{"as_of":"2026-08-10T12:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e9edcc5c6ea7d17370d5c4c053171f4dd41930c39604dbdbb183602205fd648","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:14:35.448676Z","state":"measured"},{"denominator":80,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":80,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:57:52.568195Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T21:36:15.542964Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06982","snapshot_observed_at":"2026-08-07T04:57:52.568195Z","title":"Machine learning fleet efficiency: Analyzing and optimizing large-scale google tpu systems with ml productivity goodput.arXiv preprint arXiv:2502.06982, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.09275","last_updated":"2025-06-10T22:25:29Z","snapshot_observed_at":"2026-08-09T23:54:14.495220Z","submitted_at":"2025-06-10T22:25:29Z","title":"A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO","version":1},"reference_index":129,"source":"pdf_text","source_observed_at":"2026-08-07T04:57:52.568195Z"},"links":{"cited_paper":"/paper/2502.06982","citing_paper":"/paper/2506.09275"},"observation_digest":"sha256:4b74e32cc8821244ee4d3f832a155b0353222787438747da1cf970e9e77966a9","observation_id":"41d4bd1b-5ccd-4729-80ed-24c41b4d0ce2","resolution":{"observed_at":"2026-08-07T04:57:52.568195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"cited_work":{"arxiv_id":"2502.06982","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.06982","snapshot_observed_at":"2026-07-01T21:36:15.542964Z","title":"Ma- chine learning fleet efficiency: analyzing and optimizing large-scale Google TPU systems with ML productivity goodput","venue":null,"work_id":"0a235eaf-8a18-40c5-9415-a3075a452543","year":2025},"citing_paper":{"arxiv_id":"2606.01161","last_updated":"2026-05-31T11:08:51Z","snapshot_observed_at":"2026-08-03T09:12:52.643122Z","submitted_at":"2026-05-31T11:08:51Z","title":"AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T16:37:20.774251Z"},"links":{"cited_paper":"/paper/2502.06982","citing_paper":"/paper/2606.01161"},"observation_digest":"sha256:dd36d474e9ef74d41b918942bb941a4643092394fbb57d7aaf3cdbbf90167bf2","observation_id":"56ade0ac-c6ae-4299-85e1-3e64c5c9d9e1","resolution":{"observed_at":"2026-07-01T21:36:15.545052Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06982","snapshot_observed_at":"2026-07-12T11:05:56.233115Z","title":"Machine learning fleet efficiency: Analyzing and optimizing large-scale Google TPU systems with ML productivity goodput, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.02558","last_updated":"2026-06-28T02:30:44Z","snapshot_observed_at":"2026-08-10T09:36:24.460698Z","submitted_at":"2026-06-28T02:30:44Z","title":"MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems","version":1},"reference_index":132,"source":"arxiv_source","source_observed_at":"2026-07-12T11:05:56.233115Z"},"links":{"cited_paper":"/paper/2502.06982","citing_paper":"/paper/2607.02558"},"observation_digest":"sha256:95b98366b4e399961039e29dd58f9d19c12baa1733cf5dc7008c759119c69e51","observation_id":"067f8b27-9930-4cb9-91ea-6a262abcfd68","resolution":{"observed_at":"2026-07-12T11:05:56.233115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06982","snapshot_observed_at":"2026-07-31T08:21:13.989830Z","title":"arXiv preprint arXiv:2502.06982 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28407","last_updated":"2026-07-30T15:55:29Z","snapshot_observed_at":"2026-08-07T07:35:43.518793Z","submitted_at":"2026-07-30T15:55:29Z","title":"A Taxonomy of Performance Metrics for the Distributed Computing Continuum","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-07-31T08:21:13.989830Z"},"links":{"cited_paper":"/paper/2502.06982","citing_paper":"/paper/2607.28407"},"observation_digest":"sha256:bc985a9f903e893a6bc93b10083faf10ed33ad0066644b53598013ab816cbd66","observation_id":"e937d750-074a-4c97-998a-3aa2b7833d3f","resolution":{"observed_at":"2026-07-31T08:21:13.989830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.06982/citation-record","integrity":"/paper/2502.06982/integrity","json":"/paper/2502.06982/citation-record.json","paper":"/paper/2502.06982"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:37.095395Z","title":null,"venue":null,"work_id":"7740d90b-63cf-476c-978d-7f019de0bbc7","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.159861Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:466bced1559ec5dc90cb64d19132ab5f887835bb694861dc231647d30b4f9a94","observation_id":"cea38c78-cb23-4e32-891e-8cfc71194c40","resolution":{"observed_at":"2026-08-08T14:14:37.099828Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:37.082698Z","title":null,"venue":null,"work_id":"02c98bd1-76b0-46dd-85b0-73a228d772e6","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.164732Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:2896fbd98f3c1690e48aae0a2e56b187e0be26070d9ba89d2a1696d36ccc3432","observation_id":"0b4dea27-9028-45d8-a809-974e48dd30b7","resolution":{"observed_at":"2026-08-08T14:14:37.087597Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08695","last_updated":"2016-05-31T19:46:10Z","snapshot_observed_at":"2026-08-10T07:10:18.392798Z","submitted_at":"2016-05-27T15:49:50Z","title":"TensorFlow: