{"as_of":"2026-08-08T15:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cc952e95c63c98858437e16d422608b77ab3d6465c1aa071a75e7e3759b028ea","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T14:44:19.129246Z","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":"2026-05-24T06:34:01.044705Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.02643","last_updated":"2022-02-05T21:19:41Z","snapshot_observed_at":"2026-07-06T12:34:47.800121Z","submitted_at":"2022-02-05T21:19:41Z","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","version":1},"cited_work":{"arxiv_id":"2202.02643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.02643","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training","venue":null,"work_id":"5b1a6890-b1c2-4a8d-a84f-7c9c2c68c6f3","year":2022},"citing_paper":{"arxiv_id":"2310.02277","last_updated":"2026-04-30T14:28:10Z","snapshot_observed_at":"2026-07-31T12:44:57.623721Z","submitted_at":"2023-09-29T22:55:06Z","title":"Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs \"Difficult\" Downstream Tasks in LLMs","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-24T06:33:48.456209Z"},"links":{"cited_paper":"/paper/2202.02643","citing_paper":"/paper/2310.02277"},"observation_digest":"sha256:1805b8a2b4d96ec4ef38e5a370e16ea57f4e957b07aa6a7f69a6b511433b8237","observation_id":"6d0ee260-f0d5-4c76-93af-eef4a0c64a89","resolution":{"observed_at":"2026-05-24T06:34:01.047986Z","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":"2202.02643","last_updated":"2022-02-05T21:19:41Z","snapshot_observed_at":"2026-07-06T12:34:47.800121Z","submitted_at":"2022-02-05T21:19:41Z","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.02643","snapshot_observed_at":"2026-08-07T14:44:19.129246Z","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17909","last_updated":"2025-05-23T13:53:21Z","snapshot_observed_at":"2026-08-07T18:19:48.354728Z","submitted_at":"2025-05-23T13:53:21Z","title":"NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T14:44:19.129246Z"},"links":{"cited_paper":"/paper/2202.02643","citing_paper":"/paper/2505.17909"},"observation_digest":"sha256:7a0c1e1917bdf5deea6ecc8939cda16d5bac1d420afa68ab3d1f5bd9b518be67","observation_id":"1fa92b7c-f112-4c2e-b429-afe41b7667f2","resolution":{"observed_at":"2026-08-07T14:44:19.129246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.02643","last_updated":"2022-02-05T21:19:41Z","snapshot_observed_at":"2026-07-06T12:34:47.800121Z","submitted_at":"2022-02-05T21:19:41Z","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","version":1},"cited_work":{"arxiv_id":"2202.02643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.02643","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training","venue":null,"work_id":"5b1a6890-b1c2-4a8d-a84f-7c9c2c68c6f3","year":2022},"citing_paper":{"arxiv_id":"2603.06003","last_updated":"2026-04-11T04:36:36Z","snapshot_observed_at":"2026-07-06T22:48:04.438669Z","submitted_at":"2026-03-06T08:02:58Z","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-15T14:34:48.524592Z"},"links":{"cited_paper":"/paper/2202.02643","citing_paper":"/paper/2603.06003"},"observation_digest":"sha256:055a6e2a59d1c2c9ea06e5cf585418d44e1122670054397d6a89d30d1eee6bde","observation_id":"fa3a6950-d271-4814-8003-e2ff2541f920","resolution":{"observed_at":"2026-05-15T14:35:55.571155Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2202.02643/citation-record","integrity":"/paper/2202.02643/integrity","json":"/paper/2202.02643/citation-record.json","paper":"/paper/2202.02643"},"outbound":[],"paper":{"arxiv_id":"2202.02643","last_updated":"2022-02-05T21:19:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:34:47.800121Z","submitted_at":"2022-02-05T21:19:41Z","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.02643."}