{"as_of":"2026-08-15T00:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd9d68a01cf2ef44d533eff78cfc9d90d0984891f460c9717d1330dfe0682325","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:00:40.856297Z","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":"2026-05-22T12:34:52.505633Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.02467","last_updated":"2025-08-01T09:50:22Z","snapshot_observed_at":"2026-08-12T22:31:41.076861Z","submitted_at":"2024-10-03T13:17:06Z","title":"SIDE: Surrogate Conditional Data Extraction from Diffusion Models","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02467","snapshot_observed_at":"2026-08-10T14:00:40.856297Z","title":"Towards a theoretical understanding of memorization in diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15785","last_updated":"2025-03-18T16:31:23Z","snapshot_observed_at":"2026-08-10T21:01:13.739289Z","submitted_at":"2025-01-27T05:17:06Z","title":"Memorization and Regularization in Generative Diffusion Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T14:00:40.856297Z"},"links":{"cited_paper":"/paper/2410.02467","citing_paper":"/paper/2501.15785"},"observation_digest":"sha256:c78dc8ffa71842cd61cdaaf9ab7bd50130c4803fff50dc9d047f2f30ea555855","observation_id":"6d3ce6f7-d646-4a4d-8023-6cd902bf53bd","resolution":{"observed_at":"2026-08-10T14:00:40.856297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02467","last_updated":"2025-08-01T09:50:22Z","snapshot_observed_at":"2026-08-12T22:31:41.076861Z","submitted_at":"2024-10-03T13:17:06Z","title":"SIDE: Surrogate Conditional Data Extraction from Diffusion Models","version":7},"cited_work":{"arxiv_id":"2410.02467","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02467","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Extracting training data from unconditional diffusion models","venue":null,"work_id":"350d1be6-d592-433a-967c-d4872cd0c96d","year":2024},"citing_paper":{"arxiv_id":"2508.00756","last_updated":"2026-05-21T07:46:00Z","snapshot_observed_at":"2026-07-06T22:06:24.923510Z","submitted_at":"2025-08-01T16:32:48Z","title":"LeakyCLIP: Extracting Training Data from CLIP","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T12:31:50.876655Z"},"links":{"cited_paper":"/paper/2410.02467","citing_paper":"/paper/2508.00756"},"observation_digest":"sha256:3bdacaad7c1b7beb1ab93e5aa368ade2be86ff9aebd164d62b032b7d93e55368","observation_id":"d8350e76-cb8d-4bcf-96c9-db8fcadf1160","resolution":{"observed_at":"2026-05-22T12:34:52.508598Z","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":"2410.02467","last_updated":"2025-08-01T09:50:22Z","snapshot_observed_at":"2026-08-12T22:31:41.076861Z","submitted_at":"2024-10-03T13:17:06Z","title":"SIDE: Surrogate Conditional Data Extraction from Diffusion Models","version":7},"cited_work":{"arxiv_id":"2410.02467","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02467","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Extracting training data from unconditional diffusion models","venue":null,"work_id":"350d1be6-d592-433a-967c-d4872cd0c96d","year":2024},"citing_paper":{"arxiv_id":"2511.12710","last_updated":"2026-05-18T06:50:41Z","snapshot_observed_at":"2026-07-06T22:35:55.936840Z","submitted_at":"2025-11-16T17:52:07Z","title":"Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T18:58:53.183734Z"},"links":{"cited_paper":"/paper/2410.02467","citing_paper":"/paper/2511.12710"},"observation_digest":"sha256:f48c1c9e724e7355648531b155076a58fb07c1133b96d0a30b5d34d88abeb710","observation_id":"ab1501c4-8974-4ed1-890e-c19ddb06a054","resolution":{"observed_at":"2026-05-21T19:00:30.434451Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2410.02467/citation-record","integrity":"/paper/2410.02467/integrity","json":"/paper/2410.02467/citation-record.json","paper":"/paper/2410.02467"},"outbound":[],"paper":{"arxiv_id":"2410.02467","last_updated":"2025-08-01T09:50:22Z","latest_version":7,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T22:31:41.076861Z","submitted_at":"2024-10-03T13:17:06Z","title":"SIDE: Surrogate Conditional Data Extraction from Diffusion Models"},"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 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.02467."}