A system for large-scale machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08695","snapshot_observed_at":"2026-08-08T14:14:35.173870Z","title":"Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.173870Z"},"links":{"cited_paper":"/paper/1605.08695","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:9a02edd34d53d6e534d60bf0d43f32cbd93ce3372c6c4e119bf7c61d0376f226","observation_id":"ba93c145-4b2e-4506-89aa-11ff69ed5b23","resolution":{"observed_at":"2026-08-08T14:14:35.173870Z","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":"2026-08-08T14:14:37.069700Z","title":"Banning, Sumeer Bhola, Rick Buskens, Ming Chen, Xi Chen, Yoo Chung, Qin Jia, Nick Sakharov, George T","venue":null,"work_id":"721ae1eb-76ac-4e94-8056-eb78ef285ced","year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.178126Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:5bf1d2ff572109a69c9928b97dc24c63fa3cad744feffce6d5d4aec77d684289","observation_id":"25394408-4efc-4f53-b8ec-bde0f8a2f269","resolution":{"observed_at":"2026-08-08T14:14:37.074250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:37.057629Z","title":null,"venue":null,"work_id":"bfc1c364-05bf-450f-bbe1-54032d35af93","year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.182115Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:928a8ec86d1790075e04b083fb876681cd0ba9ff24f27a12481ff43441732b1f","observation_id":"62504617-2bff-4208-b436-f941d37f44c7","resolution":{"observed_at":"2026-08-08T14:14:37.061577Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.185891Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.185891Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e45252a7ce091cef186d7b79142f700dd12af901fa97e06e18b4b657e007e952","observation_id":"31b47966-264d-4026-820a-a17fd17da18e","resolution":{"observed_at":"2026-08-08T14:14:35.185891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.12533","last_updated":"2022-03-23T16:50:53Z","snapshot_observed_at":"2026-08-09T08:54:50.746746Z","submitted_at":"2022-03-23T16:50:53Z","title":"Pathways: Asynchronous Distributed Dataflow for ML","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.12533","snapshot_observed_at":"2026-08-08T14:14:35.190079Z","title":"Thekkath, and Yonghui Wu","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.190079Z"},"links":{"cited_paper":"/paper/2203.12533","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:190b78ca8026adbbc970dc79d8d9cf0a139b1dfc494835b436d6f678f3e2d4f6","observation_id":"0bee877f-3334-4be6-b069-8149d26f08bf","resolution":{"observed_at":"2026-08-08T14:14:35.190079Z","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":"2026-08-08T14:14:37.042019Z","title":null,"venue":null,"work_id":"cd869a2a-cba3-4636-a94e-6b694c8f2515","year":2009},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.194477Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:70a4f5c6af7aa20af596299656088917c0de73932377c4e4e57fb9922be770a1","observation_id":"6aa35d2a-d3ab-4c23-80fa-86e629d65c28","resolution":{"observed_at":"2026-08-08T14:14:37.049550Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:37.028597Z","title":null,"venue":null,"work_id":"2d6b6cdc-34f3-4e5a-88c5-26fd8cedbce6","year":2008},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.198519Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:2585ab62390c344b033a993f7b1c62f033b73d334093a34bb4c70201a03f3ff2","observation_id":"c6639a02-af13-4600-b624-732fa7140ab8","resolution":{"observed_at":"2026-08-08T14:14:37.033407Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3095.14314","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:36.375004Z","title":"Cooper, and Linda Torczon","venue":null,"work_id":"1c3d2e05-6246-4a52-86bd-eb87ae5454d2","year":1992},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.201998Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:deda9f8c6c357e4b2d5f1031207623d1601b0fa09d3ab5caf861dc73afcd81bb","observation_id":"6a921add-11f4-4482-9a0c-3307790786b9","resolution":{"observed_at":"2026-08-08T14:14:36.380989Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:37.014127Z","title":null,"venue":null,"work_id":"c514cc17-9024-4ef5-82e4-31c4c4dbc88f","year":1998},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.205703Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:438fe0601b15a1bbd5737077f0b7dd9d43b493f29efa3b97a37463a2b321d27a","observation_id":"b2da1d18-b349-4355-abe8-ac2c17255023","resolution":{"observed_at":"2026-08-08T14:14:37.018889Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-08T14:14:35.209953Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.209953Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:54dee4bab7564e715e8298f8df25aa6cb06ea3a70f752c562a99a19cb22b4612","observation_id":"3d8a7939-5e56-4b69-8c42-c3783a43f834","resolution":{"observed_at":"2026-08-08T14:14:35.209953Z","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":"2026-08-08T14:14:35.214069Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.214069Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e8a2113e74fb710421246