{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZFHK7II6JXD5QMYYBWDDRION4V","short_pith_number":"pith:ZFHK7II6","schema_version":"1.0","canonical_sha256":"c94eafa11e4dc7d833180d8638a1cde54d2c6c0f455dfdf8eafb6971db00d3f4","source":{"kind":"arxiv","id":"2402.18263","version":1},"attestation_state":"computed","paper":{"title":"Max-Cut with $\\epsilon$-Accurate Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Anupam Gupta, Debmalya Panigrahi, Euiwoong Lee, Tommaso d'Orsi, Vincent Cohen-Addad","submitted_at":"2024-02-28T11:51:28Z","abstract_excerpt":"We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are given its correct label in $\\{-1,+1\\}$ with some unknown probability $1/2 + \\epsilon$, and the other (incorrect) label otherwise. In the more-informative partial predictions model, for each vertex we are given its correct label with probability $\\epsilon$ and no label otherwise. We assume only pairwise independence between vertices in both models.\n  We show how these predictions can be used to improve on the worst-case a"},"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":"2402.18263","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2024-02-28T11:51:28Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"406a831ae6327cf5c647a39f3e4b66ce20692c1f2415d7296aa094420afae27f","abstract_canon_sha256":"cdf56e40159d438f1fa053ccdf7edcba24feb38a4c1f685fb6ea6b12126d1940"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:14.487628Z","signature_b64":"EDxkz+837w0KlGXvUJWumKb1dBIowx83KtD/4KsltMARD9wWrfidgduVx4P+x44Xd7YgL0xVPSUE8kZuDkdACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c94eafa11e4dc7d833180d8638a1cde54d2c6c0f455dfdf8eafb6971db00d3f4","last_reissued_at":"2026-07-05T07:50:14.487093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:14.487093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Max-Cut with $\\epsilon$-Accurate Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Anupam Gupta, Debmalya Panigrahi, Euiwoong Lee, Tommaso d'Orsi, Vincent Cohen-Addad","submitted_at":"2024-02-28T11:51:28Z","abstract_excerpt":"We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are given its correct label in $\\{-1,+1\\}$ with some unknown probability $1/2 + \\epsilon$, and the other (incorrect) label otherwise. In the more-informative partial predictions model, for each vertex we are given its correct label with probability $\\epsilon$ and no label otherwise. We assume only pairwise independence between vertices in both models.\n  We show how these predictions can be used to improve on the worst-case a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18263","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/2402.18263/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":"2402.18263","created_at":"2026-07-05T07:50:14.487151+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18263v1","created_at":"2026-07-05T07:50:14.487151+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18263","created_at":"2026-07-05T07:50:14.487151+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFHK7II6JXD5","created_at":"2026-07-05T07:50:14.487151+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFHK7II6JXD5QMYY","created_at":"2026-07-05T07:50:14.487151+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFHK7II6","created_at":"2026-07-05T07:50:14.487151+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V","json":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V.json","graph_json":"https://pith.science/api/pith-number/ZFHK7II6JXD5QMYYBWDDRION4V/graph.json","events_json":"https://pith.science/api/pith-number/ZFHK7II6JXD5QMYYBWDDRION4V/events.json","paper":"https://pith.science/paper/ZFHK7II6"},"agent_actions":{"view_html":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V","download_json":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V.json","view_paper":"https://pith.science/paper/ZFHK7II6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18263&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFHK7II6JXD5QMYYBWDDRION4V/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFHK7II6JXD5QMYYBWDDRION4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V/action/storage_attestation","attest_author":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V/action/author_attestation","sign_citation":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V/action/citation_signature","submit_replication":"https://pith.science/pith/ZFHK7II6JXD5QMYYBWDDRION4V/action/replication_record"}},"created_at":"2026-07-05T07:50:14.487151+00:00","updated_at":"2026-07-05T07:50:14.487151+00:00"}