{"id":"https://openalex.org/W7160953267","doi":"https://doi.org/10.48550/arxiv.2605.09186","title":"Agentic MIP Research: Accelerated Constraint Handler Generation","display_name":"Agentic MIP Research: Accelerated Constraint Handler Generation","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160953267","doi":"https://doi.org/10.48550/arxiv.2605.09186"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09186","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09186","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.09186","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009952526","display_name":"Liding Xu","orcid":"https://orcid.org/0000-0002-0286-1109"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Liding","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020578263","display_name":"Yugeng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yugeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135993542","display_name":"Sebastian Pokutta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pokutta, Sebastian","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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.6485999822616577,"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"}},"topics":[{"id":"https://openalex.org/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.6485999822616577,"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"}},{"id":"https://openalex.org/T10142","display_name":"Formal Methods in Verification","score":0.05490000173449516,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.032499998807907104,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/constraint-programming","display_name":"Constraint programming","score":0.777999997138977},{"id":"https://openalex.org/keywords/debugging","display_name":"Debugging","score":0.6520000100135803},{"id":"https://openalex.org/keywords/executable","display_name":"Executable","score":0.6473000049591064},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.6341000199317932},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5997999906539917},{"id":"https://openalex.org/keywords/constraint-logic-programming","display_name":"Constraint logic programming","score":0.5554999709129333},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5286999940872192},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5188999772071838}],"concepts":[{"id":"https://openalex.org/C173404611","wikidata":"https://www.wikidata.org/wiki/Q528588","display_name":"Constraint programming","level":3,"score":0.777999997138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059000134468079},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.6520000100135803},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.6473000049591064},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.6341000199317932},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5997999906539917},{"id":"https://openalex.org/C176783269","wikidata":"https://www.wikidata.org/wiki/Q5164378","display_name":"Constraint logic programming","level":4,"score":0.5554999709129333},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5286999940872192},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5188999772071838},{"id":"https://openalex.org/C199622910","wikidata":"https://www.wikidata.org/wiki/Q1128326","display_name":"Constraint satisfaction problem","level":3,"score":0.4607999920845032},{"id":"https://openalex.org/C4924752","wikidata":"https://www.wikidata.org/wiki/Q184148","display_name":"Plug-in","level":2,"score":0.4284000098705292},{"id":"https://openalex.org/C29230964","wikidata":"https://www.wikidata.org/wiki/Q5164376","display_name":"Constraint learning","level":5,"score":0.40799999237060547},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C137105694","wikidata":"https://www.wikidata.org/wiki/Q3407510","display_name":"Local consistency","level":4,"score":0.39800000190734863},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.39430001378059387},{"id":"https://openalex.org/C164155591","wikidata":"https://www.wikidata.org/wiki/Q2067766","display_name":"Satisfiability modulo theories","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3472000062465668},{"id":"https://openalex.org/C204306468","wikidata":"https://www.wikidata.org/wiki/Q5159106","display_name":"Concurrent constraint logic programming","level":5,"score":0.33399999141693115},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.319599986076355},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2736999988555908}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09186","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09186","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.09186","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09186","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":"Preprint"},"sustainable_development_goals":[{"score":0.4778500497341156,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mixed-integer":[0],"programming":[1,109],"(MIP)":[2],"research":[3,31,207],"is":[4],"both":[5],"mathematically":[6],"sophisticated":[7],"and":[8,23,49,85,110,115,137,155],"engineering-intensive:":[9],"testing":[10],"an":[11,28],"algorithmic":[12,173],"hypothesis":[13],"within":[14,127,190],"a":[15,43,60,128,213],"branch-and-cut":[16],"solver":[17,55,216],"requires":[18],"substantial":[19],"implementation,":[20],"debugging,":[21],"tuning,":[22],"large-scale":[24],"benchmarking.":[25],"We":[26,71],"propose":[27],"agentic":[29],"MIP":[30,65,80,153,206],"framework":[32,74,101,121,166,196],"that":[33,198],"shortens":[34],"this":[35,195],"feedback":[36],"loop":[37],"by":[38,67],"embedding":[39],"LLM":[40,199],"agents":[41,132,200],"into":[42,82],"solver-aware":[44],"harness":[45],"for":[46,52,212],"generating,":[47],"verifying,":[48],"evaluating":[50],"plugins":[51],"the":[53,76,86,95,100,120,181,191,204,210],"open-source":[54],"SCIP.":[56,164],"Propagation":[57],"methods":[58,184],"play":[59],"central":[61],"role":[62],"in":[63,152,163],"accelerating":[64],"solving":[66],"exploiting":[68],"global":[69,83,104,149],"constraints.":[70],"instantiate":[72],"our":[73],"on":[75,142],"semantic":[77],"lifting":[78],"of":[79,89],"formulations":[81],"constraints":[84],"automatic":[87],"construction":[88],"propagation-only":[90,116],"SCIP":[91],"constraint":[92,105,108,113,117,140,150],"handlers.":[93,118],"On":[94],"MIPLIB":[96],"2017":[97],"benchmark":[98],"set,":[99],"successfully":[102,185],"recovers":[103],"structures":[106],"from":[107,175],"generates":[111],"executable":[112],"detectors":[114],"Furthermore,":[119],"naturally":[122],"extends":[123],"to":[124,135,147,169],"in-context":[125],"learning":[126],"sandboxed":[129],"environment,":[130],"enabling":[131],"not":[133,160],"only":[134],"tune":[136],"debug":[138],"generated":[139],"handlers":[141],"real":[143],"instances,":[144],"but":[145],"also":[146],"explore":[148],"patterns":[151],"problems":[154],"discover":[156],"novel":[157,182],"propagation":[158,183],"strategies":[159],"yet":[161],"implemented":[162],"This":[165],"allows":[167],"us":[168],"systematically":[170],"distinguish":[171],"meaningful":[172],"improvements":[174],"low-value":[176],"or":[177],"overly":[178],"costly":[179],"candidates:":[180],"solved":[186],"five":[187],"additional":[188],"instances":[189],"explored":[192],"benchmark.":[193],"Overall,":[194],"demonstrates":[197],"can":[201],"autonomously":[202],"navigate":[203],"complex":[205],"loop,":[208],"paving":[209],"way":[211],"more":[214],"automated":[215],"development":[217],"process.":[218]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}