{"as_of":"2026-08-07T19:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:747211c3d4bf48259c196dc2cdd1ef7b5a8935b5804758157a97d38df683aabf","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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":"2026-08-07T12:46:42.859488Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":2,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.04744","last_updated":"2024-02-07T10:55:59Z","snapshot_observed_at":"2026-08-07T10:39:20.795187Z","submitted_at":"2024-02-07T10:55:59Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04744","snapshot_observed_at":"2026-08-07T12:46:42.859488Z","title":"Progressive gradient flow for robust n: M sparsity training in transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23949","last_updated":"2025-05-29T18:59:43Z","snapshot_observed_at":"2026-08-07T12:36:03.040420Z","submitted_at":"2025-05-29T18:59:43Z","title":"TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:46:42.859488Z"},"links":{"cited_paper":"/paper/2402.04744","citing_paper":"/paper/2505.23949"},"observation_digest":"sha256:f93cf5c98fe7f5ec288c55b34152ebfd1fb2329153fabae7ab0ca8a3a5bf03b2","observation_id":"02d71e93-905a-4b71-974c-470125c9f663","resolution":{"observed_at":"2026-08-07T12:46:42.859488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04744","last_updated":"2024-02-07T10:55:59Z","snapshot_observed_at":"2026-08-07T10:39:20.795187Z","submitted_at":"2024-02-07T10:55:59Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04744","snapshot_observed_at":"2026-08-06T14:55:28.336038Z","title":"Progressive gradient flow for robust n:m sparsity Efficient Column-Wise N:M Pruning on RISC-V CPU 21 training in transformers, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17301","last_updated":"2025-07-23T08:06:13Z","snapshot_observed_at":"2026-08-06T14:49:22.729136Z","submitted_at":"2025-07-23T08:06:13Z","title":"Efficient Column-Wise N:M Pruning on RISC-V CPU","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:55:28.336038Z"},"links":{"cited_paper":"/paper/2402.04744","citing_paper":"/paper/2507.17301"},"observation_digest":"sha256:6754511cc95ac920213ed81545bc3d022d6e8abde4ad9032af105d7b3cda7184","observation_id":"3ee1ac38-5aff-4d23-8c93-c5580fb91aad","resolution":{"observed_at":"2026-08-06T14:55:28.336038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04744","last_updated":"2024-02-07T10:55:59Z","snapshot_observed_at":"2026-08-07T10:39:20.795187Z","submitted_at":"2024-02-07T10:55:59Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","version":1},"cited_work":{"arxiv_id":"2402.04744","doi":"10.48550/arxiv.2402.04744","metadata_source":"pith","pith_arxiv_id":"2402.04744","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","venue":"cs.LG","work_id":"2b281e01-adab-4726-a1c4-1b4bf54a5732","year":2024},"citing_paper":{"arxiv_id":"2607.17733","last_updated":"2026-07-20T09:23:10Z","snapshot_observed_at":"2026-08-07T16:27:06.302213Z","submitted_at":"2026-07-20T09:23:10Z","title":"MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T17:12:36.315756Z"},"links":{"cited_paper":"/paper/2402.04744","citing_paper":"/paper/2607.17733"},"observation_digest":"sha256:5ac9fc76a678e3b0acd3500c40ee0fe3d5d55a863a30b43504a126066692c5ca","observation_id":"0798e960-4843-403a-afec-510fafde30f7","resolution":{"observed_at":"2026-08-01T17:14:12.239179Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04744","last_updated":"2024-02-07T10:55:59Z","snapshot_observed_at":"2026-08-07T10:39:20.795187Z","submitted_at":"2024-02-07T10:55:59Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04744","snapshot_observed_at":"2026-08-06T00:24:56.382575Z","title":"Progressive gradient flow for robust N:M sparsity training in transformers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01343","last_updated":"2026-08-02T16:08:44Z","snapshot_observed_at":"2026-08-06T23:26:17.642729Z","submitted_at":"2026-08-02T16:08:44Z","title":"DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T00:24:56.382575Z"},"links":{"cited_paper":"/paper/2402.04744","citing_paper":"/paper/2608.01343"},"observation_digest":"sha256:93c2eda31d3ea2fbaf3896373ea2759c64e9eccdc0b99b96f49870fbed018092","observation_id":"30bf6e20-884a-4b73-b9c5-39b82b2665a9","resolution":{"observed_at":"2026-08-06T00:24:56.382575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.04744/citation-record","integrity":"/paper/2402.04744/integrity","json":"/paper/2402.04744/citation-record.json","paper":"/paper/2402.04744"},"outbound":[],"paper":{"arxiv_id":"2402.04744","last_updated":"2024-02-07T10:55:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T10:39:20.795187Z","submitted_at":"2024-02-07T10:55:59Z","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.04744."}