{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:JL4FJRL5ZEAAXN5JZRWV4VNJXV","short_pith_number":"pith:JL4FJRL5","canonical_record":{"source":{"id":"2308.02123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T03:51:38Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"de5cfd250681aa96b04ed95a6b9e5a8a8d6bcf859baaf86572930f5c3a2a0cb0","abstract_canon_sha256":"b3c3b0934ec1b57a966a190ca3bd1c3cb7bbbf0ee61b573d298748f3d6db8b37"},"schema_version":"1.0"},"canonical_sha256":"4af854c57dc9000bb7a9cc6d5e55a9bd5f56847cbd6848ebd20204ecd10b3acd","source":{"kind":"arxiv","id":"2308.02123","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.02123","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"arxiv_version","alias_value":"2308.02123v1","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02123","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_12","alias_value":"JL4FJRL5ZEAA","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_16","alias_value":"JL4FJRL5ZEAAXN5J","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_8","alias_value":"JL4FJRL5","created_at":"2026-07-05T06:37:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:JL4FJRL5ZEAAXN5JZRWV4VNJXV","target":"record","payload":{"canonical_record":{"source":{"id":"2308.02123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T03:51:38Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"de5cfd250681aa96b04ed95a6b9e5a8a8d6bcf859baaf86572930f5c3a2a0cb0","abstract_canon_sha256":"b3c3b0934ec1b57a966a190ca3bd1c3cb7bbbf0ee61b573d298748f3d6db8b37"},"schema_version":"1.0"},"canonical_sha256":"4af854c57dc9000bb7a9cc6d5e55a9bd5f56847cbd6848ebd20204ecd10b3acd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:38.031243Z","signature_b64":"WrTthLeo9KO5ZY/miw2e/TGQyh6djI+6vQa9Ka7bAjFa1tNnEyWJ9ALnCsCML42ULaLUKPcXxn7I9RUXY0FdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4af854c57dc9000bb7a9cc6d5e55a9bd5f56847cbd6848ebd20204ecd10b3acd","last_reissued_at":"2026-07-05T06:37:38.030801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:38.030801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.02123","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:37:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uZ4HZBf+055RoKVlNxJH7XTPb4+gd+Ges7IIXJPYJIl5byUAqWbSKlLBhqZ+xVdkQaLhvxAEZXMzizZNPCm5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:15:02.091193Z"},"content_sha256":"c032f1d903a4bc6e81c8956501623459ab075bb0c8a3b5073bd9e20eab70c951","schema_version":"1.0","event_id":"sha256:c032f1d903a4bc6e81c8956501623459ab075bb0c8a3b5073bd9e20eab70c951"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:JL4FJRL5ZEAAXN5JZRWV4VNJXV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Eva: A General Vectorized Approximation Framework for Second-order Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Bo Li, Lin Zhang, Shaohuai Shi","submitted_at":"2023-08-04T03:51:38Z","abstract_excerpt":"Second-order optimization algorithms exhibit excellent convergence properties for training deep learning models, but often incur significant computation and memory overheads. This can result in lower training efficiency than the first-order counterparts such as stochastic gradient descent (SGD). In this work, we present a memory- and time-efficient second-order algorithm named Eva with two novel techniques: 1) we construct the second-order information with the Kronecker factorization of small stochastic vectors over a mini-batch of training data to reduce memory consumption, and 2) we derive a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02123","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/2308.02123/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":"2026-07-05T06:37:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PSCdtbkbg256tHKnBxzjqBuuiNnkpuZ2PHRT9q51v2kUeX7/I1e7oHkYxSsNFMbPao7EwdA3UYqpBcROFEmsCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:15:02.091707Z"},"content_sha256":"bd3a6c8023f5c3b165d091362e7b5748984afeb50429f24d7237ad658210de21","schema_version":"1.0","event_id":"sha256:bd3a6c8023f5c3b165d091362e7b5748984afeb50429f24d7237ad658210de21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/bundle.json","state_url":"https://pith.science/