{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4TSUSIYCJRRDK4G2BG5DH4KZ25","short_pith_number":"pith:4TSUSIYC","canonical_record":{"source":{"id":"2502.15015","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T20:11:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cc404115e7146127fe848c5a2a0beac6d443998905d690629866e41e4d0de691","abstract_canon_sha256":"ba5bd3d3fa58103d80ba68f878c11f7b4889f6406b4149366039d90effae92c7"},"schema_version":"1.0"},"canonical_sha256":"e4e54923024c623570da09ba33f159d765b064e239c3490d89cb278288f7761b","source":{"kind":"arxiv","id":"2502.15015","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15015","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15015v1","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15015","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"4TSUSIYCJRRD","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_16","alias_value":"4TSUSIYCJRRDK4G2","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_8","alias_value":"4TSUSIYC","created_at":"2026-07-05T10:17:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4TSUSIYCJRRDK4G2BG5DH4KZ25","target":"record","payload":{"canonical_record":{"source":{"id":"2502.15015","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T20:11:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cc404115e7146127fe848c5a2a0beac6d443998905d690629866e41e4d0de691","abstract_canon_sha256":"ba5bd3d3fa58103d80ba68f878c11f7b4889f6406b4149366039d90effae92c7"},"schema_version":"1.0"},"canonical_sha256":"e4e54923024c623570da09ba33f159d765b064e239c3490d89cb278288f7761b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:38.224883Z","signature_b64":"XPU0U3iMo/3Xr+1WP+8/0a2wTw9M2y/Lo5uZX12G2H1rdYTyi5Sjk5LnHfVQugJdq7T+TpUmrHqkAJah60ELBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4e54923024c623570da09ba33f159d765b064e239c3490d89cb278288f7761b","last_reissued_at":"2026-07-05T10:17:38.224376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:38.224376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.15015","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":"2026-07-05T10:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EkBnw7dxbhb/oN2yVWLNzU0Um76DTUBztRFFyDXZjBCTq9lwepgtJaDvm29t6yb+EQFVXUeFnt8ACfU2q5fXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:36:56.536380Z"},"content_sha256":"1403fc1e037d2af857fe7aa8843017694a5895142c73c1d33fd957a2575f5512","schema_version":"1.0","event_id":"sha256:1403fc1e037d2af857fe7aa8843017694a5895142c73c1d33fd957a2575f5512"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4TSUSIYCJRRDK4G2BG5DH4KZ25","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Boyuan Feng, Chandramouli Shama Sastry, Edward Z. Yang, Frank Schneider, George E. Dahl, Juhan Bae, Less Wright, Mark Saroufim, Michael Rabbat, Philipp Hennig, Priya Kasimbeg, Runa Eschenhagen, Sourabh Medapati, Zachary Nado","submitted_at":"2025-02-20T20:11:54Z","abstract_excerpt":"The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to-result across multiple deep learning workloads, training on fixed hardware. This paper presents the inaugural AlgoPerf competition's results, which drew 18 diverse submissions fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15015","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/2502.15015/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":"2026-07-05T10:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q2JFB9rKITGZJE8TZGA6JUKEY7reoNQb3ZyXptFEkFr3Ki5Iw9rN3Kj9DJkmWSNkcjQfF3LMmbbbcKnhRLgLDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:36:56.536753Z"},"content_sha256":"45885178fde631591d29a71ba9f5666aa4d5b4f59984227d354de8cc23e70683","schema_version":"1.0","event_id":"sha256:45885178fde631591d29a71ba9f5666aa4d5b4f59984227d354de8cc23e70683"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/bundle.json","state_url":"https://pith.science/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/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":"2026-08-03T22:36:56Z","links":{"resolver":"https://pith.science/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25","bundle":"https://pith.science/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/bundle.json","state":"https://pith.science/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4TSUSIYCJRRDK4G2BG5DH4KZ25/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4TSUSIYCJRRDK4G2BG5DH4KZ25","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":"ba5bd3d3fa58103d80ba68f878c11f7b4889f6406b4149366039d90effae92c7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T20:11:54Z","title_canon_sha256":"cc404115e7146127fe848c5a2a0beac6d443998905d690629866e41e4d0de691"},"schema_version":"1.0","source":{"id":"2502.15015","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15015","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15015v1","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15015","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"4TSUSIYCJRRD","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_16","alias_value":"4TSUSIYCJRRDK4G2","created_at":"2026-07-05T10:17:38Z"},{"alias_kind":"pith_short_8","alias_value":"4TSUSIYC","created_at":"2026-07-05T10:17:38Z"}],"graph_snapshots":[{"event_id":"sha256:45885178fde631591d29a71ba9f5666aa4d5b4f59984227d354de8cc23e70683","target":"graph","created_at":"2026-07-05T10:17:38Z","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/2502.15015/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to-result across multiple deep learning workloads, training on fixed hardware. This paper presents the inaugural AlgoPerf competition's results, which drew 18 diverse submissions fr","authors_text":"Boyuan Feng, Chandramouli Shama Sastry, Edward Z. Yang, Frank Schneider, George E. Dahl, Juhan Bae, Less Wright, Mark Saroufim, Michael Rabbat, Philipp Hennig, Priya Kasimbeg, Runa Eschenhagen, Sourabh Medapati, Zachary Nado","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T20:11:54Z","title":"Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15015","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":"sha256:1403fc1e037d2af857fe7aa8843017694a5895142c73c1d33fd957a2575f5512","target":"record","created_at":"2026-07-05T10:17:38Z","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":"ba5bd3d3fa58103d80ba68f878c11f7b4889f6406b4149366039d90effae92c7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T20:11:54Z","title_canon_sha256":"cc404115e7146127fe848c5a2a0beac6d443998905d690629866e41e4d0de691"},"schema_version":"1.0","source":{"id":"2502.15015","kind":"arxiv","version":1}},"canonical_sha256":"e4e54923024c623570da09ba33f159d765b064e239c3490d89cb278288f7761b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4e54923024c623570da09ba33f159d765b064e239c3490d89cb278288f7761b","first_computed_at":"2026-07-05T10:17:38.224376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:17:38.224376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XPU0U3iMo/3Xr+1WP+8/0a2wTw9M2y/Lo5uZX12G2H1rdYTyi5Sjk5LnHfVQugJdq7T+TpUmrHqkAJah60ELBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:17:38.224883Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.15015","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1403fc1e037d2af857fe7aa8843017694a5895142c73c1d33fd957a2575f5512","sha256:45885178fde631591d29a71ba9f5666aa4d5b4f59984227d354de8cc23e70683"],"state_sha256":"495675a8064e8b9b27f1802f9001258e3a61876cf23a8868db88ef17edc79c76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"50brt0z/EQHoB6djZWl+jycbDvbBFEaFqqjFjReZP6aSyWFxPesHB4gZ64sz9AYXnUOTBSpKBj7bG1H/hK3zCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T22:36:56.539256Z","bundle_sha256":"7b1f8ed4ef9d7276604fd9ba2542bb8c0374e1fa62227bb9eccc7b50aa7a7a5e"}}