{"as_of":"2026-08-08T12:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5ac6354b20a521aa5f63c21bc5247c9659080484b3eaa4342e0fdfa4fdd96281","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":"2026-08-07T13:28:17.670309Z","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":"2026-08-07T12:35:24.930430Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.13768","last_updated":"2023-08-26T05:20:58Z","snapshot_observed_at":"2026-07-06T16:10:38.925065Z","submitted_at":"2023-08-26T05:20:58Z","title":"Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13768","snapshot_observed_at":"2026-08-07T13:28:17.670309Z","title":"Adversarial fine-tuning of language models: An iterative optimisation approach for the generation and detection of problematic content","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23817","last_updated":"2025-05-27T21:36:27Z","snapshot_observed_at":"2026-08-07T13:20:45.414243Z","submitted_at":"2025-05-27T21:36:27Z","title":"System Prompt Extraction Attacks and Defenses in Large Language Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:28:17.670309Z"},"links":{"cited_paper":"/paper/2308.13768","citing_paper":"/paper/2505.23817"},"observation_digest":"sha256:9d209964e2557e34f1bf38b180af58d15e012d45785df83f5f906f3e9e294190","observation_id":"24a3a16c-7ad3-457e-9e14-52f68f6040bb","resolution":{"observed_at":"2026-08-07T13:28:17.670309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13768","last_updated":"2023-08-26T05:20:58Z","snapshot_observed_at":"2026-07-06T16:10:38.925065Z","submitted_at":"2023-08-26T05:20:58Z","title":"Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content","version":1},"cited_work":{"arxiv_id":"2308.13768","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.13768","snapshot_observed_at":"2026-08-07T12:35:24.930430Z","title":"Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content","venue":"cs.CL","work_id":"cabcf63d-5a84-482f-a624-551ffc5dc313","year":2023},"citing_paper":{"arxiv_id":"2505.24369","last_updated":"2025-05-30T09:02:07Z","snapshot_observed_at":"2026-08-08T01:18:48.028080Z","submitted_at":"2025-05-30T09:02:07Z","title":"Adversarial Preference Learning for Robust LLM Alignment","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:22.267168Z"},"links":{"cited_paper":"/paper/2308.13768","citing_paper":"/paper/2505.24369"},"observation_digest":"sha256:c2d25b4c55cad96f36049b8dba3603a12a31fcf5895bde4f847fff7f325e84a1","observation_id":"d9939c42-9503-494e-b4d1-08d081a71d6c","resolution":{"observed_at":"2026-08-07T12:35:25.026989Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13768","last_updated":"2023-08-26T05:20:58Z","snapshot_observed_at":"2026-07-06T16:10:38.925065Z","submitted_at":"2023-08-26T05:20:58Z","title":"Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13768","snapshot_observed_at":"2026-08-05T23:13:03.657036Z","title":"arXiv preprint arXiv:2308.13768 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05775","last_updated":"2026-07-27T20:50:40Z","snapshot_observed_at":"2026-08-05T23:12:57.691936Z","submitted_at":"2025-08-07T18:42:16Z","title":"Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-05T23:13:03.657036Z"},"links":{"cited_paper":"/paper/2308.13768","citing_paper":"/paper/2508.05775"},"observation_digest":"sha256:48b08cf72ecc575c24a344b271ba9794951f7f69ef201b86b61ccc35b9c278dc","observation_id":"8705ad8d-9cd4-466b-b034-18c9e58deb4f","resolution":{"observed_at":"2026-08-05T23:13:03.657036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2308.13768/citation-record","integrity":"/paper/2308.13768/integrity","json":"/paper/2308.13768/citation-record.json","paper":"/paper/2308.13768"},"outbound":[],"paper":{"arxiv_id":"2308.13768","last_updated":"2023-08-26T05:20:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T16:10:38.925065Z","submitted_at":"2023-08-26T05:20:58Z","title":"Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content"},"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:2308.13768."}