{"id":"https://openalex.org/W7140107404","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.11","title":"I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search","display_name":"I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140107404","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.11"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-eacl.11","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.11","pdf_url":"https://aclanthology.org/2026.findings-eacl.11.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-eacl.11.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031164256","display_name":"Zujie Liang","orcid":"https://orcid.org/0009-0002-9736-0231"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zujie Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130348557","display_name":"Feng Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130410448","display_name":"Wujiang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wujiang Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073967625","display_name":"Yuxi Qian","orcid":"https://orcid.org/0009-0009-2685-9247"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuxi Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130399369","display_name":"Lin Veronica Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109859235","display_name":"Xinhui Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinhui Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39638382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"189","last_page":"210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5185999870300293,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5185999870300293,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11574","display_name":"Artificial Intelligence in Games","score":0.04879999905824661,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.02160000056028366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/monte-carlo-method","display_name":"Monte Carlo method","score":0.3569999933242798},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.35440000891685486},{"id":"https://openalex.org/keywords/monte-carlo-tree-search","display_name":"Monte Carlo tree search","score":0.32409998774528503},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3116999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6054999828338623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5148000121116638},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.32409998774528503},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C129671850","wikidata":"https://www.wikidata.org/wiki/Q210501","display_name":"Introspection","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2782999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2615000009536743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-eacl.11","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.11","pdf_url":"https://aclanthology.org/2026.findings-eacl.11.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-eacl.11","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.11","pdf_url":"https://aclanthology.org/2026.findings-eacl.11.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140107404.pdf","grobid_xml":"https://content.openalex.org/works/W7140107404.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,11,46,57,107,170,182],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"have":[7],"shown":[8],"remarkable":[9],"potential":[10],"automating":[12],"machine":[13],"learning":[14],"tasks.However,":[15],"existing":[16],"LLM-based":[17],"agents":[18],"often":[19],"struggle":[20],"with":[21],"low-diversity":[22],"and":[23,49,93,97],"suboptimal":[24],"code":[25],"generation.While":[26],"recent":[27],"work":[28],"(Chi":[29],"et":[30],"al.,":[31],"2024)":[32],"has":[33],"introduced":[34],"Monte":[35,72],"Carlo":[36,73],"Tree":[37,74],"Search":[38,75],"(MCTS)":[39],"to":[40,120,129,139,147,154,158,173],"address":[41],"these":[42],"issues,":[43],"limitations":[44],"persist":[45],"the":[47,58,105,108,142,159,174],"quality":[48],"diversity":[50],"of":[51,104,124],"thoughts":[52],"generated,":[53],"as":[54,56],"well":[55],"scalar":[59],"value":[60,118],"feedback":[61],"mechanisms":[62],"used":[63],"for":[64],"node":[65,106],"selection.In":[66],"this":[67],"study,":[68],"we":[69,111],"introduce":[70],"Introspective":[71],"(I-MCTS),":[76],"a":[77,101,113,166],"novel":[78],"approach":[79,164],"that":[80,89],"iteratively":[81],"expands":[82],"tree":[83],"nodes":[84,153],"through":[85],"an":[86],"introspective":[87],"process":[88],"meticulously":[90],"analyzes":[91],"solutions":[92],"results":[94],"from":[95,144],"parent":[96],"sibling":[98],"nodes.This":[99],"facilitates":[100],"continuous":[102],"refinement":[103],"search":[109],"tree.Furthermore,":[110],"integrate":[112],"Large":[114],"Language":[115],"Model":[116],"(LLM)based":[117],"model":[119],"facilitate":[121],"direct":[122],"evaluation":[123],"each":[125],"node's":[126],"solution":[127],"prior":[128],"conducting":[130],"comprehensive":[131],"computational":[132],"rollouts.A":[133],"hybrid":[134],"rewarding":[135],"mechanism":[136],"is":[137],"implemented":[138],"seamlessly":[140],"transition":[141],"Q-value":[143],"LLMestimated":[145],"scores":[146],"actual":[148],"performance":[149,171],"scores.This":[150],"allows":[151],"higher-quality":[152],"be":[155],"traversed":[156],"earlier.Applied":[157],"various":[160],"ML":[161],"tasks,":[162],"our":[163],"demonstrates":[165],"4%":[167],"absolute":[168],"improvement":[169],"compared":[172],"strong":[175],"open-source":[176],"AutoML":[177,185],"agents,":[178],"showcasing":[179],"its":[180],"effectiveness":[181],"enhancing":[183],"agentic":[184],"systems":[186],"1":[187],".":[188]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-24T00:00:00"}
