{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:T7QCQ3F7BHITZISOWSAJFMRYZJ","short_pith_number":"pith:T7QCQ3F7","canonical_record":{"source":{"id":"2402.14434","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-02-22T10:26:46Z","cross_cats_sorted":["cs.LG","math.PR","stat.CO","stat.TH"],"title_canon_sha256":"678433f5657ae2a87b874219976abe095bfaa05597a569086b5691901c41c4e1","abstract_canon_sha256":"f7b530df887b6da16500cfacb76b3190fff3ff348e7c3eebe09ce13bf8fa125e"},"schema_version":"1.0"},"canonical_sha256":"9fe0286cbf09d13ca24eb48092b238ca7bd8da952353f9d83b93928ff531114e","source":{"kind":"arxiv","id":"2402.14434","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14434","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14434v4","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14434","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_12","alias_value":"T7QCQ3F7BHIT","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_16","alias_value":"T7QCQ3F7BHITZISO","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_8","alias_value":"T7QCQ3F7","created_at":"2026-07-05T09:58:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:T7QCQ3F7BHITZISOWSAJFMRYZJ","target":"record","payload":{"canonical_record":{"source":{"id":"2402.14434","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-02-22T10:26:46Z","cross_cats_sorted":["cs.LG","math.PR","stat.CO","stat.TH"],"title_canon_sha256":"678433f5657ae2a87b874219976abe095bfaa05597a569086b5691901c41c4e1","abstract_canon_sha256":"f7b530df887b6da16500cfacb76b3190fff3ff348e7c3eebe09ce13bf8fa125e"},"schema_version":"1.0"},"canonical_sha256":"9fe0286cbf09d13ca24eb48092b238ca7bd8da952353f9d83b93928ff531114e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:18.938582Z","signature_b64":"03hlAdCuaWgMuPbhGrhRHm+bUpmY9nwjpA84tkyFTiWX2cCtK14o0TsTVH669dNPi4UD4+LNPXs8pcJPRi54AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fe0286cbf09d13ca24eb48092b238ca7bd8da952353f9d83b93928ff531114e","last_reissued_at":"2026-07-05T09:58:18.938151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:18.938151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.14434","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:58:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XBywWmFNYY+q2hjx3DFo9TxpIuhQQPkOLqQmlsUvmO2ZrQ/B2fU8qrulvCroAeGFHuXGTE0eSwp6kZ1FOOUcBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T06:16:04.021401Z"},"content_sha256":"a4c7caf3d7da98f1f799ec89aae6a192a30a37a55279beda9e409c69e8bddbbd","schema_version":"1.0","event_id":"sha256:a4c7caf3d7da98f1f799ec89aae6a192a30a37a55279beda9e409c69e8bddbbd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:T7QCQ3F7BHITZISOWSAJFMRYZJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallelized Midpoint Randomization for Langevin Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.PR","stat.CO","stat.TH"],"primary_cat":"math.ST","authors_text":"Arnak Dalalyan, Lu Yu","submitted_at":"2024-02-22T10:26:46Z","abstract_excerpt":"We study the problem of sampling from a target probability density function in frameworks where parallel evaluations of the log-density gradient are feasible. Focusing on smooth and strongly log-concave densities, we revisit the parallelized randomized midpoint method and investigate its properties using recently developed techniques for analyzing its sequential version. Through these techniques, we derive upper bounds on the Wasserstein distance between sampling and target densities. These bounds quantify the substantial runtime improvements achieved through parallel processing."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14434","kind":"arxiv","version":4},"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/2402.14434/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:58:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B3PJcpsPgWMybjGUV9MCfBGYju/9CI/0G4jNqoUiWSmRrnJe6I88pDqOnFBTtt6Y+njJvPX4TT6LABAg9NGnBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T06:16:04.021927Z"},"content_sha256":"9ceffac6f7125a7361721c27058fc9901746f4c43bc84d0b3dccef527268bc1c","schema_version":"1.0","event_id":"sha256:9ceffac6f7125a7361721c27058fc9901746f4c43bc84d0b3dccef527268bc1c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