{"as_of":"2026-08-09T11:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d9592815cf644342e0ec5f733fcdd68f08c46ec3faf84f12aa2e6dc50ea2f4a3","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T19:15:54.703346Z","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":"2026-05-21T20:54:21.566072Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.17688","last_updated":"2024-05-22T16:19:38Z","snapshot_observed_at":"2026-08-04T05:54:35.989942Z","submitted_at":"2023-10-26T17:59:06Z","title":"Managing extreme AI risks amid rapid progress","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17688","snapshot_observed_at":"2026-08-08T19:15:54.703346Z","title":"N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., Hadfield, G., Clune, J., Maharaj, T., Hutter, F., Baydin, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05475","last_updated":"2025-02-08T07:24:04Z","snapshot_observed_at":"2026-08-09T02:15:12.668653Z","submitted_at":"2025-02-08T07:24:04Z","title":"You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T19:15:54.703346Z"},"links":{"cited_paper":"/paper/2310.17688","citing_paper":"/paper/2502.05475"},"observation_digest":"sha256:42d9c4b068f247747afc074edad87124abea54226366ff1e6d906366016aca87","observation_id":"6df791a2-70c5-44fa-a2dc-c956e23ccce8","resolution":{"observed_at":"2026-08-08T19:15:54.703346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17688","last_updated":"2024-05-22T16:19:38Z","snapshot_observed_at":"2026-08-04T05:54:35.989942Z","submitted_at":"2023-10-26T17:59:06Z","title":"Managing extreme AI risks amid rapid progress","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17688","snapshot_observed_at":"2026-08-07T15:35:47.817622Z","title":"arXiv:2310.17688 [cs]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17084","last_updated":"2025-05-20T16:07:41Z","snapshot_observed_at":"2026-08-09T03:35:20.809284Z","submitted_at":"2025-05-20T16:07:41Z","title":"From nuclear safety to LLM security: Applying non-probabilistic risk management strategies to build safe and secure LLM-powered systems","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:47.817622Z"},"links":{"cited_paper":"/paper/2310.17688","citing_paper":"/paper/2505.17084"},"observation_digest":"sha256:4f67cb486e258b0ec01e5083edfc23605922b923bc66058b732be452acc32dc1","observation_id":"637325bf-e9f5-47a2-bda8-1809e08167b7","resolution":{"observed_at":"2026-08-07T15:35:47.817622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17688","last_updated":"2024-05-22T16:19:38Z","snapshot_observed_at":"2026-08-04T05:54:35.989942Z","submitted_at":"2023-10-26T17:59:06Z","title":"Managing extreme AI risks amid rapid progress","version":3},"cited_work":{"arxiv_id":"2310.17688","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.17688","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Managing ai risks in an era of rapid progress","venue":null,"work_id":"482ec80b-f05b-46b8-8f9e-f51768d1f48b","year":2023},"citing_paper":{"arxiv_id":"2510.07239","last_updated":"2026-05-17T13:01:38Z","snapshot_observed_at":"2026-08-02T19:25:51.433055Z","submitted_at":"2025-10-08T17:06:20Z","title":"Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T20:53:58.198974Z"},"links":{"cited_paper":"/paper/2310.17688","citing_paper":"/paper/2510.07239"},"observation_digest":"sha256:0de20e20159ff7087072a9c7000ab5c4d61c26f062f33d97d600245bcd7a8d32","observation_id":"118885e2-2350-4897-8b17-471190033f2b","resolution":{"observed_at":"2026-05-21T20:54:21.567987Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17688","last_updated":"2024-05-22T16:19:38Z","snapshot_observed_at":"2026-08-04T05:54:35.989942Z","submitted_at":"2023-10-26T17:59:06Z","title":"Managing extreme AI risks amid rapid progress","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17688","snapshot_observed_at":"2026-08-01T08:32:00.201622Z","title":"arXiv preprint arXiv:2310.17688 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21120","last_updated":"2026-07-23T09:55:45Z","snapshot_observed_at":"2026-08-05T03:29:01.517081Z","submitted_at":"2026-07-23T09:55:45Z","title":"Relative Value Learning","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T08:32:00.201622Z"},"links":{"cited_paper":"/paper/2310.17688","citing_paper":"/paper/2607.21120"},"observation_digest":"sha256:e0e2b1ff569530cd5ad87e181863a6a8db8b21f6c482ea08baf9957a79ea2f3a","observation_id":"54eb23d0-d9fa-4f13-9709-8c3b56151020","resolution":{"observed_at":"2026-08-01T08:32:00.201622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2310.17688/citation-record","integrity":"/paper/2310.17688/integrity","json":"/paper/2310.17688/citation-record.json","paper":"/paper/2310.17688"},"outbound":[],"paper":{"arxiv_id":"2310.17688","last_updated":"2024-05-22T16:19:38Z","latest_version":3,"primary_category":"cs.CY","snapshot_observed_at":"2026-08-04T05:54:35.989942Z","submitted_at":"2023-10-26T17:59:06Z","title":"Managing extreme AI risks amid rapid progress"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.17688."}