{"id":"https://openalex.org/W7143414036","doi":"https://doi.org/10.48550/arxiv.2603.26125","title":"CL-SEC: Cross-Layer Semantic Error Correction Empowered by Language Models","display_name":"CL-SEC: Cross-Layer Semantic Error Correction Empowered by Language Models","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7143414036","doi":"https://doi.org/10.48550/arxiv.2603.26125"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26125","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.2603.26125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130922508","display_name":"Yirun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Yirun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131003155","display_name":"Yuyang Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Yuyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130966537","display_name":"Soung Chang Liew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liew, Soung Chang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131000722","display_name":"Yuchen Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Yuchen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050282453","display_name":"Feifan Zhang","orcid":"https://orcid.org/0000-0003-4551-0365"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Feifan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130932696","display_name":"Lihao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lihao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5130922508"],"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.4968999922275543,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.4968999922275543,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0860000029206276,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.04270000010728836,"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/focus","display_name":"Focus (optics)","score":0.5756000280380249},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.5753999948501587},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5374000072479248},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5357000231742859},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5033000111579895},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4781000018119812},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.4523000121116638},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4388999938964844},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.42730000615119934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8334000110626221},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.5753999948501587},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5464000105857849},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5374000072479248},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5357000231742859},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4918000102043152},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.4523000121116638},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.3221000134944916},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3043000102043152},{"id":"https://openalex.org/C71695816","wikidata":"https://www.wikidata.org/wiki/Q3478367","display_name":"Semantic Web Rule Language","level":5,"score":0.2937999963760376},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C110903229","wikidata":"https://www.wikidata.org/wiki/Q7449064","display_name":"Semantic integration","level":4,"score":0.28110000491142273},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2671999931335449},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26125","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.2603.26125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26125","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":0.5814260244369507,"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":{"Achieving":[0],"reliable":[1],"communication":[2,138],"has":[3],"long":[4],"been":[5],"a":[6,94],"fundamental":[7],"challenge":[8],"in":[9,90,97,114],"networked":[10],"systems.":[11],"Semantic":[12],"Error":[13],"Correction":[14],"(SEC)":[15],"leverages":[16],"the":[17,47,51,56,80,145,155,161],"semantic":[18,131,137,146,162],"understanding":[19,163],"capabilities":[20,164],"of":[21,148,165],"language":[22],"models":[23],"(LMs)":[24],"to":[25,85,101,153],"perform":[26],"application-layer":[27],"error":[28,71],"correction,":[29],"complementing":[30],"conventional":[31],"channel":[32],"decoding.":[33],"While":[34],"promising,":[35],"existing":[36],"SEC":[37,67],"approaches":[38],"rely":[39],"solely":[40,142],"on":[41,143],"context":[42],"captured":[43],"by":[44],"LMs":[45,166],"at":[46,55],"application":[48,83],"layer,":[49],"ignoring":[50],"rich":[52],"information":[53,77,113],"available":[54],"physical":[57,81],"layer.":[58],"To":[59],"address":[60],"this":[61,63,102],"limitation,":[62],"paper":[64],"introduces":[65],"Cross-Layer":[66],"(CL-SEC),":[68],"an":[69],"LM-empowered":[70],"correction":[72],"framework":[73],"that":[74,111,140],"integrates":[75],"cross-layer":[76],"from":[78],"both":[79],"and":[82,130],"layers":[84],"jointly":[86],"correct":[87],"corrupted":[88],"words":[89],"text":[91],"communication.":[92],"Using":[93],"Bayesian":[95],"combination":[96],"product":[98],"form":[99],"tailored":[100],"framework,":[103],"CL-SEC":[104,117,151],"achieves":[105],"significantly":[106],"improved":[107],"performance":[108],"over":[109],"methods":[110],"process":[112],"isolated":[115],"layers.":[116],"shows":[118],"substantial":[119],"gains":[120],"across":[121],"multiple":[122],"error-correction":[123],"metrics,":[124],"including":[125],"bit-error":[126],"rate,":[127,129],"word-error":[128],"fidelity":[132],"scores.":[133],"Importantly,":[134],"unlike":[135],"most":[136],"systems":[139],"focus":[141],"recovering":[144],"meaning":[147],"transmitted":[149,157],"messages,":[150],"aims":[152],"reconstruct":[154],"original":[156],"message":[158],"verbatim,":[159],"leveraging":[160],"for":[167],"precise":[168],"reconstruction.":[169]},"counts_by_year":[],"updated_date":"2026-03-31T06:07:48.031334","created_date":"2026-03-31T00:00:00"}
