{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XYITGUXMJBJAYEUGSOLLK73OFI","short_pith_number":"pith:XYITGUXM","schema_version":"1.0","canonical_sha256":"be113352ec48520c12869396b57f6e2a264f331b0d7a25335ff98f6aa7b39db4","source":{"kind":"arxiv","id":"2503.15764","version":2},"attestation_state":"computed","paper":{"title":"Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Guangming Shi, Ping Zhang, Yong Xiao","submitted_at":"2025-03-20T00:48:44Z","abstract_excerpt":"The promising potential of AI and network convergence in improving networking performance and enabling new service capabilities has recently attracted significant interest. Existing network AI solutions, while powerful, are mainly built based on the close-loop and passive learning framework, resulting in major limitations in autonomous solution finding and dynamic environmental adaptation. Agentic AI has recently been introduced as a promising solution to address the above limitations and pave the way for true generally intelligent and beneficial AI systems. The key idea is to create a network"},"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":"2503.15764","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2025-03-20T00:48:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7bd5d17efa99f1021a3ab9534adbe76f6874581327a604704f182078c82784d9","abstract_canon_sha256":"59c710f8356fde1d7daf9d0521ce43109596bd090ba896bbba0ef58d0fca3b78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:33.357677Z","signature_b64":"8PKxVptnxzUHPXq7pXIkCSd9i32S7Gfh3yJvICwhC2Lccxg5TFUlmf/8azSckIpZQujR/9XALKB7O9NOPlf5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be113352ec48520c12869396b57f6e2a264f331b0d7a25335ff98f6aa7b39db4","last_reissued_at":"2026-07-05T11:01:33.357224Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:33.357224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Guangming Shi, Ping Zhang, Yong Xiao","submitted_at":"2025-03-20T00:48:44Z","abstract_excerpt":"The promising potential of AI and network convergence in improving networking performance and enabling new service capabilities has recently attracted significant interest. Existing network AI solutions, while powerful, are mainly built based on the close-loop and passive learning framework, resulting in major limitations in autonomous solution finding and dynamic environmental adaptation. Agentic AI has recently been introduced as a promising solution to address the above limitations and pave the way for true generally intelligent and beneficial AI systems. The key idea is to create a network"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15764","kind":"arxiv","version":2},"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/2503.15764/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":"2503.15764","created_at":"2026-07-05T11:01:33.357289+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.15764v2","created_at":"2026-07-05T11:01:33.357289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15764","created_at":"2026-07-05T11:01:33.357289+00:00"},{"alias_kind":"pith_short_12","alias_value":"XYITGUXMJBJA","created_at":"2026-07-05T11:01:33.357289+00:00"},{"alias_kind":"pith_short_16","alias_value":"XYITGUXMJBJAYEUG","created_at":"2026-07-05T11:01:33.357289+00:00"},{"alias_kind":"pith_short_8","alias_value":"XYITGUXM","created_at":"2026-07-05T11:01:33.357289+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.19973","citing_title":"A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2512.20640","citing_title":"Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02911","citing_title":"Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI","json":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI.json","graph_json":"https://pith.science/api/pith-number/XYITGUXMJBJAYEUGSOLLK73OFI/graph.json","events_json":"https://pith.science/api/pith-number/XYITGUXMJBJAYEUGSOLLK73OFI/events.json","paper":"https://pith.science/paper/XYITGUXM"},"agent_actions":{"view_html":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI","download_json":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI.json","view_paper":"https://pith.science/paper/XYITGUXM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.15764&json=true","fetch_graph":"https://pith.science/api/pith-number/XYITGUXMJBJAYEUGSOLLK73OFI/graph.json","fetch_events":"https://pith.science/api/pith-number/XYITGUXMJBJAYEUGSOLLK73OFI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI/action/storage_attestation","attest_author":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI/action/author_attestation","sign_citation":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI/action/citation_signature","submit_replication":"https://pith.science/pith/XYITGUXMJBJAYEUGSOLLK73OFI/action/replication_record"}},"created_at":"2026-07-05T11:01:33.357289+00:00","updated_at":"2026-07-05T11:01:33.357289+00:00"}