{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PISQDMH3DZGKCWQIHDKR7QYGJM","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":"ef5548ba5164a07308534efd4bc63193d9362cbf51598441c9bfe67b77002a36","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2022-05-25T15:24:03Z","title_canon_sha256":"5f2dfbe5d4c9a5a01e6c4cf8d2aa25183324e97c085c00e74daf8ec5ce3f9885"},"schema_version":"1.0","source":{"id":"2205.12850","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.12850","created_at":"2026-07-05T05:04:48Z"},{"alias_kind":"arxiv_version","alias_value":"2205.12850v2","created_at":"2026-07-05T05:04:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.12850","created_at":"2026-07-05T05:04:48Z"},{"alias_kind":"pith_short_12","alias_value":"PISQDMH3DZGK","created_at":"2026-07-05T05:04:48Z"},{"alias_kind":"pith_short_16","alias_value":"PISQDMH3DZGKCWQI","created_at":"2026-07-05T05:04:48Z"},{"alias_kind":"pith_short_8","alias_value":"PISQDMH3","created_at":"2026-07-05T05:04:48Z"}],"graph_snapshots":[{"event_id":"sha256:8f90e5857692317f3ab314e050624763fad9fa4ee36b077451f1fb4b6c865d69","target":"graph","created_at":"2026-07-05T05:04:48Z","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/2205.12850/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a novel measure for quantifying the error in input predictions. The error is based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a general template which we apply to online graph problems. The measure captures errors due to absent predicted requests as well as unpredicted actual requests; hence, predicted and actual inputs can be of arbitrary size. We achieve refined performance guarantees for previously studied network design problems in the online-list model, such as Steiner tree and facility location. Further, we initiate the study of learning-","authors_text":"Alberto Marchetti-Spaccamela, Alexander Lindermayr, Giulia Bernardini, Leen Stougie, Michelle Sweering, Nicole Megow","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2022-05-25T15:24:03Z","title":"A Universal Error Measure for Input Predictions Applied to Online Graph Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.12850","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:f2f88bd2e4ee6fc11fe91223c0d1f33c46f0e403c059cf920d3d5c797889f697","target":"record","created_at":"2026-07-05T05:04:48Z","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":"ef5548ba5164a07308534efd4bc63193d9362cbf51598441c9bfe67b77002a36","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2022-05-25T15:24:03Z","title_canon_sha256":"5f2dfbe5d4c9a5a01e6c4cf8d2aa25183324e97c085c00e74daf8ec5ce3f9885"},"schema_version":"1.0","source":{"id":"2205.12850","kind":"arxiv","version":2}},"canonical_sha256":"7a2501b0fb1e4ca15a0838d51fc3064b3af1f8bd908db340bb3fab587f9db7ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a2501b0fb1e4ca15a0838d51fc3064b3af1f8bd908db340bb3fab587f9db7ba","first_computed_at":"2026-07-05T05:04:48.828096Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:48.828096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6vltVra8dP1BZSvZUDd5q5YGpCHqfpHcJqOFroh1gDaZS4wTwxwK2gl49u8coXOS/m4/9ZF9d/mk86v/DIJJAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:48.828621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.12850","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2f88bd2e4ee6fc11fe91223c0d1f33c46f0e403c059cf920d3d5c797889f697","sha256:8f90e5857692317f3ab314e050624763fad9fa4ee36b077451f1fb4b6c865d69"],"state_sha256":"b1a022d36ebc5095fbb1db28b293d42edbc2083b1f582b59e58b081907010944"}