{"as_of":"2026-08-03T22:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d24147b6e2b3a8b77c131fcabe521bf58a06076ec0aec79a231f8e261b02dedd","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-15T19:03:22.142671Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-03T06:30:56.289259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T06:45:15.274057Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.22437","snapshot_observed_at":"2026-08-02T06:45:15.274057Z","title":"vescale-fsdp: Flexible and high-performance fsdp at scale.arXiv preprint arXiv:2602.22437, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20548","last_updated":"2026-07-13T21:47:08Z","snapshot_observed_at":"2026-08-03T03:46:04.260858Z","submitted_at":"2026-07-13T21:47:08Z","title":"SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T06:45:15.274057Z"},"links":{"cited_paper":"/paper/2602.22437","citing_paper":"/paper/2607.20548"},"observation_digest":"sha256:9cd5048fab2bf0ed6c46fd04d41bc6a549e4a3ae9f5a2c45e0a595abc3202ec2","observation_id":"afdba9c2-f340-4d9f-8127-a4445be7afed","resolution":{"observed_at":"2026-08-02T06:45:15.274057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2602.22437/citation-record","integrity":"/paper/2602.22437/integrity","json":"/paper/2602.22437/citation-record.json","paper":"/paper/2602.22437"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.10925","last_updated":"2025-08-08T19:24:38Z","snapshot_observed_at":"2026-08-01T16:27:35.664983Z","submitted_at":"2025-08-08T19:24:38Z","title":"gpt-oss-120b & gpt-oss-20b Model Card","version":1},"cited_work":{"arxiv_id":"2508.10925","doi":"10.3115/1073083.1073135","metadata_source":"pith","pith_arxiv_id":"2508.10925","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"gpt-oss-120b & gpt-oss-20b Model Card","venue":"cs.CL","work_id":"178c1f7e-4f19-4392-a45d-45a6dfa88ead","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2508.10925","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:2064ef4ecfb3cb2bc670ff27f15b060edc2327dad26ca611fb4eec726ffd08a6","observation_id":"5998d2c2-4a94-4a12-b8ed-f846976d59bf","resolution":{"observed_at":"2026-05-15T19:06:30.812760Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:19:47.962081+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:19:47.962081+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"8-bit optimizers via block-wise quantization","venue":null,"work_id":"073eb6be-f75b-4927-b8fd-d0426ed0249c","year":2022},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:6951096f90900ccc53599717282ed86cbc9d8b93dbd7fe2eed8e2cf58417420d","observation_id":"a49cee27-c101-43a0-af10-591238e2d99c","resolution":{"observed_at":"2026-05-15T19:10:17.286825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-07-06T22:12:56.764750Z","title":"The llama 3 herd of models.arXiv e-prints, pages arXiv–2407","venue":null,"work_id":"2dfe07e4-932e-4ce0-ad85-badb06bf579b","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:49b8a39a0b880519ac39b8cf088d6647bee7050c53f5489b52a7317dd12e8b26","observation_id":"6519037e-3e60-49cd-a62b-77b254e4ad24","resolution":{"observed_at":"2026-05-15T19:10:17.267671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"131a859b-80ca-441f-a9a2-41247a0f0f32","year":1975},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:7f6913666365932945a6e9c7d3e51d834e4a1ba2bc959d745e1e454749425f55","observation_id":"e231b96b-9de7-4a3e-aa13-bf630c1f8a40","resolution":{"observed_at":"2026-05-15T19:10:17.272353Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"[rfc] per-parameter-sharding fsdp","venue":null,"work_id":"9d7b39b3-0db5-4dc8-a2bc-a299d01f6ca2","year":2023},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:d4899a4e07d1ca8a2c1df424ce1806c61bda7a6b54cd919714a7cf27fd1e9975","observation_id":"027506bc-fcbf-4a85-9934-65cf7e8c92a4","resolution":{"observed_at":"2026-05-15T19:10:17.308513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Shampoo: Preconditioned stochastic tensor optimization","venue":null,"work_id":"6aae5137-dc4d-422b-9126-cc1945364764","year":2018},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:a4dadeca3603b1278a62d149f7acbfa8ca925c230cf792a27900e702d26e1c9f","observation_id":"80ef9150-478c-4eb7-ae22-8ffbecd493cf","resolution":{"observed_at":"2026-05-15T19:10:17.262636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Deepspeed is slower than fsdp","venue":null,"work_id":"80841e4a-c564-46ef-aa7c-ed402d821505","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:01ba8c2cadb037b96c9390abcf7c5d5f45457d3e8761b14a4b25e88393be1f68","observation_id":"0cf3f73e-cd5c-4584-9072-935e0fd3cec6","resolution":{"observed_at":"2026-05-15T19:10:17.312721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"In 