{"as_of":"2026-08-21T17:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f8be6d3ccfbacbabc50b17870972c72dd8bbc4353a3822385049b26057686f90","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:05:12.035275Z","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":"2026-05-21T04:33:57.784905Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.06072","last_updated":"2024-09-09T21:12:03Z","snapshot_observed_at":"2026-08-20T09:59:38.987428Z","submitted_at":"2024-09-09T21:12:03Z","title":"DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06072","snapshot_observed_at":"2026-08-10T20:05:12.035275Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09431","last_updated":"2025-01-16T09:59:45Z","snapshot_observed_at":"2026-08-15T18:24:50.553332Z","submitted_at":"2025-01-16T09:59:45Z","title":"A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:05:12.035275Z"},"links":{"cited_paper":"/paper/2409.06072","citing_paper":"/paper/2501.09431"},"observation_digest":"sha256:ca9ab82cba3d4d891139eadc0184871ece7be4638bf97ba07f002607fc07ee61","observation_id":"8c1acaf7-0090-4db8-a2ba-1fb743bd4f1a","resolution":{"observed_at":"2026-08-10T20:05:12.035275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06072","last_updated":"2024-09-09T21:12:03Z","snapshot_observed_at":"2026-08-20T09:59:38.987428Z","submitted_at":"2024-09-09T21:12:03Z","title":"DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection","version":1},"cited_work":{"arxiv_id":"2409.06072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.06072","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection","venue":null,"work_id":"9b1a60b5-a6a7-4f9d-88df-768a302e5213","year":2024},"citing_paper":{"arxiv_id":"2605.20759","last_updated":"2026-05-20T05:59:54Z","snapshot_observed_at":"2026-07-06T23:31:18.520630Z","submitted_at":"2026-05-20T05:59:54Z","title":"Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-21T04:33:04.629491Z"},"links":{"cited_paper":"/paper/2409.06072","citing_paper":"/paper/2605.20759"},"observation_digest":"sha256:d5a21f06cf31b9a02b4bc3794dc701c53397b888026d5df051136cb9957dccb8","observation_id":"da766003-663c-4413-8e78-c9b330a356a3","resolution":{"observed_at":"2026-05-21T04:33:57.787066Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2409.06072/citation-record","integrity":"/paper/2409.06072/integrity","json":"/paper/2409.06072/citation-record.json","paper":"/paper/2409.06072"},"outbound":[],"paper":{"arxiv_id":"2409.06072","last_updated":"2024-09-09T21:12:03Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-20T09:59:38.987428Z","submitted_at":"2024-09-09T21:12:03Z","title":"DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.06072."}