{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:M5E5NGJS27ZEBCASSMHBNXVJUD","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":"d412f351eec34e816fcce717946a3f113f2c43c386c0bcb433631c53bff768af","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-08-27T07:15:05Z","title_canon_sha256":"a36f7f7bb94ad38494c6e2533b6f9a7f350f90e714a7b716e06c66d622f02623"},"schema_version":"1.0","source":{"id":"2408.14827","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.14827","created_at":"2026-07-05T08:59:39Z"},{"alias_kind":"arxiv_version","alias_value":"2408.14827v1","created_at":"2026-07-05T08:59:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.14827","created_at":"2026-07-05T08:59:39Z"},{"alias_kind":"pith_short_12","alias_value":"M5E5NGJS27ZE","created_at":"2026-07-05T08:59:39Z"},{"alias_kind":"pith_short_16","alias_value":"M5E5NGJS27ZEBCAS","created_at":"2026-07-05T08:59:39Z"},{"alias_kind":"pith_short_8","alias_value":"M5E5NGJS","created_at":"2026-07-05T08:59:39Z"}],"graph_snapshots":[{"event_id":"sha256:3286cb87f47c5f563ae217efab988b8998b873b92bdd99f8e4330087a258ca90","target":"graph","created_at":"2026-07-05T08:59:39Z","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/2408.14827/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G use cases cause AI/ML model performance degradation, resulting in Service Level Agreements (SLA) violations, over- or under-provisioning of resources, etc. Retraining is essential to address the performance degradation of the AI/ML models. Existing threshold and periodic retraining approaches have potential disadvant","authors_text":"Bhargav Chirumamilla, Bheemarjuna Reddy Tamma, Koteswararao Kondepu, Luca Valcarenghi, Piero Castoldi, Venkatarami Reddy Chintapalli, Venkateswarlu Gudepu","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-08-27T07:15:05Z","title":"Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.14827","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":"sha256:32f3a305c21110a83a19eb161fba2741d4df0c9c1a7cc1013b3218e9db519f7d","target":"record","created_at":"2026-07-05T08:59:39Z","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":"d412f351eec34e816fcce717946a3f113f2c43c386c0bcb433631c53bff768af","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-08-27T07:15:05Z","title_canon_sha256":"a36f7f7bb94ad38494c6e2533b6f9a7f350f90e714a7b716e06c66d622f02623"},"schema_version":"1.0","source":{"id":"2408.14827","kind":"arxiv","version":1}},"canonical_sha256":"6749d69932d7f2408812930e16dea9a0c0c0c518572f5fb827e35ce89b377ec1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6749d69932d7f2408812930e16dea9a0c0c0c518572f5fb827e35ce89b377ec1","first_computed_at":"2026-07-05T08:59:39.495147Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:59:39.495147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7HO9JEPrncvbRdsU/0D3baVbrGJ3tRk5WJrb6mJgyg6gO29QK8UwajTtxmRt9PV6/JPUKq9Bw7N6O5ZkRyv6Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T08:59:39.495692Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.14827","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32f3a305c21110a83a19eb161fba2741d4df0c9c1a7cc1013b3218e9db519f7d","sha256:3286cb87f47c5f563ae217efab988b8998b873b92bdd99f8e4330087a258ca90"],"state_sha256":"f533822f112d264750708600961fdccfa0c2c518d76dc10021cbf3a27b895aea"}