{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KDCFMN6EYRTUAIQ3T4EM4RYGA2","short_pith_number":"pith:KDCFMN6E","schema_version":"1.0","canonical_sha256":"50c45637c4c46740221b9f08ce47060687dd8725a7786b79ec465ff43f840751","source":{"kind":"arxiv","id":"2403.06362","version":1},"attestation_state":"computed","paper":{"title":"An Alternative to Stride-Based RNG for Monte Carlo Transport","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"physics.comp-ph","authors_text":"Braxton S.Cuneo, Ilham Variansyah","submitted_at":"2024-03-11T01:19:42Z","abstract_excerpt":"The techniques used to generate pseudo-random numbers for Monte Carlo (MC) applications bear many implications on the quality and speed of that programs work. As a random number generator (RNG) slows, the production of random numbers begins to dominate runtime. As RNG output grows in correlation, the final product becomes less reliable.\n  These difficulties are further compounded by the need for reproducibility and parallelism. For reproducibility, the numbers generated to determine any outcome must be the same each time a simulation is run. However, the concurrency that comes with most parall"},"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":"2403.06362","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-03-11T01:19:42Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"d0c5a33228ad23c86c764dcf1effa29f0e92e6a71318192abddcf17f1a37bb9e","abstract_canon_sha256":"ff7f1c948697c316607b3bfc19a95f20f8efd8dd4c1a50eb22f6ef2b1539ef08"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:22.492375Z","signature_b64":"SSqpaUnj3app/k0l8WoI31z1iHTXcf2PRaJMT5/AGZrxU5nCeypn9U3nniHh6vhjU4T9Oela5hfwl4jxE3AgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50c45637c4c46740221b9f08ce47060687dd8725a7786b79ec465ff43f840751","last_reissued_at":"2026-07-05T07:54:22.491873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:22.491873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Alternative to Stride-Based RNG for Monte Carlo Transport","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"physics.comp-ph","authors_text":"Braxton S.Cuneo, Ilham Variansyah","submitted_at":"2024-03-11T01:19:42Z","abstract_excerpt":"The techniques used to generate pseudo-random numbers for Monte Carlo (MC) applications bear many implications on the quality and speed of that programs work. As a random number generator (RNG) slows, the production of random numbers begins to dominate runtime. As RNG output grows in correlation, the final product becomes less reliable.\n  These difficulties are further compounded by the need for reproducibility and parallelism. For reproducibility, the numbers generated to determine any outcome must be the same each time a simulation is run. However, the concurrency that comes with most parall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06362","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/2403.06362/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":"2403.06362","created_at":"2026-07-05T07:54:22.491935+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06362v1","created_at":"2026-07-05T07:54:22.491935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06362","created_at":"2026-07-05T07:54:22.491935+00:00"},{"alias_kind":"pith_short_12","alias_value":"KDCFMN6EYRTU","created_at":"2026-07-05T07:54:22.491935+00:00"},{"alias_kind":"pith_short_16","alias_value":"KDCFMN6EYRTUAIQ3","created_at":"2026-07-05T07:54:22.491935+00:00"},{"alias_kind":"pith_short_8","alias_value":"KDCFMN6E","created_at":"2026-07-05T07:54:22.491935+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/KDCFMN6EYRTUAIQ3T4EM4RYGA2","json":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2.json","graph_json":"https://pith.science/api/pith-number/KDCFMN6EYRTUAIQ3T4EM4RYGA2/graph.json","events_json":"https://pith.science/api/pith-number/KDCFMN6EYRTUAIQ3T4EM4RYGA2/events.json","paper":"https://pith.science/paper/KDCFMN6E"},"agent_actions":{"view_html":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2","download_json":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2.json","view_paper":"https://pith.science/paper/KDCFMN6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06362&json=true","fetch_graph":"https://pith.science/api/pith-number/KDCFMN6EYRTUAIQ3T4EM4RYGA2/graph.json","fetch_events":"https://pith.science/api/pith-number/KDCFMN6EYRTUAIQ3T4EM4RYGA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2/action/storage_attestation","attest_author":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2/action/author_attestation","sign_citation":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2/action/citation_signature","submit_replication":"https://pith.science/pith/KDCFMN6EYRTUAIQ3T4EM4RYGA2/action/replication_record"}},"created_at":"2026-07-05T07:54:22.491935+00:00","updated_at":"2026-07-05T07:54:22.491935+00:00"}