{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TBL7K3DYIFTND47ASGN6GKXY4J","short_pith_number":"pith:TBL7K3DY","canonical_record":{"source":{"id":"2509.02197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-02T11:09:45Z","cross_cats_sorted":["cs.PF","cs.PL"],"title_canon_sha256":"24308387f4cb2add384c55cf8199a8ae929952e8eb338acaf32d0a953ab2c56f","abstract_canon_sha256":"262939c20696bc20b5f61b0ae8a6995047629952448a5ef299e2b3e952586258"},"schema_version":"1.0"},"canonical_sha256":"9857f56c784166d1f3e0919be32af8e279dc485c74b7e18610b59eff3974cfa4","source":{"kind":"arxiv","id":"2509.02197","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.02197","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"arxiv_version","alias_value":"2509.02197v1","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02197","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_12","alias_value":"TBL7K3DYIFTN","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_16","alias_value":"TBL7K3DYIFTND47A","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_8","alias_value":"TBL7K3DY","created_at":"2026-07-05T12:03:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TBL7K3DYIFTND47ASGN6GKXY4J","target":"record","payload":{"canonical_record":{"source":{"id":"2509.02197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-02T11:09:45Z","cross_cats_sorted":["cs.PF","cs.PL"],"title_canon_sha256":"24308387f4cb2add384c55cf8199a8ae929952e8eb338acaf32d0a953ab2c56f","abstract_canon_sha256":"262939c20696bc20b5f61b0ae8a6995047629952448a5ef299e2b3e952586258"},"schema_version":"1.0"},"canonical_sha256":"9857f56c784166d1f3e0919be32af8e279dc485c74b7e18610b59eff3974cfa4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:33.795433Z","signature_b64":"pOSwn9XLTfd/RTgc7EYhWvouxdZk6fYdYX/BHOV3uQf9Rtd0r1Ie5jZQogEoYazqc+2K8+715FS8tTFQ0/0/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9857f56c784166d1f3e0919be32af8e279dc485c74b7e18610b59eff3974cfa4","last_reissued_at":"2026-07-05T12:03:33.794983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:33.794983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.02197","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":"2026-07-05T12:03:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W1eQLrqF915ai1XQsMDl+RCdeDMy7W/GjQwvwm3PC91RWxJQzBOllQeMYPA+jOWCUb4wKYiX2Z2QtL5VN3ITDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:53:46.965656Z"},"content_sha256":"777e30cb2f4691041ea0961f7831bd20b20c766f4c324785265682aecbd6e6fa","schema_version":"1.0","event_id":"sha256:777e30cb2f4691041ea0961f7831bd20b20c766f4c324785265682aecbd6e6fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TBL7K3DYIFTND47ASGN6GKXY4J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF","cs.PL"],"primary_cat":"cs.LG","authors_text":"Afif Boudaoud, Alexandru Calotoiu, Marcin Copik, Torsten Hoefler","submitted_at":"2025-09-02T11:09:45Z","abstract_excerpt":"Automatic differentiation (AD) is a set of techniques that systematically applies the chain rule to compute the gradients of functions without requiring human intervention. Although the fundamentals of this technology were established decades ago, it is experiencing a renaissance as it plays a key role in efficiently computing gradients for backpropagation in machine learning algorithms. AD is also crucial for many applications in scientific computing domains, particularly emerging techniques that integrate machine learning models within scientific simulations and schemes. Existing AD framewor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02197","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/2509.02197/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":"2026-07-05T12:03:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0wvAOU2RaAsmceHX7/4ivsClRmooEhJfuLEHvhA95Vpkf4xXFYgHvvNBd9CRVMU34xR4lLRpdodVa+DFb0bVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:53:46.966387Z"},"content_sha256":"1763a0292c308fd5fa758bf44ba70ee4cb89eb00c99ea61ac2eedf6e40b715e5","schema_version":"1.0","event_id":"sha256:1763a0292c308fd5fa758bf44ba70ee4cb89eb00c99ea61ac2eedf6e40b715e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TBL7K3DYIFTND47ASGN6GKXY4J/bundle.json","state_url":"https://pith.science