{"as_of":"2026-08-08T12:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a3bb607c3ced4618520f42b512f28c46fdf70413a3b0fc418ae8a03840812147","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:52:15.482880Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.02197/citation-record","integrity":"/paper/2509.02197/integrity","json":"/paper/2509.02197/citation-record.json","paper":"/paper/2509.02197"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:13.450929Z","title":"Naumann, The Art of Differentiating Computer Programs","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:13.450929Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f63d96126cc54da17cbb4f6361b71ad81cfb21ef562aba88bdddbaed98a0f634","observation_id":"7fb51daf-a286-451e-87ea-892a392707f0","resolution":{"observed_at":"2026-08-05T11:52:13.450929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:13.571096Z","title":"A review of automatic differentiation and its efficient implementation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:13.571096Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:e0618dfb98a1369f78022dee84de93f5686c93e76700493881c2b9458b18f562","observation_id":"6684147e-4670-4148-8824-ba46e5d3a835","resolution":{"observed_at":"2026-08-05T11:52:13.571096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.976850Z","title":"Learning representations by back-propagating errors,","venue":null,"work_id":"cc77f5f7-0250-4032-b698-d3c37a8eb1f2","year":1986},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:13.742481Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:9ebd2e1e9e39a202eac87d499a478c906055eacdf7ea3c3f17e7b9a9dbcabe10","observation_id":"2b92cea5-e18b-41f7-b76b-2baa6f27dfa8","resolution":{"observed_at":"2026-08-05T11:52:17.983130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.949907Z","title":"30 years of adaptive neural networks: perceptron, madaline, and backpropagation,","venue":null,"work_id":"a851fbcb-e26e-4165-8ae6-9cb40e07a38d","year":1990},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:13.799658Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:cf7bb88692a4e7f89b8d454cb3af9771e2c6f3342a3ac27c8c8b48cc019f73ff","observation_id":"10a9849d-42c1-4eca-b66c-4184e79bec81","resolution":{"observed_at":"2026-08-05T11:52:17.956557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-05T11:52:14.093502Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.093502Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:83d5ef20572d823ea4e9475dceadf85960e2fc77c29504b0b9e947c323b0f36f","observation_id":"a8aa85ee-4420-4d8d-9da7-2f5c9faa4fe9","resolution":{"observed_at":"2026-08-05T11:52:14.093502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.910941Z","title":"Identification and review of sensitivity analysis methods,","venue":null,"work_id":"c4603296-8c44-4110-9b80-cdae6d4c2970","year":2002},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.250873Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:8d522f41b057602300ee12cf14f990a9d796160e0625573d07a844da48a9fd10","observation_id":"6f4fcbe0-0ab5-4e0f-a1ec-b8d579395539","resolution":{"observed_at":"2026-08-05T11:52:17.921330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.881959Z","title":null,"venue":null,"work_id":"1113dbde-7459-4dbc-ae91-03b0c65c123b","year":1977},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.337115Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:ce5e7f3318e866d4a0ed642c5f1ab788067720cd83484e07a8ff4da45b698745","observation_id":"e83fb998-1fdb-4ede-b30e-afc0805f1904","resolution":{"observed_at":"2026-08-05T11:52:17.889625Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.852863Z","title":"Data assimilation concepts and methods march 1999,","venue":null,"work_id":"7268198b-cf7f-448f-b423-2c8ce7ae4e60","year":1999},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.444188Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:58f2268c60ccf012d8eb15785c0eb3fa8735cf5208bfabc7d08551765e5c3c74","observation_id":"e64596e0-a3fd-44dd-ad92-b1b8a2e27118","resolution":{"observed_at":"2026-08-05T11:52:17.862613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07222","last_updated":"2024-03-08T02:14:15Z","snapshot_observed_at":"2026-07-06T16:46:30.470666Z","submitted_at":"2023-11-13T10:40:17Z","title":"Neural General Circulation Models for Weather and Climate","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07222","snapshot_observed_at":"2026-08-05T11:52:14.536601Z","title":"Neural general circulation models for weather and climate,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.536601Z"},"links":{"cited_paper":"/paper/2311.07222","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:8b855a3d215f6e0f7243e0d933bab8ff2d23119d1ccb08371404b9dcd07c567c","observation_id":"808fa792-b39c-4ae1-8727-4d896af7b939","resolution":{"observed_at":"2026-08-05T11:52:14.536601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.827912Z","title":"Advances