21st USENIX Symposium on NetworkedSystems Design and Implementation (NSDI 24), pages 745–760","venue":null,"work_id":"6f68419e-8f2c-4f6b-9d7e-daf421d30482","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:732159fa9638cbe96d3de87b3f30dd3d15f817e84c77adab403b2aa84c4c45bd","observation_id":"a7d68984-808a-41bc-8204-96cb476f1e50","resolution":{"observed_at":"2026-05-15T19:10:17.282568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Muon: An optimizer for hidden layers in neural networks","venue":null,"work_id":"5c4ac5b1-b573-4c40-be96-5d28a7681ae4","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:e210a232b1b3d26f6beec2c94693d0689f2eb0c123705425f38e6c263f618655","observation_id":"e2a1e1be-94fe-4a22-8e64-7c4594f490e6","resolution":{"observed_at":"2026-05-15T19:10:17.258610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":"2001.08361","doi":"10.1145/3616855.3635845","metadata_source":"pith","pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Scaling Laws for Neural Language Models","venue":"cs.LG","work_id":"b7dd8749-9c45-4977-ab9b-64478dce1ae8","year":2020},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:c45d80bf8c3496d5157aa33284b200a661b59ecbbaf5e543b26e83d8455f94d1","observation_id":"9ca1e60a-00ac-4367-b35f-fe4c0874a85e","resolution":{"observed_at":"2026-05-15T19:06:30.818563Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16668","last_updated":"2020-06-30T10:42:02Z","snapshot_observed_at":"2026-07-06T09:33:58.857566Z","submitted_at":"2020-06-30T10:42:02Z","title":"GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding","version":1},"cited_work":{"arxiv_id":"2006.16668","doi":"10.48550/arxiv.2006.16668","metadata_source":"pith","pith_arxiv_id":"2006.16668","snapshot_observed_at":"2026-07-11T01:07:44.079601Z","title":"GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding","venue":"cs.CL","work_id":"52b3c9a6-2a27-45a7-ba2b-ebe4b5bb5a5f","year":2020},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2006.16668","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:d9ca9f699c802ff5cbd8df7dce44f16685c159330ed8382000a02516fa8ec840","observation_id":"1118bdd3-03a1-4a75-ac2e-742e6525213d","resolution":{"observed_at":"2026-05-15T19:06:30.828498Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.15704","last_updated":"2020-06-28T20:39:45Z","snapshot_observed_at":"2026-07-06T09:33:26.082100Z","submitted_at":"2020-06-28T20:39:45Z","title":"PyTorch Distributed: Experiences on Accelerating Data Parallel Training","version":1},"cited_work":{"arxiv_id":"2006.15704","doi":"10.48550/arxiv.2006.15704","metadata_source":"pith","pith_arxiv_id":"2006.15704","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"PyTorch Distributed: Experiences on Accelerating Data Parallel Training","venue":"cs.DC","work_id":"353279b8-3b33-45fd-9b64-41e5bd1708b9","year":2020},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2006.15704","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:060b553bfe6bee6e03a71578c834c61b1e49ee1aaa07c0afb0947c0fbc3d7a88","observation_id":"d5ac6670-8585-460d-9ea8-314d3bfbf209","resolution":{"observed_at":"2026-05-15T19:06:30.792076Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-05-20T18:22:38.135957+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T18:22:38.135957+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.07003","last_updated":"2025-09-05T19:29:00Z","snapshot_observed_at":"2026-07-06T22:26:03.668339Z","submitted_at":"2025-09-05T19:29:00Z","title":"veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD","version":1},"cited_work":{"arxiv_id":"2509.07003","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.07003","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD","venue":null,"work_id":"2432cf46-6ecb-41ad-b473-10108ed4eade","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2509.07003","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:c9a0f03212ab76c5ad3f92db11303f6d5d63f0a490ceae2b6600a6636f9fae21","observation_id":"73653535-1729-4a50-8806-38b7e51d874e","resolution":{"observed_at":"2026-05-15T19:06:30.797758Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":"2412.19437","doi":"10.1016/j.neucom.2023.127063.url:https://www.sciencedirect","metadata_source":"pith","pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeek-V3 Technical