{"id":"https://openalex.org/W7160415045","doi":"https://doi.org/10.48550/arxiv.2605.03075","title":"Refining Compositional Diffusion for Reliable Long-Horizon Planning","display_name":"Refining Compositional Diffusion for Reliable Long-Horizon Planning","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160415045","doi":"https://doi.org/10.48550/arxiv.2605.03075"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03075","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03075","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.2605.03075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078048007","display_name":"Kyowoon Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Kyowoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135521145","display_name":"Yunhao Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yunhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134408568","display_name":"Anh Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong, Anh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052985764","display_name":"Jaesik Choi","orcid":"https://orcid.org/0000-0002-4663-3263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jaesik","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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.6960999965667725,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.6960999965667725,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.12300000339746475,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.03840000182390213,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.685699999332428},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6169000267982483},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5436000227928162},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5062000155448914},{"id":"https://openalex.org/keywords/image-stitching","display_name":"Image stitching","score":0.4643000066280365},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4512999951839447},{"id":"https://openalex.org/keywords/refining","display_name":"Refining (metallurgy)","score":0.44020000100135803}],"concepts":[{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.685699999332428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6389999985694885},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6169000267982483},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5436000227928162},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5062000155448914},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C60044698","wikidata":"https://www.wikidata.org/wiki/Q1283324","display_name":"Refining (metallurgy)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43369999527931213},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.319599986076355},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03075","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03075","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.2605.03075","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03075","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.793075442314148}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Compositional":[0,47],"diffusion":[1,71],"planning":[2],"generates":[3],"long-horizon":[4,111],"trajectories":[5],"by":[6],"stitching":[7],"together":[8],"overlapping":[9],"short-horizon":[10],"segments":[11],"through":[12],"score":[13],"composition.":[14],"However,":[15],"when":[16],"local":[17,31],"plan":[18],"distributions":[19],"are":[20,37],"multimodal,":[21],"existing":[22,127],"compositional":[23,56],"methods":[24],"suffer":[25],"from":[26,113],"mode-averaging,":[27],"where":[28],"averaging":[29],"incompatible":[30],"modes":[32],"leads":[33],"to":[34],"plans":[35,104],"that":[36,54,88,96,105,123],"neither":[38],"locally":[39],"feasible":[40],"nor":[41],"globally":[42,60],"coherent.":[43],"We":[44,94],"propose":[45],"Refining":[46],"Diffusion":[48],"(RCD),":[49],"a":[50,69,74],"training-free":[51],"guidance":[52,99],"method":[53],"steers":[55],"sampling":[57,101],"toward":[58],"high-density,":[59],"coherent":[61],"plans.":[62],"RCD":[63,124],"leverages":[64],"the":[65,77,97],"self-reconstruction":[66],"error":[67],"of":[68,79],"pretrained":[70],"model":[72],"as":[73],"proxy":[75],"for":[76],"log-density":[78],"composed":[80],"plans,":[81],"combined":[82,98],"with":[83],"an":[84],"overlap":[85],"consistency":[86,90],"term":[87],"enforces":[89],"at":[91],"segment":[92],"boundaries.":[93],"show":[95],"concentrates":[100],"on":[102,109],"high-density":[103],"mitigate":[106],"mode-averaging.":[107],"Experiments":[108],"challenging":[110],"tasks":[112],"OGBench,":[114],"including":[115],"locomotion,":[116],"object":[117],"manipulation,":[118],"and":[119],"pixel-based":[120],"observations,":[121],"demonstrate":[122],"consistently":[125],"outperforms":[126],"methods.":[128]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}