{"id":"https://openalex.org/W7164156848","doi":"https://doi.org/10.48550/arxiv.2606.10314","title":"Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints","display_name":"Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164156848","doi":"https://doi.org/10.48550/arxiv.2606.10314"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10314","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10314","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.2606.10314","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138320003","display_name":"Yueyang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yueyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138324608","display_name":"Joon-Seok Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Joon-Seok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5095886499","display_name":"Andreas Z\u00fcfle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Z\u00fcfle, Andreas","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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.8737999796867371,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.8737999796867371,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.02889999933540821,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.02019999921321869,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7437999844551086},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5203999876976013},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4853000044822693},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.475600004196167},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.44620001316070557},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4413999915122986},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.4092999994754791},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.4009999930858612},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.36570000648498535}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7437999844551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325000166893005},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4853000044822693},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42089998722076416},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3971000015735626},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.3125},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.3066999912261963},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C191485582","wikidata":"https://www.wikidata.org/wiki/Q6887309","display_name":"Mobility model","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C55508974","wikidata":"https://www.wikidata.org/wiki/Q190763","display_name":"Venn diagram","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C176743888","wikidata":"https://www.wikidata.org/wiki/Q862797","display_name":"Observational methods in psychology","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10314","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10314","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.2606.10314","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10314","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"the":[1,27,55,63,72,124,171,179,190,195],"study":[2],"of":[3,23,29,59,65,75],"human":[4,34,98],"trajectory":[5,35,99,117],"anomalies":[6,100,118,156],"is":[7,51,79],"critical":[8],"for":[9],"advancing":[10],"spatial":[11,169,201],"data":[12,78,130],"mining,":[13],"empirical":[14],"research":[15],"remains":[16],"severely":[17],"hindered":[18],"by":[19,54,82,136,205],"a":[20,96,108,199],"pervasive":[21],"lack":[22,45],"ground-truth":[24],"datasets.":[25],"Despite":[26],"availability":[28],"several":[30],"real-world":[31,133],"and":[32,44,85,94,131,162,207],"simulated":[33,141],"collections,":[36],"these":[37,91,183],"datasets":[38],"exclusively":[39],"capture":[40],"normal":[41],"mobility":[42,77,129],"patterns":[43],"annotated":[46,103],"anomalies.":[47],"This":[48],"specific":[49],"scarcity":[50],"fundamentally":[52],"driven":[53],"inherent":[56],"statistical":[57],"rarity":[58],"anomalous":[60],"events,":[61],"precluding":[62],"feasibility":[64],"conventional":[66],"observational":[67],"methods.":[68],"Compounding":[69],"this":[70],"challenge,":[71],"systematic":[73],"acquisition":[74],"large-scale":[76],"strictly":[80],"bottlenecked":[81],"prohibitive":[83],"costs":[84],"stringent":[86],"privacy":[87],"regulations.":[88],"To":[89,166],"overcome":[90],"fundamental":[92],"limitations":[93],"establish":[95],"reliable":[97],"dataset":[101],"with":[102,198],"ground":[104],"truth,":[105],"we":[106,193],"introduce":[107],"novel,":[109],"end-to-end":[110],"generative":[111],"framework":[112],"designed":[113],"to":[114,150,177,188,210],"synthesize":[115],"realistic":[116],"at":[119],"scale.":[120],"Our":[121],"architecture":[122],"bridges":[123],"gap":[125],"between":[126,182],"purely":[127],"synthetic":[128],"complex":[132],"physical":[134,180],"constraints":[135],"operating":[137],"directly":[138],"on":[139],"baseline":[140],"trajectories.":[142],"We":[143],"employ":[144],"Large":[145],"Language":[146],"Model":[147],"(LLM)":[148],"agents":[149],"systematically":[151],"inject":[152],"semantically":[153],"meaningful":[154],"behavioral":[155],"such":[157],"as":[158],"irregular":[159],"out-of-distribution":[160],"check-ins":[161],"skipped":[163],"routine":[164],"visits.":[165],"ensure":[167],"rigorous":[168],"validity,":[170],"system":[172],"leverages":[173],"map-constrained":[174],"routing":[175],"reconstruction":[176],"recalculate":[178],"transitions":[181],"LLM":[184],"agent-modified":[185],"staypoints.":[186],"Moreover,":[187],"narrow":[189],"simulation-to-reality":[191],"gap,":[192],"augment":[194],"resulting":[196],"trajectories":[197],"context-aware":[200],"noise":[202],"model,":[203],"parameterized":[204],"environmental":[206],"location-specific":[208],"variables,":[209],"accurately":[211],"emulate":[212],"heterogeneous":[213],"GPS":[214],"sensor":[215],"degradation.":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}