/bundle.json","state_url":"https://pith.science/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T06:16:04Z","links":{"resolver":"https://pith.science/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ","bundle":"https://pith.science/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/bundle.json","state":"https://pith.science/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T7QCQ3F7BHITZISOWSAJFMRYZJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:T7QCQ3F7BHITZISOWSAJFMRYZJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f7b530df887b6da16500cfacb76b3190fff3ff348e7c3eebe09ce13bf8fa125e","cross_cats_sorted":["cs.LG","math.PR","stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-02-22T10:26:46Z","title_canon_sha256":"678433f5657ae2a87b874219976abe095bfaa05597a569086b5691901c41c4e1"},"schema_version":"1.0","source":{"id":"2402.14434","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14434","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14434v4","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14434","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_12","alias_value":"T7QCQ3F7BHIT","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_16","alias_value":"T7QCQ3F7BHITZISO","created_at":"2026-07-05T09:58:18Z"},{"alias_kind":"pith_short_8","alias_value":"T7QCQ3F7","created_at":"2026-07-05T09:58:18Z"}],"graph_snapshots":[{"event_id":"sha256:9ceffac6f7125a7361721c27058fc9901746f4c43bc84d0b3dccef527268bc1c","target":"graph","created_at":"2026-07-05T09:58:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.14434/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the problem of sampling from a target probability density function in frameworks where parallel evaluations of the log-density gradient are feasible. Focusing on smooth and strongly log-concave densities, we revisit the parallelized randomized midpoint method and investigate its properties using recently developed techniques for analyzing its sequential version. Through these techniques, we derive upper bounds on the Wasserstein distance between sampling and target densities. These bounds quantify the substantial runtime improvements achieved through parallel processing.","authors_text":"Arnak Dalalyan, Lu Yu","cross_cats":["cs.LG","math.PR","stat.CO","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-02-22T10:26:46Z","title":"Parallelized Midpoint Randomization for Langevin Monte Carlo"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14434","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a4c7caf3d7da98f1f799ec89aae6a192a30a37a55279beda9e409c69e8bddbbd","target":"record","created_at":"2026-07-05T09:58:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f7b530df887b6da16500cfacb76b3190fff3ff348e7c3eebe09ce13bf8fa125e","cross_cats_sorted":["cs.LG","math.PR","stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-02-22T10:26:46Z","title_canon_sha256":"678433f5657ae2a87b874219976abe095bfaa05597a569086b5691901c41c4e1"},"schema_version":"1.0","source":{"id":"2402.14434","kind":"arxiv","version":4}},"canonical_sha256":"9fe0286cbf09d13ca24eb48092b238ca7bd8da952353f9d83b93928ff531114e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9fe0286cbf09d13ca24eb48092b238ca7bd8da952353f9d83b93928ff531114e","first_computed_at":"2026-07-05T09:58:18.938151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:18.938151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"03hlAdCuaWgMuPbhGrhRHm+bUpmY9nwjpA84tkyFTiWX2cCtK14o0TsTVH669dNPi4UD4+LNPXs8pcJPRi54AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:18.938582Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.14434","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4c7caf3d7da98f1f799ec89aae6a192a30a37a55279beda9e409c69e8bddbbd","sha256:9ceffac6f7125a7361721c27058fc9901746f4c43bc84d0b3dccef527268bc1c"],"state_sha256":"f1f3623cdff4bfe0e566bc2c29ce4db86d8b300e6e01e4292f357d8593d112a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h5bcEltFm1R++nlesiEvopoVXgMOMNWfjbaG5XCblmmjgyHqdlPFthEgpcmwTT630712BSd+WYtPZ/FnJ+ESDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T06:16:04.039432Z","bundle_sha256":"34c76195b580d2e3c11d4fced0afbe770053ee2bac7f825ca7de4bab047c5822"}}