{"id":"https://openalex.org/W7163665316","doi":"https://doi.org/10.48550/arxiv.2606.05570","title":"TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework","display_name":"TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163665316","doi":"https://doi.org/10.48550/arxiv.2606.05570"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05570","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":"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.2606.05570","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019401621","display_name":"Bobby Yan","orcid":"https://orcid.org/0009-0002-6792-6222"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Bobby","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137995838","display_name":"Fredrik Kjolstad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kjolstad, Fredrik","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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.3488999903202057,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.3488999903202057,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10260","display_name":"Software Engineering Research","score":0.21950000524520874,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10126","display_name":"Logic, programming, and type systems","score":0.0794999971985817,"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":0.637499988079071},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6344000101089478},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5860000252723694},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5587000250816345},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4779999852180481},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.39149999618530273},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.38499999046325684},{"id":"https://openalex.org/keywords/code-refactoring","display_name":"Code refactoring","score":0.38040000200271606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7411999702453613},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.637499988079071},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6344000101089478},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42750000953674316},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3946000039577484},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C152752567","wikidata":"https://www.wikidata.org/wiki/Q116877","display_name":"Code refactoring","level":3,"score":0.38040000200271606},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34200000762939453},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30410000681877136},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05570","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":"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.2606.05570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05570","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":"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Repository-level":[0],"coding":[1,132],"benchmarks":[2],"face":[3],"a":[4,34,108],"trade-off":[5],"between":[6],"task":[7],"difficulty":[8],"and":[9,39,56,73,86,99,120,139],"evaluation":[10],"reliability:":[11],"tasks":[12,41],"that":[13,48,111],"challenge":[14],"frontier":[15,136],"models":[16],"often":[17],"involve":[18],"large":[19],"codebases":[20],"with":[21,51,174],"incomplete":[22],"test":[23,90],"coverage,":[24],"while":[25],"human":[26],"review":[27],"does":[28],"not":[29],"scale.":[30],"We":[31,129],"introduce":[32],"TensorBench,":[33],"benchmark":[35],"of":[36,164],"199":[37],"feature-addition":[38,106],"refactoring":[40],"on":[42],"an":[43],"open-source":[44],"compiler-based":[45],"tensor":[46],"framework":[47],"extends":[49],"PyTorch":[50],"first-class":[52],"support":[53],"for":[54,125,151,157,177],"dense":[55,64],"sparse":[57,62],"tensors.":[58],"Tasks":[59],"cover":[60],"new":[61],"formats,":[63],"optimization":[65],"passes,":[66],"IR":[67],"transformations,":[68],"scheduler":[69],"changes,":[70],"runtime":[71],"components,":[72],"high-level":[74],"numerical":[75],"operators.":[76],"TensorBench":[77],"grades":[78],"each":[79],"run":[80],"by":[81],"applying":[82],"the":[83,88,94,102,112,116,122,126,152,158,178],"agent's":[84],"patch":[85],"running":[87],"framework's":[89],"suite,":[91],"which":[92],"includes":[93],"pre-existing":[95,118],"randomized":[96],"regression":[97],"tests":[98,101],"any":[100],"agent":[103,154],"adds.":[104],"For":[105],"tasks,":[107],"pass":[109,161],"means":[110],"patched":[113],"repository":[114],"preserves":[115],"tested":[117],"behavior":[119],"satisfies":[121],"agent-added":[123],"checks":[124],"requested":[127],"feature.":[128],"evaluate":[130],"seven":[131],"agents":[133],"spanning":[134],"three":[135],"model":[137],"families":[138],"one":[140],"open-weight":[141],"model.":[142],"Pass":[143],"rates":[144],"under":[145],"this":[146],"criterion":[147],"range":[148],"from":[149,170],"$64.8\\%$":[150],"strongest":[153,180],"to":[155,172],"$22.1\\%$":[156],"weakest.":[159],"Agents":[160],"different":[162],"subsets":[163],"tasks:":[165],"pairwise":[166],"Cohen's":[167],"$\u03ba$":[168],"ranges":[169],"$-0.07$":[171],"$0.43$,":[173],"$\u03ba=":[175],"0.05$":[176],"two":[179],"agents.":[181]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-06T00:00:00"}