in weather prediction,","venue":null,"work_id":"eed1ddd6-8fd5-4f22-90f6-2834ec0b16d2","year":2019},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.667139Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:8591d72ebaa3a6d516df90fbf446370266863b352cdc81df8b08bc7375efcb54","observation_id":"813b660e-ef91-4075-a5c8-18805fb9591a","resolution":{"observed_at":"2026-08-05T11:52:17.834811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.797488Z","title":"Adifor 2.0: automatic differentiation of fortran 77 programs,","venue":null,"work_id":"b2eda099-f09d-4cf5-8f27-c8707d027f8f","year":1996},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:14.830883Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f883e830c0affda3dd9a39b96b791164351b79ca1efad13ba8f1cda9440dc33a","observation_id":"d6cd8330-8270-4a6d-aa04-e7b484270ff5","resolution":{"observed_at":"2026-08-05T11:52:17.806562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.768349Z","title":"Compiling machine learning programs via high-level tracing,","venue":null,"work_id":"a008bdcc-7175-4f26-b030-56fe62ceca68","year":2018},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.003167Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f1874e58df3412c02e4c69313ac587c8d6a5439cc1481aefa193fe437ccf378f","observation_id":"7e16b619-1690-4ca6-868f-a49bb541a828","resolution":{"observed_at":"2026-08-05T11:52:17.776203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.07587","last_updated":"2019-07-18T12:56:11Z","snapshot_observed_at":"2026-08-02T05:19:13.622327Z","submitted_at":"2019-07-17T15:35:04Z","title":"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.07587","snapshot_observed_at":"2026-08-05T11:52:15.158273Z","title":"A differentiable programming system to bridge machine learning and scientific computing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.158273Z"},"links":{"cited_paper":"/paper/1907.07587","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f54c5a4eef61eea847b753d1f79dd0dff6f93ae765e7f67691feda53b87e3630","observation_id":"0f23ed03-a921-4b74-9c1f-0995dc8dac14","resolution":{"observed_at":"2026-08-05T11:52:15.158273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.739609Z","title":"Instead of rewriting foreign code for ma- chine learning, automatically synthesize fast gradients,","venue":null,"work_id":"dc30cd20-e640-4630-8f42-2c8bca0a9bf4","year":2020},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.167744Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:cff833491f01a48cefef9aa7ac5a52b8605de6c90585175ad848b51467f6326d","observation_id":"577e2391-a275-4cbd-a5c5-c42ee4747085","resolution":{"observed_at":"2026-08-05T11:52:17.746595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.175895Z","title":"Automatic differentiation in pytorch,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.175895Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:3268de48f39db251ee2a939f3ed2eedfb3da73ad556f3681047e5af6510e909f","observation_id":"4404c9cd-2d5b-4b03-b5d9-b509b480c4ec","resolution":{"observed_at":"2026-08-05T11:52:15.175895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.197991Z","title":"Griewank and A","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.197991Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:82aebf53d125902ac46ea506179d86d7da40bced364072187ef4ac9df76802bd","observation_id":"4aa8a928-3f50-4649-b14c-56a0648e2de3","resolution":{"observed_at":"2026-08-05T11:52:15.197991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0606042","last_updated":"2006-06-09T16:01:46Z","snapshot_observed_at":"2026-07-07T03:08:02.689910Z","submitted_at":"2006-06-09T16:01:46Z","title":"Enabling user-driven Checkpointing strategies in Reverse-mode Automatic Differentiation","version":1},"cited_work":{"arxiv_id":"cs/0606042","doi":null,"metadata_source":"pith","pith_arxiv_id":"cs/0606042","snapshot_observed_at":"2026-08-05T11:52:17.096834Z","title":"Enabling user-driven Checkpointing strategies in Reverse-mode Automatic Differentiation","venue":"cs.DS","work_id":"25253ad9-bee4-4112-a093-7b45154463fb","year":2006},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.207232Z"},"links":{"cited_paper":"/paper/cs/0606042","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:a3ca4f10df84b7cb1e2c90760d7e0c5ae8f97bd963382048581c635785ead083","observation_id":"2264037b-5a06-49e9-a515-55a8f0de10d2","resolution":{"observed_at":"2026-08-05T11:52:17.117541Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0153.24501","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:16.930067Z","title":"The tapenade automatic differentiation tool: Principles, model, and