{"id":"https://openalex.org/W7161588528","doi":"https://doi.org/10.48550/arxiv.2605.15766","title":"BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks","display_name":"BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161588528","doi":"https://doi.org/10.48550/arxiv.2605.15766"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15766","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15766","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":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.2605.15766","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136376604","display_name":"Loka Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Loka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136445257","display_name":"Duzhen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Duzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136397909","display_name":"Xingbo Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Xingbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136378392","display_name":"Leonard Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Leonard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136449260","display_name":"Zixiao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zixiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136366310","display_name":"Assanali Aukenov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aukenov, Assanali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122286711","display_name":"Noel Thomas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas, Noel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136425302","display_name":"Shakhnazar Sailaukan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sailaukan, Shakhnazar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136005323","display_name":"Yonghan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yonghan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101667541","display_name":"Feilong Chen","orcid":"https://orcid.org/0000-0002-4860-8483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Feilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014443478","display_name":"Jiahua Dong","orcid":"https://orcid.org/0000-0001-8545-4447"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Jiahua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136397793","display_name":"Kun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136370390","display_name":"Bin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136451676","display_name":"Le Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Le","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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.40310001373291016,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.40310001373291016,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.07680000364780426,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.05400000140070915,"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/benchmarking","display_name":"Benchmarking","score":0.664900004863739},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5260999798774719},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5200999975204468},{"id":"https://openalex.org/keywords/executable","display_name":"Executable","score":0.4900999963283539},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4171999990940094},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3041999936103821}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7591000199317932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7059000134468079},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.664900004863739},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4171999990940094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15766","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15766","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":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.2605.15766","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15766","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5135815143585205,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"model":[2,53,208],"(LLM)":[3],"agents":[4,49],"are":[5,119],"increasingly":[6],"capable":[7],"of":[8,11,33,173],"automating":[9],"components":[10],"machine":[12,42],"learning":[13,43],"development,":[14],"yet":[15],"existing":[16],"biomedical":[17,34,41,60,85,96,215],"benchmarks":[18],"mainly":[19],"focus":[20],"on":[21],"question":[22],"answering,":[23],"reasoning,":[24],"and":[25,58,87,108,128,151,201,214,233],"tool":[26],"usage,":[27],"or":[28],"evaluate":[29,47,155],"only":[30],"narrow":[31],"aspects":[32],"ML":[35],"coding.":[36],"We":[37,154,189,221],"present":[38],"BioXArena,":[39],"a":[40,99,113,160],"benchmark":[44,226],"designed":[45],"to":[46,112,115,121,204],"whether":[48],"can":[50],"generate":[51,129],"task-specific":[52],"training":[54],"pipelines":[55],"for":[56,131],"heterogeneous":[57],"multi-modal":[59],"datasets.":[61],"BioXArena":[62],"contains":[63],"76":[64],"end-to-end":[65],"tasks":[66,136],"across":[67,186],"9":[68],"domains,":[69],"including":[70,141],"sequence":[71],"modeling,":[72,84],"single-cell":[73],"analysis,":[74],"structural":[75],"biology,":[76,78,80],"network":[77],"chemical":[79],"perturbation":[81],"dynamics,":[82],"phenotype-disease":[83],"imaging,":[86],"text-integrated":[88],"learning.":[89],"Each":[90],"task":[91],"is":[92],"curated":[93],"from":[94],"primary":[95],"sources":[97],"into":[98],"unified":[100],"evaluation":[101],"framework":[102],"with":[103,166,178],"hidden":[104],"labels,":[105],"held-out":[106],"graders,":[107,228],"biology-aware":[109],"metrics":[110],"normalized":[111],"0":[114],"1":[116],"scale.":[117],"Agents":[118],"required":[120],"write":[122],"executable":[123],"code,":[124],"train":[125],"predictive":[126],"models,":[127],"submissions":[130],"private":[132],"test":[133],"samples.":[134],"Most":[135],"involve":[137],"multiple":[138],"input":[139],"modalities,":[140],"tabular":[142],"data,":[143],"images,":[144],"natural":[145],"language,":[146],"molecular":[147],"sequences,":[148],"omics":[149],"matrices,":[150],"protein":[152],"structures.":[153],"11":[156],"agent":[157,183,210,234],"configurations":[158],"in":[159],"standardized":[161],"2-hour":[162],"single-GPU":[163],"environment.":[164],"MLEvolve":[165],"Gemini-3.1-Pro":[167],"achieves":[168],"the":[169],"highest":[170],"average":[171],"score":[172],"0.666,":[174],"followed":[175],"by":[176],"GPT-5.4":[177],"0.636,":[179],"while":[180],"no":[181],"single":[182],"consistently":[184],"dominates":[185],"all":[187,225],"domains.":[188],"additionally":[190],"perform":[191],"extensive":[192],"ablation":[193],"studies,":[194],"robustness":[195],"evaluations,":[196],"scaling":[197],"analyses,":[198,200],"cost":[199],"failure-mode":[202],"investigations":[203],"better":[205],"understand":[206],"how":[207],"backbones,":[209],"scaffolds,":[211],"inference":[212],"budgets,":[213],"domains":[216],"influence":[217],"BioML":[218],"coding":[219],"performance.":[220],"will":[222],"publicly":[223],"release":[224],"tasks,":[227],"execution":[229],"runners,":[230],"leaderboard":[231],"results,":[232],"trajectories.":[235]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-19T00:00:00"}
