{"as_of":"2026-08-09T03:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9a3036023ad7aba0b49a313e4d9d524985b09358ff08edb2a528743a18d11486","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:33:05.355547Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T14:33:10.437367Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.03158","last_updated":"2025-07-31T13:53:50Z","snapshot_observed_at":"2026-08-09T00:20:17.156468Z","submitted_at":"2024-02-05T16:27:59Z","title":"Optimal and Near-Optimal Adaptive Vector Quantization","version":2},"cited_work":{"arxiv_id":"2402.03158","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.03158","snapshot_observed_at":"2026-08-07T14:33:10.437367Z","title":"Optimal and Near-Optimal Adaptive Vector Quantization","venue":"cs.LG","work_id":"e820f836-9d49-4d61-b061-3bd923b74dfe","year":2024},"citing_paper":{"arxiv_id":"2505.18563","last_updated":"2025-05-24T07:06:36Z","snapshot_observed_at":"2026-08-07T14:27:16.669960Z","submitted_at":"2025-05-24T07:06:36Z","title":"PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:33:05.355547Z"},"links":{"cited_paper":"/paper/2402.03158","citing_paper":"/paper/2505.18563"},"observation_digest":"sha256:0ba8df0fa4e155e7b9d4c192ebde00f5aafacf9170d98bc7c100a3594a9c4860","observation_id":"660b1518-15a4-463c-8e75-2399b1d5d1ed","resolution":{"observed_at":"2026-08-07T14:33:10.495984Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2402.03158/citation-record","integrity":"/paper/2402.03158/integrity","json":"/paper/2402.03158/citation-record.json","paper":"/paper/2402.03158"},"outbound":[],"paper":{"arxiv_id":"2402.03158","last_updated":"2025-07-31T13:53:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T00:20:17.156468Z","submitted_at":"2024-02-05T16:27:59Z","title":"Optimal and Near-Optimal Adaptive Vector Quantization"},"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 1 inbound Pith citation observation for arXiv:2402.03158."}