{"id":"https://openalex.org/W7154396959","doi":"https://doi.org/10.48550/arxiv.2604.09741","title":"ExecTune: Effective Steering of Black-Box LLMs with Guide Models","display_name":"ExecTune: Effective Steering of Black-Box LLMs with Guide Models","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7154396959","doi":"https://doi.org/10.48550/arxiv.2604.09741"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09741","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09741","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.09741","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057449678","display_name":"Vijay Lingam","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lingam, Vijay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026595166","display_name":"Aditya Golatkar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golatkar, Aditya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133554690","display_name":"Anwesan Pal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pal, Anwesan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133610414","display_name":"Ben Vo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Ben","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133620067","display_name":"Narayanan Sadagopan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sadagopan, Narayanan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065386783","display_name":"Alessandro Achille","orcid":"https://orcid.org/0000-0002-8163-8326"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Achille, Alessandro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133571649","display_name":"Jun Huan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huan, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000621526","display_name":"Anoop Deoras","orcid":"https://orcid.org/0009-0007-4566-8767"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deoras, Anoop","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133619779","display_name":"Stefano Soatto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soatto, Stefano","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5057449678"],"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/T10028","display_name":"Topic Modeling","score":0.4027000069618225,"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/T10028","display_name":"Topic Modeling","score":0.4027000069618225,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.18000000715255737,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.05739999935030937,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5149000287055969},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5101000070571899},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.42640000581741333},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.42170000076293945},{"id":"https://openalex.org/keywords/toolbox","display_name":"Toolbox","score":0.4020000100135803},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.38929998874664307},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3467000126838684},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3425000011920929}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7466999888420105},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5149000287055969},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4894999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48159998655319214},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3425000011920929},{"id":"https://openalex.org/C108710211","wikidata":"https://www.wikidata.org/wiki/Q11538","display_name":"Mathematical proof","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.29100000858306885},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09741","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09741","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.09741","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09741","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5627383589744568,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"For":[0],"large":[1],"language":[2],"models":[3],"deployed":[4],"through":[5],"black-box":[6,55],"APIs,":[7],"recurring":[8],"inference":[9,184],"costs":[10],"often":[11,116],"exceed":[12],"one-time":[13],"training":[14,140],"costs.":[15],"This":[16,58],"motivates":[17],"composed":[18],"agentic":[19],"systems":[20],"that":[21,50,85,95,112,142],"amortize":[22],"expensive":[23],"reasoning":[24,165],"into":[25],"reusable":[26],"intermediate":[27],"representations.":[28],"We":[29,75],"study":[30],"a":[31,43,47,54,79,96,138],"broad":[32],"class":[33],"of":[34,212],"such":[35],"systems,":[36],"termed":[37],"Guide-Core":[38],"Policies":[39],"(GCoP),":[40],"in":[41,69,125],"which":[42,66],"guide":[44,72,101,229],"model":[45],"generates":[46],"structured":[48],"strategy":[49,97],"is":[51,73,88],"executed":[52,105],"by":[53,90,99,106,132,174,186,226],"core":[56],"model.":[57],"abstraction":[59],"subsumes":[60],"base,":[61],"supervised,":[62],"and":[63,83,128,149,160,166,202,205],"advisor-style":[64],"approaches,":[65],"differ":[67],"primarily":[68],"how":[70],"the":[71,93,100,107,228,232],"trained.":[74],"formalize":[76],"GCoP":[77,114,169,221],"under":[78,121],"cost-sensitive":[80],"utility":[81],"objective":[82],"show":[84],"end-to-end":[86],"performance":[87],"governed":[89],"guide-averaged":[91],"executability:":[92],"probability":[94],"generated":[98],"can":[102],"be":[103],"faithfully":[104],"core.":[108,233],"Our":[109],"analysis":[110],"shows":[111],"existing":[113],"instantiations":[115],"fail":[117],"to":[118,153,176,188,195,206],"optimize":[119,155],"executability":[120],"deployment":[122],"constraints,":[123],"resulting":[124],"brittle":[126],"strategies":[127],"inefficient":[129],"computation.":[130],"Motivated":[131],"these":[133],"insights,":[134],"we":[135],"propose":[136],"ExecTune,":[137],"principled":[139],"recipe":[141],"combines":[143],"teacher-guided":[144],"acceptance":[145],"sampling,":[146],"supervised":[147],"fine-tuning,":[148],"structure-aware":[150],"reinforcement":[151],"learning":[152],"directly":[154],"syntactic":[156],"validity,":[157],"execution":[158],"success,":[159],"cost":[161,185],"efficiency.":[162],"Across":[163],"mathematical":[164],"code-generation":[167],"benchmarks,":[168],"with":[170],"ExecTune":[171],"improves":[172],"accuracy":[173,211],"up":[175,187],"9.2%":[177],"over":[178],"prior":[179],"state-of-the-art":[180],"baselines":[181],"while":[182],"reducing":[183],"22.4%.":[189],"It":[190],"enables":[191],"Claude":[192],"Haiku":[193],"3.5":[194,198],"outperform":[196],"Sonnet":[197,213],"on":[199],"both":[200],"math":[201],"code":[203],"tasks,":[204],"come":[207],"within":[208],"1.7%":[209],"absolute":[210],"4":[214],"at":[215],"38%":[216],"lower":[217],"cost.":[218],"Beyond":[219],"efficiency,":[220],"also":[222],"supports":[223],"modular":[224],"adaptation":[225],"updating":[227],"without":[230],"retraining":[231]},"counts_by_year":[],"updated_date":"2026-04-15T06:04:33.058270","created_date":"2026-04-15T00:00:00"}