{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L7KUPMPIX7GGXW7WRXBX4E7TYA","short_pith_number":"pith:L7KUPMPI","schema_version":"1.0","canonical_sha256":"5fd547b1e8bfcc6bdbf68dc37e13f3c01d3c217a249ae4c3a83f3e46b933132c","source":{"kind":"arxiv","id":"2505.22311","version":1},"attestation_state":"computed","paper":{"title":"From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.NI","eess.SP"],"primary_cat":"cs.AI","authors_text":"Cunhua Pan, Feibo Jiang, Kezhi Wang, Li Dong, Merouane Debbah, Octavia A. Dobre","submitted_at":"2025-05-28T12:54:07Z","abstract_excerpt":"With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers a comprehensive overview of cutting-edge technologies and practical guidance. First, we outline the background of 6G communications, review t"},"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":"2505.22311","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-28T12:54:07Z","cross_cats_sorted":["cs.CY","cs.NI","eess.SP"],"title_canon_sha256":"6edb69faf47c3df68c288d7fea94e0c5aa591835a6f763064c21da03051eba0f","abstract_canon_sha256":"841e0cb3db6289751eeb3b6fd5d8f176870b20cb114bcf7a4ecf772a37a1a2f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:13.013168Z","signature_b64":"lu0HoXmqYB0MX+r3vMX7+NHMC8raBuusY/dap+MHSLd8P5SbubgUvQU1aZnAqoVzCswdw/uExH1ifbUvxApBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fd547b1e8bfcc6bdbf68dc37e13f3c01d3c217a249ae4c3a83f3e46b933132c","last_reissued_at":"2026-07-05T11:11:13.012649Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:13.012649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.NI","eess.SP"],"primary_cat":"cs.AI","authors_text":"Cunhua Pan, Feibo Jiang, Kezhi Wang, Li Dong, Merouane Debbah, Octavia A. Dobre","submitted_at":"2025-05-28T12:54:07Z","abstract_excerpt":"With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers a comprehensive overview of cutting-edge technologies and practical guidance. First, we outline the background of 6G communications, review t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22311","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/2505.22311/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":"2505.22311","created_at":"2026-07-05T11:11:13.012715+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.22311v1","created_at":"2026-07-05T11:11:13.012715+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.22311","created_at":"2026-07-05T11:11:13.012715+00:00"},{"alias_kind":"pith_short_12","alias_value":"L7KUPMPIX7GG","created_at":"2026-07-05T11:11:13.012715+00:00"},{"alias_kind":"pith_short_16","alias_value":"L7KUPMPIX7GGXW7W","created_at":"2026-07-05T11:11:13.012715+00:00"},{"alias_kind":"pith_short_8","alias_value":"L7KUPMPI","created_at":"2026-07-05T11:11:13.012715+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.12043","citing_title":"Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2601.16472","citing_title":"Secure Intellicise Wireless Network: Agentic AI for Coverless Semantic Steganography Communication","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2603.16876","citing_title":"Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13558","citing_title":"AgentComm: Semantic Communication for Embodied Agents","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA","json":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA.json","graph_json":"https://pith.science/api/pith-number/L7KUPMPIX7GGXW7WRXBX4E7TYA/graph.json","events_json":"https://pith.science/api/pith-number/L7KUPMPIX7GGXW7WRXBX4E7TYA/events.json","paper":"https://pith.science/paper/L7KUPMPI"},"agent_actions":{"view_html":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA","download_json":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA.json","view_paper":"https://pith.science/paper/L7KUPMPI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.22311&json=true","fetch_graph":"https://pith.science/api/pith-number/L7KUPMPIX7GGXW7WRXBX4E7TYA/graph.json","fetch_events":"https://pith.science/api/pith-number/L7KUPMPIX7GGXW7WRXBX4E7TYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA/action/storage_attestation","attest_author":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA/action/author_attestation","sign_citation":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA/action/citation_signature","submit_replication":"https://pith.science/pith/L7KUPMPIX7GGXW7WRXBX4E7TYA/action/replication_record"}},"created_at":"2026-07-05T11:11:13.012715+00:00","updated_at":"2026-07-05T11:11:13.012715+00:00"}