dbc8d516e7a7951c63cc2e80ac99a12efcd222ab649","observation_id":"06eea592-b20f-4e4b-8d92-9c1fd16bf40b","resolution":{"observed_at":"2026-08-08T14:14:35.214069Z","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":"2026-08-08T14:14:36.993445Z","title":null,"venue":null,"work_id":"9066b108-a6fc-4682-8bf2-0183598f9861","year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.221745Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:fe0ad44eaf26168042a06ae9e2ebf0ef389a34e080b22ace37879bc0dac265fe","observation_id":"9f709f81-142b-4e48-b57a-23358e43ca57","resolution":{"observed_at":"2026-08-08T14:14:36.998340Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.217913Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.217913Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:a404b1f989d43cc3efa44743a3bbefae74c01a8c20716c10441bd9020d5cc03a","observation_id":"3249a713-8368-4699-b59e-6da122993ffa","resolution":{"observed_at":"2026-08-08T14:14:35.217913Z","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":"2026-08-08T14:14:36.970498Z","title":null,"venue":null,"work_id":"402402c0-37e8-4491-8a41-ceeebeebabfb","year":2010},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.229279Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:8f4cf47a80d98014576116a7d4363f7c632703f4a4d2b8294ddd732dc30a5cd4","observation_id":"1cdcf7a7-8a22-45ad-bcc6-363b710d7867","resolution":{"observed_at":"2026-08-08T14:14:36.973987Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.981692Z","title":null,"venue":null,"work_id":"58c5e2a0-fcc9-4bed-820e-e47f3a83c532","year":2012},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.225561Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:96151b8d7e4193fa07cc3690252f34bcb842ee157b892169abbdd58c7d20389b","observation_id":"3da094ce-d419-4776-90da-4cf7a41b2857","resolution":{"observed_at":"2026-08-08T14:14:36.986304Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.958983Z","title":null,"venue":null,"work_id":"15852cc8-5cc9-4f5f-8666-bb5555753ee2","year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.236342Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:2c99b36d3ea45520e2c4dc1ebd22ec253f0aaa43e7e34b3b2047072a89fe7ff0","observation_id":"271ae118-3e51-49a6-8c22-5fca3a4c9e90","resolution":{"observed_at":"2026-08-08T14:14:36.963328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3453.80819","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:36.278700Z","title":"Emer and Douglas W","venue":null,"work_id":"281de91d-b720-469b-80d3-c3afa6908f1b","year":1984},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.232556Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e3a5b4f341453a2f72c0e214e54cea0ae15fcbcdaa895efdc1a83a269e736e73","observation_id":"5d71858a-c111-4394-91c4-3007c6bd2bb5","resolution":{"observed_at":"2026-08-08T14:14:36.283984Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.936131Z","title":null,"venue":null,"work_id":"84fa5ac2-6754-4f60-b42e-624122d28652","year":2018},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.243564Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:0cfa77b1a2aaa6ce86f815e3f236c0b2cfc61711902489a13de5eeff1d86f8c9","observation_id":"3ada0bbe-4023-4306-949b-5ad8a1d8acc4","resolution":{"observed_at":"2026-08-08T14:14:36.939914Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.947896Z","title":null,"venue":null,"work_id":"33136d46-c479-46c5-926a-bc75752659c9","year":2004},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.239690Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:06d7464f3b078a7c68679826734f9c3fcb64fc90408366a9df5f05fdf2b08761","observation_id":"44db8799-8f3c-4dc5-badf-05346876a859","resolution":{"observed_at":"2026-08-08T14:14:36.951919Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.912784Z","title":null,"venue":null,"work_id":"44bf13ab-b122-400c-b7c8-4bab85ba9cb0","year":2007},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.250803Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:3709d2eb7a7091d4941792080e23201bd7e08c0cb7b129a0919cbb59d275f744","observation_id":"87fb889a-4061-43d3-bf8e-740bf237af1d","resolution":{"observed_at":"2026-08-08T14:14:36.916849Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.924720Z","title":null,"venue":null,"work_id":"7a784c83-7117-4995-87e0-897eaac1c156","year":2003},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.247043Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:f32322026000d831e35f7640800ee4c4d20349c4563a9421dfe968c30dcaaf08","observation_id":"aeaf1fd6-7599-4aaf-8326-02135d738274","resolution":{"observed_at":"2026-08-08T14:14:36.928462Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.886415Z","title":null,"venue":null,"work_id":"995786ad-a087-44bf-abec-29b4cae3b20f","year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.258072Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:676b7f97b7f0ab14ecd51e3c60b3b4bb02192d0a158c5d49e1b0a970ba7c47dd","observation_id":"3fb2793b-0597-426b-835a-8a363aa6a5ab","resolution":{"observed_at":"2026-08-08T14:14:36.890153Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.898526Z","title":"Hennessy and David