{"as_of":"2026-08-09T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:233de51d8b569f3ae6f356a96b2935590de1a2d38458758949e67287420dc52d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:57:52.270151Z","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":"2026-07-04T14:19:54.683703Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-08-07T04:57:52.270151Z","title":"Echo: Simulating distributed training at scale.arXiv preprint arXiv:2412.12487, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09275","last_updated":"2025-06-10T22:25:29Z","snapshot_observed_at":"2026-08-07T14:32:01.746757Z","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":35,"source":"pdf_text","source_observed_at":"2026-08-07T04:57:52.270151Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2506.09275"},"observation_digest":"sha256:52a75547720151bf7056b4e1b07baaceb2f8edb892a94f33c28c48ac4634073d","observation_id":"48018a51-2778-4d1c-b45a-7798bea4a185","resolution":{"observed_at":"2026-08-07T04:57:52.270151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2604.12090","last_updated":"2026-04-13T21:59:03Z","snapshot_observed_at":"2026-08-05T22:06:20.829268Z","submitted_at":"2026-04-13T21:59:03Z","title":"Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T15:02:42.806073Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2604.12090"},"observation_digest":"sha256:54713cf7a60fb67d3dbc7b97c2a211f0d4090f2039c577c24facd210a327fe98","observation_id":"3d940012-5e46-486e-9282-043ede15da10","resolution":{"observed_at":"2026-05-11T11:16:09.565279Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2605.17164","last_updated":"2026-05-19T23:51:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-16T21:28:22Z","title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-20T14:11:58.106397Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2605.17164"},"observation_digest":"sha256:40db94564b740515893af1901a6860aef53bee3fad7d794e27890fd92eba9f3f","observation_id":"72983f21-48ff-474d-8117-3f19c1ad5e24","resolution":{"observed_at":"2026-05-20T14:13:21.214924Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2605.17164","last_updated":"2026-05-19T23:51:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-16T21:28:22Z","title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-21T08:55:31.298030Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2605.17164"},"observation_digest":"sha256:2ed078e7e00123f9cca8fe094db65871f7f85c926f5e5b10e03a5ddbe3a33124","observation_id":"d27df8cd-b947-4f38-a8d1-6a9bc076b057","resolution":{"observed_at":"2026-05-21T08:59:55.763826Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2605.21312","last_updated":"2026-05-20T15:40:18Z","snapshot_observed_at":"2026-08-02T01:15:29.579250Z","submitted_at":"2026-05-20T15:40:18Z","title":"Frontier: Towards Comprehensive and Accurate LLM Inference Simulation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-21T03:47:49.835773Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2605.21312"},"observation_digest":"sha256:2bed0195a9afa8fa1b89f0b974f1656095e847ab8b3087f11c4a6c8491f0aa6f","observation_id":"d34f3392-d6a5-4ad3-8c64-81cc8b7f32f5","resolution":{"observed_at":"2026-05-21T03:49:31.117985Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2606.03077","last_updated":"2026-06-10T06:28:18Z","snapshot_observed_at":"2026-07-06T23:43:22.513369Z","submitted_at":"2026-06-02T03:09:13Z","title":"Libra: Efficient Resource Management for Agentic RL Post-Training","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T11:02:00.385932Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2606.03077"},"observation_digest":"sha256:55c0dca22f926b48aa27da99dc339c7403af45558d7a081d0a41a72ec5062dfa","observation_id":"c1023920-e9a2-406c-8119-6488de3b26b2","resolution":{"observed_at":"2026-07-02T02:16:27.216566Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":"2412.12487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-07-04T14:19:54.683703Z","title":"Echo: Simulating distributed training at scale","venue":null,"work_id":"9dfc4b80-82a3-49a7-91d8-c09d97a4a8d9","year":2024},"citing_paper":{"arxiv_id":"2606.26633","last_updated":"2026-06-25T05:50:46Z","snapshot_observed_at":"2026-08-06T17:28:20.433752Z","submitted_at":"2026-06-25T05:50:46Z","title":"Simulating Unified Tensor Resharding in heterogeneous AI systems","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T04:00:26.409243Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2606.26633"},"observation_digest":"sha256:0084ae19ac581a2d2513bee6d39a34d660de7cb628465e4e4542754819701e92","observation_id":"ff382ffe-b05d-47ba-bf32-5e5ebf626e98","resolution":{"observed_at":"2026-07-04T14:19:54.686367Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-08-01T04:49:45.597984Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.597984Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ddbd5ccc8cd7ccc3c6da01105657a169421063a6ca15b8383ef2b31cc6bd9528","observation_id":"d50c1229-7fe5-4da3-912f-165a901a2dd1","resolution":{"observed_at":"2026-08-01T04:49:45.597984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.12487/citation-record","integrity":"/paper/2412.12487/integrity","json":"/paper/2412.12487/citation-record.json","paper":"/paper/2412.12487"},"outbound":[],"paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T20:08:16.471575Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2412.12487."}