specification,","venue":null,"work_id":"b73f1ac3-9c92-4418-bf7e-946b145360b9","year":2013},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.221378Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:4961631985eca61634eeca1ff7b471634f9487b7297722d523ed53a9a305d3ad","observation_id":"ec397770-73be-4d0c-ae57-6b15b8533c8c","resolution":{"observed_at":"2026-08-05T11:52:16.940101Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.699508Z","title":"Stateful dataflow multigraphs: A data-centric model for performance portability on heterogeneous architectures,","venue":null,"work_id":"71095c3f-0929-47f8-8041-4d3e1bb9a886","year":2019},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.230527Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:2d9e4d1162707923f79acebf4bff26a046767eec1d78cf4518b01fae081db3b6","observation_id":"aefb1365-7ce7-4939-95d5-f0580a9a845a","resolution":{"observed_at":"2026-08-05T11:52:17.707994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.674266Z","title":"Open neural network exchange (onnx),","venue":null,"work_id":"d604d1ac-56ba-435a-92ad-3e0792b7143c","year":2023},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.237235Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f218eb7f5f8f8f643bf1a95e4f16762c1710b8b6a3090c166d7e41079a06ec32","observation_id":"60482231-2bf8-4109-90d7-195f3e05180b","resolution":{"observed_at":"2026-08-05T11:52:17.684377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7818.34603","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:16.803645Z","title":"Npbench: A benchmarking suite for high-performance numpy,","venue":null,"work_id":"2da8858e-198c-48b4-9a8b-9880c9064551","year":2021},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.243601Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:661fd0c83cb1522d2b29a1a464d59829f6d074adeea6292c6b09a77dc5a40441","observation_id":"f63c47c6-e73c-4dc0-900e-6e4a661cf338","resolution":{"observed_at":"2026-08-05T11:52:16.818324Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10256","last_updated":"2020-06-18T03:39:27Z","snapshot_observed_at":"2026-07-06T09:30:17.443375Z","submitted_at":"2020-06-18T03:39:27Z","title":"Array Programming with NumPy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10256","snapshot_observed_at":"2026-08-05T11:52:15.254720Z","title":"Array programming with numpy,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.254720Z"},"links":{"cited_paper":"/paper/2006.10256","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:109fd4a5019c0bd830c44d2dfadc09506cb9f8b56d04651eb6e9f197b65c27a4","observation_id":"bd840a34-07f4-4936-9daa-547859181437","resolution":{"observed_at":"2026-08-05T11:52:15.254720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.10802","last_updated":"2022-08-29T23:16:19Z","snapshot_observed_at":"2026-07-06T12:00:03.070535Z","submitted_at":"2021-10-20T22:07:40Z","title":"A Data-Centric Optimization Framework for Machine Learning","version":3},"cited_work":{"arxiv_id":"2110.10802","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.10802","snapshot_observed_at":"2026-08-05T11:52:16.647743Z","title":"A Data-Centric Optimization Framework for Machine Learning","venue":"cs.LG","work_id":"c2e1b99d-ec3c-4eb8-8965-eaf899492edf","year":2021},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.261022Z"},"links":{"cited_paper":"/paper/2110.10802","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:6bbb056271fe82db65b04ad2d3bac7b5f3b6479288ff01270f3381f61fd8896d","observation_id":"de90c4e7-d19b-418e-ad3f-ba9d1114c4c1","resolution":{"observed_at":"2026-08-05T11:52:16.657864Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.645955Z","title":"Spivak, Calculus, 3rd ed","venue":null,"work_id":"3684d509-48ae-49cf-b4d6-764f26698982","year":1994},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.268307Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:9609fffd055bffda698291390a4fd65b25e567c7dea2e1feddc33da36d47cde7","observation_id":"6bc1817a-3205-48b2-a582-ed92f68b3cd6","resolution":{"observed_at":"2026-08-05T11:52:17.657279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.277071Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.277071Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:ba442916a3696d64629bc45bbc9c86f854d328f94f75e92b42b4dc2efe62a7c2","observation_id":"fcc48324-5fa3-4b35-84b8-2bf493724230","resolution":{"observed_at":"2026-08-05T11:52:15.277071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5008.35450","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:16.567824Z","title":"Automatic differentiation of parallel loops with formal