A","venue":null,"work_id":"ff7d4c7a-28b4-4a4f-8b9f-9691da27fcdf","year":2019},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.254605Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:8195854d1c7b1814593fbeea9115a989c5bb471d1ca312f72a85c759f8d6aabb","observation_id":"18c5a57b-994b-45ff-ae44-aeb52ba82a7c","resolution":{"observed_at":"2026-08-08T14:14:36.903414Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.860825Z","title":null,"venue":null,"work_id":"cfe3bfd7-82ea-4270-a787-4618a54c185f","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.265555Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:9d3d891976c12d755aec185c0ea2b43c78d67f527423384d27cf73feff8f6c46","observation_id":"fa0088d6-7fc3-43aa-b781-393a2d03c52e","resolution":{"observed_at":"2026-08-08T14:14:36.865391Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.873513Z","title":null,"venue":null,"work_id":"565bd790-ed42-4901-beba-341147cfa9e5","year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.261802Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:7857d865f6552a66477a37354b4139a284a04b35d7fb6f3c531488912539398b","observation_id":"fab075ab-53f4-40d7-8332-10a85963d716","resolution":{"observed_at":"2026-08-08T14:14:36.878769Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.01433","last_updated":"2023-04-20T22:25:51Z","snapshot_observed_at":"2026-07-06T15:11:43.266427Z","submitted_at":"2023-04-04T00:52:46Z","title":"TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.01433","snapshot_observed_at":"2026-08-08T14:14:35.272822Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.272822Z"},"links":{"cited_paper":"/paper/2304.01433","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:b438b40f2bcc5f2c63df1a3e0312c5f2f6d76270f2f30fc4dc32aa19f2bb0602","observation_id":"a383b0ba-cb36-47ff-a4f3-5a3c501ef85f","resolution":{"observed_at":"2026-08-08T14:14:35.272822Z","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":"2026-08-08T14:14:35.269272Z","title":"Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.269272Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:f0aec5352ac591912aceac7ed5832b20e822c7faa7579c98f62d69c5e8f9584e","observation_id":"a04529fe-3d6e-4562-a63d-1f61baacdcc2","resolution":{"observed_at":"2026-08-08T14:14:35.269272Z","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":"2008.45362","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:36.109190Z","title":null,"venue":null,"work_id":"2f50beec-bb15-40cf-8a79-e99a44cb033e","year":2008},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.284853Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e5f433adb89e25b3d8d3e1018a98e95d92a9e5f219ddc2b21682b293b0bce436","observation_id":"f9abe851-84b4-4daf-96cf-3ffaf96d86d2","resolution":{"observed_at":"2026-08-08T14:14:36.116560Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.846332Z","title":"Jouppi, Cliff Young, Nishant Patil, David A","venue":null,"work_id":"1ab8a017-5dae-410d-991c-f8ff5f0d9803","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.276616Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:db20f86c2c27b92f910d615dd8b7d612a8ae20ccf3125a4437b28e71ae162296","observation_id":"7e35396e-d937-4097-995a-bb982854c260","resolution":{"observed_at":"2026-08-08T14:14:36.852533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.821777Z","title":null,"venue":null,"work_id":"e257bb8d-32a9-4022-9037-562e7b8fa730","year":2012},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.291882Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:3fd03e3444ba7dff912bdeb00cbae48edbcb14494fc8c2b6b8e2bd79c25efcac","observation_id":"04723395-cbe5-4fd1-9ddc-be9bcb5f78dc","resolution":{"observed_at":"2026-08-08T14:14:36.825662Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.806831Z","title":null,"venue":null,"work_id":"61dc47b4-9030-4746-97af-97da11024177","year":2022},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.295663Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:48ab1fc37c4d5ea5ae526ebdf3c1af2c24aa6f16bbb0724b36fb7bd267b1edc0","observation_id":"40174fd1-2797-43c3-ae8e-0389fe5574ee","resolution":{"observed_at":"2026-08-08T14:14:36.811728Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.834340Z","title":null,"venue":null,"work_id":"dfaea593-9822-4439-9326-be23734f069c","year":2015},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.288453Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:bf4795c669c81bc04a082d0a13177c3a281c4d39c8f7544c0829410ef0d152f0","observation_id":"8aa90ba5-3253-4e87-9805-7f31fa76a548","resolution":{"observed_at":"2026-08-08T14:14:36.838478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.782211Z","title":null,"venue":null,"work_id":"a345a1ff-8187-4939-b1a9-0e7635f93e37","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.303755Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e88424d422be65eb6c337c5af733cbeb1e99c0363282ce0939b1b7f6a880fad3","observation_id":"4ac63b1a-3cd3-4886-8bbc-eb203aa1bbfd","resolution":{"observed_at":"2026-08-08T14:14:36.786613Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.758712Z","title":null,"venue":null,"work_id":"c0267f73-4236-487e-8d10-c45268d6588f","year":2014},