{"as_of":"2026-08-09T23:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32b2be19e700e70c38087ad6acdcbc4078a9ccfa20d6ece4189f9cf27e573a22","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T11:44:26.187901Z","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":"2026-08-05T18:11:10.803191Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1706.04983","last_updated":"2017-06-18T16:15:21Z","snapshot_observed_at":"2026-07-06T05:47:02.250306Z","submitted_at":"2017-06-15T17:35:15Z","title":"FreezeOut: Accelerate Training by Progressively Freezing Layers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.04983","snapshot_observed_at":"2026-08-08T11:44:26.187901Z","title":"Freeze- out: Accelerate training by progressively freezing layers,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.07750","last_updated":"2025-02-19T04:21:58Z","snapshot_observed_at":"2026-08-09T04:57:03.070045Z","submitted_at":"2025-02-11T18:25:48Z","title":"PFedDST: Personalized Federated Learning with Decentralized Selection Training","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T11:44:26.187901Z"},"links":{"cited_paper":"/paper/1706.04983","citing_paper":"/paper/2502.07750"},"observation_digest":"sha256:434607d371d3a1e14f9437b09c43190dea0325887a3977f3c852562808a0a2fe","observation_id":"cf36a89b-bd8f-4e9c-b60f-882f457ad468","resolution":{"observed_at":"2026-08-08T11:44:26.187901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04983","last_updated":"2017-06-18T16:15:21Z","snapshot_observed_at":"2026-07-06T05:47:02.250306Z","submitted_at":"2017-06-15T17:35:15Z","title":"FreezeOut: Accelerate Training by Progressively Freezing Layers","version":2},"cited_work":{"arxiv_id":"1706.04983","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04983","snapshot_observed_at":"2026-08-05T18:11:10.803191Z","title":"FreezeOut: Accelerate Training by Progressively Freezing Layers","venue":"stat.ML","work_id":"8e0f008a-6202-4a73-9878-362d2ee67ae1","year":2017},"citing_paper":{"arxiv_id":"2508.15036","last_updated":"2025-08-20T20:02:35Z","snapshot_observed_at":"2026-08-08T01:51:47.464406Z","submitted_at":"2025-08-20T20:02:35Z","title":"MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T18:11:08.695617Z"},"links":{"cited_paper":"/paper/1706.04983","citing_paper":"/paper/2508.15036"},"observation_digest":"sha256:46da755d310dc94aec220c5bc992428fc13a81cadca2c583b60f7af8a7154b12","observation_id":"aa7f1628-683d-4f2a-8900-34a66b7513cf","resolution":{"observed_at":"2026-08-05T18:11:10.823484Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1706.04983/citation-record","integrity":"/paper/1706.04983/integrity","json":"/paper/1706.04983/citation-record.json","paper":"/paper/1706.04983"},"outbound":[],"paper":{"arxiv_id":"1706.04983","last_updated":"2017-06-18T16:15:21Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T05:47:02.250306Z","submitted_at":"2017-06-15T17:35:15Z","title":"FreezeOut: Accelerate Training by Progressively Freezing Layers"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1706.04983."}