{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YII4AGPI5X4DXQ7MSF2CGUCY4P","short_pith_number":"pith:YII4AGPI","canonical_record":{"source":{"id":"2310.10879","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T23:14:56Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"28ad7ba8f5783f84797190e1ede831ad2b774ad3681ade34bd4bcad62e8b1a5c","abstract_canon_sha256":"da5c8ef01266ffe7ef12d7f3fc5e4dff1b09c7138ff50d35510d1e06967f7665"},"schema_version":"1.0"},"canonical_sha256":"c211c019e8edf83bc3ec9174235058e3f99dcfa65d084b7d3c106713836a0f3b","source":{"kind":"arxiv","id":"2310.10879","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10879","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10879v2","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10879","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_12","alias_value":"YII4AGPI5X4D","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_16","alias_value":"YII4AGPI5X4DXQ7M","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_8","alias_value":"YII4AGPI","created_at":"2026-07-05T08:12:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YII4AGPI5X4DXQ7MSF2CGUCY4P","target":"record","payload":{"canonical_record":{"source":{"id":"2310.10879","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T23:14:56Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"28ad7ba8f5783f84797190e1ede831ad2b774ad3681ade34bd4bcad62e8b1a5c","abstract_canon_sha256":"da5c8ef01266ffe7ef12d7f3fc5e4dff1b09c7138ff50d35510d1e06967f7665"},"schema_version":"1.0"},"canonical_sha256":"c211c019e8edf83bc3ec9174235058e3f99dcfa65d084b7d3c106713836a0f3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:14.321511Z","signature_b64":"xehFEeXgVzBa5wdlzE6rRmt4Oguoe9tlSgeEwW7ueArdlcE94jhzMVIaDyUTjdpxTt405vIQq5DQU/TW/N2wBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c211c019e8edf83bc3ec9174235058e3f99dcfa65d084b7d3c106713836a0f3b","last_reissued_at":"2026-07-05T08:12:14.321064Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:14.321064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.10879","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":"2026-07-05T08:12:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JyAYmg7oQWjnyof6ZZUFXaMrI5diFN8+vR30h+dFpn4DRIG4th9SVsAcrMVLw9dUVFCI+jBpEegF4wzsMGHeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:51:38.636760Z"},"content_sha256":"c14f01a490c895ac34134b9b6fe40c09f16962924694e4c382236c38245c46b8","schema_version":"1.0","event_id":"sha256:c14f01a490c895ac34134b9b6fe40c09f16962924694e4c382236c38245c46b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YII4AGPI5X4DXQ7MSF2CGUCY4P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"A. S. M. Iftekhar, B. S. Manjunath, Raphael Ruschel, Suya You","submitted_at":"2023-10-16T23:14:56Z","abstract_excerpt":"The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resultin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10879","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/2310.10879/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":"2026-07-05T08:12:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UtEVFijWReTJwC11wc+3D4GTW3XlQw6qsffmtGm1M/smjksSayz8B8I/CKwLKlOh+AAKQsufEi60xRaN+pojDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:51:38.637137Z"},"content_sha256":"1d111df85e170c4e5e749ab419aba78f234cc24d3200a3a27f0576c39abb44cb","schema_version":"1.0","event_id":"sha256:1d111df85e170c4e5e749ab419aba78f234cc24d3200a3a27f0576c39abb44cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/bundle.json","state_url":"https://pith.science/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/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":"2026-07-09T06:51:38Z","links":{"resolver":"https://pith.science/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P","bundle":"https://pith.science/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/bundle.json","state":"https://pith.science/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YII4AGPI5X4DXQ7MSF2CGUCY4P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YII4AGPI5X4DXQ7MSF2CGUCY4P","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":"da5c8ef01266ffe7ef12d7f3fc5e4dff1b09c7138ff50d35510d1e06967f7665","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T23:14:56Z","title_canon_sha256":"28ad7ba8f5783f84797190e1ede831ad2b774ad3681ade34bd4bcad62e8b1a5c"},"schema_version":"1.0","source":{"id":"2310.10879","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10879","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10879v2","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10879","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_12","alias_value":"YII4AGPI5X4D","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_16","alias_value":"YII4AGPI5X4DXQ7M","created_at":"2026-07-05T08:12:14Z"},{"alias_kind":"pith_short_8","alias_value":"YII4AGPI","created_at":"2026-07-05T08:12:14Z"}],"graph_snapshots":[{"event_id":"sha256:1d111df85e170c4e5e749ab419aba78f234cc24d3200a3a27f0576c39abb44cb","target":"graph","created_at":"2026-07-05T08:12:14Z","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/2310.10879/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resultin","authors_text":"A. S. M. Iftekhar, B. S. Manjunath, Raphael Ruschel, Suya You","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T23:14:56Z","title":"BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10879","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:c14f01a490c895ac34134b9b6fe40c09f16962924694e4c382236c38245c46b8","target":"record","created_at":"2026-07-05T08:12:14Z","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":"da5c8ef01266ffe7ef12d7f3fc5e4dff1b09c7138ff50d35510d1e06967f7665","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T23:14:56Z","title_canon_sha256":"28ad7ba8f5783f84797190e1ede831ad2b774ad3681ade34bd4bcad62e8b1a5c"},"schema_version":"1.0","source":{"id":"2310.10879","kind":"arxiv","version":2}},"canonical_sha256":"c211c019e8edf83bc3ec9174235058e3f99dcfa65d084b7d3c106713836a0f3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c211c019e8edf83bc3ec9174235058e3f99dcfa65d084b7d3c106713836a0f3b","first_computed_at":"2026-07-05T08:12:14.321064Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:12:14.321064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xehFEeXgVzBa5wdlzE6rRmt4Oguoe9tlSgeEwW7ueArdlcE94jhzMVIaDyUTjdpxTt405vIQq5DQU/TW/N2wBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:12:14.321511Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10879","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c14f01a490c895ac34134b9b6fe40c09f16962924694e4c382236c38245c46b8","sha256:1d111df85e170c4e5e749ab419aba78f234cc24d3200a3a27f0576c39abb44cb"],"state_sha256":"6fe0e66441f751774ec780ccf271f4efc0330180afdb5b6612373903bf9f28c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ot9Mwr28QWRIQCbJNLSlv/LPaiOQN1SAPDskAv/5Fv8JfljOhs1ceXqndweJmqViIWz+Z16JmZFB5RNN2IhACw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:51:38.639525Z","bundle_sha256":"b73c2477e05dce2251396a879fdd1f32ded7f3a098b23dc748cc360b7941aeae"}}