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.312034Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:5193583a1cf83a7d1dcda19c6ab675a993f175d09b1fcd63af06c8413f35a74c","observation_id":"68f558cb-ed27-41ae-bfcc-4886d4f215bd","resolution":{"observed_at":"2026-08-08T14:14:36.762357Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.794813Z","title":null,"venue":null,"work_id":"774bacac-0a34-4862-acfd-91c8c7aefe62","year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.300028Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:2729127aa5bcc0fc972073ea055ee83b825c0ac19b0c0101d2e99990c4980f40","observation_id":"edde4ab9-8bd1-46dd-8c57-a6843fd05f34","resolution":{"observed_at":"2026-08-08T14:14:36.799045Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.724020Z","title":null,"venue":null,"work_id":"cb6fee4a-520a-4539-9779-6916322d1a09","year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.323018Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:1863466cf527e20ea0ce663b41e188b76225a6b4e2a1c0dfac05684569238b09","observation_id":"cd04e0e4-912f-47fb-a144-b30e5033e5a5","resolution":{"observed_at":"2026-08-08T14:14:36.728319Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.326881Z","title":"Mustafa Rafique, Franck Cappello, and Bogdan Nicolae","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.326881Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:139f657d1c93ba0c756bf2865c67f13f3c85a80e4dcd341f6a22782e75b2af31","observation_id":"ab82423b-1998-4c8e-bc28-340e0a3c3a3e","resolution":{"observed_at":"2026-08-08T14:14:35.326881Z","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":"2026-08-08T14:14:36.711530Z","title":null,"venue":null,"work_id":"241e2ed1-ff55-41e1-bc74-49617fa1563c","year":2005},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.330529Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:8106410ad91b7d3587d078f0fbaf904357fd1787da23f81a71b2933cbe58e820","observation_id":"b89ee389-a29c-4bf0-adc7-c68a0b78d1f3","resolution":{"observed_at":"2026-08-08T14:14:36.716124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.315520Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.315520Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:d7fcca13cb8b08a9866e5e6ef293136e89bca8ca04469eda4dec1f013045a4aa","observation_id":"1fd3196a-60c5-4b1d-b348-6451670d219c","resolution":{"observed_at":"2026-08-08T14:14:35.315520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.00091","last_updated":"2019-05-31T21:51:16Z","snapshot_observed_at":"2026-08-02T13:53:54.252223Z","submitted_at":"2019-05-31T21:51:16Z","title":"Deep Learning Recommendation Model for Personalization and Recommendation Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.00091","snapshot_observed_at":"2026-08-08T14:14:35.337895Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.337895Z"},"links":{"cited_paper":"/paper/1906.00091","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:7a3397e75695ee7fd7b4ff74506436a944dec6d673251594aa4e582c5e13ece8","observation_id":"d8522ad1-6365-45dc-9a64-2d62889e1620","resolution":{"observed_at":"2026-08-08T14:14:35.337895Z","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":"2026-08-08T14:14:35.341659Z","title":"Wozniak, George Bosilca, Matthieu Dorier, and Franck Cappello","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.341659Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:cac87f0fccadf265a9405bf8a6e348d2a4c177c11ee1ed80b4dfb167a89b20c6","observation_id":"46a99af2-e73d-43ec-bd45-7fc432253e3d","resolution":{"observed_at":"2026-08-08T14:14:35.341659Z","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":"2026-08-08T14:14:36.698837Z","title":"Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, David Stafford, Tony Tung, and Venkateshwaran Venkataramani","venue":null,"work_id":"7d103ab0-2080-4a1b-b5ba-9e8664132a82","year":2013},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.345317Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:56c8cdd06bf79b3b1ea8bbfb82e1c254ced32206251289dfcc8e5a917c96445c","observation_id":"d7582782-e7b2-40e7-aa22-f70f980fc257","resolution":{"observed_at":"2026-08-08T14:14:36.703233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.00075","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:35.856689Z","title":null,"venue":null,"work_id":"1be2fb53-949d-4245-8a8e-6d4970ab2be4","year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.348829Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:39b0819347f23e5a3974e12398df555f2372cff15fbb646f27aa7208a0ca9491","observation_id":"e0bd02fb-8097-4562-94ec-647cb41c74ff","resolution":{"observed_at":"2026-08-08T14:14:35.862508Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.12127","last_updated":"2021-02-23T22:56:12Z","snapshot_observed_at":"2026-07-06T10:36:31.297391Z","submitted_at":"2021-01-28T17:16:46Z","title":"tf.data: A Machine Learning Data Processing Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.12127","snapshot_observed_at":"2026-08-08T14:14:35.334066Z","title":"Murray, Jiri Simsa, Ana Klimovic, and Ihor