{"id":"https://openalex.org/W7162696696","doi":"https://doi.org/10.48550/arxiv.2605.27703","title":"Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models","display_name":"Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162696696","doi":"https://doi.org/10.48550/arxiv.2605.27703"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27703","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.2605.27703","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119932375","display_name":"Joan Vendrell Gallart","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gallart, Joan Vendrell","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137207592","display_name":"Russell Bent","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bent, Russell","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111109272","display_name":"Michael Grosskopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grosskopf, Michael","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":0.16609999537467957,"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":0.16609999537467957,"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/T10028","display_name":"Topic Modeling","score":0.1137000024318695,"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":0.050599999725818634,"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/schema","display_name":"Schema (genetic algorithms)","score":0.4799000024795532},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4519999921321869},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.42399999499320984},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.40950000286102295},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.37720000743865967},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3666999936103821},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.36010000109672546},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3481999933719635},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.34779998660087585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7807000279426575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5218999981880188},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41659998893737793},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.37720000743865967},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2912999987602234},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C2775851571","wikidata":"https://www.wikidata.org/wiki/Q6045205","display_name":"Interaction protocol","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27703","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.2605.27703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27703","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":[],"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],"Models":[2],"are":[3],"increasingly":[4],"deployed":[5],"inside":[6],"agentic":[7],"systems,":[8],"where":[9],"they":[10],"must":[11],"follow":[12],"structured":[13],"protocols,":[14],"adapt":[15],"to":[16,69],"evolving":[17],"states,":[18],"and":[19,24,53,87,98,122,155,159,164],"operate":[20],"under":[21,103],"memory,":[22],"latency,":[23],"cost":[25],"constraints.":[26],"In":[27],"such":[28],"regimes,":[29],"prompt":[30,43,96,130],"extension":[31],"is":[32,66],"unreliable:":[33],"growing":[34],"contexts":[35],"can":[36],"push":[37],"compact":[38,64],"models":[39],"outside":[40],"their":[41],"effective":[42,129],"domain,":[44,97],"while":[45],"deployment-time":[46],"fine-tuning":[47,102],"remains":[48],"limited":[49],"by":[50,78],"scarce":[51],"data":[52],"compute.":[54],"We":[55,118],"propose":[56],"a":[57,63,94,144,150],"hierarchical":[58],"control-and-learning":[59],"framework":[60],"in":[61],"which":[62],"model":[65],"first":[67],"distilled":[68],"learn":[70],"the":[71,128],"required":[72],"output":[73],"schema,":[74],"then":[75],"supervised":[76],"online":[77],"an":[79],"oracle-controller":[80],"loop.":[81],"The":[82],"controller":[83],"monitors":[84],"protocol":[85],"validity":[86],"semantic":[88,113],"performance,":[89],"projects":[90],"accumulated":[91],"histories":[92],"into":[93],"feasible":[95],"triggers":[99],"lightweight":[100],"oracle-supervised":[101],"drift.":[104],"This":[105],"separates":[106],"schema":[107],"learning":[108],"for":[109,115],"communication":[110],"compatibility":[111],"from":[112],"adaptation":[114],"task-level":[116],"correction.":[117],"formalize":[119],"prompt-domain":[120],"feasibility":[121],"attention-induced":[123],"saturation,":[124],"motivating":[125],"control":[126],"of":[127],"state":[131],"rather":[132],"than":[133],"reliance":[134],"on":[135],"nominal":[136],"context":[137],"length.":[138],"Using":[139],"Multi-Fidelity":[140],"Bayesian":[141],"Optimization":[142],"as":[143],"controlled":[145],"sequential":[146],"testbed,":[147],"we":[148],"characterize":[149],"core":[151],"deployment":[152],"failure":[153],"mode":[154],"show":[156],"improved":[157],"reliability":[158],"cost-efficiency":[160],"over":[161],"non-hierarchical,":[162],"distillation-only,":[163],"non-distilled":[165],"baselines.":[166]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}