methods,","venue":null,"work_id":"50e3601b-1150-4cd7-82bc-e621272fe85a","year":2023},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.297373Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:31178a1ff479ffa3fc7d3f318fca422773655aa050be690a68bd976f3ec38255","observation_id":"9ebe8031-fd5e-4d7b-9e54-f11581f124f2","resolution":{"observed_at":"2026-08-05T11:52:16.579942Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.307867Z","title":"The complex-step derivative approximation,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.307867Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:427cd7eb7b78d8f183992a7b6466a334a3e44240dae9f777ee86a2435158226d","observation_id":"7a46468a-4c42-4c83-8a2d-e8a2b5c8888c","resolution":{"observed_at":"2026-08-05T11:52:15.307867Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.587687Z","title":"John Wiley & Sons, Ltd, 2020, ch","venue":null,"work_id":"a8747b4e-2d85-44e4-8a4c-f84c2eb26d5c","year":2020},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.318314Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:e39de1a483332d27eb0aa149b505769ef4097a684609bf4ce6951900c21328c3","observation_id":"25bb58ac-d3ba-4e7c-b372-670bffb6eef4","resolution":{"observed_at":"2026-08-05T11:52:17.600437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.525104Z","title":"The icon (icosahedral non-hydrostatic) modelling framework of dwd and mpi-m: Description of the non-hydrostatic dynamical core,","venue":null,"work_id":"0e17a3d7-8dca-4875-bec6-1278a249164f","year":2015},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.328818Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:98d65bc03c72188cf93d468102ff0d954b5afd098f5d7e566234dd5354a543df","observation_id":"69f5f203-5857-4adb-9952-4dd81673d0fa","resolution":{"observed_at":"2026-08-05T11:52:17.539336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.500815Z","title":"Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results,","venue":null,"work_id":"b38dcd5e-157c-40e4-b65d-02b258bccf57","year":null},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.335317Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:ca3090374fe05a883a308b31585546e00d832ed7bbb42e32ffd24f1821802bfb","observation_id":"f6735d2e-0073-47fb-9773-7853482fe016","resolution":{"observed_at":"2026-08-05T11:52:17.507868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.480601Z","title":null,"venue":null,"work_id":"7157fa62-fa15-4a04-af1d-2f080a42d498","year":2014},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.355119Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:be8b4669879b99b4bc864e644c4242f7f6320bb5804c25d94a1a626ce11fb64f","observation_id":"b731b52d-dfbb-4d61-9e42-0da3284e2728","resolution":{"observed_at":"2026-08-05T11:52:17.487377Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.555697Z","title":"Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/ 9781119606475.ch1","venue":null,"work_id":"c324b0d3-cfcd-40d0-8733-d29a6d4e912c","year":null},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.323861Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:222edbb3ccf32f22e06b2f58e3c5b0fdf094f9901377b931a1a85022d8864e3a","observation_id":"ac454c01-a5ac-40e0-a30d-4acd835d0a5c","resolution":{"observed_at":"2026-08-05T11:52:17.562976Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.459429Z","title":"Dense linear algebra solvers for multicore with gpu accelerators,","venue":null,"work_id":"8b9fd661-977b-4b1c-a1f1-b3bad8797953","year":2010},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.368100Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:dcc02409d7bfa5bf7722968de3504f76fb7b50e2c53613eac674de79005312d6","observation_id":"f1be8caa-0a9b-4400-bc76-7ca5a5895851","resolution":{"observed_at":"2026-08-05T11:52:17.465536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/2904901","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.540543Z","title":"Adijac – automatic differentiation of java classfiles,","venue":null,"work_id":"ba224c2c-755b-4894-bd2f-1c901fba9148","year":2016},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.378063Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:e4f7f1ea4fea6eba1ec14c10ffe8b7e0f1b4aacfaea119255da8d9d3a422fcc6","observation_id":"3ecea77d-11a5-47d1-84fc-5a804a68255a","resolution":{"observed_at":"2026-08-05T11:52:15.550993Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.343105Z","title":"Available: https://doi.org/10.1145/2807591.2807644","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.343105Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:94be8053c7ed3128aba2e1d5d926a3440dc1cbab9abf4d26355c7d5b1ce834ce","observation_id":"3a90f71a-a700-4d7f-a879-3f8d0e1151cf","resolution":{"observed_at":"2026-08-05T11:52:15.343105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.376703Z","title":"Llvm compiler