{"id":"https://openalex.org/W7168289016","doi":"https://doi.org/10.48550/arxiv.2607.09784","title":"Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion","display_name":"Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7168289016","doi":"https://doi.org/10.48550/arxiv.2607.09784"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.09784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09784","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":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.2607.09784","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140616568","display_name":"Yongseong Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Yongseong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140715691","display_name":"Joeun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Joeun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140628922","display_name":"HoEun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, HoEun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140629589","display_name":"Young-Sik Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Young-Sik","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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.36399999260902405,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.36399999260902405,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.14630000293254852,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11739999800920486,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anchoring","display_name":"Anchoring","score":0.7932999730110168},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.7915999889373779},{"id":"https://openalex.org/keywords/asymmetry","display_name":"Asymmetry","score":0.6471999883651733},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5139999985694885},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41670000553131104},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.39430001378059387},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.38769999146461487},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.36309999227523804}],"concepts":[{"id":"https://openalex.org/C18483071","wikidata":"https://www.wikidata.org/wiki/Q168432","display_name":"Anchoring","level":2,"score":0.7932999730110168},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.7915999889373779},{"id":"https://openalex.org/C38976095","wikidata":"https://www.wikidata.org/wiki/Q752641","display_name":"Asymmetry","level":2,"score":0.6471999883651733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5685999989509583},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5651999711990356},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5139999985694885},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3492000102996826},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31299999356269836},{"id":"https://openalex.org/C207850805","wikidata":"https://www.wikidata.org/wiki/Q269608","display_name":"Reverse engineering","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C164602753","wikidata":"https://www.wikidata.org/wiki/Q567544","display_name":"Anomalous diffusion","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.274399995803833},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C2984485829","wikidata":"https://www.wikidata.org/wiki/Q100766421","display_name":"Noise level","level":3,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.09784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09784","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":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.2607.09784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09784","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":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":{"Real-image":[0],"diffusion":[1,32,43],"inversion":[2,80,83,128,187],"is":[3,84,152],"governed":[4],"by":[5,176],"a":[6,31,94,103,126,132,141],"tight":[7],"quality-cost":[8],"trade-off,":[9],"with":[10,140,163],"costs":[11],"incurred":[12],"in":[13],"computation,":[14],"storage,":[15],"or":[16,68],"per-image":[17],"optimization.":[18],"We":[19],"study":[20],"this":[21],"trade-off":[22],"through":[23],"the":[24,71,89,149],"forward":[25,91],"Gaussian":[26],"noise":[27,44],"anchor":[28,92,136,195],"that":[29,130],"defines":[30],"trajectory":[33,151],"and":[34,86,93,114,137,173,197],"isolate":[35],"two":[36],"mechanisms":[37],"behind":[38],"effective":[39],"stored-noise":[40,79],"inversion.":[41],"First,":[42],"exhibits":[45],"an":[46],"element-wise":[47,72],"compression":[48,193],"asymmetry:":[49],"int8":[50,134],"full-dimensional":[51],"anchors":[52],"preserve":[53],"reconstruction,":[54],"whereas":[55],"low-dimensional":[56],"subspace":[57,74],"summaries":[58],"are":[59,98],"much":[60],"less":[61,186],"reliable,":[62],"often":[63],"collapsing":[64],"even":[65],"at":[66],"comparable":[67],"smaller":[69],"payloads;":[70],"over":[73,179],"ordering":[75],"persists":[76],"across":[77],"five":[78,169],"methods.":[81],"Second,":[82],"trajectory-bound":[85],"score-prior":[87],"coupled:":[88],"matched":[90],"trained":[95],"score":[96],"network":[97],"both":[99],"necessary,":[100],"arguing":[101],"against":[102],"purely":[104],"algebraic-identity":[105],"explanation.":[106],"Together,":[107],"these":[108],"findings":[109],"specify":[110],"what":[111],"to":[112,116,121,202],"store":[113],"how":[115],"use":[117],"it.":[118],"They":[119],"lead":[120],"Noise-Anchored":[122],"Reverse":[123],"Correction":[124],"(NARC),":[125],"training-free":[127],"primitive":[129],"stores":[131],"single":[133],"latent":[135],"reuses":[138],"it":[139],"fixed,":[142],"noise-level-dependent":[143],"anchor-weight":[144],"schedule:":[145],"strong":[146],"anchoring":[147,156],"when":[148],"reverse":[150],"noise-dominated,":[153],"then":[154],"relaxed":[155],"as":[157],"image":[158],"detail":[159],"emerges.":[160],"On":[161],"PIE-Bench++":[162],"Stable":[164],"Diffusion":[165],"1.5,":[166],"NARC":[167],"outperforms":[168],"modern":[170],"non-exact":[171],"baselines":[172],"improves":[174],"PSNR":[175],"+3.24":[177],"dB":[178],"PnP":[180,190],"DirectInv":[181],"while":[182],"using":[183],"about":[184],"400x":[185],"storage":[188],"than":[189],"DirectInv.":[191],"The":[192],"asymmetry,":[194],"specificity,":[196],"editing":[198],"plug-in":[199],"also":[200],"transfer":[201],"SDXL":[203],"1024^2.":[204]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}