{"id":"https://openalex.org/W7155065074","doi":"https://doi.org/10.48550/arxiv.2604.16555","title":"LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search","display_name":"LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7155065074","doi":"https://doi.org/10.48550/arxiv.2604.16555"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.16555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16555","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.2604.16555","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102086565","display_name":"Masakazu Yoshimura","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoshimura, Masakazu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055322544","display_name":"Zitang Sun","orcid":"https://orcid.org/0000-0003-2267-421X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zitang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023805039","display_name":"Yuiko Sakuma","orcid":"https://orcid.org/0000-0003-1933-5196"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sakuma, Yuiko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066409845","display_name":"Junji Otsuka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Otsuka, Junji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108791840","display_name":"Atsushi Irie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Irie, Atsushi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113687209","display_name":"Takeshi Ohashi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ohashi, Takeshi","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.23980000615119934,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.23980000615119934,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1242000013589859,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0908999964594841,"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/executable","display_name":"Executable","score":0.6212999820709229},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5557000041007996},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5414999723434448},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.48249998688697815},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47279998660087585},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.43880000710487366},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.429500013589859},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.40380001068115234},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3280999958515167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.776199996471405},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.6212999820709229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5755000114440918},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5557000041007996},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5414999723434448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.499099999666214},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.48249998688697815},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3237999975681305},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32260000705718994},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C207024777","wikidata":"https://www.wikidata.org/wiki/Q621673","display_name":"Search tree","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C199505168","wikidata":"https://www.wikidata.org/wiki/Q3267529","display_name":"Evolutionary robotics","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.16555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16555","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.2604.16555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16555","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"Architecture":[1],"Search":[2],"(NAS)":[3],"aims":[4],"to":[5,24,47,136,149],"automatically":[6,95],"discover":[7],"high-performing":[8],"deep":[9],"neural":[10],"network":[11],"(DNN)":[12],"architectures.":[13],"However,":[14],"conventional":[15],"algorithm-driven":[16],"NAS":[17,85,186],"relies":[18],"on":[19,194],"carefully":[20],"hand-crafted":[21],"search":[22,74],"spaces":[23],"ensure":[25,150],"executability,":[26],"which":[27],"restricts":[28],"open-ended":[29,90],"exploration.":[30],"Recent":[31],"coding-based":[32],"agentic":[33,166],"approaches":[34],"using":[35],"large":[36],"language":[37],"models":[38],"(LLMs)":[39],"reduce":[40],"manual":[41],"design,":[42],"but":[43],"current":[44],"LLMs":[45],"struggle":[46],"reliably":[48],"generate":[49],"complex,":[50],"valid":[51],"architectures,":[52],"and":[53,89,103,155,191,197],"their":[54,67],"proposals":[55],"are":[56],"often":[57],"biased":[58],"toward":[59],"a":[60,82,129,151],"narrow":[61],"set":[62],"of":[63,147,164],"patterns":[64],"observed":[65],"in":[66,178],"training":[68],"data.":[69],"To":[70],"bridge":[71],"reliable":[72,113],"algorithmic":[73],"with":[75],"powerful":[76],"LLM":[77,142,167],"assistance,":[78],"we":[79],"propose":[80],"LLMasTool,":[81],"hierarchical":[83,108],"tree-based":[84],"framework":[86],"for":[87],"stable":[88],"model":[91],"evolution.":[92],"Our":[93,181],"method":[94,170,182],"extracts":[96],"reusable":[97],"modules":[98],"from":[99],"arbitrary":[100],"source":[101],"code":[102,118],"represents":[104],"full":[105],"architectures":[106],"as":[107],"trees,":[109],"enabling":[110],"evolution":[111,122],"through":[112],"tree":[114],"transformations":[115],"rather":[116],"than":[117],"generation.":[119],"At":[120],"each":[121],"step,":[123],"coarse-level":[124],"planning":[125],"is":[126],"governed":[127],"by":[128,188],"diversity-guided":[130],"algorithm":[131],"that":[132],"leverages":[133],"Bayesian":[134],"modeling":[135],"improve":[137],"exploration":[138],"efficiency,":[139],"while":[140],"the":[141,144,175,179],"resolves":[143],"remaining":[145],"degrees":[146],"freedom":[148],"meaningful":[152],"evolutionary":[153],"trajectory":[154],"an":[156],"executable":[157],"generated":[158],"architecture.":[159],"With":[160],"this":[161],"formulation,":[162],"instead":[163],"fully":[165],"approaches,":[168],"our":[169],"explores":[171],"diverse":[172],"directions":[173],"beyond":[174],"inherent":[176],"biases":[177],"LLM.":[180],"improves":[183],"over":[184],"existing":[185],"methods":[187],"0.69,":[189],"1.83,":[190],"2.68":[192],"points":[193],"CIFAR-10,":[195],"CIFAR-100,":[196],"ImageNet16-120,":[198],"demonstrating":[199],"its":[200],"effectiveness.":[201]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-22T00:00:00"}
