{"as_of":"2026-08-15T01:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74d75b1bbe63fcab2acc8e59ecbaa688368b1fdac32a5a1d37e822dad8d78b07","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:10:26.200465Z","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":"2026-05-15T00:28:23.411114Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00544","snapshot_observed_at":"2026-08-11T23:10:26.200465Z","title":"Towards improving adversarial training of nlp models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02795","last_updated":"2024-12-03T19:54:32Z","snapshot_observed_at":"2026-08-14T18:06:17.422619Z","submitted_at":"2024-12-03T19:54:32Z","title":"Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:10:26.200465Z"},"links":{"cited_paper":"/paper/2109.00544","citing_paper":"/paper/2412.02795"},"observation_digest":"sha256:91c4413c75f4009d8f5a9721075bbc20cee770669269c1a8f952a06142967895","observation_id":"e30d6c7b-9ffa-4adb-9292-1808aba96710","resolution":{"observed_at":"2026-08-11T23:10:26.200465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00544","snapshot_observed_at":"2026-08-10T23:21:41.821380Z","title":"Towards improving adversarial training of nlp models.arXiv preprint arXiv:2109.00544, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.00066","last_updated":"2025-06-07T11:27:26Z","snapshot_observed_at":"2026-08-14T11:24:12.417233Z","submitted_at":"2024-12-29T15:55:35Z","title":"On Adversarial Robustness of Language Models in Transfer Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:21:41.821380Z"},"links":{"cited_paper":"/paper/2109.00544","citing_paper":"/paper/2501.00066"},"observation_digest":"sha256:f4ab9f21e2e7923d1d501145e57371fff71b535266a9a3cde5f8e3d72d81d55c","observation_id":"79f74199-6f23-4c0a-9917-2c8a5331cb09","resolution":{"observed_at":"2026-08-10T23:21:41.821380Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00544","snapshot_observed_at":"2026-08-07T00:46:10.490308Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.12699","last_updated":"2025-06-19T06:30:24Z","snapshot_observed_at":"2026-08-14T17:40:51.768214Z","submitted_at":"2025-06-15T03:14:03Z","title":"SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation","version":2},"reference_index":134,"source":"pdf_text","source_observed_at":"2026-08-07T00:46:10.490308Z"},"links":{"cited_paper":"/paper/2109.00544","citing_paper":"/paper/2506.12699"},"observation_digest":"sha256:a4d68ae83d9dcfca4bc8a906c870d2fa78a638f3ca6e141d857ece5645210584","observation_id":"5f836170-2d87-4ba3-822b-bf0a40083432","resolution":{"observed_at":"2026-08-07T00:46:10.490308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP Models","version":2},"cited_work":{"arxiv_id":"2109.00544","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.00544","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f611f601-ae2a-4282-a860-cd1057f844db","year":2021},"citing_paper":{"arxiv_id":"2604.14163","last_updated":"2026-07-04T02:44:53Z","snapshot_observed_at":"2026-08-10T18:35:47.453098Z","submitted_at":"2026-03-23T18:21:13Z","title":"SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T00:26:09.423198Z"},"links":{"cited_paper":"/paper/2109.00544","citing_paper":"/paper/2604.14163"},"observation_digest":"sha256:142a1400715ab440060a5c0d2b2264873bf1c1a562600f39688aff0baf3d3ca3","observation_id":"e850cd37-500d-46cf-8bd6-4f854682800d","resolution":{"observed_at":"2026-05-15T00:28:23.412616Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00544","snapshot_observed_at":"2026-08-01T01:26:18.169995Z","title":"arXiv preprint arXiv:2109.00544 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25814","last_updated":"2026-07-28T14:58:28Z","snapshot_observed_at":"2026-08-09T15:34:32.407220Z","submitted_at":"2026-07-28T14:58:28Z","title":"Evaluation of Adversarial Robustness in Arabic Language Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T01:26:18.169995Z"},"links":{"cited_paper":"/paper/2109.00544","citing_paper":"/paper/2607.25814"},"observation_digest":"sha256:079e9b9755ce1a4da46181e718af3a1eb7989229985f8194bb61e11e5593086e","observation_id":"e4442878-02ba-4e5e-b645-ee873f73d6cd","resolution":{"observed_at":"2026-08-01T01:26:18.169995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2109.00544/citation-record","integrity":"/paper/2109.00544/integrity","json":"/paper/2109.00544/citation-record.json","paper":"/paper/2109.00544"},"outbound":[],"paper":{"arxiv_id":"2109.00544","last_updated":"2021-09-11T09:16:05Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-14T13:49:26.253440Z","submitted_at":"2021-09-01T17:14:26Z","title":"Towards Improving Adversarial Training of NLP 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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2109.00544."}