{"as_of":"2026-08-14T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d1e66d19733b97cf480bd7c0dee1114379106c96081c4bd2a4a55580ff2da78","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:25:54.735983Z","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":"2026-05-13T06:57:27.710403Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.01850","last_updated":"2024-07-01T23:25:30Z","snapshot_observed_at":"2026-08-13T01:57:14.863803Z","submitted_at":"2024-07-01T23:25:30Z","title":"Purple-teaming LLMs with Adversarial Defender Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01850","snapshot_observed_at":"2026-08-10T22:25:54.735983Z","title":"Purple-teaming llms with adversarial defender training, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01830","last_updated":"2025-01-03T14:30:14Z","snapshot_observed_at":"2026-08-12T09:57:48.731669Z","submitted_at":"2025-01-03T14:30:14Z","title":"Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T22:25:54.735983Z"},"links":{"cited_paper":"/paper/2407.01850","citing_paper":"/paper/2501.01830"},"observation_digest":"sha256:5d60226eada43e74a15abfdc24d2bf09c6bd989025df7a38158e236238364f51","observation_id":"6df875c8-52c5-4bb8-91f3-6c76500f792e","resolution":{"observed_at":"2026-08-10T22:25:54.735983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01850","last_updated":"2024-07-01T23:25:30Z","snapshot_observed_at":"2026-08-13T01:57:14.863803Z","submitted_at":"2024-07-01T23:25:30Z","title":"Purple-teaming LLMs with Adversarial Defender Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01850","snapshot_observed_at":"2026-08-07T12:01:10.911575Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00782","last_updated":"2025-06-01T02:19:46Z","snapshot_observed_at":"2026-08-07T11:55:45.889580Z","submitted_at":"2025-06-01T02:19:46Z","title":"Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:01:10.911575Z"},"links":{"cited_paper":"/paper/2407.01850","citing_paper":"/paper/2506.00782"},"observation_digest":"sha256:b252c56448b99fe73ba2b78a3ea92b760986c110d7ecea457e1d02d03654eb50","observation_id":"ed3ab69c-a3f5-45fb-a68a-4206b040c247","resolution":{"observed_at":"2026-08-07T12:01:10.911575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01850","last_updated":"2024-07-01T23:25:30Z","snapshot_observed_at":"2026-08-13T01:57:14.863803Z","submitted_at":"2024-07-01T23:25:30Z","title":"Purple-teaming LLMs with Adversarial Defender Training","version":1},"cited_work":{"arxiv_id":"2407.01850","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.01850","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3944bcfa-ab1b-451f-842a-e44bc72f7597","year":2024},"citing_paper":{"arxiv_id":"2605.11730","last_updated":"2026-05-12T08:12:18Z","snapshot_observed_at":"2026-08-12T19:23:05.248363Z","submitted_at":"2026-05-12T08:12:18Z","title":"Persona-Conditioned Adversarial Prompting: Multi-Identity Red-Teaming for Adversarial Discovery and Mitigation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-13T06:55:55.638358Z"},"links":{"cited_paper":"/paper/2407.01850","citing_paper":"/paper/2605.11730"},"observation_digest":"sha256:8a869b0ed6cdded2cd8642be7f14130fb0af1049884a9cddc2888814ea664e21","observation_id":"bd307bd1-cd8c-46e8-bb5b-f65949bb724c","resolution":{"observed_at":"2026-05-13T06:57:27.713430Z","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":"2407.01850","last_updated":"2024-07-01T23:25:30Z","snapshot_observed_at":"2026-08-13T01:57:14.863803Z","submitted_at":"2024-07-01T23:25:30Z","title":"Purple-teaming LLMs with Adversarial Defender Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01850","snapshot_observed_at":"2026-08-06T05:11:45.995659Z","title":"Purple-teaming llms with adversarial defender training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05108","last_updated":"2026-08-05T17:44:09Z","snapshot_observed_at":"2026-08-13T19:35:02.625564Z","submitted_at":"2026-08-05T17:44:09Z","title":"Agent Against Agent: An Agentic System for Automatic Prompt Injection Red Teaming","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T05:11:45.995659Z"},"links":{"cited_paper":"/paper/2407.01850","citing_paper":"/paper/2608.05108"},"observation_digest":"sha256:9db89ed3cbf5c2fea34ee1a816f676e52f7aa6f6b7fafb5b4bafbac80d9cc8d3","observation_id":"fccb7cd8-214d-4419-9e71-61df4f9a1633","resolution":{"observed_at":"2026-08-06T05:11:45.995659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.01850/citation-record","integrity":"/paper/2407.01850/integrity","json":"/paper/2407.01850/citation-record.json","paper":"/paper/2407.01850"},"outbound":[],"paper":{"arxiv_id":"2407.01850","last_updated":"2024-07-01T23:25:30Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T01:57:14.863803Z","submitted_at":"2024-07-01T23:25:30Z","title":"Purple-teaming LLMs with Adversarial Defender Training"},"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 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.01850."}