{"id":"https://openalex.org/W7155529329","doi":"https://doi.org/10.48550/arxiv.2604.21830","title":"GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward","display_name":"GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155529329","doi":"https://doi.org/10.48550/arxiv.2604.21830"},"language":"en","primary_location":{"id":"pmh:doi:10.3929/ethz-c-000799224","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/799224","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":"Working Paper"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11850/799224","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134508147","display_name":"Florian Holeczek","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Holeczek, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066639486","display_name":"Andreas Hinterreiter","orcid":"https://orcid.org/0000-0003-4101-5180"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hinterreiter, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134494317","display_name":"Alex Hernandez-Garcia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hernandez-Garcia, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134510518","display_name":"Marc Streit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Streit, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134551762","display_name":"Christina Humer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Humer, Christina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5134508147"],"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/T10799","display_name":"Data Visualization and Analytics","score":0.4758000075817108,"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/T10799","display_name":"Data Visualization and Analytics","score":0.4758000075817108,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.2273000031709671,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.07339999824762344,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7954999804496765},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5796999931335449},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.49959999322891235},{"id":"https://openalex.org/keywords/visual-analytics","display_name":"Visual analytics","score":0.4715999960899353},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46869999170303345},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4555000066757202},{"id":"https://openalex.org/keywords/debugging","display_name":"Debugging","score":0.451200008392334},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4474000036716461},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.42500001192092896}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7954999804496765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7936999797821045},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5796999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5770000219345093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.550599992275238},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.49959999322891235},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.4715999960899353},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46459999680519104},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.451200008392334},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44209998846054077},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.42500001192092896},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3882000148296356},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.2939000129699707},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.2903999984264374},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C42525527","wikidata":"https://www.wikidata.org/wiki/Q1209955","display_name":"Formative assessment","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.3929/ethz-c-000799224","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/799224","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":"Working Paper"},{"id":"doi:10.48550/arxiv.2604.21830","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21830","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:10.3929/ethz-c-000799224","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/799224","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":"Working Paper"},"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":{"We":[0],"present":[1],"GFlowState,":[2,84],"a":[3,22,30,112,117,120,128],"visual":[4],"analytics":[5],"system":[6,160],"designed":[7],"to":[8,29,37,54,87,96,138,146],"illuminate":[9],"the":[10,70,92,101,124,159,165,174],"training":[11,50,102,153],"process":[12],"of":[13,114,123,152,167,177],"Generative":[14],"Flow":[15],"Networks":[16],"(GFlowNets":[17],"or":[18,76],"GFNs).":[19],"GFlowNets":[20,34,168,178],"are":[21],"probabilistic":[23],"framework":[24],"for":[25],"generating":[26],"samples":[27],"proportionally":[28],"reward":[31],"function.":[32],"While":[33],"have":[35],"proved":[36],"be":[38],"powerful":[39],"tools":[40,59],"in":[41,190],"applications":[42],"such":[43],"as":[44],"molecule":[45],"and":[46,99,127,136,142,145,150,163,185],"material":[47],"discovery,":[48],"their":[49,183],"dynamics":[51,176],"remain":[52],"difficult":[53],"interpret.":[55],"Standard":[56],"machine":[57],"learning":[58],"allow":[60],"metric":[61],"tracking":[62],"but":[63],"do":[64],"not":[65],"reveal":[66],"how":[67,158],"models":[68],"explore":[69],"sample":[71,74,93],"space,":[72],"construct":[73],"trajectories,":[75,90],"shift":[77],"sampling":[78,89,140],"probabilities":[79],"during":[80],"training.":[81],"Our":[82],"solution,":[83],"allows":[85],"users":[86,137],"analyze":[88,100],"compare":[91],"space":[94],"relative":[95],"reference":[97],"datasets,":[98],"dynamics.":[103],"To":[104],"this":[105],"end,":[106],"we":[107],"introduce":[108],"multiple":[109],"views,":[110],"including":[111],"chart":[113],"candidate":[115],"rankings,":[116],"state":[118],"projection,":[119],"node-link":[121],"diagram":[122],"trajectory":[125],"network,":[126],"transition":[129],"heatmap.":[130],"These":[131],"visualizations":[132],"enable":[133],"GFlowNet":[134,188],"developers":[135],"investigate":[139],"behavior":[141],"policy":[143],"evolution,":[144],"identify":[147],"underexplored":[148],"regions":[149],"sources":[151],"failure.":[154],"Case":[155],"studies":[156],"demonstrate":[157],"supports":[161],"debugging":[162],"assessing":[164],"quality":[166],"across":[169],"application":[170],"domains.":[171],"By":[172],"making":[173],"structural":[175],"observable,":[179],"our":[180],"work":[181],"enhances":[182],"interpretability":[184],"can":[186],"accelerate":[187],"development":[189],"practice.":[191]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-25T00:00:00"}
