{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G4R5QL4YZGQ6KIG5S2JNAZDPP7","short_pith_number":"pith:G4R5QL4Y","schema_version":"1.0","canonical_sha256":"3723d82f98c9a1e520dd9692d0646f7fc5480079bab905baf8f0e05f40479e0d","source":{"kind":"arxiv","id":"2407.20970","version":1},"attestation_state":"computed","paper":{"title":"Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Alakesh Kalita","submitted_at":"2024-07-30T16:57:41Z","abstract_excerpt":"With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.20970","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2024-07-30T16:57:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"13e7690fe8c2f844cf28747da564a05ab68efd3482285d6a6bd1fa317a779920","abstract_canon_sha256":"0d8e7a632098075080660c1cc7aa3c80345bf9ab0f5397d14d7c61edb1a93e8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:21.615918Z","signature_b64":"uDdtpSZwkg5oR4ERYBG5TOqsfk1gWMyb+vnTnwAWjUgx19j3x1tbDLNPKsYrXa4ak7UHCYtkpaO7aNg2uobUCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3723d82f98c9a1e520dd9692d0646f7fc5480079bab905baf8f0e05f40479e0d","last_reissued_at":"2026-07-05T08:50:21.615457Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:21.615457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Alakesh Kalita","submitted_at":"2024-07-30T16:57:41Z","abstract_excerpt":"With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.20970","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.20970/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.20970","created_at":"2026-07-05T08:50:21.615515+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.20970v1","created_at":"2026-07-05T08:50:21.615515+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.20970","created_at":"2026-07-05T08:50:21.615515+00:00"},{"alias_kind":"pith_short_12","alias_value":"G4R5QL4YZGQ6","created_at":"2026-07-05T08:50:21.615515+00:00"},{"alias_kind":"pith_short_16","alias_value":"G4R5QL4YZGQ6KIG5","created_at":"2026-07-05T08:50:21.615515+00:00"},{"alias_kind":"pith_short_8","alias_value":"G4R5QL4Y","created_at":"2026-07-05T08:50:21.615515+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17865","citing_title":"Talk with the Things: Integrating LLMs into IoT Networks","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7","json":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7.json","graph_json":"https://pith.science/api/pith-number/G4R5QL4YZGQ6KIG5S2JNAZDPP7/graph.json","events_json":"https://pith.science/api/pith-number/G4R5QL4YZGQ6KIG5S2JNAZDPP7/events.json","paper":"https://pith.science/paper/G4R5QL4Y"},"agent_actions":{"view_html":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7","download_json":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7.json","view_paper":"https://pith.science/paper/G4R5QL4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.20970&json=true","fetch_graph":"https://pith.science/api/pith-number/G4R5QL4YZGQ6KIG5S2JNAZDPP7/graph.json","fetch_events":"https://pith.science/api/pith-number/G4R5QL4YZGQ6KIG5S2JNAZDPP7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7/action/storage_attestation","attest_author":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7/action/author_attestation","sign_citation":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7/action/citation_signature","submit_replication":"https://pith.science/pith/G4R5QL4YZGQ6KIG5S2JNAZDPP7/action/replication_record"}},"created_at":"2026-07-05T08:50:21.615515+00:00","updated_at":"2026-07-05T08:50:21.615515+00:00"}