{"id":"https://openalex.org/W7167056845","doi":"https://doi.org/10.48550/arxiv.2607.00022","title":"When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy","display_name":"When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7167056845","doi":"https://doi.org/10.48550/arxiv.2607.00022"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00022","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.2607.00022","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025317164","display_name":"Xianyao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xianyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139856308","display_name":"Yuhai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuhai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129881246","display_name":"Hu Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139906028","display_name":"Kaleb Smith","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smith, Kaleb","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139898493","display_name":"Gilbert Yang Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Gilbert Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139854664","display_name":"Eric Jing Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Eric Jing","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/T10709","display_name":"Social Robot Interaction and HRI","score":0.4674000144004822,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.4674000144004822,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.2484000027179718,"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"}},{"id":"https://openalex.org/T10789","display_name":"Interactive and Immersive Displays","score":0.030799999833106995,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/personalization","display_name":"Personalization","score":0.6858000159263611},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5770000219345093},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5482000112533569},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5317999720573425},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5235999822616577},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45579999685287476},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.454800009727478},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.38260000944137573}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6858000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6564000248908997},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5482000112533569},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5235999822616577},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5184000134468079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4812000095844269},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C106934330","wikidata":"https://www.wikidata.org/wiki/Q1971873","display_name":"Trait","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3366999924182892},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00022","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.2607.00022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00022","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":{"Service":[0],"robots":[1],"searching":[2],"for":[3,132,142,150],"household":[4],"objects":[5,134],"rely":[6],"on":[7,166],"spatial":[8],"priors":[9,94],"to":[10,31,71,91,152],"reduce":[11],"search":[12,195],"cost,":[13],"yet":[14],"object":[15],"locations":[16],"can":[17],"vary":[18],"with":[19,50,202],"resident":[20],"traits.":[21],"Collecting":[22],"longitudinal,":[23],"trait-specific":[24],"in-home":[25],"trajectories":[26],"is":[27,59,129],"invasive":[28],"and":[29,38,73,81,95,121],"hard":[30],"scale.":[32],"We":[33],"study":[34,103],"when":[35,56,151],"personalization":[36,128],"helps":[37],"propose":[39],"PerSim,":[40],"a":[41,47,51,67,83,100,124,147,161,185],"rigidity-gated":[42],"hybrid":[43],"policy":[44],"that":[45,85,108,191],"combines":[46],"trait-conditioned":[48],"prior":[49],"population-frequency":[52,138],"baseline,":[53],"personalizing":[54],"only":[55],"placement":[57],"behavior":[58],"variable.":[60],"To":[61],"scale":[62],"resident-conditioned":[63],"dynamics,":[64],"we":[65,159,189],"employ":[66],"human-calibrated":[68],"simulation":[69],"pipeline":[70],"generate":[72],"validate":[74],"object-placement":[75],"transitions":[76,111],"in":[77,123,179,184],"diverse":[78],"home":[79,186],"layouts,":[80],"train":[82],"predictor":[84],"injects":[86],"continuous":[87,168],"Big":[88],"Five":[89],"vectors":[90,170],"output":[92],"room-level":[93],"within-room":[96,203],"co-occurrence":[97],"cues.":[98],"In":[99,154],"unified":[101],"human":[102],"(N=200),":[104],"dual-layer":[105],"validation":[106],"shows":[107],"(i)":[109],"synthetic":[110],"are":[112],"judged":[113],"behaviorally":[114],"plausible":[115],"(mean":[116],"3.85/5,":[117],"p":[118],"&lt;":[119],"1e-6),":[120],"(ii)":[122],"blinded":[125],"A/B":[126],"comparison,":[127],"favored":[130],"primarily":[131],"low-rigidity":[133],"(p=0.005),":[135],"while":[136],"the":[137],"baseline":[139],"remains":[140],"strong":[141],"universally":[143],"placed":[144],"items,":[145],"yielding":[146],"decision":[148],"rule":[149],"personalize.":[153],"an":[155],"offline":[156],"objective":[157],"test,":[158],"observe":[160],"small":[162],"but":[163],"significant":[164],"improvement":[165],"unseen":[167],"trait":[169,181],"over":[171],"nearest":[172],"discrete":[173],"configuration":[174],"matching":[175],"(p=0.035),":[176],"supporting":[177],"interpolation":[178],"five-dimensional":[180],"space.":[182],"Finally,":[183],"digital":[187],"twin":[188],"show":[190],"PerSim":[192],"reduces":[193],"expected":[194],"cost":[196],"by":[197],"combining":[198],"room":[199],"visitation":[200],"effort":[201],"cue":[204],"checking,":[205],"demonstrating":[206],"end-to-end":[207],"gains":[208],"beyond":[209],"isolated":[210],"prediction":[211],"metrics.":[212]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}
