{"id":"https://openalex.org/W7168133340","doi":"https://doi.org/10.48550/arxiv.2607.08781","title":"Reward Transport: Property Control in Flow Matching via Noise-Space Alignment","display_name":"Reward Transport: Property Control in Flow Matching via Noise-Space Alignment","publication_year":2026,"publication_date":"2026-06-13","ids":{"openalex":"https://openalex.org/W7168133340","doi":"https://doi.org/10.48550/arxiv.2607.08781"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.08781","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08781","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.08781","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140563761","display_name":"Kehan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Kehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140559845","display_name":"Yili Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yili","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140556289","display_name":"Yujun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yujun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140562560","display_name":"Yue Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011693067","display_name":"Chujie Gao","orcid":"https://orcid.org/0000-0003-0771-3399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Chujie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134823984","display_name":"Shiyi Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Shiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140518919","display_name":"Xiangliang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiangliang","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/T11948","display_name":"Machine Learning in Materials Science","score":0.26489999890327454,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.26489999890327454,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.14069999754428864,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.0689999982714653,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.6872000098228455},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6449000239372253},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6327999830245972},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5601000189781189},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/monotone-polygon","display_name":"Monotone polygon","score":0.46639999747276306},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4634000062942505},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.44830000400543213}],"concepts":[{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.6872000098228455},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6449000239372253},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6327999830245972},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5601000189781189},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.5270000100135803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49720001220703125},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C2834757","wikidata":"https://www.wikidata.org/wiki/Q4925424","display_name":"Monotone polygon","level":2,"score":0.46639999747276306},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.44830000400543213},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.39329999685287476},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37529999017715454},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C14103023","wikidata":"https://www.wikidata.org/wiki/Q11681459","display_name":"Pairing","level":3,"score":0.32350000739097595},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C80551277","wikidata":"https://www.wikidata.org/wiki/Q11210","display_name":"Coordinate system","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28690001368522644},{"id":"https://openalex.org/C110521144","wikidata":"https://www.wikidata.org/wiki/Q193460","display_name":"Scalar field","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C205875254","wikidata":"https://www.wikidata.org/wiki/Q17156857","display_name":"Decentralised system","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C162291796","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Coordinate space","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.08781","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08781","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.08781","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08781","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,171],"coupling":[1,26,67],"in":[2],"flow":[3,53,180],"matching":[4,35],"--":[5,14],"the":[6,51,86,101,108,128,145],"rule":[7],"pairing":[8],"noise":[9,36],"vectors":[10],"with":[11,77],"data":[12,38],"points":[13],"is":[15,173,193],"typically":[16],"treated":[17],"as":[18,30],"a":[19,41,73,115,167,183],"computational":[20],"choice.":[21],"We":[22],"show":[23],"that":[24],"this":[25,57,83,105],"can":[27],"instead":[28],"serve":[29],"an":[31,91],"alignment":[32,192],"interface:":[33],"by":[34],"and":[37,125,135,178],"according":[39],"to":[40,71,175],"target":[42],"molecular":[43,78],"property,":[44],"it":[45],"embeds":[46],"controllable":[47],"structure":[48],"directly":[49],"into":[50],"learned":[52],"field.":[54],"Building":[55],"on":[56,123],"view,":[58],"we":[59],"introduce":[60],"Reward":[61],"Transport,":[62],"which":[63,164],"uses":[64],"optimal":[65],"transport":[66],"at":[68,80],"training":[69],"time":[70],"align":[72],"scalar":[74,129],"noise-space":[75],"coordinate":[76,84,106],"rewards;":[79],"inference,":[81],"varying":[82],"steers":[85],"generated":[87],"distribution":[88],"without":[89],"requiring":[90],"oracle,":[92],"reward":[93,112],"model,":[94],"gradient":[95],"guidance,":[96],"or":[97],"additional":[98],"computation.":[99],"In":[100],"coupling-preserving":[102],"limit,":[103],"thresholding":[104],"recovers":[107],"Cross-Entropy":[109],"Method's":[110],"truncated":[111],"distribution,":[113],"providing":[114],"principled,":[116],"continuously":[117],"adjustable":[118],"distribution-level":[119],"control":[120,132,138],"knob.":[121],"Empirically,":[122],"ZINC-250K":[124],"GuacaMol,":[126],"sweeping":[127],"induces":[130],"monotone":[131],"of":[133],"logP":[134,158],"consistent":[136],"QED":[137],"over":[139],"its":[140],"operating":[141],"range;":[142],"most":[143],"tellingly,":[144],"same":[146],"knob":[147],"produces":[148],"opposite":[149],"structural":[150],"responses":[151],"for":[152,157,162],"different":[153],"targets,":[154],"growing":[155],"molecules":[156],"but":[159],"shrinking":[160],"them":[161],"QED,":[163],"rules":[165],"out":[166],"generic":[168],"size":[169],"bias.":[170],"interface":[172],"complementary":[174],"classifier-free":[176],"guidance":[177],"conditional":[179],"matching,":[181],"while":[182],"negative":[184],"result":[185],"under":[186],"epsilon-prediction":[187],"diffusion":[188],"clarifies":[189],"where":[190],"coupling-level":[191],"structurally":[194],"absent.":[195],"Code:":[196],"https://github.com/KehanGuo2/reward-transport":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-14T00:00:00"}