Indyk","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.334066Z"},"links":{"cited_paper":"/paper/2101.12127","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:40b9df9c8838ebc565bb545fe4a615a1429b0176f6fd00343f3cb40dc3f3541e","observation_id":"0ecf53de-5d5d-4554-aa1e-9b436101f1bf","resolution":{"observed_at":"2026-08-08T14:14:35.334066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-08T14:14:35.356616Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.356616Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:6146ebcb051b63d6bc914bc1901659784cd49ec5ab722fd71a184576eabbd0cb","observation_id":"4364c9d7-070c-4f5b-9c59-84e7e8ba1517","resolution":{"observed_at":"2026-08-08T14:14:35.356616Z","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":"2026-08-08T14:14:36.674851Z","title":null,"venue":null,"work_id":"83949f02-0477-476c-bd0e-504a63bd42c7","year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.360582Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:13e4a5553ab9b403607f0684a708d8912f653a1f3061f37cc463021576e39e8b","observation_id":"f925e958-84f0-4b51-ac10-d7dce73ce6dd","resolution":{"observed_at":"2026-08-08T14:14:36.678965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.364656Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.364656Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:a2fe2f1043328d13a5c9658809d0d497da8304cab9b3ba9e806b9bd9d98b99be","observation_id":"47937785-8b0a-4ee3-ab3f-aeec2bed55c5","resolution":{"observed_at":"2026-08-08T14:14:35.364656Z","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":"2024.34094","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:14:35.757656Z","title":null,"venue":null,"work_id":"fee981ba-b386-4754-9fa7-778e8d50a624","year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.373018Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:8993012c3c2bdb2029faecee0bbbb553a1a4e94a1b02336567ab8519cbc18945","observation_id":"f0baefb9-e41d-4293-a820-61eca9060050","resolution":{"observed_at":"2026-08-08T14:14:35.763370Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.687347Z","title":null,"venue":null,"work_id":"cbc44844-d1bb-40da-bb1f-4274dd7a72a1","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.352212Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:d76327e1bbff09f533405bc1167b4bc93655e5de90f5c8c0b219052e3bb4f3c3","observation_id":"c3a6bd37-7003-4a44-a5c7-ce46522edf43","resolution":{"observed_at":"2026-08-08T14:14:36.691185Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.630898Z","title":null,"venue":null,"work_id":"7c1d3355-af69-40d8-9993-93836af6293b","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.380584Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:abd27739cdd202257211f6479c7b7608ebbe6c83e4270fea27c630391cfb9d07","observation_id":"fe464644-1d9c-4018-b17d-0f4c6115eb9f","resolution":{"observed_at":"2026-08-08T14:14:36.634749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.618265Z","title":null,"venue":null,"work_id":"c132df6a-6d1d-4119-a510-29b6d44a4bca","year":2009},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.383699Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:9f3b6ce2f67608e099eab6f646c8244b062ef11568d3bd5746594a6bd581c430","observation_id":"890106eb-518b-4e56-ba70-5d9027342048","resolution":{"observed_at":"2026-08-08T14:14:36.622281Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-07-06T05:27:13.416519Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-08T14:14:35.387292Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.387292Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:bd52637c21a56b77d45ecdc3463d4556d1627a3ea0321b03189a17234661ec7c","observation_id":"d26cc555-a91d-4763-94ad-c2d91c7904f3","resolution":{"observed_at":"2026-08-08T14:14:35.387292Z","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":"2026-08-08T14:14:36.654616Z","title":"In Proceedings of Ma- chine Learning and Systems , D","venue":null,"work_id":"60f50d26-d10f-4586-bc34-e959f0f6c8fb","year":2023},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.368606Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:45927829b8ccbaf29ec3ebc8070b40a3ea3f88e45bf358050a2fcb99c279061e","observation_id":"7a8fa365-89e1-4dae-a7c3-a0c6220d91bb","resolution":{"observed_at":"2026-08-08T14:14:36.659203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.593123Z","title":null,"venue":null,"work_id":"38656d99-dfe5-4e1c-8537-4a22eab6384d","year":2019},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.399359Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:b91160c1e99e98f4817046eaaec586630f85f737690b83029c6216748f4742de","observation_id":"5906e66f-f389-487c-b4ce-db3b8e78b1b2","resolution":{"observed_at":"2026-08-08T14:14:36.596928Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.642444Z","title":null,"venue":null,"work_id":"5887ea51-58cd-4f19-9a61-1d6fcedd265d","year":2015},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.376716Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:ee33accfd47bf9f45a028e7ac686e13dcaf1b492147947b02dcdd3b2238fbf52","observation_id":"ec8ef137-7b5d-4351-ace9-ab1db94c451f","resolution":{"observed_at":"2026-08-08T14:14:36.646435Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-08T14:14:35.406525Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