"operationalizes":[37],"ideation":[38,70,166],"structured":[41],"sequence":[42],"eight":[44],"cognitive":[45,168],"stages":[46],"(Pirolli":[47],"\\&":[48],"Card,":[49],"2005).":[50],"construct":[52],"SCISENSE-Traj,":[53],"100K-scale":[55],"dataset":[56],"citation-conditioned":[58],"research":[59,149,188],"trajectories":[60,147],"two":[62],"modes:":[63],"Target,":[64],"where":[65,83],"LLM":[67,85],"reconstructs":[68],"the":[69,84,90,110],"path":[71],"leading":[72],"to":[73,105,109,175],"known":[75],"paper":[76],"from":[77,89],"cited":[79],"works,":[80],"Infer,":[82],"proposes":[86],"novel":[87,134],"directions":[88],"same":[91],"citations.":[92],"distill":[94],"these":[95],"into":[96],"SCISENSE-LM,":[97],"family":[99],"sensemaking":[101],"LLMs":[102],"spanning":[103],"3B":[104],"70B":[106],"parameters.":[107],"Contrary":[108],"assumption":[111],"looser":[113],"supervision":[114],"promotes":[115],"greater":[116],"exploration,":[117],"Target-trained":[118],"models":[119,129],"achieve":[120],"2.0\\%":[122],"improvement":[123],"trajectory":[125],"quality":[126,155],"over":[127],"Infer-trained":[128],"while":[130],"also":[131],"producing":[132],"more":[133,177],"diverse":[136],"outputs.":[137],"This":[138,162],"advantage":[139],"propagates":[140],"downstream:":[141],"coding":[142],"agents":[143],"conditioned":[144,158],"on":[145,159,170],"Target":[146],"produce":[148],"artifacts":[150],"with":[151],"higher":[152],"executability":[153],"than":[156],"those":[157],"Infer":[160],"trajectories.":[161],"suggests":[163],"targeted":[165],"reduces":[167],"burden":[169],"downstream":[171],"agents,":[172],"freeing":[173],"them":[174],"explore":[176],"creatively.":[178],"SCISENSE":[179],"offers":[180],"both":[181],"practical":[183],"tool":[184],"for":[185,194],"augmenting":[186],"LLM-driven":[187],"workflows":[189],"principled":[192],"testbed":[193],"studying":[195],"how":[196],"planning":[197],"shapes":[198],"scientific":[199],"discovery.":[200]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-05T00:00:00"}