{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6LIYUUFCH67A5QTCH5QDNUBC7M","short_pith_number":"pith:6LIYUUFC","canonical_record":{"source":{"id":"1910.00643","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-01T20:06:48Z","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"title_canon_sha256":"14f08c8e4a1e8c3a2d752ecc2b8bc57df336747f240e2f45ff2bf53ccc223595","abstract_canon_sha256":"e2cba41f0dbe0f2dc190cc074dcbea4898e629dab3fad03a50e44582ec1b4209"},"schema_version":"1.0"},"canonical_sha256":"f2d18a50a23fbe0ec2623f6036d022fb3d64d7aca0884151514741590217bc5a","source":{"kind":"arxiv","id":"1910.00643","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.00643","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1910.00643v2","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.00643","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"6LIYUUFCH67A","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_16","alias_value":"6LIYUUFCH67A5QTC","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_8","alias_value":"6LIYUUFC","created_at":"2026-07-05T00:42:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6LIYUUFCH67A5QTCH5QDNUBC7M","target":"record","payload":{"canonical_record":{"source":{"id":"1910.00643","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-01T20:06:48Z","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"title_canon_sha256":"14f08c8e4a1e8c3a2d752ecc2b8bc57df336747f240e2f45ff2bf53ccc223595","abstract_canon_sha256":"e2cba41f0dbe0f2dc190cc074dcbea4898e629dab3fad03a50e44582ec1b4209"},"schema_version":"1.0"},"canonical_sha256":"f2d18a50a23fbe0ec2623f6036d022fb3d64d7aca0884151514741590217bc5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:42:32.496532Z","signature_b64":"EfWwK/7gZtaGG1+cT/kw5/HvbGR29UjaiczkJB7XHL9vrWWKelh/xqFJa3ktNZFTsYf7l+otqaPhdM629w5aBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2d18a50a23fbe0ec2623f6036d022fb3d64d7aca0884151514741590217bc5a","last_reissued_at":"2026-07-05T00:42:32.496047Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:42:32.496047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.00643","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mgkyPsar5eHC8Qch0hhVpxnvpYqgbNfEAox/0P1Yho+5uMd1F2rshCeGVx2KoOSwz/DjJh7+0QJO5AH+YkHYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:48:53.627239Z"},"content_sha256":"d43c13078dfd0bc53ec995606e322abb3e4f9424a573b1fa9502087c6f3a92d0","schema_version":"1.0","event_id":"sha256:d43c13078dfd0bc53ec995606e322abb3e4f9424a573b1fa9502087c6f3a92d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6LIYUUFCH67A5QTCH5QDNUBC7M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jianyu Wang, Michael Rabbat, Nicolas Ballas, Vinayak Tantia","submitted_at":"2019-10-01T20:06:48Z","abstract_excerpt":"Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.00643","kind":"arxiv","version":2},"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/1910.00643/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":"2026-07-05T00:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HMRqJxNk2T4E7MoEyuFsnmPnHOl6bzUWUor0bANrgc4ZkH9BV/jfm+U0WJSjzT23cUGp9g9JCkhjeHQWHS1pBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:48:53.627733Z"},"content_sha256":"b657d6e3bfd8d9dea978fbb0de1fda0a15bdc0ad700acdd4707bc5e20d49f445","schema_version":"1.0","event_id":"sha256:b657d6e3bfd8d9dea978fbb0de1fda0a15bdc0ad700acdd4707bc5e20d49f445"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/bundle.json","state_url":"https://pith.science/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/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":"2026-08-06T14:48:53Z","links":{"resolver":"https://pith.science/pith/6LIYUUFCH67A5QTCH5QDNUBC7M","bundle":"https://pith.science/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/bundle.json","state":"https://pith.science/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6LIYUUFCH67A5QTCH5QDNUBC7M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6LIYUUFCH67A5QTCH5QDNUBC7M","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":"e2cba41f0dbe0f2dc190cc074dcbea4898e629dab3fad03a50e44582ec1b4209","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-01T20:06:48Z","title_canon_sha256":"14f08c8e4a1e8c3a2d752ecc2b8bc57df336747f240e2f45ff2bf53ccc223595"},"schema_version":"1.0","source":{"id":"1910.00643","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.00643","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1910.00643v2","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.00643","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"6LIYUUFCH67A","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_16","alias_value":"6LIYUUFCH67A5QTC","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_8","alias_value":"6LIYUUFC","created_at":"2026-07-05T00:42:32Z"}],"graph_snapshots":[{"event_id":"sha256:b657d6e3bfd8d9dea978fbb0de1fda0a15bdc0ad700acdd4707bc5e20d49f445","target":"graph","created_at":"2026-07-05T00:42:32Z","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/1910.00643/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we","authors_text":"Jianyu Wang, Michael Rabbat, Nicolas Ballas, Vinayak Tantia","cross_cats":["cs.DC","math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-01T20:06:48Z","title":"SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.00643","kind":"arxiv","version":2},"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:d43c13078dfd0bc53ec995606e322abb3e4f9424a573b1fa9502087c6f3a92d0","target":"record","created_at":"2026-07-05T00:42:32Z","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":"e2cba41f0dbe0f2dc190cc074dcbea4898e629dab3fad03a50e44582ec1b4209","cross_cats_sorted":["cs.DC","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-01T20:06:48Z","title_canon_sha256":"14f08c8e4a1e8c3a2d752ecc2b8bc57df336747f240e2f45ff2bf53ccc223595"},"schema_version":"1.0","source":{"id":"1910.00643","kind":"arxiv","version":2}},"canonical_sha256":"f2d18a50a23fbe0ec2623f6036d022fb3d64d7aca0884151514741590217bc5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2d18a50a23fbe0ec2623f6036d022fb3d64d7aca0884151514741590217bc5a","first_computed_at":"2026-07-05T00:42:32.496047Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:42:32.496047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EfWwK/7gZtaGG1+cT/kw5/HvbGR29UjaiczkJB7XHL9vrWWKelh/xqFJa3ktNZFTsYf7l+otqaPhdM629w5aBg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:42:32.496532Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.00643","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d43c13078dfd0bc53ec995606e322abb3e4f9424a573b1fa9502087c6f3a92d0","sha256:b657d6e3bfd8d9dea978fbb0de1fda0a15bdc0ad700acdd4707bc5e20d49f445"],"state_sha256":"f47f4fb250a9bf56f04de13fc27f9a9e5d8234da7f50ac594137aa55544f7f2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bpmj0ChYVoAR90RtjmVZbOp9Zybf4jx8TJOpnPrW5gaaEglXi5Hwt+55F5hwcObe3r4yvEYoCaVSItxuyxUCBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T14:48:53.630768Z","bundle_sha256":"5ed0c2b2c7954979d34db6b87ea4b29c51230460afa7b384a8ad44516dec9550"}}