{"as_of":"2026-08-08T02:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:79b21c7004bb0157f46e6d33d936e2389beef6e420e232cb06292d33bb056ca9","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:28:01.051898Z","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":"2026-07-03T08:07:45.450496Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2211.00593","last_updated":"2022-11-01T17:08:44Z","snapshot_observed_at":"2026-08-05T06:04:21.031867Z","submitted_at":"2022-11-01T17:08:44Z","title":"Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-13T17:13:51.408311Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2211.00593"},"observation_digest":"sha256:026e1d5e1095da65aa67d2d1b22a5d5d8f071ea2e456b1368da78b8a41b4622e","observation_id":"9f534c03-5115-4764-b4f2-8f1ad9c98b99","resolution":{"observed_at":"2026-05-13T17:13:51.482305Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2211.09527","last_updated":"2022-11-17T13:43:20Z","snapshot_observed_at":"2026-07-06T14:19:47.424778Z","submitted_at":"2022-11-17T13:43:20Z","title":"Ignore Previous Prompt: Attack Techniques For Language Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-11T13:59:31.213830Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2211.09527"},"observation_digest":"sha256:5ede8bc4248212b6f9f04924c72ae2590c0cc7bc4339c1820468278e331fdbc9","observation_id":"103b5b22-d284-4b3f-b9c0-53f0002e19e9","resolution":{"observed_at":"2026-05-11T13:59:31.544138Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2306.12001","last_updated":"2023-10-09T22:57:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-21T03:35:06Z","title":"An Overview of Catastrophic AI Risks","version":6},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-05-25T05:03:46.615765Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2306.12001"},"observation_digest":"sha256:8a709c71e54a85e28e79f347b4d99ea822e28ea69fe3f378abde1f27fe6b33d7","observation_id":"4985d374-2f0a-40a6-bbe0-c529e13c535d","resolution":{"observed_at":"2026-05-25T05:03:46.822237Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2503.02574","last_updated":"2026-05-18T17:54:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-04T12:55:07Z","title":"LLM-Safety Evaluations Lack Robustness","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-23T01:26:45.402983Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2503.02574"},"observation_digest":"sha256:53184275cd54685f6cf0cb332346e5648e91b6f2ce1ebb8648badfa97462c8fd","observation_id":"3b423cd9-f4bc-4771-bfef-63de6dca0abb","resolution":{"observed_at":"2026-05-23T01:27:21.217977Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-08-07T13:28:01.051898Z","title":"and Mazeika, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21800","last_updated":"2025-05-27T22:14:54Z","snapshot_observed_at":"2026-08-07T13:20:35.749933Z","submitted_at":"2025-05-27T22:14:54Z","title":"From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:28:01.051898Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2505.21800"},"observation_digest":"sha256:f9fa1a1e478186ed2781fb8a63662a2cf67dbd76c6c537b63d057076550dccde","observation_id":"bec5cdc0-ee92-4958-b014-b4345c467e14","resolution":{"observed_at":"2026-08-07T13:28:01.051898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-08-06T19:56:06.411529Z","title":"Hendryckset al., X-risk analysis for AI research, arXiv preprint arXiv:2206.05862 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08014","last_updated":"2025-07-06T08:41:30Z","snapshot_observed_at":"2026-08-06T19:48:50.296360Z","submitted_at":"2025-07-06T08:41:30Z","title":"Mass-Scale Analysis of In-the-Wild Conversations Reveals Complexity Bounds on LLM Jailbreaking","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:06.411529Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2507.08014"},"observation_digest":"sha256:dc0434ee502295779c38eea046ac1906143bb548956edda5f1276a5eb73c7b29","observation_id":"7c473b14-3971-45b1-a93f-99e6e27f6d27","resolution":{"observed_at":"2026-08-06T19:56:06.411529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2511.05914","last_updated":"2026-04-14T09:51:54Z","snapshot_observed_at":"2026-07-06T22:35:13.699594Z","submitted_at":"2025-11-08T08:22:16Z","title":"Designing Incident Reporting Systems for Harms from General-Purpose AI","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-18T00:16:17.173186Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2511.05914"},"observation_digest":"sha256:ea2b243bba6a005756733d1d1bed54e93a443f59b1b25b7b5eb19ee9fc1b9492","observation_id":"efac98cb-8dff-4469-9874-5a9cd2d33826","resolution":{"observed_at":"2026-05-18T00:20:32.301641Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2604.20805","last_updated":"2026-04-22T17:36:52Z","snapshot_observed_at":"2026-08-02T12:21:26.094257Z","submitted_at":"2026-04-22T17:36:52Z","title":"Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-09T23:02:34.564375Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2604.20805"},"observation_digest":"sha256:db8174c1821f1fbc7890bdff535a2fbed8cb9a01bafb8d7363c2dc1e7cd3450e","observation_id":"6cbed3c3-de55-49e3-a9e6-55fe8d9a00d3","resolution":{"observed_at":"2026-05-09T23:04:17.717141Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":"2206.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-07-03T08:07:45.450496Z","title":"X-Risk Analysis for AI Research","venue":null,"work_id":"bf80b5c8-e3d0-4a74-98cc-d60d4b26fa3d","year":2022},"citing_paper":{"arxiv_id":"2606.10720","last_updated":"2026-06-09T11:30:25Z","snapshot_observed_at":"2026-07-06T23:49:51.672382Z","submitted_at":"2026-06-09T11:30:25Z","title":"A Pigouvian Matchmaker Mechanism for De-escalating the AGI Race","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T11:03:42.443180Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2606.10720"},"observation_digest":"sha256:8bd83b08965e53c4d939524cf567d4986232bee5e7888853eafa8c114bac49b6","observation_id":"cafb87ec-a7d5-4378-a3d1-6b72c56bcaa8","resolution":{"observed_at":"2026-07-03T08:07:45.452278Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.05862/citation-record","integrity":"/paper/2206.05862/integrity","json":"/paper/2206.05862/citation-record.json","paper":"/paper/2206.05862"},"outbound":[],"paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","latest_version":7,"primary_category":"cs.CY","snapshot_observed_at":"2026-07-31T03:28:58.752082Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2206.05862."}