Report","venue":"cs.CL","work_id":"57d2791d-2219-4c31-a077-afc04b12a75c","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:774982f4b02e1a65a71cf4662566777faca3e4de052e38ec1ece96451b3dbf73","observation_id":"0d16e34b-b1cc-43ed-950d-36234cd3badf","resolution":{"observed_at":"2026-05-15T19:06:30.802475Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.02317","last_updated":"2025-08-07T10:31:09Z","snapshot_observed_at":"2026-07-06T22:07:26.271331Z","submitted_at":"2025-08-04T11:33:04Z","title":"VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo","version":3},"cited_work":{"arxiv_id":"2508.02317","doi":"10.48550/arxiv.2508.02317","metadata_source":"pith","pith_arxiv_id":"2508.02317","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Veomni: Scaling any modality model training with model-centric distributed recipe zoo","venue":"cs.CL","work_id":"98e8be4a-3e49-42c6-a20b-0106ca5b2488","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2508.02317","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:f4336c3ab92224722f56f27cd795410359b77c249f99414fa9481129b8812663","observation_id":"8779bd6b-f133-47e5-8b1c-001139cddd9e","resolution":{"observed_at":"2026-05-15T19:06:30.840282Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Mcore custom fully sharded data parallel (fsdp)","venue":null,"work_id":"e690c33e-91bf-4e90-98bd-d5325f276de9","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:60419c6d7dc1cda9ed9d4589c4bdf50ea96bf3e9cf0902ae996ca01426deccb1","observation_id":"f1f0ee44-af00-4279-ba8a-33ae9f9017d2","resolution":{"observed_at":"2026-05-15T19:10:17.304265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Regarding the allgather bandwidth with different byte alignment under different protocols","venue":null,"work_id":"0bd5d820-0ac0-4fd8-96be-5475dbc48782","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:151fad1713cc74ed7fc848b52906bfb735889bb6267a3d5ab12d11d5d34d615e","observation_id":"6ff394d9-9f9e-45af-94ce-390bf4f878ba","resolution":{"observed_at":"2026-05-15T19:10:17.236415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Nccl: Collective operations","venue":null,"work_id":"73a2c4f9-9420-4386-8e4d-cf548fbb1fd7","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:b2ed00c77ab452a4b1ac16e93cdd134b83163814838aa15d5e7caaa284aa8537","observation_id":"656b1bf8-bb2b-4397-853b-122cabb0ded3","resolution":{"observed_at":"2026-05-15T19:10:17.246851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Fully sharded data parallel (fsdp2)","venue":null,"work_id":"0f60e2c8-ce1d-479d-8ffb-1b7919ff46ec","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:c171637c2857458e122206a5274278c618df5b31eca0b5219950cd3fc8027e4d","observation_id":"ed1c5ddd-fc0a-4d8e-932c-0a156925cbe5","resolution":{"observed_at":"2026-05-15T19:10:17.316929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Pytorch jaggedtensor","venue":null,"work_id":"90c04450-33d3-446a-ac9b-b5852afa1468","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:3f4d9bc3fa0406598b3bbc6f662a4688075e140e5976ccb8472c24b5c6d91705","observation_id":"46177dfc-ea04-4159-b6ff-5a5227a99fed","resolution":{"observed_at":"2026-05-15T19:10:17.296019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Pytorch nestedtensor","venue":null,"work_id":"2d30b231-a152-4cd8-a512-b60b40887048","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:eb7fc8426ca515c87eb7fabeaed3cad2861393d4017af30e9190a79c5fde0735","observation_id":"a79305f6-4403-4fe4-a8ab-600b7189a206","resolution":{"observed_at":"2026-05-15T19:10:17.257243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Distributed checkpoint","venue":null,"work_id":"8ee6628f-e4c6-4d83-9782-9b2a349dd9d3","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:84f7c59c39d4d27d583b6055eccd8537ba841d4194b05fad39f9249392b567f1","observation_id":"6789fd79-6ad8-4de2-8c4d-f8189dd999da","resolution":{"observed_at":"2026-05-15T19:10:17.331374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Meta pytorch team 2026 h1 roadmaps","venue":null,"work_id":"2418c1a7-1ab2-4d67-af29-80287ef4d872","year":2026},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:20a4397e9373e0fe517d79222b1fe53a205e2b03eec5688202ffa325254976b1","observation_id":"44f340a4-47b8-4655-956e-4f79ac928450","resolution":{"observed_at":"2026-05-15T19:10:17.251837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Zero: Memory optimizations toward training trillion parameter