{"id":"https://openalex.org/W7161952937","doi":"https://doi.org/10.48550/arxiv.2605.20619","title":"SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front","display_name":"SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161952937","doi":"https://doi.org/10.48550/arxiv.2605.20619"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20619","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20619","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20619","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114179175","display_name":"Liuyuan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Liuyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109695916","display_name":"Chentong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Chentong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136630097","display_name":"Lisha Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lisha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9420999884605408,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9420999884605408,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.00989999994635582,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.009499999694526196,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/traverse","display_name":"Traverse","score":0.8003000020980835},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.6280999779701233},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.5713000297546387},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47609999775886536},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.46219998598098755},{"id":"https://openalex.org/keywords/pareto-optimal","display_name":"Pareto optimal","score":0.4325000047683716},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4081000089645386},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.39890000224113464}],"concepts":[{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.8003000020980835},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.6280999779701233},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5989999771118164},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5965999960899353},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.5713000297546387},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47609999775886536},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.4325000047683716},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.3946000039577484},{"id":"https://openalex.org/C108882938","wikidata":"https://www.wikidata.org/wiki/Q274506","display_name":"Uniform distribution (continuous)","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C25432639","wikidata":"https://www.wikidata.org/wiki/Q3711790","display_name":"Weight distribution","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37929999828338623},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C2777551076","wikidata":"https://www.wikidata.org/wiki/Q842332","display_name":"Front (military)","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3637000024318695},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3573000133037567},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C134466208","wikidata":"https://www.wikidata.org/wiki/Q1520657","display_name":"Weight function","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C135692309","wikidata":"https://www.wikidata.org/wiki/Q111124","display_name":"Square (algebra)","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20619","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20619","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.20619","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20619","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":"article"},"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":{"Scalarization":[0],"is":[1,18],"widely":[2],"used":[3],"in":[4],"multi-objective":[5,168],"optimization":[6],"owing":[7],"to":[8,19,157],"its":[9],"simplicity":[10],"and":[11,129,144,167],"scalability.":[12],"In":[13],"many":[14],"applications,":[15],"the":[16,32,46,57,61,65,69,112,130],"goal":[17],"generate":[20],"solutions":[21,67],"that":[22,97,150,172],"represent":[23],"diverse":[24],"user":[25],"preferences,":[26],"ideally":[27],"with":[28,71],"uniform":[29,99,177],"coverage":[30,44,179],"of":[31,45,56],"Pareto":[33],"front":[34],"(PF).":[35],"However,":[36],"uniformly":[37],"sampling":[38,134],"scalarization":[39,58,62],"weights":[40,96],"usually":[41],"induces":[42,79],"non-uniform":[43,74],"PF.":[47],"We":[48],"explain":[49],"this":[50,87,104,126],"mismatch":[51],"through":[52],"a":[53,72,91],"geometric":[54],"analysis":[55],"path.":[59],"As":[60],"weight":[63,133,145],"varies,":[64],"corresponding":[66],"trace":[68],"PF":[70,100,178],"generally":[73],"traversal":[75],"speed.":[76],"This":[77],"speed":[78],"an":[80,158],"arc-length":[81],"cumulative":[82],"distribution":[83],"function":[84],"(CDF);":[85],"inverting":[86],"CDF":[88,127,142],"map":[89,128],"yields":[90],"principled":[92],"rule":[93],"for":[94,125],"selecting":[95],"produce":[98],"coverage.":[101],"Building":[102],"on":[103,164],"insight,":[105],"we":[106,121,148],"propose":[107],"SURF":[108,139,154,173],"(Sampling":[109],"Uniformly":[110],"along":[111],"PaReto":[113],"Front).":[114],"For":[115,136],"structured":[116],"problems,":[117,138],"including":[118],"bi-objective":[119],"bandits,":[120,165],"derive":[122],"closed-form":[123],"expressions":[124],"resulting":[131],"PF-aware":[132],"rule.":[135],"general":[137],"alternates":[140],"between":[141],"reconstruction":[143],"sampling.":[146],"Theoretically,":[147],"show":[149],"under":[151],"provable":[152],"conditions,":[153],"converges":[155],"linearly":[156],"unavoidable":[159],"finite-sampling":[160],"floor.":[161],"Empirically,":[162],"experiments":[163],"multi-objective-gymnasium,":[166],"LLM":[169],"alignment":[170],"demonstrate":[171],"efficiently":[174],"achieves":[175],"more":[176],"than":[180],"baselines.":[181]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-22T00:00:00"}