{"id":"https://openalex.org/W7162415801","doi":"https://doi.org/10.48550/arxiv.2605.25701","title":"Neural Router: Semantic Content Matching for Agentic AI","display_name":"Neural Router: Semantic Content Matching for Agentic AI","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162415801","doi":"https://doi.org/10.48550/arxiv.2605.25701"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25701","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.2605.25701","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136993455","display_name":"Lauri Lov\u00e9n","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lov\u00e9n, Lauri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137009175","display_name":"Abhishek Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Abhishek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137018971","display_name":"Alexander Engelhardt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Engelhardt, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040236852","display_name":"Alaa Saleh","orcid":"https://orcid.org/0009-0009-6317-2823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saleh, Alaa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076263478","display_name":"Roberto Morabito","orcid":"https://orcid.org/0000-0002-4240-9934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morabito, Roberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137028578","display_name":"Xiaoli Liu","orcid":"https://orcid.org/0000-0001-7479-4251"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137057512","display_name":"Naser Hossein Motlagh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Motlagh, Naser Hossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054443906","display_name":"Sasu Tarkoma","orcid":"https://orcid.org/0000-0003-4220-3650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tarkoma, Sasu","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/T10028","display_name":"Topic Modeling","score":0.20029999315738678,"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":0.20029999315738678,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.18639999628067017,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.11580000072717667,"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/pipeline","display_name":"Pipeline (software)","score":0.6722000241279602},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5302000045776367},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5059999823570251},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.49480000138282776},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.4611000120639801},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4498000144958496},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.42739999294281006},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3959999978542328},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.3605000078678131},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.34360000491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7559999823570251},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6722000241279602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.607699990272522},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5009999871253967},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4253999888896942},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3605000078678131},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.3154999911785126},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C101097943","wikidata":"https://www.wikidata.org/wiki/Q5176983","display_name":"Counterintuitive","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27300000190734863},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25701","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.2605.25701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25701","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":[{"score":0.41357019543647766,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.40089938044548035,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,116],"(LLMs)":[3],"can":[4],"serve":[5],"as":[6,36],"the":[7,19,24,106,135],"semantic-matching":[8],"engine":[9],"of":[10,87,94],"a":[11,59,69,91,147],"content-based":[12],"publish/subscribe":[13],"broker":[14],"for":[15,151],"agentic":[16],"AI":[17],"across":[18],"edge-cloud":[20],"computing":[21],"continuum,":[22],"bridging":[23],"vocabulary":[25],"and":[26,32,47,76,97,113,121,146],"modality":[27],"gaps":[28],"that":[29],"defeat":[30],"keyword":[31],"embedding":[33],"filters.":[34],"Framed":[35],"offline":[37],"multi-label":[38],"retrieval":[39],"over":[40],"three":[41,143],"public":[42],"datasets":[43],"spanning":[44],"social-media,":[45],"legal,":[46],"smart-home":[48],"sensor":[49],"domains":[50],"(six":[51],"LLMs,":[52],"seven":[53],"baselines),":[54],"our":[55],"central":[56],"contribution":[57],"is":[58,134],"two-crossover":[60],"cost-accuracy":[61],"characterisation:":[62],"an":[63,77],"analytical":[64],"context-window":[65],"crossover":[66,80],"below":[67],"which":[68,82],"CoverAndMerge":[70],"compression":[71,109],"pipeline":[72,132],"reduces":[73],"LLM":[74],"invocations,":[75],"empirical":[78],"discrimination-capacity":[79],"above":[81,105],"matching":[83],"accuracy":[84,112],"collapses":[85],"independently":[86],"context":[88],"budget,":[89],"by":[90],"model-dependent":[92],"factor":[93],"parameter":[95],"count":[96],"training":[98],"generation.":[99],"Two":[100],"findings":[101],"carry":[102],"practical":[103],"weight:":[104],"discrimination":[107],"crossover,":[108],"cannot":[110],"recover":[111],"only":[114],"frontier-scale":[115],"clear":[117],"large":[118],"subscription":[119],"sets;":[120],"there":[122],"backend":[123],"choice":[124],"dominates":[125],"configuration":[126],"choice,":[127],"so":[128],"model":[129],"selection,":[130],"not":[131],"tuning,":[133],"primary":[136],"operator":[137],"lever.":[138],"We":[139],"accompany":[140],"this":[141],"with":[142],"composable":[144],"algorithms":[145],"per-cluster":[148],"Quality-of-Experience":[149],"framework":[150],"autonomic":[152],"LLM-tier":[153],"selection.":[154]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}