{"id":"https://openalex.org/W4416694266","doi":"https://doi.org/10.48550/arxiv.2509.01543","title":"Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior","display_name":"Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4416694266","doi":"https://doi.org/10.48550/arxiv.2509.01543"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.01543","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01543","pdf_url":"https://arxiv.org/pdf/2509.01543","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.01543","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055690712","display_name":"Konstantin Mark","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark, Konstantin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059380178","display_name":"Leonard Galustian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Galustian, Leonard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118588130","display_name":"Maximilian P.-P. Kovar","orcid":"https://orcid.org/0009-0009-2321-1656"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kovar, Maximilian P. -P.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018855407","display_name":"Esther Heid","orcid":"https://orcid.org/0000-0002-8404-6596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heid, Esther","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":1,"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/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.3720000088214874,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.3720000088214874,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.0763000026345253,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.07209999859333038,"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/matching","display_name":"Matching (statistics)","score":0.6301000118255615},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5644000172615051},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5246000289916992},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4934000074863434},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.412200003862381},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4004000127315521}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874000191688538},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5338000059127808},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.412200003862381},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35600000619888306},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2612999975681305},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26030001044273376},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.01543","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01543","pdf_url":"https://arxiv.org/pdf/2509.01543","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.01543","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.01543","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":"pmh:oai:arXiv.org:2509.01543","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01543","pdf_url":"https://arxiv.org/pdf/2509.01543","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Conditional":[0],"Flow":[1],"Matching(CFM)":[2],"represents":[3],"a":[4,87,99,104,143],"fast":[5],"and":[6,157],"high-quality":[7],"approach":[8,85],"to":[9,20,52,147,160],"generative":[10],"modelling,":[11],"but":[12],"in":[13,98],"many":[14],"applications":[15],"it":[16],"is":[17,103,164],"of":[18,89,95,116,125,129,150],"interest":[19],"steer":[21],"the":[22,66,75,93,114,121,133,137,151],"generated":[23,126],"samples":[24],"towards":[25],"precise":[26],"requirements.":[27],"While":[28],"steering":[29,37,40,79,109],"approaches":[30,55],"like":[31],"gradient-based":[32],"guidance,":[33],"sequential":[34],"Monte":[35],"Carlo":[36],"or":[38,139],"Feynman-Kac":[39,78,117],"are":[41],"well":[42],"established":[43],"for":[44,74,80,108],"diffusion":[45],"models,":[46],"they":[47],"have":[48,142],"not":[49],"been":[50],"extended":[51],"flow":[53],"matching":[54],"yet.":[56],"In":[57],"this":[58,62,162],"work,":[59],"we":[60],"formulate":[61],"requirement":[63],"as":[64],"tilting":[65],"output":[67],"with":[68,132],"an":[69],"energy":[70],"potential.":[71],"We":[72,82,111],"derive,":[73],"first":[76],"time,":[77],"CFM.":[81],"evaluate":[83],"our":[84],"on":[86,120],"set":[88],"synthetic":[90],"tasks,":[91],"including":[92],"generation":[94],"tilted":[96],"distributions":[97],"high-dimensional":[100],"space,":[101],"which":[102],"particularly":[105],"challenging":[106],"case":[107],"approaches.":[110],"then":[112],"demonstrate":[113],"impact":[115],"steered":[118],"CFM":[119],"previously":[122],"unsolved":[123],"challenge":[124],"transition":[127],"states":[128],"chemical":[130],"reactions":[131],"correct":[134],"chirality,":[135],"where":[136],"reactants":[138,156],"products":[140],"can":[141],"different":[144],"handedness,":[145],"leading":[146],"geometric":[148],"constraints":[149],"viable":[152],"reaction":[153],"pathways":[154],"connecting":[155],"products.":[158],"Code":[159],"reproduce":[161],"study":[163],"avaiable":[165],"open-source":[166],"at":[167],"https://github.com/heid-lab/fkflow.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