/pith/TBL7K3DYIFTND47ASGN6GKXY4J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TBL7K3DYIFTND47ASGN6GKXY4J/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":"2026-08-08T12:53:46Z","links":{"resolver":"https://pith.science/pith/TBL7K3DYIFTND47ASGN6GKXY4J","bundle":"https://pith.science/pith/TBL7K3DYIFTND47ASGN6GKXY4J/bundle.json","state":"https://pith.science/pith/TBL7K3DYIFTND47ASGN6GKXY4J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TBL7K3DYIFTND47ASGN6GKXY4J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TBL7K3DYIFTND47ASGN6GKXY4J","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":"262939c20696bc20b5f61b0ae8a6995047629952448a5ef299e2b3e952586258","cross_cats_sorted":["cs.PF","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-02T11:09:45Z","title_canon_sha256":"24308387f4cb2add384c55cf8199a8ae929952e8eb338acaf32d0a953ab2c56f"},"schema_version":"1.0","source":{"id":"2509.02197","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.02197","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"arxiv_version","alias_value":"2509.02197v1","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02197","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_12","alias_value":"TBL7K3DYIFTN","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_16","alias_value":"TBL7K3DYIFTND47A","created_at":"2026-07-05T12:03:33Z"},{"alias_kind":"pith_short_8","alias_value":"TBL7K3DY","created_at":"2026-07-05T12:03:33Z"}],"graph_snapshots":[{"event_id":"sha256:1763a0292c308fd5fa758bf44ba70ee4cb89eb00c99ea61ac2eedf6e40b715e5","target":"graph","created_at":"2026-07-05T12:03:33Z","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/2509.02197/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic differentiation (AD) is a set of techniques that systematically applies the chain rule to compute the gradients of functions without requiring human intervention. Although the fundamentals of this technology were established decades ago, it is experiencing a renaissance as it plays a key role in efficiently computing gradients for backpropagation in machine learning algorithms. AD is also crucial for many applications in scientific computing domains, particularly emerging techniques that integrate machine learning models within scientific simulations and schemes. Existing AD framewor","authors_text":"Afif Boudaoud, Alexandru Calotoiu, Marcin Copik, Torsten Hoefler","cross_cats":["cs.PF","cs.PL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02197","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":"sha256:777e30cb2f4691041ea0961f7831bd20b20c766f4c324785265682aecbd6e6fa","target":"record","created_at":"2026-07-05T12:03:33Z","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":"262939c20696bc20b5f61b0ae8a6995047629952448a5ef299e2b3e952586258","cross_cats_sorted":["cs.PF","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-02T11:09:45Z","title_canon_sha256":"24308387f4cb2add384c55cf8199a8ae929952e8eb338acaf32d0a953ab2c56f"},"schema_version":"1.0","source":{"id":"2509.02197","kind":"arxiv","version":1}},"canonical_sha256":"9857f56c784166d1f3e0919be32af8e279dc485c74b7e18610b59eff3974cfa4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9857f56c784166d1f3e0919be32af8e279dc485c74b7e18610b59eff3974cfa4","first_computed_at":"2026-07-05T12:03:33.794983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:03:33.794983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pOSwn9XLTfd/RTgc7EYhWvouxdZk6fYdYX/BHOV3uQf9Rtd0r1Ie5jZQogEoYazqc+2K8+715FS8tTFQ0/0/DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:03:33.795433Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.02197","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:777e30cb2f4691041ea0961f7831bd20b20c766f4c324785265682aecbd6e6fa","sha256:1763a0292c308fd5fa758bf44ba70ee4cb89eb00c99ea61ac2eedf6e40b715e5"],"state_sha256":"4131fcc73687c2a2d5fddff3e3f02c94144259d497562b8ce99c3ca4a19ca1d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"te3c6bpvDDL2t+Bsu+cixxEiplmK9ZkocHlG6apjRwQS4OIhRMhx1z+ooqhyBm/wP3ownPQ8npkU1SCEENu1BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:53:46.971767Z","bundle_sha256":"e334c12cf2d8f53bc19c846d8943154b6abc7daec4c345fe2c4a42aa50980eaa"}}