{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UJ2MTZP4V3W5SPUCKJDQMV6AVN","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":"396bd043530c333a1749dd402f596a21261d61ce87da785174ea76bbc2907a50","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-12-05T23:02:02Z","title_canon_sha256":"225938819543a0cf1e64f64948877185ed13e0a92cba98e3b236952e4c4b4366"},"schema_version":"1.0","source":{"id":"2412.04657","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04657","created_at":"2026-07-05T11:05:37Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04657v2","created_at":"2026-07-05T11:05:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04657","created_at":"2026-07-05T11:05:37Z"},{"alias_kind":"pith_short_12","alias_value":"UJ2MTZP4V3W5","created_at":"2026-07-05T11:05:37Z"},{"alias_kind":"pith_short_16","alias_value":"UJ2MTZP4V3W5SPUC","created_at":"2026-07-05T11:05:37Z"},{"alias_kind":"pith_short_8","alias_value":"UJ2MTZP4","created_at":"2026-07-05T11:05:37Z"}],"graph_snapshots":[{"event_id":"sha256:005a8f1bc9a642ba800c8abe7d600ea02d2a5a1dc144e6861cca16bc58607a3a","target":"graph","created_at":"2026-07-05T11:05:37Z","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/2412.04657/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, many industries have utilized machine learning (ML) models in their systems. Ideally, ML models should be trained on and applied to data from the same distributions. However, the data evolves over time in many application areas, leading to concept drift, which in turn causes the performance of the ML models to degrade over time. Therefore, maintaining up-to-date ML models plays a critical role in the MLOps pipeline. Existing ML model maintenance approaches are often computationally resource-intensive, costly, time-consuming, and model-dependent. Thus, we propose an improved ML","authors_text":"Amin Nikanjam, Forough Majidi, Foutse Khomh, Heng Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-12-05T23:02:02Z","title":"An Efficient Model Maintenance Approach for MLOps"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04657","kind":"arxiv","version":2},"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":"sha256:2f318041d0c4c63b3660f8fe3b3d6fa78d909cf7b36f937c50c7a256922d8fc0","target":"record","created_at":"2026-07-05T11:05:37Z","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":"396bd043530c333a1749dd402f596a21261d61ce87da785174ea76bbc2907a50","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-12-05T23:02:02Z","title_canon_sha256":"225938819543a0cf1e64f64948877185ed13e0a92cba98e3b236952e4c4b4366"},"schema_version":"1.0","source":{"id":"2412.04657","kind":"arxiv","version":2}},"canonical_sha256":"a274c9e5fcaeedd93e8252470657c0ab7d6879c42265fc92b8a5711fa4985361","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a274c9e5fcaeedd93e8252470657c0ab7d6879c42265fc92b8a5711fa4985361","first_computed_at":"2026-07-05T11:05:37.111224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:37.111224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Cm7GjVco4xdYxzbhGdiUo3waQLhFf5Z3LmX3eB829SWLvzHJjCDKx34yzaauOHo7VtlwHtiMpmHjWtG7Pb4mDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:37.111758Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04657","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f318041d0c4c63b3660f8fe3b3d6fa78d909cf7b36f937c50c7a256922d8fc0","sha256:005a8f1bc9a642ba800c8abe7d600ea02d2a5a1dc144e6861cca16bc58607a3a"],"state_sha256":"8d16dcddf34267e16c7b3a249753aee889a81ebf2f0c10678265c1a10dc0bf59"}