{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5JWH7VF5NJ3TVGEEM4CDAO32WZ","short_pith_number":"pith:5JWH7VF5","canonical_record":{"source":{"id":"2405.17454","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-05-22T14:56:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4d7fecc126af779e6ad32ab7a62e26a1b3f5e597422c6a6b8d6ce789035d880e","abstract_canon_sha256":"b41f59ae830665eccc8cff97225cb337f7c78252079bb30f0708858e31cda0b1"},"schema_version":"1.0"},"canonical_sha256":"ea6c7fd4bd6a773a98846704303b7ab64b07739fcfb74ba51123444885925e6c","source":{"kind":"arxiv","id":"2405.17454","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17454","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17454v1","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17454","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"5JWH7VF5NJ3T","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_16","alias_value":"5JWH7VF5NJ3TVGEE","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_8","alias_value":"5JWH7VF5","created_at":"2026-07-05T08:23:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5JWH7VF5NJ3TVGEEM4CDAO32WZ","target":"record","payload":{"canonical_record":{"source":{"id":"2405.17454","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-05-22T14:56:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4d7fecc126af779e6ad32ab7a62e26a1b3f5e597422c6a6b8d6ce789035d880e","abstract_canon_sha256":"b41f59ae830665eccc8cff97225cb337f7c78252079bb30f0708858e31cda0b1"},"schema_version":"1.0"},"canonical_sha256":"ea6c7fd4bd6a773a98846704303b7ab64b07739fcfb74ba51123444885925e6c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:44.715449Z","signature_b64":"XUIJGVXRdZDr+ro5kSqc0tcbR2b0yWBl7kWqlxf9s8t4o9d7XUYayJAABDk6RoFxSp0zNhe30XGtUZbpgCyFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea6c7fd4bd6a773a98846704303b7ab64b07739fcfb74ba51123444885925e6c","last_reissued_at":"2026-07-05T08:23:44.714925Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:44.714925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.17454","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cVRfzHFznXl6fdLAF6g2d2jTC2Sm9z4mzViC4gydMmfOmzTfLn6HXBd/TiWP77xjP3W0C2olDkguEw0/NjVMBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:10:59.492859Z"},"content_sha256":"0d7e548102cf738cd3f87eedc1679da57aff4472000e64c5ec557ce2d706e2f4","schema_version":"1.0","event_id":"sha256:0d7e548102cf738cd3f87eedc1679da57aff4472000e64c5ec557ce2d706e2f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5JWH7VF5NJ3TVGEEM4CDAO32WZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Ekram Hossain, Fahime Khoramnejad","submitted_at":"2024-05-22T14:56:25Z","abstract_excerpt":"Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike traditional optimization techniques and other machine learning methods, GAI excels at learning from real-world network data, capturing its intricacies. This enables safe, offline exploration of various configurations and generation of diverse, unseen scenarios, empowering proactive, data-driven exploration and optimization for xG networks. Additionally, GAI's"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17454","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/2405.17454/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5plnFtkxidnao64y87Nky0j08/lua2fkXE4jfyjVovu+DaTVn6O3E8beqzIUCu5NUbsWvHYoXhGpEc2nYLr4Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:10:59.493554Z"},"content_sha256":"172001e638dc2de9dea9f9536e24342a0377b07b3f9a63324bde0e47958a5b52","schema_version":"1.0","event_id":"sha256:172001e638dc2de9dea9f9536e24342a0377b07b3f9a63324bde0e47958a5b52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/bundle.json","state_url":"https://pith.science/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T12:10:59Z","links":{"resolver":"https://pith.science/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ","bundle":"https://pith.science/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/bundle.json","state":"https://pith.science/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5JWH7VF5NJ3TVGEEM4CDAO32WZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5JWH7VF5NJ3TVGEEM4CDAO32WZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b41f59ae830665eccc8cff97225cb337f7c78252079bb30f0708858e31cda0b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-05-22T14:56:25Z","title_canon_sha256":"4d7fecc126af779e6ad32ab7a62e26a1b3f5e597422c6a6b8d6ce789035d880e"},"schema_version":"1.0","source":{"id":"2405.17454","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17454","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17454v1","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17454","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"5JWH7VF5NJ3T","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_16","alias_value":"5JWH7VF5NJ3TVGEE","created_at":"2026-07-05T08:23:44Z"},{"alias_kind":"pith_short_8","alias_value":"5JWH7VF5","created_at":"2026-07-05T08:23:44Z"}],"graph_snapshots":[{"event_id":"sha256:172001e638dc2de9dea9f9536e24342a0377b07b3f9a63324bde0e47958a5b52","target":"graph","created_at":"2026-07-05T08:23:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.17454/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike traditional optimization techniques and other machine learning methods, GAI excels at learning from real-world network data, capturing its intricacies. This enables safe, offline exploration of various configurations and generation of diverse, unseen scenarios, empowering proactive, data-driven exploration and optimization for xG networks. Additionally, GAI's","authors_text":"Ekram Hossain, Fahime Khoramnejad","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-05-22T14:56:25Z","title":"Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17454","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0d7e548102cf738cd3f87eedc1679da57aff4472000e64c5ec557ce2d706e2f4","target":"record","created_at":"2026-07-05T08:23:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b41f59ae830665eccc8cff97225cb337f7c78252079bb30f0708858e31cda0b1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-05-22T14:56:25Z","title_canon_sha256":"4d7fecc126af779e6ad32ab7a62e26a1b3f5e597422c6a6b8d6ce789035d880e"},"schema_version":"1.0","source":{"id":"2405.17454","kind":"arxiv","version":1}},"canonical_sha256":"ea6c7fd4bd6a773a98846704303b7ab64b07739fcfb74ba51123444885925e6c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea6c7fd4bd6a773a98846704303b7ab64b07739fcfb74ba51123444885925e6c","first_computed_at":"2026-07-05T08:23:44.714925Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:23:44.714925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XUIJGVXRdZDr+ro5kSqc0tcbR2b0yWBl7kWqlxf9s8t4o9d7XUYayJAABDk6RoFxSp0zNhe30XGtUZbpgCyFBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:23:44.715449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17454","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d7e548102cf738cd3f87eedc1679da57aff4472000e64c5ec557ce2d706e2f4","sha256:172001e638dc2de9dea9f9536e24342a0377b07b3f9a63324bde0e47958a5b52"],"state_sha256":"d5bc8fb6540541ebbaf734767e45de36acf6e5e818f01adfae569be1878e4ecb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u5bdCebdsr5letEdHUP7S/Ez/W2HQLDIhMSW0thUeWONfXxQ6jh/qyvY6qV9Ai66UNUrH5kGIgt86ckmqAEoBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T12:10:59.499565Z","bundle_sha256":"c5fccc5ef8848e12979b20f9aa08acc74157606424fa26fc25e716ec16b0cfd9"}}