{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:V2E6XVYVR7AANR2X2BXCPKQ4NE","short_pith_number":"pith:V2E6XVYV","schema_version":"1.0","canonical_sha256":"ae89ebd7158fc006c757d06e27aa1c6929f6323b2693be393ebcc1c3edc45d2c","source":{"kind":"arxiv","id":"2605.25078","version":1},"attestation_state":"computed","paper":{"title":"The Dirichlet Mechanism for rounding with strong negative correlation, with applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"David G. Harris, George Z. Li, Nitya Raju, Renata Valieva","submitted_at":"2026-05-24T13:41:44Z","abstract_excerpt":"Many optimization and scheduling problems can be abstracted in terms of a bipartite ``assignment graph\" $G = (L \\cup R, E)$, where the goal is to select exactly one edge for each right-node. For example, a right-node may correspond to a job, and a left-node to a possible machine assignment. A common strategy to solve such problems is to obtain a fractional relaxation $x_e$ for each edge $e$, and then have each right-node independently select an edge with probability $x_e$. However, this may cause the left-nodes to become unevenly loaded, leading to suboptimal solutions for some problems.\n  To "},"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":"2605.25078","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2026-05-24T13:41:44Z","cross_cats_sorted":[],"title_canon_sha256":"90380831cd318f5e9a07751d6a5404235d2437d148555fbf2379f6aab8d583a0","abstract_canon_sha256":"cc058889fb4c67a07176693fc9afc3b3bcdc1d43225ae8fadbb600f9de2e73f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:03:38.993728Z","signature_b64":"ruEFC+sLSJbi3WqBbmVyRf7o18enj+7o7bF7AdHd+tdcRluIqbNe2igtdQCPSbZ35lDsCpOt7xMs78K7A7s6CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae89ebd7158fc006c757d06e27aa1c6929f6323b2693be393ebcc1c3edc45d2c","last_reissued_at":"2026-05-26T02:03:38.992905Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:03:38.992905Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Dirichlet Mechanism for rounding with strong negative correlation, with applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"David G. Harris, George Z. Li, Nitya Raju, Renata Valieva","submitted_at":"2026-05-24T13:41:44Z","abstract_excerpt":"Many optimization and scheduling problems can be abstracted in terms of a bipartite ``assignment graph\" $G = (L \\cup R, E)$, where the goal is to select exactly one edge for each right-node. For example, a right-node may correspond to a job, and a left-node to a possible machine assignment. A common strategy to solve such problems is to obtain a fractional relaxation $x_e$ for each edge $e$, and then have each right-node independently select an edge with probability $x_e$. However, this may cause the left-nodes to become unevenly loaded, leading to suboptimal solutions for some problems.\n  To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25078","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/2605.25078/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":"2605.25078","created_at":"2026-05-26T02:03:38.993040+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.25078v1","created_at":"2026-05-26T02:03:38.993040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25078","created_at":"2026-05-26T02:03:38.993040+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2E6XVYVR7AA","created_at":"2026-05-26T02:03:38.993040+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2E6XVYVR7AANR2X","created_at":"2026-05-26T02:03:38.993040+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2E6XVYV","created_at":"2026-05-26T02:03:38.993040+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/V2E6XVYVR7AANR2X2BXCPKQ4NE","json":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE.json","graph_json":"https://pith.science/api/pith-number/V2E6XVYVR7AANR2X2BXCPKQ4NE/graph.json","events_json":"https://pith.science/api/pith-number/V2E6XVYVR7AANR2X2BXCPKQ4NE/events.json","paper":"https://pith.science/paper/V2E6XVYV"},"agent_actions":{"view_html":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE","download_json":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE.json","view_paper":"https://pith.science/paper/V2E6XVYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.25078&json=true","fetch_graph":"https://pith.science/api/pith-number/V2E6XVYVR7AANR2X2BXCPKQ4NE/graph.json","fetch_events":"https://pith.science/api/pith-number/V2E6XVYVR7AANR2X2BXCPKQ4NE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE/action/storage_attestation","attest_author":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE/action/author_attestation","sign_citation":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE/action/citation_signature","submit_replication":"https://pith.science/pith/V2E6XVYVR7AANR2X2BXCPKQ4NE/action/replication_record"}},"created_at":"2026-05-26T02:03:38.993040+00:00","updated_at":"2026-05-26T02:03:38.993040+00:00"}