infrastructure,","venue":null,"work_id":"45b4f2dd-5e71-46e4-9ee2-8a3cf39affb9","year":2003},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.392874Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:47124f10cd73d7a884f2caf8d17a54918d8bea63a88afca98e2b59d05b9c8498","observation_id":"717dbc09-da0d-4e0c-b07b-27b2eb10a151","resolution":{"observed_at":"2026-08-05T11:52:17.397565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6052.13560","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:16.205792Z","title":"Anatomy of high-performance matrix multiplication,","venue":null,"work_id":"55ba20ae-4c10-4d19-8ed0-251eb414e4a4","year":2008},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.362020Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:d1c66447ef5d98ad7402ab4a6d6cabe397301520c1497e6e2268a655c84fe26f","observation_id":"d1472ef1-29b6-49a7-82d3-d73b022c913e","resolution":{"observed_at":"2026-08-05T11:52:16.238021Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.323233Z","title":"Scalable automatic differen- tiation of multiple parallel paradigms through compiler augmentation,","venue":null,"work_id":"ee44ea1c-73c8-4cf0-8db5-1de604715cdf","year":2022},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.412645Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:811e9ce7f3942b17f23502e33307baa3f555fa29aa3bc1e177dd7aa8abdc1a12","observation_id":"83e46222-4b3a-4b80-8d31-07a6e6fcfe87","resolution":{"observed_at":"2026-08-05T11:52:17.341316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.293343Z","title":"Schanen, S","venue":null,"work_id":"654b4ea5-4919-4161-a29f-50e9b0d2a531","year":2023},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.421805Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:ba10f3a7a41542ea03d087eed7673e6bcf4b2cac2f050d5dbd539e7b36110b59","observation_id":"f7cf7972-20c6-4ec1-9af0-9de33b95c015","resolution":{"observed_at":"2026-08-05T11:52:17.298544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.385924Z","title":"{TensorFlow}: a system for {Large-Scale} machine learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.385924Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:b03647042819c0c4370841b73a07f1106b3e510dac766c44b44249955b48b8ae","observation_id":"519625e4-804f-4a19-9acf-e45a07aede94","resolution":{"observed_at":"2026-08-05T11:52:15.385924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03401","last_updated":"2016-06-10T17:20:39Z","snapshot_observed_at":"2026-07-06T04:59:33.186233Z","submitted_at":"2016-06-10T17:20:39Z","title":"Memory-Efficient Backpropagation Through Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.03401","snapshot_observed_at":"2026-08-05T11:52:15.436419Z","title":"Memory-efficient backpropagation through time,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.436419Z"},"links":{"cited_paper":"/paper/1606.03401","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:f5a0d63fdf4db53f1e72728351e068dcae9944ae422fcf650207accef2ea6702","observation_id":"9e96aa21-4d0b-4ae1-be2c-d3707553f07f","resolution":{"observed_at":"2026-08-05T11:52:15.436419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.404024Z","title":"Reverse-mode automatic differentiation and optimization of gpu kernels via enzyme,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.404024Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:adbb27658a73de80fb3294326b929a0e37c5e3e006993ffc50bfde4a6c03c708","observation_id":"dd68ca5f-cb00-46d3-b779-3def8993d7a1","resolution":{"observed_at":"2026-08-05T11:52:15.404024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.06174","last_updated":"2016-04-22T19:21:36Z","snapshot_observed_at":"2026-08-08T09:03:25.135475Z","submitted_at":"2016-04-21T04:15:27Z","title":"Training Deep Nets with Sublinear Memory Cost","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.06174","snapshot_observed_at":"2026-08-05T11:52:15.429173Z","title":"Training deep nets with sublinear memory cost,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.429173Z"},"links":{"cited_paper":"/paper/1604.06174","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:0176a66fa847a336a7cc0c7dbdbdcd837c3847673a96d83743198a61a988ba37","observation_id":"bbe212b4-ee89-4432-910c-367122542696","resolution":{"observed_at":"2026-08-05T11:52:15.429173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:15.462272Z","title":"Available: