{"as_of":"2026-08-17T20:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d863ca1a5f550524ad04df16719753da5cafc4c00497313ed46a9751dc9a3925","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":"2026-08-17T06:30:58.91139+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":"2026-08-06T21:41:17.021203Z","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":"2026-05-17T20:55:15.163500Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1703.02573","last_updated":"2017-03-07T19:56:26Z","snapshot_observed_at":"2026-08-14T21:13:00.987582Z","submitted_at":"2017-03-07T19:56:26Z","title":"Data Noising as Smoothing in Neural Network Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.02573","snapshot_observed_at":"2026-08-06T21:41:17.021203Z","title":"Data noising as smoothing in neural network language models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.23799","last_updated":"2025-07-02T22:50:21Z","snapshot_observed_at":"2026-08-16T01:48:50.793482Z","submitted_at":"2025-06-30T12:44:28Z","title":"KAIROS: Scalable Model-Agnostic Data Valuation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:41:17.021203Z"},"links":{"cited_paper":"/paper/1703.02573","citing_paper":"/paper/2506.23799"},"observation_digest":"sha256:752f8b8557edf7f04167837bd78d1e051c29af1a85be7c64aea1bb674d3360d0","observation_id":"565cfb40-5605-4f6a-af68-7a546b82ba8b","resolution":{"observed_at":"2026-08-06T21:41:17.021203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.02573","last_updated":"2017-03-07T19:56:26Z","snapshot_observed_at":"2026-08-14T21:13:00.987582Z","submitted_at":"2017-03-07T19:56:26Z","title":"Data Noising as Smoothing in Neural Network Language Models","version":1},"cited_work":{"arxiv_id":"1703.02573","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.02573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data Noising as Smoothing in Neural Network Language Models","venue":"cs.LG","work_id":"84ed1187-3537-4ccf-b95b-a6063568df38","year":2017},"citing_paper":{"arxiv_id":"2511.16136","last_updated":"2026-04-10T12:24:35Z","snapshot_observed_at":"2026-08-15T11:37:08.800712Z","submitted_at":"2025-11-20T08:16:24Z","title":"How Noise Benefits AI-generated Image Detection","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-17T20:53:31.985118Z"},"links":{"cited_paper":"/paper/1703.02573","citing_paper":"/paper/2511.16136"},"observation_digest":"sha256:9a93c255801343f5e1f3627aa380ea49cb44e8e80a4e82f57930a59ac558c44a","observation_id":"028036c6-cb3e-41b3-a2fb-61ad1d6d23ac","resolution":{"observed_at":"2026-05-17T20:55:15.165174Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.02573","last_updated":"2017-03-07T19:56:26Z","snapshot_observed_at":"2026-08-14T21:13:00.987582Z","submitted_at":"2017-03-07T19:56:26Z","title":"Data Noising as Smoothing in Neural Network Language Models","version":1},"cited_work":{"arxiv_id":"1703.02573","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.02573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data Noising as Smoothing in Neural Network Language Models","venue":"cs.LG","work_id":"84ed1187-3537-4ccf-b95b-a6063568df38","year":2017},"citing_paper":{"arxiv_id":"2605.13436","last_updated":"2026-05-13T12:31:04Z","snapshot_observed_at":"2026-08-13T05:34:25.467602Z","submitted_at":"2026-05-13T12:31:04Z","title":"Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-14T19:29:22.229589Z"},"links":{"cited_paper":"/paper/1703.02573","citing_paper":"/paper/2605.13436"},"observation_digest":"sha256:5bcb0710a09aa8edf9bb54c7384b806d924ace19cdff936e4c4c7253e8f3661d","observation_id":"73cd72e4-066c-4aac-83cf-70f63d9d4502","resolution":{"observed_at":"2026-05-14T19:32:52.672452Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1703.02573/citation-record","integrity":"/paper/1703.02573/integrity","json":"/paper/1703.02573/citation-record.json","paper":"/paper/1703.02573"},"outbound":[],"paper":{"arxiv_id":"1703.02573","last_updated":"2017-03-07T19:56:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T21:13:00.987582Z","submitted_at":"2017-03-07T19:56:26Z","title":"Data Noising as Smoothing in Neural Network Language Models"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1703.02573."}