{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NOCWGRS2M7VM5WBMLMP2RV22LS","short_pith_number":"pith:NOCWGRS2","schema_version":"1.0","canonical_sha256":"6b8563465a67eaced82c5b1fa8d75a5cbf6cca6bd2bd5fde4730a0603c57aff3","source":{"kind":"arxiv","id":"2507.13494","version":1},"attestation_state":"computed","paper":{"title":"Random Variate Generation with Formal Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"cs.PL","authors_text":"Feras A. Saad, Wonyeol Lee","submitted_at":"2025-07-17T19:05:07Z","abstract_excerpt":"This article introduces a new approach to principled and practical random variate generation with formal guarantees. The key idea is to first specify the desired probability distribution in terms of a finite-precision numerical program that defines its cumulative distribution function (CDF), and then generate exact random variates according to this CDF. We present a universal and fully automated method to synthesize exact random variate generators given any numerical CDF implemented in any binary number format, such as floating-point, fixed-point, and posits. The method is guaranteed to operat"},"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":"2507.13494","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2025-07-17T19:05:07Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"454b9412ed973e58d7e945d10177c707f8457724f7517a86a803d7386f41ec38","abstract_canon_sha256":"e68568c8de85048964fb1144f4d803c8391514adc7246dfcd35818e2f216e608"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:18.278425Z","signature_b64":"kGW/gznPsI+1aZt5fgQ/kYRcXikihPCxTtb9ITaEuq8wskSU1EZoGbgmF5TeSWK4hjU1wLIKAaeaLRoxJc/2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b8563465a67eaced82c5b1fa8d75a5cbf6cca6bd2bd5fde4730a0603c57aff3","last_reissued_at":"2026-07-05T11:39:18.277996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:18.277996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Random Variate Generation with Formal Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"cs.PL","authors_text":"Feras A. Saad, Wonyeol Lee","submitted_at":"2025-07-17T19:05:07Z","abstract_excerpt":"This article introduces a new approach to principled and practical random variate generation with formal guarantees. The key idea is to first specify the desired probability distribution in terms of a finite-precision numerical program that defines its cumulative distribution function (CDF), and then generate exact random variates according to this CDF. We present a universal and fully automated method to synthesize exact random variate generators given any numerical CDF implemented in any binary number format, such as floating-point, fixed-point, and posits. The method is guaranteed to operat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13494","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/2507.13494/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":"2507.13494","created_at":"2026-07-05T11:39:18.278053+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.13494v1","created_at":"2026-07-05T11:39:18.278053+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13494","created_at":"2026-07-05T11:39:18.278053+00:00"},{"alias_kind":"pith_short_12","alias_value":"NOCWGRS2M7VM","created_at":"2026-07-05T11:39:18.278053+00:00"},{"alias_kind":"pith_short_16","alias_value":"NOCWGRS2M7VM5WBM","created_at":"2026-07-05T11:39:18.278053+00:00"},{"alias_kind":"pith_short_8","alias_value":"NOCWGRS2","created_at":"2026-07-05T11:39:18.278053+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/NOCWGRS2M7VM5WBMLMP2RV22LS","json":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS.json","graph_json":"https://pith.science/api/pith-number/NOCWGRS2M7VM5WBMLMP2RV22LS/graph.json","events_json":"https://pith.science/api/pith-number/NOCWGRS2M7VM5WBMLMP2RV22LS/events.json","paper":"https://pith.science/paper/NOCWGRS2"},"agent_actions":{"view_html":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS","download_json":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS.json","view_paper":"https://pith.science/paper/NOCWGRS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.13494&json=true","fetch_graph":"https://pith.science/api/pith-number/NOCWGRS2M7VM5WBMLMP2RV22LS/graph.json","fetch_events":"https://pith.science/api/pith-number/NOCWGRS2M7VM5WBMLMP2RV22LS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS/action/storage_attestation","attest_author":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS/action/author_attestation","sign_citation":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS/action/citation_signature","submit_replication":"https://pith.science/pith/NOCWGRS2M7VM5WBMLMP2RV22LS/action/replication_record"}},"created_at":"2026-07-05T11:39:18.278053+00:00","updated_at":"2026-07-05T11:39:18.278053+00:00"}