{"id":"https://openalex.org/W7161079297","doi":"https://doi.org/10.48550/arxiv.2605.11547","title":"Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching","display_name":"Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161079297","doi":"https://doi.org/10.48550/arxiv.2605.11547"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11547","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":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.2605.11547","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136044886","display_name":"Aditi Gupta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Aditi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055366983","display_name":"Soon Hoe Lim","orcid":"https://orcid.org/0000-0002-4649-673X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Soon Hoe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048046732","display_name":"Annan Yu","orcid":"https://orcid.org/0009-0007-4827-6830"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Annan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136023073","display_name":"N. Benjamin Erichson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erichson, N. Benjamin","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2606000006198883,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2606000006198883,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.17679999768733978,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.06729999929666519,"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/sampling","display_name":"Sampling (signal processing)","score":0.5855000019073486},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.5755000114440918},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5361999869346619},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.47999998927116394},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.46299999952316284},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42149999737739563},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.414000004529953},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4106999933719635},{"id":"https://openalex.org/keywords/euler-method","display_name":"Euler method","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5855000019073486},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.5755000114440918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5375000238418579},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5361999869346619},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47839999198913574},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4106999933719635},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40400001406669617},{"id":"https://openalex.org/C75380026","wikidata":"https://www.wikidata.org/wiki/Q868454","display_name":"Euler method","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C62884695","wikidata":"https://www.wikidata.org/wiki/Q184871","display_name":"Euler's formula","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11547","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.11547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11547","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":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":{"Flow":[0],"matching":[1],"models":[2],"generate":[3],"samples":[4],"by":[5,63,89],"numerically":[6],"integrating":[7],"a":[8,17,26,54,59,80,90,94,118,127,141,150],"learned":[9,67],"velocity":[10,68],"field,":[11],"with":[12,110],"each":[13],"integration":[14,109],"step":[15],"requiring":[16],"neural":[18],"network":[19],"evaluation.":[20],"Fast":[21],"generation":[22],"therefore":[23],"requires":[24],"using":[25,124],"small":[27],"fixed":[28,175],"evaluation":[29],"budget":[30],"effectively:":[31],"the":[32,41,45,66,111,134,155,162],"key":[33],"question":[34],"is":[35,85,159],"not":[36],"only":[37],"how":[38],"to":[39],"integrate":[40],"flow,":[42],"but":[43],"where":[44,65],"sampler":[46,56,158],"should":[47],"spend":[48],"its":[49],"steps.":[50],"We":[51,121],"propose":[52],"SharpEuler,":[53],"training-free":[55],"that":[57,154,169],"profiles":[58],"pretrained":[60],"model":[61,115],"offline":[62],"estimating":[64],"field":[69],"changes":[70],"most":[71],"rapidly":[72],"along":[73],"calibration":[74],"trajectories.":[75],"This":[76],"finite-difference":[77],"estimate":[78],"defines":[79],"solver-aware":[81],"sharpness":[82],"profile,":[83],"which":[84],"smoothed":[86],"and":[87,149,180],"converted":[88],"quantile":[91],"transform":[92],"into":[93],"timestep":[95,147],"grid":[96],"for":[97],"any":[98],"desired":[99],"inference":[100],"budget.":[101],"At":[102],"test":[103],"time,":[104],"sampling":[105],"remains":[106],"ordinary":[107],"Euler":[108,138],"same":[112],"number":[113],"of":[114,137],"evaluations":[116],"as":[117,133],"uniform":[119],"schedule.":[120],"justify":[122],"SharpEuler":[123,170],"three":[125],"principles:":[126],"numerical":[128],"principle":[129,143],"identifying":[130],"trajectory":[131],"acceleration":[132],"leading":[135],"source":[136],"discretization":[139],"error,":[140],"variational":[142],"deriving":[144],"sharpness-based":[145],"power-law":[146],"densities,":[148],"statistical":[151],"guarantee":[152],"showing":[153],"finite-sample":[156],"calibrated":[157],"stable":[160],"at":[161,174],"terminal":[163],"distribution":[164],"level.":[165],"Our":[166],"experiments":[167],"show":[168],"improves":[171],"sample":[172],"quality":[173],"budgets,":[176],"reducing":[177],"inter-mode":[178],"leakage":[179],"increasing":[181],"mode":[182],"coverage.":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}