models","venue":null,"work_id":"a90290fc-a462-4e0b-a104-44399619beab","year":2020},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:6bdca4291e47f331ca778b218b03d9295a45b415fdde082c9cc1ab87203c18c9","observation_id":"770a8be2-be77-4dc2-8dff-2d75c04f6e9c","resolution":{"observed_at":"2026-05-15T19:10:17.276884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08053","last_updated":"2020-03-13T23:45:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-09-17T19:42:54Z","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","version":4},"cited_work":{"arxiv_id":"1909.08053","doi":"10.48550/arxiv.1909.08053","metadata_source":"pith","pith_arxiv_id":"1909.08053","snapshot_observed_at":"2026-07-10T16:37:23.051852Z","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","venue":"cs.CL","work_id":"c888e6d1-0b1d-43d6-9ef5-f0912a0efa1b","year":2019},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/1909.08053","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:7d7bc79a7e63ecac9ab7dcf8b129e335807c6887193c9dd3f293e91067be93ee","observation_id":"dd8a1d3e-c76f-45b2-ba2d-8ed17c7c3eea","resolution":{"observed_at":"2026-05-15T19:06:30.807753Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-09T10:48:33.392193+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T10:48:33.392193+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11990","last_updated":"2022-02-04T18:02:23Z","snapshot_observed_at":"2026-07-06T12:32:10.267841Z","submitted_at":"2022-01-28T08:59:57Z","title":"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model","version":3},"cited_work":{"arxiv_id":"2201.11990","doi":"10.48550/arxiv.2201.11990","metadata_source":"pith","pith_arxiv_id":"2201.11990","snapshot_observed_at":"2026-07-10T16:37:23.049330Z","title":"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model","venue":"cs.CL","work_id":"7db569c6-f66a-40a1-9974-3de1eb611cc1","year":2022},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2201.11990","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:822cec3d9e958448b608a69bab7db48a33350fcb918eb49829b4ea8281288369","observation_id":"50005be3-08b2-4d3c-8d04-6b26200316c7","resolution":{"observed_at":"2026-05-15T19:06:30.823510Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05530","last_updated":"2024-12-16T17:39:39Z","snapshot_observed_at":"2026-07-06T17:41:42.995949Z","submitted_at":"2024-03-08T18:54:20Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","version":5},"cited_work":{"arxiv_id":"2403.05530","doi":"10.48550/arxiv.2403.05530","metadata_source":"pith","pith_arxiv_id":"2403.05530","snapshot_observed_at":"2026-07-11T03:37:46.178537Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","venue":"cs.CL","work_id":"80e3e977-f1bb-4c83-8d0c-1ab0a0c5c3f1","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2403.05530","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:1ba23f518dae4b646c52cd2a8b6ec7ed945f21a1636b16129cccb426cb540306","observation_id":"0bc41cea-3580-433e-862e-d6fbef769fcd","resolution":{"observed_at":"2026-05-15T19:06:30.868363Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-02T03:08:14.426583+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T03:08:14.426583+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20534","last_updated":"2026-02-03T04:57:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-28T05:35:43Z","title":"Kimi K2: Open Agentic Intelligence","version":2},"cited_work":{"arxiv_id":"2507.20534","doi":"10.1145/3448609","metadata_source":"pith","pith_arxiv_id":"2507.20534","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Kimi K2: Open Agentic Intelligence","venue":"cs.LG","work_id":"7f18284c-12d3-4137-bea1-1da97e8cf3c1","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2507.20534","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:6d909c2b56a76e020a6ef2b3d5df2d1db37d554789610a8ffcf0fa148bf0644e","observation_id":"fc522266-a8f0-40fa-a53c-189dffcd370e","resolution":{"observed_at":"2026-05-15T19:06:30.862712Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-05-25T01:23:16.170083+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T01:23:16.170083+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Tensorflow ragged tensors","venue":null,"work_id":"7cc7e062-3296-49e2-96e5-30d52d73f53f","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:b19fcb509d2e15f696889ff39a8c4f85d05c2849f7c37e56871bd397b59da9ea","observation_id":"9abdcc9b-9db2-427a-9105-b1d3b1c0b6ca","resolution":{"observed_at":"2026-05-15T19:10:17.321010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"PyTorch DTensor (Distributed Tensor).https://pytorch.org/docs/stable/distributed","venue":null,"work_id":"28205b20-2dd3-47e8-bd27-8b6ef3fba701","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:8ed29083a876a7a52b0b609543541a29877892a28b2f766281c21e9a0697ff8a","observation_id":"acfc63f7-2a1a-4ece-bc77-1d29cebe501e","resolution":{"observed_at":"2026-05-15T19:10:17.327775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02046","last_updated":"2025-09-04T19:22:04Z","snapshot_observed_at":"2026-08-02T20:37:51.914753Z","submitted_at":"2025-09-02T07:43:22Z","title":"Fantastic Pretraining Optimizers and Where to Find Them","version":2},"cited_work":{"arxiv_id":"2509.02046","doi":"10.48550/arxiv.2509.02046","metadata_source":"arxiv_reference","pith_arxiv_id":"2509.02046","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Fantastic