{"as_of":"2026-08-14T09:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8cfd07ee2a29407b79ffe255c7ba23d744c3ec220e09ae021723296124917b3d","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:26:04.469761Z","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":"2026-05-21T08:59:55.774756Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.05370","last_updated":"2025-08-07T13:15:59Z","snapshot_observed_at":"2026-08-05T23:24:02.256639Z","submitted_at":"2025-08-07T13:15:59Z","title":"Simulating LLM training workloads for heterogeneous compute and network infrastructure","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.05370","snapshot_observed_at":"2026-08-05T23:26:04.469761Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.05377","last_updated":"2025-08-07T13:21:00Z","snapshot_observed_at":"2026-08-13T07:49:29.241741Z","submitted_at":"2025-08-07T13:21:00Z","title":"Does Multimodality Improve Recommender Systems as Expected? A Critical Analysis and Future Directions","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T23:26:04.469761Z"},"links":{"cited_paper":"/paper/2508.05370","citing_paper":"/paper/2508.05377"},"observation_digest":"sha256:d4795d20a3df071bc3e4b618a9576f51c18a2297334be472b9b4415a955e5889","observation_id":"592b554b-db53-4385-be69-af70bd19443d","resolution":{"observed_at":"2026-08-05T23:26:04.469761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.05370","last_updated":"2025-08-07T13:15:59Z","snapshot_observed_at":"2026-08-05T23:24:02.256639Z","submitted_at":"2025-08-07T13:15:59Z","title":"Simulating LLM training workloads for heterogeneous compute and network infrastructure","version":1},"cited_work":{"arxiv_id":"2508.05370","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.05370","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Simulating llm training work- loads for heterogeneous compute and network in- frastructure","venue":null,"work_id":"0a95e6fa-3277-491f-a79f-349b835f2ed3","year":2025},"citing_paper":{"arxiv_id":"2605.17164","last_updated":"2026-05-19T23:51:14Z","snapshot_observed_at":"2026-08-14T07:01:53.166681Z","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":6,"source":"pdf_text","source_observed_at":"2026-05-20T14:11:58.106397Z"},"links":{"cited_paper":"/paper/2508.05370","citing_paper":"/paper/2605.17164"},"observation_digest":"sha256:6a081454b5be0391af0244463039e50a8de00d455671ac248a1ec4d8b642fc43","observation_id":"911a80a2-2dee-4981-9c2d-65c73f9342ed","resolution":{"observed_at":"2026-05-20T14:13:21.190468Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.05370","last_updated":"2025-08-07T13:15:59Z","snapshot_observed_at":"2026-08-05T23:24:02.256639Z","submitted_at":"2025-08-07T13:15:59Z","title":"Simulating LLM training workloads for heterogeneous compute and network infrastructure","version":1},"cited_work":{"arxiv_id":"2508.05370","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.05370","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Simulating llm training work- loads for heterogeneous compute and network in- frastructure","venue":null,"work_id":"0a95e6fa-3277-491f-a79f-349b835f2ed3","year":2025},"citing_paper":{"arxiv_id":"2605.17164","last_updated":"2026-05-19T23:51:14Z","snapshot_observed_at":"2026-08-14T07:01:53.166681Z","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":8,"source":"pdf_text","source_observed_at":"2026-05-21T08:55:31.298030Z"},"links":{"cited_paper":"/paper/2508.05370","citing_paper":"/paper/2605.17164"},"observation_digest":"sha256:c1c9895c6d26a7f29626531add8c095f1f35d800c0706c3f3246340f5f873660","observation_id":"53d0dac3-c8d3-442f-ab96-880925d3946b","resolution":{"observed_at":"2026-05-21T08:59:55.776519Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.05370/citation-record","integrity":"/paper/2508.05370/integrity","json":"/paper/2508.05370/citation-record.json","paper":"/paper/2508.05370"},"outbound":[],"paper":{"arxiv_id":"2508.05370","last_updated":"2025-08-07T13:15:59Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-05T23:24:02.256639Z","submitted_at":"2025-08-07T13:15:59Z","title":"Simulating LLM training workloads for heterogeneous compute and network infrastructure"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2508.05370."}