http://arxiv.org/abs/1911.13214","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.462272Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:5a5e62a8ca16e0f49f9ab41910d12ba500664393db08f43e4722225c352c4581","observation_id":"7cada5e3-1e61-438e-9d9c-912a4973aaea","resolution":{"observed_at":"2026-08-05T11:52:15.462272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7837.34784","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:52:17.031735Z","title":"Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation,","venue":null,"work_id":"c2640d97-8c45-434a-9983-0e6f4aaed0ad","year":2000},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.482880Z"},"links":{"citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:d5993b795142746064383fcce6623738930172d8dafcf72636bab80b73819501","observation_id":"bb4aef34-595c-4c07-b0fc-34edf1fe528d","resolution":{"observed_at":"2026-08-05T11:52:17.049468Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1209.5145","last_updated":"2012-09-24T03:55:45Z","snapshot_observed_at":"2026-08-01T14:12:06.954753Z","submitted_at":"2012-09-24T03:55:45Z","title":"Julia: A Fast Dynamic Language for Technical Computing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1209.5145","snapshot_observed_at":"2026-08-05T11:52:15.191958Z","title":"Available: http://arxiv.org/abs/1209.5145","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.191958Z"},"links":{"cited_paper":"/paper/1209.5145","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:a550f127488f993d9f6a7071fc5d2ff37012df6d49cf15e2e9eacd818d9626f1","observation_id":"12c14c94-2aa5-4e7c-b3da-b2e8cc0483bb","resolution":{"observed_at":"2026-08-05T11:52:15.191958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.05767","last_updated":"2018-02-05T15:57:57Z","snapshot_observed_at":"2026-07-06T04:09:45.600447Z","submitted_at":"2015-02-20T04:20:47Z","title":"Automatic differentiation in machine learning: a survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.05767","snapshot_observed_at":"2026-08-05T11:52:13.989046Z","title":"Available: http://arxiv.org/abs/1502.05767","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:13.989046Z"},"links":{"cited_paper":"/paper/1502.05767","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:c90ca66a5c857ee67098b50a6dad1418afa415ab3a942e02a6dc534361e4ba56","observation_id":"9578e571-17d4-4fb7-9566-f379dbf0c2c2","resolution":{"observed_at":"2026-08-05T11:52:13.989046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.02653","last_updated":"2020-05-14T17:46:43Z","snapshot_observed_at":"2026-07-06T08:27:21.780015Z","submitted_at":"2019-10-07T07:54:06Z","title":"Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.02653","snapshot_observed_at":"2026-08-05T11:52:15.447984Z","title":"Available: http://arxiv.org/abs/1910.02653","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.447984Z"},"links":{"cited_paper":"/paper/1910.02653","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:211df8134f30ea4dc10c5879092d57e4df6ae0d853952100768e3c519a762bed","observation_id":"e641ad89-2d96-406c-9d69-99ce535abf8c","resolution":{"observed_at":"2026-08-05T11:52:15.447984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.14501","last_updated":"2021-04-03T00:32:47Z","snapshot_observed_at":"2026-07-06T10:09:06.864468Z","submitted_at":"2020-10-27T17:57:34Z","title":"Memory Optimization for Deep Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.14501","snapshot_observed_at":"2026-08-05T11:52:15.475551Z","title":"Available: https://arxiv.org/abs/2010.14501","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.475551Z"},"links":{"cited_paper":"/paper/2010.14501","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:77f5a9c8ef9d5b4cf9f2615cd3866a254c219a2180bd770535c1ed54ad14463b","observation_id":"78aee140-aa55-43c0-865b-c7a6badfba57","resolution":{"observed_at":"2026-08-05T11:52:15.475551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.01861","last_updated":"2021-11-02T19:40:59Z","snapshot_observed_at":"2026-08-04T02:05:27.671695Z","submitted_at":"2021-11-02T19:40:59Z","title":"Source-to-Source Automatic Differentiation of OpenMP Parallel Loops","version":1},"cited_work":{"arxiv_id":"2111.01861","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.01861","snapshot_observed_at":"2026-08-05T11:52:16.607350Z","title":"Source-to-Source Automatic Differentiation of OpenMP Parallel Loops","venue":"cs.MS","work_id":"b2110cb8-cdef-4a79-9310-40f5034561d4","year":2021},"citing_paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-05T11:52:15.291460Z"},"links":{"cited_paper":"/paper/2111.01861","citing_paper":"/paper/2509.02197"},"observation_digest":"sha256:98abf8f30c68095238d931152fddcbec87bb3582bbfba1ac08a289f9d2a49e93","observation_id":"58ebe02a-c507-4b24-aceb-325baabb419c","resolution":{"observed_at":"2026-08-05T11:52:16.615307Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.02197","last_updated":"2025-09-02T11:09:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T08:01:45.628491Z","submitted_at":"2025-09-02T11:09:45Z","title":"DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":2,"metadata_mismatch":6,"parse_uncertain":0,"unresolved":21,"verified_exact":3,"verified_fuzzy":18},"total_outbound_references":50},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2509.02197."}