pretraining optimizers and where to find them.arXiv preprint arXiv:2509.02046","venue":null,"work_id":"692b6526-174d-4275-b06f-8afe06e28a6c","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2509.02046","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:85e199665937ec1ea0c3ed8afedeec6cb5101adea00f5573f196553697d94ce6","observation_id":"eae161c7-baa0-42d0-bf11-6d220841f47d","resolution":{"observed_at":"2026-05-15T19:06:30.851781Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09226","last_updated":"2025-08-03T03:46:44Z","snapshot_observed_at":"2026-07-06T21:40:02.335215Z","submitted_at":"2025-06-10T20:30:31Z","title":"Terabyte-Scale Analytics in the Blink of an Eye","version":2},"cited_work":{"arxiv_id":"2506.09226","doi":"10.48550/arxiv.2506.09226","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.09226","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Terabyte-scale analytics in the blink of an eye","venue":null,"work_id":"b445b8af-0ee2-4b34-8acf-710408b06dde","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2506.09226","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:c6e070385554a0cef1468eca5b38e48978d255aa98928bdbf8d6f8aa5e77a2d0","observation_id":"edf0d5a8-2a2d-444d-bbdd-98eb02feb5c7","resolution":{"observed_at":"2026-05-15T19:06:30.857247Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"FSDP & CUDACachingAllocator","venue":null,"work_id":"b328ac56-7de3-4552-8f7b-36eaa2a96daa","year":2024},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:259877adc1c264568ca219326b9aa0a197f987000418676fca4543595c2225d8","observation_id":"5765d192-40fc-4c02-b4af-24b1d691d513","resolution":{"observed_at":"2026-05-15T19:10:17.291619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04663","last_updated":"2021-12-23T21:29:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-05-10T20:54:58Z","title":"GSPMD: General and Scalable Parallelization for ML Computation Graphs","version":2},"cited_work":{"arxiv_id":"2105.04663","doi":"10.48550/arxiv.2105.04663","metadata_source":"pith","pith_arxiv_id":"2105.04663","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"GSPMD: General and Scalable Parallelization for ML Computation Graphs","venue":"cs.DC","work_id":"0ab74606-fb17-4ead-898b-8086ae8cb3af","year":2021},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2105.04663","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:cef73661d8bc782775aab2406b458ddde2b3a4f8bf37d82082b52797ecb689e7","observation_id":"ebacae73-2268-49af-95bf-8e16d75a85ff","resolution":{"observed_at":"2026-05-18T12:36:36.562059Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11277","last_updated":"2023-09-12T16:28:00Z","snapshot_observed_at":"2026-08-01T19:01:47.393546Z","submitted_at":"2023-04-21T23:52:27Z","title":"PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel","version":2},"cited_work":{"arxiv_id":"2304.11277","doi":"10.48550/arxiv.2304.11277","metadata_source":"pith","pith_arxiv_id":"2304.11277","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel","venue":"cs.DC","work_id":"bee7755e-b855-401d-813a-06ae9451d768","year":2023},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"cited_paper":"/paper/2304.11277","citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:f7063b897d34eb04427bc4d58297758dbee0e7f93b5dc93adf75a61a75ccf9c3","observation_id":"e8b2ffe0-b410-4f20-a2d0-d32ae53856af","resolution":{"observed_at":"2026-05-15T19:06:30.833896Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-05-20T14:22:13.496008+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T14:22:13.496008+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Fsdp1 post backward reduce","venue":null,"work_id":"9071d636-716f-42c3-996a-9236b7ac72a5","year":2025},"citing_paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-15T19:03:22.142671Z"},"links":{"citing_paper":"/paper/2602.22437"},"observation_digest":"sha256:aff99a74f2dc689a4d45bf2306a4ff1650a7520f48bc8b6f092c6491744ae6b3","observation_id":"96a6dd7f-9eb4-4b6a-ac64-b70aeb3e1513","resolution":{"observed_at":"2026-05-15T19:10:17.300047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2602.22437","last_updated":"2026-04-21T21:24:42Z","latest_version":3,"primary_category":"cs.DC","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-25T21:55:43Z","title":"veScale-FSDP: Flexible and High-Performance FSDP at Scale"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":15,"verified_fuzzy":20},"total_outbound_references":36},"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":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"thesis":"As of 3 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2602.22437."}