{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:75EPLF6SWLX43MCGCSOIFF274D","short_pith_number":"pith:75EPLF6S","schema_version":"1.0","canonical_sha256":"ff48f597d2b2efcdb046149c82975fe0f14d88e582ee893b2075357c05495e52","source":{"kind":"arxiv","id":"2408.13298","version":1},"attestation_state":"computed","paper":{"title":"Large Language Models for Zero Touch Network Configuration Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Nelson L. S. da Fonseca, Oscar G. Lira, Oscar M. Caicedo","submitted_at":"2024-08-23T16:37:50Z","abstract_excerpt":"The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verifi"},"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":"2408.13298","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-08-23T16:37:50Z","cross_cats_sorted":[],"title_canon_sha256":"e8fb142aa72a81b4c5f17b89c808deaf72f7f171e5f6acb3520ee29fa6e3c86f","abstract_canon_sha256":"53559c37837ecc07be9edf5128f30dccfb1d708c87a3393f4d31c9c462802f6d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:48.197748Z","signature_b64":"vCiEEmmJL+hE1vPTsvsbWFhzS7PMjJDj1SVCFvhH+TUwk+d4NV5MD4vE7B4GtCNq2PxMMtBk9Ss+m80F6dncAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff48f597d2b2efcdb046149c82975fe0f14d88e582ee893b2075357c05495e52","last_reissued_at":"2026-07-05T08:58:48.197295Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:48.197295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models for Zero Touch Network Configuration Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Nelson L. S. da Fonseca, Oscar G. Lira, Oscar M. Caicedo","submitted_at":"2024-08-23T16:37:50Z","abstract_excerpt":"The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.13298","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/2408.13298/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":"2408.13298","created_at":"2026-07-05T08:58:48.197370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.13298v1","created_at":"2026-07-05T08:58:48.197370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.13298","created_at":"2026-07-05T08:58:48.197370+00:00"},{"alias_kind":"pith_short_12","alias_value":"75EPLF6SWLX4","created_at":"2026-07-05T08:58:48.197370+00:00"},{"alias_kind":"pith_short_16","alias_value":"75EPLF6SWLX43MCG","created_at":"2026-07-05T08:58:48.197370+00:00"},{"alias_kind":"pith_short_8","alias_value":"75EPLF6S","created_at":"2026-07-05T08:58:48.197370+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.19823","citing_title":"A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions","ref_index":111,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D","json":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D.json","graph_json":"https://pith.science/api/pith-number/75EPLF6SWLX43MCGCSOIFF274D/graph.json","events_json":"https://pith.science/api/pith-number/75EPLF6SWLX43MCGCSOIFF274D/events.json","paper":"https://pith.science/paper/75EPLF6S"},"agent_actions":{"view_html":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D","download_json":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D.json","view_paper":"https://pith.science/paper/75EPLF6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.13298&json=true","fetch_graph":"https://pith.science/api/pith-number/75EPLF6SWLX43MCGCSOIFF274D/graph.json","fetch_events":"https://pith.science/api/pith-number/75EPLF6SWLX43MCGCSOIFF274D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D/action/storage_attestation","attest_author":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D/action/author_attestation","sign_citation":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D/action/citation_signature","submit_replication":"https://pith.science/pith/75EPLF6SWLX43MCGCSOIFF274D/action/replication_record"}},"created_at":"2026-07-05T08:58:48.197370+00:00","updated_at":"2026-07-05T08:58:48.197370+00:00"}