{"as_of":"2026-08-08T23:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5ec3ad4e9c8e0f9c529653c825c2fef8d69210c79a6066cebc4216fc1f91478","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":"2026-08-07T15:04:28.694089Z","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":"2026-05-13T22:58:24.106000Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.10341","last_updated":"2024-01-18T19:09:47Z","snapshot_observed_at":"2026-07-06T17:17:35.111617Z","submitted_at":"2024-01-18T19:09:47Z","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10341","snapshot_observed_at":"2026-08-07T15:04:28.694089Z","title":"Elrt: Efficient low-rank training for compact convolutional neural networks.arXiv preprint arXiv:2401.10341, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16531","last_updated":"2025-09-10T10:50:10Z","snapshot_observed_at":"2026-08-07T21:09:49.874887Z","submitted_at":"2025-05-22T11:20:35Z","title":"HOFT: Householder Orthogonal Fine-tuning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:28.694089Z"},"links":{"cited_paper":"/paper/2401.10341","citing_paper":"/paper/2505.16531"},"observation_digest":"sha256:4d04ce3b79c82a71cacc34c8fc788cae282fb2f63460683acec1a46fc0e8fe5e","observation_id":"bb3b69a0-6621-4a00-8fb1-ac9d1a58c56d","resolution":{"observed_at":"2026-08-07T15:04:28.694089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10341","last_updated":"2024-01-18T19:09:47Z","snapshot_observed_at":"2026-07-06T17:17:35.111617Z","submitted_at":"2024-01-18T19:09:47Z","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10341","snapshot_observed_at":"2026-08-06T11:40:30.926660Z","title":"Elrt: Efficient low-rank training for compact convolutional neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22527","last_updated":"2025-07-30T09:56:18Z","snapshot_observed_at":"2026-08-07T10:59:25.777185Z","submitted_at":"2025-07-30T09:56:18Z","title":"FGFP: A Fractional Gaussian Filter and Pruning for Deep Neural Networks Compression","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T11:40:30.926660Z"},"links":{"cited_paper":"/paper/2401.10341","citing_paper":"/paper/2507.22527"},"observation_digest":"sha256:1be00d7408659fbdfd36ff8e2d63e91c938c0f6076c4bdeae4f0f0c0e1e85f68","observation_id":"345c33b4-d012-4372-b412-c71e11877baa","resolution":{"observed_at":"2026-08-06T11:40:30.926660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10341","last_updated":"2024-01-18T19:09:47Z","snapshot_observed_at":"2026-07-06T17:17:35.111617Z","submitted_at":"2024-01-18T19:09:47Z","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":"2401.10341","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.10341","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5f332758-7698-4bb7-9fab-87562bd5f9f9","year":2024},"citing_paper":{"arxiv_id":"2604.00733","last_updated":"2026-04-05T12:23:35Z","snapshot_observed_at":"2026-08-08T05:01:12.246593Z","submitted_at":"2026-04-01T10:53:56Z","title":"Spectral Compact Training: Pre-Training Large Language Models via Permanent Truncated SVD and Stiefel QR Retraction","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-13T22:54:02.492105Z"},"links":{"cited_paper":"/paper/2401.10341","citing_paper":"/paper/2604.00733"},"observation_digest":"sha256:c311626d9971c43517884c32fc376c3f2670d1e489c770f8d1b6da58a4a18eb8","observation_id":"7165b1d9-d297-49cb-9ce7-cb4e1dba393f","resolution":{"observed_at":"2026-05-13T22:58:24.108491Z","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/2401.10341/citation-record","integrity":"/paper/2401.10341/integrity","json":"/paper/2401.10341/citation-record.json","paper":"/paper/2401.10341"},"outbound":[],"paper":{"arxiv_id":"2401.10341","last_updated":"2024-01-18T19:09:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T17:17:35.111617Z","submitted_at":"2024-01-18T19:09:47Z","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks"},"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:2401.10341."}