{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7UJSMQY2H2XBPW4J7R63C2IEI6","short_pith_number":"pith:7UJSMQY2","schema_version":"1.0","canonical_sha256":"fd1326431a3eae17db89fc7db1690447827b5665e5f789915e7d5676c62fb3eb","source":{"kind":"arxiv","id":"2502.13476","version":1},"attestation_state":"computed","paper":{"title":"Integration of Agentic AI with 6G Networks for Mission-Critical Applications: Use-case and Challenges","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.AI","authors_text":"Engin Zeydan, Kapal Dev, Merouane Debbah, Muhammad Salman Pathan, Sunder Ali Khowaja","submitted_at":"2025-02-19T07:00:53Z","abstract_excerpt":"We are in a transformative era, and advances in Artificial Intelligence (AI), especially the foundational models, are constantly in the news. AI has been an integral part of many applications that rely on automation for service delivery, and one of them is mission-critical public safety applications. The problem with AI-oriented mission-critical applications is the humanin-the-loop system and the lack of adaptability to dynamic conditions while maintaining situational awareness. Agentic AI (AAI) has gained a lot of attention recently due to its ability to analyze textual data through a context"},"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":"2502.13476","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-02-19T07:00:53Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"002013fe49fd8bb774e7d622db03764bc2326ad45298b6dea19986bdee003d7f","abstract_canon_sha256":"83ce9b270217880c79936b5eccd5c1eff171d5549989b55818659ba6f7fbcac4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:16:47.794802Z","signature_b64":"EjVxcu/NzgYWW08gDO8Fc01t4TNOk4pnlVOCZqjSXqZN8J1tZ5eAt8Bu5NTmOnM4U9GwVRbFIeYT2DFHoTweAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd1326431a3eae17db89fc7db1690447827b5665e5f789915e7d5676c62fb3eb","last_reissued_at":"2026-07-05T10:16:47.794366Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:16:47.794366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integration of Agentic AI with 6G Networks for Mission-Critical Applications: Use-case and Challenges","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.AI","authors_text":"Engin Zeydan, Kapal Dev, Merouane Debbah, Muhammad Salman Pathan, Sunder Ali Khowaja","submitted_at":"2025-02-19T07:00:53Z","abstract_excerpt":"We are in a transformative era, and advances in Artificial Intelligence (AI), especially the foundational models, are constantly in the news. AI has been an integral part of many applications that rely on automation for service delivery, and one of them is mission-critical public safety applications. The problem with AI-oriented mission-critical applications is the humanin-the-loop system and the lack of adaptability to dynamic conditions while maintaining situational awareness. Agentic AI (AAI) has gained a lot of attention recently due to its ability to analyze textual data through a context"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13476","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/2502.13476/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":"2502.13476","created_at":"2026-07-05T10:16:47.794423+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13476v1","created_at":"2026-07-05T10:16:47.794423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13476","created_at":"2026-07-05T10:16:47.794423+00:00"},{"alias_kind":"pith_short_12","alias_value":"7UJSMQY2H2XB","created_at":"2026-07-05T10:16:47.794423+00:00"},{"alias_kind":"pith_short_16","alias_value":"7UJSMQY2H2XBPW4J","created_at":"2026-07-05T10:16:47.794423+00:00"},{"alias_kind":"pith_short_8","alias_value":"7UJSMQY2","created_at":"2026-07-05T10:16:47.794423+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01546","citing_title":"6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6","json":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6.json","graph_json":"https://pith.science/api/pith-number/7UJSMQY2H2XBPW4J7R63C2IEI6/graph.json","events_json":"https://pith.science/api/pith-number/7UJSMQY2H2XBPW4J7R63C2IEI6/events.json","paper":"https://pith.science/paper/7UJSMQY2"},"agent_actions":{"view_html":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6","download_json":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6.json","view_paper":"https://pith.science/paper/7UJSMQY2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13476&json=true","fetch_graph":"https://pith.science/api/pith-number/7UJSMQY2H2XBPW4J7R63C2IEI6/graph.json","fetch_events":"https://pith.science/api/pith-number/7UJSMQY2H2XBPW4J7R63C2IEI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6/action/storage_attestation","attest_author":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6/action/author_attestation","sign_citation":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6/action/citation_signature","submit_replication":"https://pith.science/pith/7UJSMQY2H2XBPW4J7R63C2IEI6/action/replication_record"}},"created_at":"2026-07-05T10:16:47.794423+00:00","updated_at":"2026-07-05T10:16:47.794423+00:00"}