{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ESV2LRXC2TBJL26PTTVPAQVNQT","short_pith_number":"pith:ESV2LRXC","schema_version":"1.0","canonical_sha256":"24aba5c6e2d4c295ebcf9ceaf042ad84f75bd5db8d1dd793d38434ed01f6fec8","source":{"kind":"arxiv","id":"2501.15928","version":1},"attestation_state":"computed","paper":{"title":"Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Geng Sun, Jiacheng Wang, Lianfen Huang, Xianbin Wang, Zhang Liu, Zhibin Gao","submitted_at":"2025-01-27T10:27:15Z","abstract_excerpt":"Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered s"},"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":"2501.15928","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2025-01-27T10:27:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"7b5ae1d69ff434fdcd8261d6e705eb1d58b06814ed500b440261291c3d315423","abstract_canon_sha256":"e71b4111ed8cee568d1b149d5842579d41d130d30cf925230abadfb3c71eec36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:52.232991Z","signature_b64":"1iwRNcB+lHGczFetfuidIlHE3UkZcaptFGG6AyuoGP6BlT68PJZ+YP8EiG5e6Hv69HWXjILhtiyFM/G8Yo40Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24aba5c6e2d4c295ebcf9ceaf042ad84f75bd5db8d1dd793d38434ed01f6fec8","last_reissued_at":"2026-07-05T10:05:52.232570Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:52.232570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Geng Sun, Jiacheng Wang, Lianfen Huang, Xianbin Wang, Zhang Liu, Zhibin Gao","submitted_at":"2025-01-27T10:27:15Z","abstract_excerpt":"Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15928","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/2501.15928/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":"2501.15928","created_at":"2026-07-05T10:05:52.232630+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15928v1","created_at":"2026-07-05T10:05:52.232630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15928","created_at":"2026-07-05T10:05:52.232630+00:00"},{"alias_kind":"pith_short_12","alias_value":"ESV2LRXC2TBJ","created_at":"2026-07-05T10:05:52.232630+00:00"},{"alias_kind":"pith_short_16","alias_value":"ESV2LRXC2TBJL26P","created_at":"2026-07-05T10:05:52.232630+00:00"},{"alias_kind":"pith_short_8","alias_value":"ESV2LRXC","created_at":"2026-07-05T10:05:52.232630+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21933","citing_title":"Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT","json":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT.json","graph_json":"https://pith.science/api/pith-number/ESV2LRXC2TBJL26PTTVPAQVNQT/graph.json","events_json":"https://pith.science/api/pith-number/ESV2LRXC2TBJL26PTTVPAQVNQT/events.json","paper":"https://pith.science/paper/ESV2LRXC"},"agent_actions":{"view_html":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT","download_json":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT.json","view_paper":"https://pith.science/paper/ESV2LRXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15928&json=true","fetch_graph":"https://pith.science/api/pith-number/ESV2LRXC2TBJL26PTTVPAQVNQT/graph.json","fetch_events":"https://pith.science/api/pith-number/ESV2LRXC2TBJL26PTTVPAQVNQT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT/action/storage_attestation","attest_author":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT/action/author_attestation","sign_citation":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT/action/citation_signature","submit_replication":"https://pith.science/pith/ESV2LRXC2TBJL26PTTVPAQVNQT/action/replication_record"}},"created_at":"2026-07-05T10:05:52.232630+00:00","updated_at":"2026-07-05T10:05:52.232630+00:00"}