{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OQU2ZKMXZ5RBXF5J4E2YKF65PS","short_pith_number":"pith:OQU2ZKMX","schema_version":"1.0","canonical_sha256":"7429aca997cf621b97a9e1358517dd7cb6e729e4f98640102a0e0d4abc6e7798","source":{"kind":"arxiv","id":"2309.06342","version":1},"attestation_state":"computed","paper":{"title":"Making Network Configuration Human Friendly","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Alireza Farshin, Changjie Wang, Dejan Kostic, Marco Chiesa, Mariano Scazzariello","submitted_at":"2023-09-12T16:02:07Z","abstract_excerpt":"This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices and minimizing errors. We examine the effectiveness of these models in translating high-level policies and requirements (i.e., specified in natural language) into low-level network APIs, which requires understanding the hardware and protocols. More specifically, we propose NETBUDDY for generating network configurations from scratch and modifying them at runtime. NETBUDDY splits the generation of network configurations into fine"},"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":"2309.06342","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2023-09-12T16:02:07Z","cross_cats_sorted":[],"title_canon_sha256":"6d0cd8788f1a417440f7e4b5e1f2e81b1899675bf0a8e9698c9436b314376c13","abstract_canon_sha256":"3e5bbe89a2b4746f16aae29d7176c15c152fee6cc6aa6b2b874a9fd5dfbc8e40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:06.586640Z","signature_b64":"v9ncf5p2iMYdSGD25qZ/TldCR0s/O8s9CyhI2lagjCoFuRD/g0skos//Axz7qu4o6XD83GOvruRnN27N60RrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7429aca997cf621b97a9e1358517dd7cb6e729e4f98640102a0e0d4abc6e7798","last_reissued_at":"2026-07-05T06:50:06.586223Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:06.586223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Making Network Configuration Human Friendly","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Alireza Farshin, Changjie Wang, Dejan Kostic, Marco Chiesa, Mariano Scazzariello","submitted_at":"2023-09-12T16:02:07Z","abstract_excerpt":"This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices and minimizing errors. We examine the effectiveness of these models in translating high-level policies and requirements (i.e., specified in natural language) into low-level network APIs, which requires understanding the hardware and protocols. More specifically, we propose NETBUDDY for generating network configurations from scratch and modifying them at runtime. NETBUDDY splits the generation of network configurations into fine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06342","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/2309.06342/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":"2309.06342","created_at":"2026-07-05T06:50:06.586290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.06342v1","created_at":"2026-07-05T06:50:06.586290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06342","created_at":"2026-07-05T06:50:06.586290+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQU2ZKMXZ5RB","created_at":"2026-07-05T06:50:06.586290+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQU2ZKMXZ5RBXF5J","created_at":"2026-07-05T06:50:06.586290+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQU2ZKMX","created_at":"2026-07-05T06:50:06.586290+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.13131","citing_title":"MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09678","citing_title":"NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09015","citing_title":"Generative AI Agent Empowered Power Allocation for HAP Propulsion and Communication Systems","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS","json":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS.json","graph_json":"https://pith.science/api/pith-number/OQU2ZKMXZ5RBXF5J4E2YKF65PS/graph.json","events_json":"https://pith.science/api/pith-number/OQU2ZKMXZ5RBXF5J4E2YKF65PS/events.json","paper":"https://pith.science/paper/OQU2ZKMX"},"agent_actions":{"view_html":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS","download_json":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS.json","view_paper":"https://pith.science/paper/OQU2ZKMX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.06342&json=true","fetch_graph":"https://pith.science/api/pith-number/OQU2ZKMXZ5RBXF5J4E2YKF65PS/graph.json","fetch_events":"https://pith.science/api/pith-number/OQU2ZKMXZ5RBXF5J4E2YKF65PS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS/action/storage_attestation","attest_author":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS/action/author_attestation","sign_citation":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS/action/citation_signature","submit_replication":"https://pith.science/pith/OQU2ZKMXZ5RBXF5J4E2YKF65PS/action/replication_record"}},"created_at":"2026-07-05T06:50:06.586290+00:00","updated_at":"2026-07-05T06:50:06.586290+00:00"}