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/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":"2026-08-11T09:15:02Z","links":{"resolver":"https://pith.science/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV","bundle":"https://pith.science/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/bundle.json","state":"https://pith.science/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JL4FJRL5ZEAAXN5JZRWV4VNJXV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:JL4FJRL5ZEAAXN5JZRWV4VNJXV","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":"b3c3b0934ec1b57a966a190ca3bd1c3cb7bbbf0ee61b573d298748f3d6db8b37","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T03:51:38Z","title_canon_sha256":"de5cfd250681aa96b04ed95a6b9e5a8a8d6bcf859baaf86572930f5c3a2a0cb0"},"schema_version":"1.0","source":{"id":"2308.02123","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.02123","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"arxiv_version","alias_value":"2308.02123v1","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02123","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_12","alias_value":"JL4FJRL5ZEAA","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_16","alias_value":"JL4FJRL5ZEAAXN5J","created_at":"2026-07-05T06:37:38Z"},{"alias_kind":"pith_short_8","alias_value":"JL4FJRL5","created_at":"2026-07-05T06:37:38Z"}],"graph_snapshots":[{"event_id":"sha256:bd3a6c8023f5c3b165d091362e7b5748984afeb50429f24d7237ad658210de21","target":"graph","created_at":"2026-07-05T06:37:38Z","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/2308.02123/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Second-order optimization algorithms exhibit excellent convergence properties for training deep learning models, but often incur significant computation and memory overheads. This can result in lower training efficiency than the first-order counterparts such as stochastic gradient descent (SGD). In this work, we present a memory- and time-efficient second-order algorithm named Eva with two novel techniques: 1) we construct the second-order information with the Kronecker factorization of small stochastic vectors over a mini-batch of training data to reduce memory consumption, and 2) we derive a","authors_text":"Bo Li, Lin Zhang, Shaohuai Shi","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T03:51:38Z","title":"Eva: A General Vectorized Approximation Framework for Second-order Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02123","kind":"arxiv","version":1},"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:c032f1d903a4bc6e81c8956501623459ab075bb0c8a3b5073bd9e20eab70c951","target":"record","created_at":"2026-07-05T06:37:38Z","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":"b3c3b0934ec1b57a966a190ca3bd1c3cb7bbbf0ee61b573d298748f3d6db8b37","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T03:51:38Z","title_canon_sha256":"de5cfd250681aa96b04ed95a6b9e5a8a8d6bcf859baaf86572930f5c3a2a0cb0"},"schema_version":"1.0","source":{"id":"2308.02123","kind":"arxiv","version":1}},"canonical_sha256":"4af854c57dc9000bb7a9cc6d5e55a9bd5f56847cbd6848ebd20204ecd10b3acd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4af854c57dc9000bb7a9cc6d5e55a9bd5f56847cbd6848ebd20204ecd10b3acd","first_computed_at":"2026-07-05T06:37:38.030801Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:37:38.030801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WrTthLeo9KO5ZY/miw2e/TGQyh6djI+6vQa9Ka7bAjFa1tNnEyWJ9ALnCsCML42ULaLUKPcXxn7I9RUXY0FdAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:37:38.031243Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.02123","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c032f1d903a4bc6e81c8956501623459ab075bb0c8a3b5073bd9e20eab70c951","sha256:bd3a6c8023f5c3b165d091362e7b5748984afeb50429f24d7237ad658210de21"],"state_sha256":"1f9da464eda067f23621041f3b82f3d7e424d5deee10157dc61803a05aeb92cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lpc407Ry2yoYGp3ghiyAv7Jxbvu8p4F+coLOJ/GQrvlmtI5q3zkgrrMLDTqn0WdyKG1qAtQU3J1if/uKk8l1Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T09:15:02.096889Z","bundle_sha256":"9257fc2b2506f959521939e902f9480cdcaf54018fe155d196f24e6fda2dde31"}}