{"as_of":"2026-08-20T14:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f3c3f83cd79b61df190ed1c31e8efa3864e351607190353b5f34c66f65a39ecc","coverage":[{"denominator":5,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T21:09:14.032276Z","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":"2026-08-20T06:33:59.587034+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":"2026-05-11T01:16:36.082783Z","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":"2026-05-11T20:06:18.457464Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"cited_work":{"arxiv_id":"2511.16940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2511.16940","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multipriv: Benchmark- ing individual-level privacy reasoning in vision-language models","venue":null,"work_id":"0e9734f1-c1a3-40b5-9525-fa1286b36014","year":2025},"citing_paper":{"arxiv_id":"2605.05340","last_updated":"2026-05-08T01:54:08Z","snapshot_observed_at":"2026-08-11T20:56:09.474568Z","submitted_at":"2026-05-06T18:10:51Z","title":"How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T16:29:44.865799Z"},"links":{"cited_paper":"/paper/2511.16940","citing_paper":"/paper/2605.05340"},"observation_digest":"sha256:8b2932a95e0f2845d172f31ceb0fa8c6b5017f9738c90ed6d8340780e8aed9a0","observation_id":"b041df1c-e7c1-4604-9c4c-f382c93d74a5","resolution":{"observed_at":"2026-06-01T02:02:21.957785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"cited_work":{"arxiv_id":"2511.16940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2511.16940","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multipriv: Benchmark- ing individual-level privacy reasoning in vision-language models","venue":null,"work_id":"0e9734f1-c1a3-40b5-9525-fa1286b36014","year":2025},"citing_paper":{"arxiv_id":"2605.05340","last_updated":"2026-05-08T01:54:08Z","snapshot_observed_at":"2026-08-11T20:56:09.474568Z","submitted_at":"2026-05-06T18:10:51Z","title":"How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-11T01:16:36.082783Z"},"links":{"cited_paper":"/paper/2511.16940","citing_paper":"/paper/2605.05340"},"observation_digest":"sha256:4ea5e381f858c3f8da7917e3ee65d2580123488c8a20b71dc767a342d45531d2","observation_id":"d0e1a3a1-d58b-409b-94b0-61f926d20a8c","resolution":{"observed_at":"2026-06-01T02:02:21.957785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"cited_work":{"arxiv_id":"2511.16940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2511.16940","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multipriv: Benchmark- ing individual-level privacy reasoning in vision-language models","venue":null,"work_id":"0e9734f1-c1a3-40b5-9525-fa1286b36014","year":2025},"citing_paper":{"arxiv_id":"2605.06230","last_updated":"2026-05-08T02:31:22Z","snapshot_observed_at":"2026-08-16T07:22:27.044390Z","submitted_at":"2026-05-07T13:21:15Z","title":"Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-08T10:12:58.421050Z"},"links":{"cited_paper":"/paper/2511.16940","citing_paper":"/paper/2605.06230"},"observation_digest":"sha256:675c9f0cfbc9b5c517d29bac45102a385b5751d5c76e44ed26d54ec15c936757","observation_id":"b027d3a9-39d6-410f-8dc2-4234d6b1ac23","resolution":{"observed_at":"2026-06-01T02:02:21.957785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"cited_work":{"arxiv_id":"2511.16940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2511.16940","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multipriv: Benchmark- ing individual-level privacy reasoning in vision-language models","venue":null,"work_id":"0e9734f1-c1a3-40b5-9525-fa1286b36014","year":2025},"citing_paper":{"arxiv_id":"2605.06230","last_updated":"2026-05-08T02:31:22Z","snapshot_observed_at":"2026-08-16T07:22:27.044390Z","submitted_at":"2026-05-07T13:21:15Z","title":"Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-11T00:56:48.838028Z"},"links":{"cited_paper":"/paper/2511.16940","citing_paper":"/paper/2605.06230"},"observation_digest":"sha256:5bd83dd35d6e85936b9765dcf87f13871633d958dbbb487c929946f41bdb8f32","observation_id":"f0754315-2403-4f9a-8b12-241c6f12bbc2","resolution":{"observed_at":"2026-06-01T02:02:21.957785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2511.16940/citation-record","integrity":"/paper/2511.16940/integrity","json":"/paper/2511.16940/citation-record.json","paper":"/paper/2511.16940"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:09:14.032276Z","title":"\"The image contains indirect identifiers such as barcodes, postal codes, and reference numbers like \\","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:14.032276Z"},"links":{"citing_paper":"/paper/2511.16940"},"observation_digest":"sha256:892e49fb89647ae2205ffbc0ad08ad9df502e9a50926f6ce73da6bb5bc0c76f9","observation_id":"80767aa9-1d6d-4631-bd25-9ab085d10655","resolution":{"observed_at":"2026-08-03T21:09:14.032276Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06261","last_updated":"2025-12-19T14:25:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-07T17:36:04Z","title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06261","snapshot_observed_at":"2026-08-03T21:09:13.708820Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:13.708820Z"},"links":{"cited_paper":"/paper/2507.06261","citing_paper":"/paper/2511.16940"},"observation_digest":"sha256:06efbf7efc80777d246acd7a22ca206b48958657196190ebd09c256061e67fd4","observation_id":"80b51d6f-65dc-46ec-af64-d148eda45e3a","resolution":{"observed_at":"2026-08-03T21:09:13.708820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07298","last_updated":"2024-05-06T15:52:03Z","snapshot_observed_at":"2026-08-20T07:02:18.685141Z","submitted_at":"2023-10-11T08:32:46Z","title":"Beyond Memorization: Violating Privacy Via Inference with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07298","snapshot_observed_at":"2026-08-03T21:09:13.933921Z","title":"correct\\","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:13.933921Z"},"links":{"cited_paper":"/paper/2310.07298","citing_paper":"/paper/2511.16940"},"observation_digest":"sha256:06e8036f6598e41cab8f5c15d7225b4ce009e7b77207977b4377d1f0565fa53d","observation_id":"0de2b4eb-c81f-4ddb-ac02-53d3012c919b","resolution":{"observed_at":"2026-08-03T21:09:13.933921Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-08-15T14:02:47.366139Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-03T21:09:13.798052Z","title":"InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 939–948","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:13.798052Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2511.16940"},"observation_digest":"sha256:9b670ea5deae9700766ee863ad50e3f7ee60c9d435caee7eecb58fd6fdb7f128","observation_id":"b227eecb-fc76-48ce-8a4c-1e98d468b424","resolution":{"observed_at":"2026-08-03T21:09:13.798052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11468","last_updated":"2025-04-10T16:54:05Z","snapshot_observed_at":"2026-08-16T22:33:00.173650Z","submitted_at":"2025-04-10T16:54:05Z","title":"SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11468","snapshot_observed_at":"2026-08-03T21:09:13.251555Z","title":"China Computer Federation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:13.251555Z"},"links":{"cited_paper":"/paper/2504.11468","citing_paper":"/paper/2511.16940"},"observation_digest":"sha256:5c18a729e3dd7eab7987e75f5867337ecd66e2f2c5add5903550419e593e9cb2","observation_id":"4c20dbad-0a8a-46b7-bf7b-cb81c16b9fea","resolution":{"observed_at":"2026-08-03T21:09:13.251555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.16940","last_updated":"2026-05-29T07:25:18Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T22:33:40.558917Z","submitted_at":"2025-11-21T04:33:11Z","title":"MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models"},"reference_resolution":{"displayed":5,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":5},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 4 inbound Pith citation observations for arXiv:2511.16940."}