{"as_of":"2026-08-22T09:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8f36b2ad10abbb091eee36231af43a123b5f8546d4a215806cf42e8462fc8042","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:38:18.471303Z","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":"2026-07-02T01:56:27.991147Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12991","snapshot_observed_at":"2026-08-07T14:59:38.419559Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16785","last_updated":"2025-05-22T15:28:25Z","snapshot_observed_at":"2026-08-21T11:45:10.394079Z","submitted_at":"2025-05-22T15:28:25Z","title":"CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T14:59:38.419559Z"},"links":{"cited_paper":"/paper/2402.12991","citing_paper":"/paper/2505.16785"},"observation_digest":"sha256:55489e58e8468a890eaf8bb82a37ab544507bd70e3604bb3ad7020a9f2ae98e9","observation_id":"6739901b-064b-46b5-af26-3d7ba1726e2d","resolution":{"observed_at":"2026-08-07T14:59:38.419559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12991","snapshot_observed_at":"2026-08-15T16:38:18.471303Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03058","last_updated":"2025-09-03T06:40:57Z","snapshot_observed_at":"2026-08-20T05:52:39.336087Z","submitted_at":"2025-09-03T06:40:57Z","title":"EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T16:38:18.471303Z"},"links":{"cited_paper":"/paper/2402.12991","citing_paper":"/paper/2509.03058"},"observation_digest":"sha256:910913a9ac66e6159377f1cc27a56811169282b96ba8b330f9b9bf0709bf97f9","observation_id":"dcfb835e-8064-41d7-a582-1f573ef21a5d","resolution":{"observed_at":"2026-08-15T16:38:18.471303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12991","snapshot_observed_at":"2026-08-05T05:54:46.316289Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09703","last_updated":"2025-09-05T05:59:50Z","snapshot_observed_at":"2026-08-14T07:57:06.501745Z","submitted_at":"2025-09-05T05:59:50Z","title":"CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T05:54:46.316289Z"},"links":{"cited_paper":"/paper/2402.12991","citing_paper":"/paper/2509.09703"},"observation_digest":"sha256:01fefe6ee52f69294270bcf538cbf5ea9d3f8a2ea466c68a518e353d61165a6d","observation_id":"88c36346-bf00-4577-9e23-48d2474b47e4","resolution":{"observed_at":"2026-08-05T05:54:46.316289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","version":2},"cited_work":{"arxiv_id":"2402.12991","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12991","snapshot_observed_at":"2026-07-02T01:56:27.991147Z","title":"Trap: Targeted ran- dom adversarial prompt honeypot for black-box identification.arXiv preprint arXiv:2402.12991","venue":null,"work_id":"da0e8b1c-25cb-407e-99ac-a6faced1ed7e","year":null},"citing_paper":{"arxiv_id":"2509.25448","last_updated":"2026-05-19T03:11:28Z","snapshot_observed_at":"2026-08-17T07:16:17.583079Z","submitted_at":"2025-09-29T19:54:36Z","title":"Fingerprinting LLMs via Prompt Injection","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T21:29:26.153307Z"},"links":{"cited_paper":"/paper/2402.12991","citing_paper":"/paper/2509.25448"},"observation_digest":"sha256:517e69d3bd51018530613d1d1e0a63250c945c19284c0c834cf6cea716f7cf7d","observation_id":"b919f991-4a24-4673-ac1b-c9abf5798997","resolution":{"observed_at":"2026-05-21T21:30:39.359964Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","version":2},"cited_work":{"arxiv_id":"2402.12991","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12991","snapshot_observed_at":"2026-07-02T01:56:27.991147Z","title":"Trap: Targeted ran- dom adversarial prompt honeypot for black-box identification.arXiv preprint arXiv:2402.12991","venue":null,"work_id":"da0e8b1c-25cb-407e-99ac-a6faced1ed7e","year":null},"citing_paper":{"arxiv_id":"2606.03330","last_updated":"2026-06-02T08:39:50Z","snapshot_observed_at":"2026-08-02T20:44:01.368085Z","submitted_at":"2026-06-02T08:39:50Z","title":"FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-28T11:24:01.547119Z"},"links":{"cited_paper":"/paper/2402.12991","citing_paper":"/paper/2606.03330"},"observation_digest":"sha256:496f8e7d6c9afb2ffdffda92f1057dcfe97881c2084bc8b726006eb4ddfa5702","observation_id":"fdcbd972-58cb-4fec-ab09-a3f128d2f366","resolution":{"observed_at":"2026-07-02T01:56:27.993035Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.12991/citation-record","integrity":"/paper/2402.12991/integrity","json":"/paper/2402.12991/citation-record.json","paper":"/paper/2402.12991"},"outbound":[],"paper":{"arxiv_id":"2402.12991","last_updated":"2024-06-06T17:46:48Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T17:14:26.945481Z","submitted_at":"2024-02-20T13:20:39Z","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.12991."}