{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2JDDXM7HXH36POHR56PQJ5MMNW","short_pith_number":"pith:2JDDXM7H","schema_version":"1.0","canonical_sha256":"d2463bb3e7b9f7e7b8f1ef9f04f58c6d9c9e4fc5944ce8162b03787e78221470","source":{"kind":"arxiv","id":"2412.10107","version":1},"attestation_state":"computed","paper":{"title":"NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.NI","authors_text":"Abdulkadir Celik, Abdullatif Albaseer, Ahmed M. Eltawil, Asmaa Abdallah, Mohamed Abdallah","submitted_at":"2024-12-13T12:48:15Z","abstract_excerpt":"The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. However, the practical application of LLMs in wireless network orchestration and management remains largely unexplored. Existing literature predominantly offers visio"},"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":"2412.10107","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2024-12-13T12:48:15Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG"],"title_canon_sha256":"470847cfb0747e1876fda9c0d564ff841e05d3298ea8c3cb35711aaa032a7ecd","abstract_canon_sha256":"50a21c89e4dfd8b958c2d0952cb513b6ab266f59e30ef6e6019c0a6e22e420b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:47.157929Z","signature_b64":"ok6pMUuXfadg/tV9QDKXPBnDMoZR5lcE6ir1LzoHKNXxlIKRLWf2AhvpgXWIl/mrQrPMEeyYi20kmi7c9AEpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2463bb3e7b9f7e7b8f1ef9f04f58c6d9c9e4fc5944ce8162b03787e78221470","last_reissued_at":"2026-07-05T09:48:47.157420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:47.157420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"cs.NI","authors_text":"Abdulkadir Celik, Abdullatif Albaseer, Ahmed M. Eltawil, Asmaa Abdallah, Mohamed Abdallah","submitted_at":"2024-12-13T12:48:15Z","abstract_excerpt":"The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. However, the practical application of LLMs in wireless network orchestration and management remains largely unexplored. Existing literature predominantly offers visio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10107","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/2412.10107/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":"2412.10107","created_at":"2026-07-05T09:48:47.157483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.10107v1","created_at":"2026-07-05T09:48:47.157483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10107","created_at":"2026-07-05T09:48:47.157483+00:00"},{"alias_kind":"pith_short_12","alias_value":"2JDDXM7HXH36","created_at":"2026-07-05T09:48:47.157483+00:00"},{"alias_kind":"pith_short_16","alias_value":"2JDDXM7HXH36POHR","created_at":"2026-07-05T09:48:47.157483+00:00"},{"alias_kind":"pith_short_8","alias_value":"2JDDXM7H","created_at":"2026-07-05T09:48:47.157483+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03215","citing_title":"Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW","json":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW.json","graph_json":"https://pith.science/api/pith-number/2JDDXM7HXH36POHR56PQJ5MMNW/graph.json","events_json":"https://pith.science/api/pith-number/2JDDXM7HXH36POHR56PQJ5MMNW/events.json","paper":"https://pith.science/paper/2JDDXM7H"},"agent_actions":{"view_html":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW","download_json":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW.json","view_paper":"https://pith.science/paper/2JDDXM7H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.10107&json=true","fetch_graph":"https://pith.science/api/pith-number/2JDDXM7HXH36POHR56PQJ5MMNW/graph.json","fetch_events":"https://pith.science/api/pith-number/2JDDXM7HXH36POHR56PQJ5MMNW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW/action/storage_attestation","attest_author":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW/action/author_attestation","sign_citation":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW/action/citation_signature","submit_replication":"https://pith.science/pith/2JDDXM7HXH36POHR56PQJ5MMNW/action/replication_record"}},"created_at":"2026-07-05T09:48:47.157483+00:00","updated_at":"2026-07-05T09:48:47.157483+00:00"}