{"as_of":"2026-08-09T05:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:604f14dab5ce6e4023876abaedf48dd18a9e5a5b1779e4b86c4665de61f0c653","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:58:05.322950Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":57,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":"2104.07857","doi":"10.48550/arxiv.2104.07857","metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zero-inﬁnity: Breaking the gpu memory wall for extreme scale deep learning","venue":"arXiv (Cornell University)","work_id":"4546e09d-ef90-46df-b6f2-7a3849a53009","year":2021},"citing_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},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-24T12:10:49.690618Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2201.11990"},"observation_digest":"sha256:5e54359e6a107be99a9b09d6c2863f33f9e7cafb61b40d155b52e6a3c6a00c7a","observation_id":"47c79d72-47c9-41b2-83e8-d4f6d4285e35","resolution":{"observed_at":"2026-05-24T12:14:26.603797Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":"2104.07857","doi":"10.48550/arxiv.2104.07857","metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zero-inﬁnity: Breaking the gpu memory wall for extreme scale deep learning","venue":"arXiv (Cornell University)","work_id":"4546e09d-ef90-46df-b6f2-7a3849a53009","year":2021},"citing_paper":{"arxiv_id":"2208.07339","last_updated":"2022-11-10T18:14:31Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-08-15T17:08:50Z","title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-05-13T13:35:35.972596Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2208.07339"},"observation_digest":"sha256:3bb4531f2c159e160e53fa3de7502ec38984214d1e3177b78bbc55a34e470880","observation_id":"5b7311e8-64ae-46d4-93bf-d08657db1649","resolution":{"observed_at":"2026-05-13T13:35:36.112167Z","resolver_source":"arxiv_id","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":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-07T12:58:05.322950Z","title":"ZeRO- Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23072","last_updated":"2025-05-29T04:24:56Z","snapshot_observed_at":"2026-08-09T05:07:42.156283Z","submitted_at":"2025-05-29T04:24:56Z","title":"Speeding up Model Loading with fastsafetensors","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:58:05.322950Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2505.23072"},"observation_digest":"sha256:e6eba1df168b20aa55109fd9630aefd79e11639ed1cef8ec1ab7edba8065b3d8","observation_id":"eac066d1-55ad-4914-9134-18f059fbd4c8","resolution":{"observed_at":"2026-08-07T12:58:05.322950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-06T22:01:23.734112Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22950","last_updated":"2025-06-28T16:52:29Z","snapshot_observed_at":"2026-08-06T21:52:06.556706Z","submitted_at":"2025-06-28T16:52:29Z","title":"Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:01:23.734112Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2506.22950"},"observation_digest":"sha256:0933969a7bf42a114a9cd629f27d8a8798d7fc7e34fcd53e138d4d112c5886c7","observation_id":"e2b3638e-38a1-4326-946c-6c6f81a290f7","resolution":{"observed_at":"2026-08-06T22:01:23.734112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":"2104.07857","doi":"10.48550/arxiv.2104.07857","metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zero-inﬁnity: Breaking the gpu memory wall for extreme scale deep learning","venue":"arXiv (Cornell University)","work_id":"4546e09d-ef90-46df-b6f2-7a3849a53009","year":2021},"citing_paper":{"arxiv_id":"2604.27085","last_updated":"2026-04-29T18:26:13Z","snapshot_observed_at":"2026-07-06T23:12:38.453388Z","submitted_at":"2026-04-29T18:26:13Z","title":"Efficient Training on Multiple Consumer GPUs with RoundPipe","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-07T10:37:22.251566Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2604.27085"},"observation_digest":"sha256:e948708c0c9156f64a273ab919e4ffa934530a2b5a9a6f769faef5dd049f1005","observation_id":"c6a546ef-7146-4d16-a77d-769bc003fe8f","resolution":{"observed_at":"2026-05-12T09:31:26.792934Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":"2104.07857","doi":"10.48550/arxiv.2104.07857","metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zero-inﬁnity: Breaking the gpu memory wall for extreme scale deep learning","venue":"arXiv (Cornell University)","work_id":"4546e09d-ef90-46df-b6f2-7a3849a53009","year":2021},"citing_paper":{"arxiv_id":"2605.16184","last_updated":"2026-05-15T17:03:55Z","snapshot_observed_at":"2026-07-31T13:45:58.520033Z","submitted_at":"2026-05-15T17:03:55Z","title":"Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-19T18:30:24.657592Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2605.16184"},"observation_digest":"sha256:a6ea76efe48bd9537ae14cf60bbd108f3c0d9a2a439d4b8778ea01300c3f12a1","observation_id":"f9e91246-80d2-4687-afbb-3f511f6ea6a3","resolution":{"observed_at":"2026-05-19T18:32:42.956504Z","resolver_source":"arxiv_id","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":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":"2104.07857","doi":"10.48550/arxiv.2104.07857","metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zero-inﬁnity: Breaking the gpu memory wall for extreme scale deep learning","venue":"arXiv (Cornell University)","work_id":"4546e09d-ef90-46df-b6f2-7a3849a53009","year":2021},"citing_paper":{"arxiv_id":"2606.24937","last_updated":"2026-07-27T15:17:17Z","snapshot_observed_at":"2026-08-02T23:19:25.465662Z","submitted_at":"2026-06-22T17:48:54Z","title":"The Hitchhiker's Guide to Agentic AI: From Foundations to Systems","version":1},"reference_index":234,"source":"pdf_text","source_observed_at":"2026-06-26T08:09:57.542558Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2606.24937"},"observation_digest":"sha256:4276c92d4fa8fee2c719471a0ff862ca320409623c77fb4954639c52aa22c8bb","observation_id":"ed54f167-bd48-4bdf-90c2-ab70536557d3","resolution":{"observed_at":"2026-07-04T11:09:46.154734Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07857","snapshot_observed_at":"2026-08-02T10:27:18.533828Z","title":"ZeRO- Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning.arXiv Preprint arXiv:2104.07857, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.24937","last_updated":"2026-07-27T15:17:17Z","snapshot_observed_at":"2026-08-02T23:19:25.465662Z","submitted_at":"2026-06-22T17:48:54Z","title":"The Hitchhiker's Guide to Agentic AI: From Foundations to Systems","version":2},"reference_index":234,"source":"pdf_text","source_observed_at":"2026-08-02T10:27:18.533828Z"},"links":{"cited_paper":"/paper/2104.07857","citing_paper":"/paper/2606.24937"},"observation_digest":"sha256:288ab78e9ffda4ef5cd628a441a01a1a8808dbbf427dfcfbce398b5890f4a110","observation_id":"2ab751d5-1005-4adb-8649-1c5012e0d69b","resolution":{"observed_at":"2026-08-02T10:27:18.533828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2104.07857/citation-record","integrity":"/paper/2104.07857/integrity","json":"/paper/2104.07857/citation-record.json","paper":"/paper/2104.07857"},"outbound":[],"paper":{"arxiv_id":"2104.07857","last_updated":"2021-04-16T02:22:12Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-08T13:19:57.834040Z","submitted_at":"2021-04-16T02:22:12Z","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2104.07857."}