{"id":"https://openalex.org/W7154458008","doi":"https://doi.org/10.48550/arxiv.2604.12421","title":"Agentic Insight Generation in VSM Simulations","display_name":"Agentic Insight Generation in VSM Simulations","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154458008","doi":"https://doi.org/10.48550/arxiv.2604.12421"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12421","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.12421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123759920","display_name":"Micha Selak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Selak, Micha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012587775","display_name":"Dirk Krechel","orcid":"https://orcid.org/0000-0003-0984-5918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krechel, Dirk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133687185","display_name":"Adrian Ulges","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ulges, Adrian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013008127","display_name":"Sven Spieckermann","orcid":"https://orcid.org/0000-0002-2228-0360"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spieckermann, Sven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133720873","display_name":"Niklas Stoehr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stoehr, Niklas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031164341","display_name":"Andreas Loehr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Loehr, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.46050000190734863,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.46050000190734863,"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/T10028","display_name":"Topic Modeling","score":0.13079999387264252,"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/T10799","display_name":"Data Visualization and Analytics","score":0.03739999979734421,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.708899974822998},{"id":"https://openalex.org/keywords/orchestration","display_name":"Orchestration","score":0.646399974822998},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5595999956130981},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4397999942302704},{"id":"https://openalex.org/keywords/situation-awareness","display_name":"Situation awareness","score":0.42089998722076416},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4115000069141388},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.39320001006126404},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7760000228881836},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.708899974822998},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.646399974822998},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4397999942302704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C145804949","wikidata":"https://www.wikidata.org/wiki/Q478123","display_name":"Situation awareness","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37720000743865967},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C517642484","wikidata":"https://www.wikidata.org/wiki/Q2388514","display_name":"Intelligence analysis","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C2780977526","wikidata":"https://www.wikidata.org/wiki/Q42417149","display_name":"Data exploration","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.26660001277923584},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12421","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.12421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12421","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":"article"},"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":{"Extracting":[0],"actionable":[1],"insights":[2],"from":[3,75,113],"complex":[4],"value":[5],"stream":[6],"map":[7],"simulations":[8],"can":[9],"be":[10],"challenging,":[11],"time-consuming,":[12],"and":[13,99,132],"error-prone.":[14],"Recent":[15],"advances":[16],"in":[17,58],"large":[18,116],"language":[19,117],"models":[20,118,125],"offer":[21],"new":[22],"avenues":[23],"to":[24,38,45,53,94,130],"support":[25],"users":[26],"with":[27,85,123],"this":[28,59,63],"task.":[29],"While":[30],"existing":[31],"approaches":[32],"excel":[33],"at":[34],"processing":[35],"raw":[36],"data":[37,56,76,82,97,104],"gain":[39],"information,":[40],"they":[41],"are":[42],"structurally":[43],"unfit":[44],"pick":[46],"up":[47,129],"on":[48],"subtle":[49],"situational":[50],"differences":[51],"needed":[52],"distinguish":[54],"similar":[55],"sources":[57,98],"domain.":[60],"To":[61],"address":[62],"issue,":[64],"we":[65],"propose":[66],"a":[67,108],"decoupled,":[68],"two-step":[69],"agentic":[70],"architecture.":[71],"By":[72],"separating":[73],"orchestration":[74,93],"analysis,":[77],"the":[78,92,120],"system":[79],"leverages":[80],"progressive":[81],"discovery":[83],"infused":[84],"domain":[86],"expert":[87],"knowledge.":[88],"This":[89],"architecture":[90],"allows":[91],"intelligently":[95],"select":[96],"perform":[100],"multi-hop":[101],"reasoning":[102],"across":[103,136],"structures":[105],"while":[106],"maintaining":[107],"slim":[109],"internal":[110],"context.":[111],"Results":[112],"multiple":[114],"state-of-the-art":[115],"demonstrate":[119],"framework's":[121],"viability:":[122],"top-tier":[124],"achieving":[126],"accuracies":[127],"of":[128],"86%":[131],"demonstrating":[133],"high":[134],"robustness":[135],"evaluation":[137],"runs.":[138]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-16T00:00:00"}