.406525Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:87b94963d525646263cdbc7e3f95531cc95d5c2d466dc8f7982e5e05bffbbb4f","observation_id":"08807a89-72ff-4ac9-bd5d-27972fda8f58","resolution":{"observed_at":"2026-08-08T14:14:35.406525Z","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":"2026-08-08T14:14:36.568314Z","title":null,"venue":null,"work_id":"24ca1aa4-2da5-4e14-8a53-e5acab0b756b","year":2023},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.410326Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e05af21073828672b7a70cb6dedb522bb83b9b71097364465714f3cb5493fc6c","observation_id":"853f7608-fb94-49a3-bd3f-3d51f898e901","resolution":{"observed_at":"2026-08-08T14:14:36.572563Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.555230Z","title":null,"venue":null,"work_id":"26c982ed-a8cc-4449-8cf7-80a5a59c6a80","year":2008},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.413646Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:e23e945ed3399d2488738bbe5720667cb690fba57cfc91a46fe5a9e7d92f1a12","observation_id":"904ed2ba-818e-456a-b955-e57284ea0b3a","resolution":{"observed_at":"2026-08-08T14:14:36.559271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.604515Z","title":null,"venue":null,"work_id":"40a47d3e-0dde-420e-bed8-c7335f72be2e","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.391018Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:d2b847df983e41697a74f5fe645ef55b364be286dcd7479eb63f5f0435e2820e","observation_id":"ac4921a7-110b-4bfb-85dc-a81c9c424164","resolution":{"observed_at":"2026-08-08T14:14:36.609749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.535685Z","title":"Korupolu, David Oppenheimer, Eric Tune, and John Wilkes","venue":null,"work_id":"56170e9a-0bd6-4409-8a2b-01b43012640d","year":2015},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.422067Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:11d1c5f9c5118202639ecc19a8844d16c8d9c8dad65e7eff132bd96a8702c120","observation_id":"1c7deb3f-fc0a-47f7-aeee-3d452d9d5be2","resolution":{"observed_at":"2026-08-08T14:14:36.539676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.523667Z","title":null,"venue":null,"work_id":"3f7a3c8f-f3bf-4aa1-bcf5-aa9726bea2bb","year":2022},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.425471Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:20fdcb27b3c8517882038c45c80331cf54d018cb1a2be50f35c5d6ef906fbc9b","observation_id":"1bdd4758-21ba-4026-91b0-1158d0faa395","resolution":{"observed_at":"2026-08-08T14:14:36.527478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:36.581023Z","title":null,"venue":null,"work_id":"e09943f9-394f-4c54-9027-333c8566925c","year":2016},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.402764Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:9f4c9caa1b55099003f447ed15d22fcb0907b650b040ab40a881595d6747d151","observation_id":"6118bd45-25b0-4fc1-914f-1ddd3305fad0","resolution":{"observed_at":"2026-08-08T14:14:36.585251Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10423","last_updated":"2022-10-11T16:02:43Z","snapshot_observed_at":"2026-08-10T08:14:28.799425Z","submitted_at":"2021-02-20T19:25:09Z","title":"An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.10423","snapshot_observed_at":"2026-08-08T14:14:35.432953Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.432953Z"},"links":{"cited_paper":"/paper/2102.10423","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:b77aca76b67f85a02400b8e425b8ad6185e7585969b3cc9f52f5bd50b240c2c3","observation_id":"fdf12497-143c-417d-9602-cacddbb99a31","resolution":{"observed_at":"2026-08-08T14:14:35.432953Z","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":"2026-08-08T14:14:36.510899Z","title":"Yoo, Morris A","venue":null,"work_id":"3b49d28e-b1e7-4e91-995d-a11b4cf22ce6","year":2003},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.436777Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:6167a213e54c50226c727842101e31603e87c4700bdd63e1359ecdda35abb327","observation_id":"39d43346-6e2b-489e-b7cb-18c1aa711eda","resolution":{"observed_at":"2026-08-08T14:14:36.515371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.09373","last_updated":"2022-04-22T23:51:04Z","snapshot_observed_at":"2026-07-06T11:40:17.025130Z","submitted_at":"2021-08-20T21:09:34Z","title":"Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training","version":4},"cited_work":{"arxiv_id":"2108.09373","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.09373","snapshot_observed_at":"2026-08-08T14:14:35.562320Z","title":"Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training","venue":"cs.DC","work_id":"5a9c0b67-0b85-41b3-873f-80c07c0eb9a7","year":2021},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.440366Z"},"links":{"cited_paper":"/paper/2108.09373","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:6380851ab297fbd4e652e6128498819b19d931b515362a01470326712947d215","observation_id":"10ca42e5-1ffb-4ccd-ba5b-00e93d4f239b","resolution":{"observed_at":"2026-08-08T14:14:35.568865Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.417476Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.417476Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:4d9cec041c978a7024a68a2d865c739e02576488346f1f96c445928fe319eda3","observation_id":"3c795c23-a7ed-4002-a3c5-1ffc392fa3d6","resolution":{"observed_at":"2026-08-08T14:14:35.417476Z","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":"2026-08-08T14:14:36.498571Z","title":null,"venue":null,"work_id":"d4f32faa-ac89-495a-b024-875add003cf4","year":2024},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.448676Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:348af6966e3525dd8dfbaf446fa211bcb0c543ee2f0bec8039a701e7c7087b28","observation_id":"db9cc7a8-a2bb-4a3e-ab44-a1e8936db368","resolution":{"observed_at":"2026-08-08T14:14:36.502598Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:14:35.429521Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.429521Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:d37302715c5077ce0e6f905dbdfdff0e69996b62cf6bb2d2d1cd8cc4e3ff9376","observation_id":"758981c4-9a02-457b-af2b-5a42ce20c145","resolution":{"observed_at":"2026-08-08T14:14:35.429521Z","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":"2026-08-08T14:14:35.444334Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.444334Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:ad5f01d3d4dd83c8f4edf83a318d9f43096c51fd80ac6073d649872a9aa8489c","observation_id":"fc3ae17a-213b-4917-98b6-7e871893365c","resolution":{"observed_at":"2026-08-08T14:14:35.444334Z","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":"2026-08-08T14:14:36.737376Z","title":"In Proceedings of the 44th annual IEEE/ACM International Symposium on Microarchitecture","venue":null,"work_id":"30109c51-3dd0-42d1-80f2-3f7576f489f9","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.319225Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:137efe41e511d1aa3463d68e7bb0dc01fe2e30ac02de7253b421b1095e1ae9de","observation_id":"2dfe1f75-e9ed-41f0-a3d4-35e47168f5fb","resolution":{"observed_at":"2026-08-08T14:14:36.741531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.04467","last_updated":"2016-03-16T16:57:12Z","snapshot_observed_at":"2026-07-06T04:49:28.300758Z","submitted_at":"2016-03-14T20:50:20Z","title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.04467","snapshot_observed_at":"2026-08-08T14:14:35.169430Z","title":"arXiv:1603.04467 [cs.DC] https://arxiv.org/abs/1603.04467","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.169430Z"},"links":{"cited_paper":"/paper/1603.04467","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:eca557db28789ca962e8f21126ab987b658044de1f82d5391b15b33b58ff3033","observation_id":"a8f6a0e4-5c66-44ee-ab88-74857485dbf5","resolution":{"observed_at":"2026-08-08T14:14:35.169430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04760","last_updated":"2017-04-16T12:07:54Z","snapshot_observed_at":"2026-08-07T03:34:50.918938Z","submitted_at":"2017-04-16T12:07:54Z","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04760","snapshot_observed_at":"2026-08-08T14:14:35.280728Z","title":"CoRR abs/1704.04760 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.280728Z"},"links":{"cited_paper":"/paper/1704.04760","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:449ccba08c46b9b91d67a6081db86fce186f9e98f0ae4f96c36cc4e1578cacdb","observation_id":"82e0ff50-14c6-4299-ae00-bc1275b92bbc","resolution":{"observed_at":"2026-08-08T14:14:35.280728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02075","last_updated":"2020-10-05T15:12:52Z","snapshot_observed_at":"2026-07-06T10:01:35.418341Z","submitted_at":"2020-10-05T15:12:52Z","title":"Learned Hardware/Software Co-Design of Neural Accelerators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02075","snapshot_observed_at":"2026-08-08T14:14:35.395499Z","title":"CoRR abs/2010.02075 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.395499Z"},"links":{"cited_paper":"/paper/2010.02075","citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:a7ddac4d7e8c8cbc8364a80c7efd9111ca401c1d2c6bbd79f1317e0066ae18e3","observation_id":"39ad9462-27af-42f6-a0ae-b7ac694bcc37","resolution":{"observed_at":"2026-08-08T14:14:35.395499Z","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":"2026-08-08T14:14:36.770136Z","title":"In International Conference on High Performance Computing","venue":null,"work_id":"b173ec8b-c141-4816-8b4a-fa293c3153d9","year":null},"citing_paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-08T14:14:35.308185Z"},"links":{"citing_paper":"/paper/2502.06982"},"observation_digest":"sha256:8a1792028eb6a8a69c9affef767da1c26e3cb848b923965faf7a2ae18a20a6b8","observation_id":"2ba9a89e-3730-4418-9db9-519699aa3567","resolution":{"observed_at":"2026-08-08T14:14:36.774606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.06982","last_updated":"2025-04-20T08:02:36Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T11:06:24.298477Z","submitted_at":"2025-02-10T19:20:02Z","title":"Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":1,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":61,"verified_exact":2,"verified_fuzzy":8},"total_outbound_references":76},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 4 inbound Pith citation observations for arXiv:2502.06982."}