{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GGV4TYOZWXRQF5OSYULTI7BFA2","short_pith_number":"pith:GGV4TYOZ","schema_version":"1.0","canonical_sha256":"31abc9e1d9b5e302f5d2c517347c2506afc885c2a189e94ee4b3def7828338d3","source":{"kind":"arxiv","id":"2506.18746","version":1},"attestation_state":"computed","paper":{"title":"The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.ML"],"primary_cat":"stat.CO","authors_text":"Bob Carpenter, Nawaf Bou-Rabee, Sifan Liu, Tore Selland Kleppe","submitted_at":"2025-06-23T15:20:46Z","abstract_excerpt":"Locally adapting parameters within Markov chain Monte Carlo methods while preserving reversibility is notoriously difficult. The success of the No-U-Turn Sampler (NUTS) largely stems from its clever local adaptation of the integration time in Hamiltonian Monte Carlo via a geometric U-turn condition. However, posterior distributions frequently exhibit multi-scale geometries with extreme variations in scale, making it necessary to also adapt the leapfrog integrator's step size locally and dynamically. Despite its practical importance, this problem has remained largely open since the introduction"},"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":"2506.18746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2025-06-23T15:20:46Z","cross_cats_sorted":["math.PR","stat.ML"],"title_canon_sha256":"308d053050f2aec35cfbb8b2b25e16298eb0ab70713511ef07bc31757b589818","abstract_canon_sha256":"6023b4dc4b75fd74b4da9683ab2cfe6f2dd15e0830c18ab8dbc2db062e41fb4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:55.337650Z","signature_b64":"rGH/0FU8xSUGlcNQITmBWY77dN+983WHrw88PFHqaE9UYVoaHdD8gzvnpI2FMq03G8u0zrMjRDAIIIHwHU5TCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31abc9e1d9b5e302f5d2c517347c2506afc885c2a189e94ee4b3def7828338d3","last_reissued_at":"2026-07-05T11:25:55.337179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:55.337179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.ML"],"primary_cat":"stat.CO","authors_text":"Bob Carpenter, Nawaf Bou-Rabee, Sifan Liu, Tore Selland Kleppe","submitted_at":"2025-06-23T15:20:46Z","abstract_excerpt":"Locally adapting parameters within Markov chain Monte Carlo methods while preserving reversibility is notoriously difficult. The success of the No-U-Turn Sampler (NUTS) largely stems from its clever local adaptation of the integration time in Hamiltonian Monte Carlo via a geometric U-turn condition. However, posterior distributions frequently exhibit multi-scale geometries with extreme variations in scale, making it necessary to also adapt the leapfrog integrator's step size locally and dynamically. Despite its practical importance, this problem has remained largely open since the introduction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18746","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/2506.18746/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":"2506.18746","created_at":"2026-07-05T11:25:55.337237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.18746v1","created_at":"2026-07-05T11:25:55.337237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18746","created_at":"2026-07-05T11:25:55.337237+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGV4TYOZWXRQ","created_at":"2026-07-05T11:25:55.337237+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGV4TYOZWXRQF5OS","created_at":"2026-07-05T11:25:55.337237+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGV4TYOZ","created_at":"2026-07-05T11:25:55.337237+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2606.19909","citing_title":"Establishing an $\\Omega(\\sqrt{d})$ complexity lower bound for PDMP samplers and how to break it: a sub-$\\sqrt{d}$ algorithm for Gaussian-tailed targets","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2606.05935","citing_title":"Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2603.18640","citing_title":"A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2604.09832","citing_title":"Adaptive Riemannian Manifold Hamiltonian Monte Carlo with Hierarchical Metric","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2","json":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2.json","graph_json":"https://pith.science/api/pith-number/GGV4TYOZWXRQF5OSYULTI7BFA2/graph.json","events_json":"https://pith.science/api/pith-number/GGV4TYOZWXRQF5OSYULTI7BFA2/events.json","paper":"https://pith.science/paper/GGV4TYOZ"},"agent_actions":{"view_html":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2","download_json":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2.json","view_paper":"https://pith.science/paper/GGV4TYOZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.18746&json=true","fetch_graph":"https://pith.science/api/pith-number/GGV4TYOZWXRQF5OSYULTI7BFA2/graph.json","fetch_events":"https://pith.science/api/pith-number/GGV4TYOZWXRQF5OSYULTI7BFA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2/action/storage_attestation","attest_author":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2/action/author_attestation","sign_citation":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2/action/citation_signature","submit_replication":"https://pith.science/pith/GGV4TYOZWXRQF5OSYULTI7BFA2/action/replication_record"}},"created_at":"2026-07-05T11:25:55.337237+00:00","updated_at":"2026-07-05T11:25:55.337237+00:00"}