{"id":"https://openalex.org/W7111194813","doi":"https://doi.org/10.48550/arxiv.2512.07150","title":"FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers","display_name":"FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7111194813","doi":"https://doi.org/10.48550/arxiv.2512.07150"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.07150","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07150","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.07150","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Park, Jonghyun","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Park, Jonghyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Ye, Jong Chul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Jong Chul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":0.2160000056028366,"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":0.2160000056028366,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.14190000295639038,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.08810000121593475,"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/prior-probability","display_name":"Prior probability","score":0.6331999897956848},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.6219000220298767},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.5145999789237976},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5073000192642212},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.47200000286102295},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4302999973297119},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.41819998621940613},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.40380001068115234},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.3953999876976013}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6331999897956848},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.6219000220298767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5705000162124634},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5437999963760376},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5073000192642212},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.45739999413490295},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4302999973297119},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.41819998621940613},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3962000012397766},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.37209999561309814},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3163999915122986},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C2780004032","wikidata":"https://www.wikidata.org/wiki/Q6485978","display_name":"Langevin dynamics","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.07150","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07150","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":"doi:10.48550/arxiv.2512.07150","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07150","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":"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":{"Deep":[0],"generative":[1],"models":[2,16],"are":[3],"powerful":[4],"priors":[5],"for":[6,13,107],"imaging":[7],"inverse":[8,79,149,169],"problems,":[9],"but":[10,28,58],"training-free":[11,76],"solvers":[12],"latent":[14,32,77],"flow":[15,78],"face":[17],"a":[18,75,91,155],"practical":[19],"finite-step":[20],"trade-off.":[21],"Optimization-heavy":[22],"methods":[23,53],"quickly":[24],"improve":[25,119],"measurement":[26,120,159],"consistency,":[27],"in":[29,101],"highly":[30],"nonlinear":[31],"spaces,":[33],"their":[34],"results":[35],"can":[36],"depend":[37],"strongly":[38],"on":[39,82,142,167],"where":[40],"local":[41,108,113],"refinement":[42,116],"is":[43],"initialized,":[44],"often":[45,59],"degrading":[46],"perceptual":[47,162],"realism.":[48],"In":[49],"contrast,":[50],"stochastic":[51,105],"sampling":[52],"better":[54],"preserve":[55],"posterior":[56],"exploration,":[57],"require":[60],"many":[61],"iterations":[62],"to":[63,95,117,132],"obtain":[64],"sharp,":[65],"measurement-consistent":[66],"reconstructions.":[67],"To":[68],"address":[69],"this":[70],"trade-off,":[71],"we":[72],"propose":[73],"FlowLPS,":[74],"solver":[80],"based":[81],"Langevin-Proximal":[83],"Sampling.":[84],"At":[85],"each":[86],"reverse":[87,135],"step,":[88],"FlowLPS":[89,153],"uses":[90],"few":[92],"Langevin":[93],"updates":[94],"perturb":[96],"the":[97,123,134],"model-predicted":[98],"clean":[99],"estimate":[100],"posterior-oriented":[102],"directions,":[103],"providing":[104],"initializations":[106],"refinement.":[109],"It":[110],"then":[111],"applies":[112],"MAP-style":[114],"proximal":[115],"rapidly":[118],"consistency":[121],"from":[122],"Langevin-updated":[124],"estimate.":[125],"We":[126],"additionally":[127],"use":[128],"controlled":[129],"pCN-style":[130],"re-noising":[131],"stabilize":[133],"trajectory":[136,139],"while":[137],"retaining":[138],"coherence.":[140],"Experiments":[141],"FFHQ":[143],"and":[144,161,171],"DIV2K":[145],"across":[146],"five":[147],"linear":[148],"problems":[150,170],"show":[151],"that":[152],"achieves":[154],"strong":[156],"balance":[157],"between":[158],"fidelity":[160],"quality,":[163],"with":[164],"additional":[165],"experiments":[166],"pixel-space":[168],"phase":[172],"retrieval.":[173]},"counts_by_year":[],"updated_date":"2026-05-14T06:09:40.864956","created_date":"2025-12-10T00:00:00"}
