{"id":"https://openalex.org/W7165115425","doi":"https://doi.org/10.48550/arxiv.2606.18315","title":"Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation","display_name":"Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165115425","doi":"https://doi.org/10.48550/arxiv.2606.18315"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.18315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18315","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":null,"license_id":null,"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.2606.18315","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138923395","display_name":"Tianyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138956705","display_name":"Ying Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138929532","display_name":"Zhihao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062473662","display_name":"Xi Vincent Wang","orcid":"https://orcid.org/0000-0001-9694-0483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xi Vincent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138881813","display_name":"Lihui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lihui","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.45329999923706055,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.45329999923706055,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.17659999430179596,"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/T11170","display_name":"Biomimetic flight and propulsion mechanisms","score":0.05990000069141388,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/attractor","display_name":"Attractor","score":0.54339998960495},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5040000081062317},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4449999928474426},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.4036000072956085},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.3898000121116638},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3824999928474426},{"id":"https://openalex.org/keywords/continuation","display_name":"Continuation","score":0.37369999289512634},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.3718000054359436}],"concepts":[{"id":"https://openalex.org/C164380108","wikidata":"https://www.wikidata.org/wiki/Q507187","display_name":"Attractor","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5350000262260437},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4449999928474426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4361000061035156},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3779999911785126},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C140479938","wikidata":"https://www.wikidata.org/wiki/Q5254619","display_name":"Iterated function","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C163415756","wikidata":"https://www.wikidata.org/wiki/Q126473","display_name":"Contraction (grammar)","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29120001196861267},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C2781204021","wikidata":"https://www.wikidata.org/wiki/Q6497091","display_name":"Lattice (music)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2646999955177307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.18315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18315","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.18315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18315","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":null,"license_id":null,"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":{"Sequential":[0],"output":[1],"generation":[2,41],"with":[3,15,24,51,72,101,121,135],"large-scale":[4],"Transformer":[5,170],"and":[6,42,74,87,94,124,176,181,226],"diffusion":[7],"decoders":[8,27,186],"pays":[9],"a":[10,48,60,69,76,117,122,155,167,216,222,230],"memory":[11],"cost":[12],"that":[13,35],"grows":[14],"sequence":[16],"length,":[17],"plus":[18],"iterative":[19],"per-step":[20],"computation.":[21],"Replacing":[22],"them":[23],"small":[25],"feed-forward":[26,223],"restores":[28],"efficiency":[29],"but":[30],"produces":[31,75],"unstructured":[32],"latent":[33,44,49,66,214],"representations":[34],"limit":[36],"closed-loop":[37,207],"control:":[38],"phase-conditioned":[39],"action":[40,157],"cross-step":[43],"carry-over":[45],"both":[46],"require":[47],"geometry":[50],"stable":[52],"basins.":[53],"This":[54],"article":[55],"proposes":[56],"Ghost":[57,118,151,161],"Attractor":[58],"Networks,":[59],"theoretically":[61],"derived":[62],"dynamical":[63],"decoder":[64],"whose":[65],"evolves":[67],"under":[68],"learned":[70],"potential":[71],"drift":[73],"basin-attractor":[77],"structure":[78],"by":[79,140,199],"construction.":[80],"Three":[81],"desiderata":[82],"(multi-modality,":[83],"decoder-level":[84],"single-pass":[85],"switching,":[86],"constant":[88],"memory)":[89],"motivate":[90],"the":[91,128,136,163,205],"potential-drift":[92],"form,":[93],"mode":[95],"transitions":[96],"arise":[97],"as":[98,154],"saddle-node":[99],"bifurcations":[100],"ghost-attractor":[102],"escape.":[103],"A":[104,159],"hierarchical":[105],"phase-space":[106],"decomposition":[107],"separates":[108],"first-order":[109],"basin":[110],"convergence":[111],"from":[112],"second-order":[113],"proprioceptive":[114],"refinement.":[115],"Empirically,":[116],"trained":[119],"end-to-end":[120],"behavioral-cloning":[123],"contrastive":[125],"objective":[126],"exhibits":[127],"predicted":[129],"gradient-flow":[130],"contraction":[131],"in":[132],"its":[133],"potential,":[134],"gradient":[137],"norm":[138],"decaying":[139],"67":[141],"percent":[142,232],"across":[143],"five":[144,183],"integration":[145],"steps":[146],"on":[147,194,211],"1430":[148],"held-out":[149],"samples.":[150],"is":[152],"evaluated":[153],"robotic":[156],"decoder.":[158],"2.3-million-parameter":[160],"matches":[162],"offline":[164,195],"accuracy":[165],"of":[166],"1.07-billion-parameter":[168],"Diffusion":[169],"at":[171],"462":[172],"times":[173,178],"fewer":[174],"parameters":[175],"32":[177],"lower":[179],"latency,":[180],"beats":[182],"alternative":[184],"2M-parameter":[185],"(MLP,":[187],"Neural":[188],"ODE,":[189],"CVAE,":[190],"Transformer,":[191],"1-step":[192],"Diffusion)":[193],"mean":[196],"squared":[197],"error":[198],"5.9":[200],"to":[201],"29":[202],"percent.":[203],"On":[204],"LIBERO-10":[206],"benchmark,":[208],"phase":[209],"conditioning":[210],"Ghost's":[212],"basin-structured":[213],"yields":[215],"13.5":[217],"percentage-point":[218],"success-rate":[219],"gain":[220],"over":[221],"MLP":[224],"baseline,":[225],"persistent-latent":[227],"ensembling":[228],"reaches":[229],"95.7":[231],"final":[233],"success":[234],"rate.":[235]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-19T00:00:00"}
