{"as_of":"2026-08-15T07:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c70e5284b3901ff53bfae93c53f4020aa5ac20c4720b70bef9a9490dcf13367a","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:15:47.263691Z","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":"2026-06-29T22:44:01.321727Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-08-05T19:06:23.701939Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large mod- els","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13470","last_updated":"2025-08-19T03:03:29Z","snapshot_observed_at":"2026-08-10T06:06:18.503386Z","submitted_at":"2025-08-19T03:03:29Z","title":"STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:06:23.701939Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2508.13470"},"observation_digest":"sha256:8e08cfcb4cbc9ec432bb50e2ace0a43853ba69473bf00ea144004690e37438ed","observation_id":"4b115999-15e5-4575-8c98-d5ceb4a12853","resolution":{"observed_at":"2026-08-05T19:06:23.701939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":"2505.12589","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-06-29T22:44:01.321727Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large models","venue":null,"work_id":"b7018331-7dc7-4d0d-b421-069522ddc09f","year":2025},"citing_paper":{"arxiv_id":"2512.03479","last_updated":"2026-05-08T15:07:43Z","snapshot_observed_at":"2026-08-10T21:44:53.860497Z","submitted_at":"2025-12-03T06:14:26Z","title":"ProcObject-10K: Benchmarking Object-Centric Procedural Understanding in Instructional Videos","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-17T03:05:42.773193Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2512.03479"},"observation_digest":"sha256:27a55992d604271233bf84796fae53c580566309919278610f97d359aac0b4bf","observation_id":"239e5431-98ec-4e19-9a00-164b5ca3ca63","resolution":{"observed_at":"2026-05-17T03:08:56.600848Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":"2505.12589","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-06-29T22:44:01.321727Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large models","venue":null,"work_id":"b7018331-7dc7-4d0d-b421-069522ddc09f","year":2025},"citing_paper":{"arxiv_id":"2604.08457","last_updated":"2026-04-10T14:52:27Z","snapshot_observed_at":"2026-08-12T16:10:35.618218Z","submitted_at":"2026-04-09T16:52:04Z","title":"CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T17:09:57.040135Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2604.08457"},"observation_digest":"sha256:09f0b409e6391f11f391774fc87e859a7364e0ac13a35004ba3bb13dad8becfb","observation_id":"b1c66789-5806-49e3-8774-61e562d6ebc8","resolution":{"observed_at":"2026-05-11T07:30:57.908140Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":"2505.12589","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-06-29T22:44:01.321727Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large models","venue":null,"work_id":"b7018331-7dc7-4d0d-b421-069522ddc09f","year":2025},"citing_paper":{"arxiv_id":"2605.21917","last_updated":"2026-07-08T19:55:21Z","snapshot_observed_at":"2026-08-13T17:46:01.346846Z","submitted_at":"2026-05-21T02:44:27Z","title":"MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T07:36:09.562362Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2605.21917"},"observation_digest":"sha256:c093166c63d5e582d587444a90ed502407c6aa73a45e12f9c72b96cfe65402ea","observation_id":"3eb130d2-a3de-47cf-ae0d-9389acf53a4b","resolution":{"observed_at":"2026-05-22T07:36:13.989290Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-07-12T16:17:21.074502Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.21917","last_updated":"2026-07-08T19:55:21Z","snapshot_observed_at":"2026-08-13T17:46:01.346846Z","submitted_at":"2026-05-21T02:44:27Z","title":"MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-12T16:17:21.074502Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2605.21917"},"observation_digest":"sha256:aa720b86ec4313f913634d3cb2fa721d2c6d77e77ec3f46be43826a62344c1b9","observation_id":"29ab977d-45d4-4862-a0a2-88ef6011c0f5","resolution":{"observed_at":"2026-07-12T16:17:21.074502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":"2505.12589","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-06-29T22:44:01.321727Z","title":"Surveillancevqa-589k: A benchmark for comprehensive surveillance video-language understanding with large models","venue":null,"work_id":"b7018331-7dc7-4d0d-b421-069522ddc09f","year":2025},"citing_paper":{"arxiv_id":"2605.25461","last_updated":"2026-05-25T06:12:19Z","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:12:19Z","title":"MetaphorVU: Towards Metaphorical Video Understanding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T22:43:20.101830Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2605.25461"},"observation_digest":"sha256:dc497b283924bba392ccbdee17ce68a557776af4ede88b5385ff6071c61b8b79","observation_id":"d59e0931-11dc-4425-8c61-dece5328e698","resolution":{"observed_at":"2026-06-29T22:44:01.323382Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12589","snapshot_observed_at":"2026-08-14T04:15:47.263691Z","title":"SurveillanceVQA-589K: A benchmark for comprehensive surveillance video-language understanding with large models.arXiv preprint arXiv:2505.12589, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.10317","last_updated":"2026-08-10T23:40:07Z","snapshot_observed_at":"2026-08-14T23:10:29.979915Z","submitted_at":"2026-08-10T23:40:07Z","title":"From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T04:15:47.263691Z"},"links":{"cited_paper":"/paper/2505.12589","citing_paper":"/paper/2608.10317"},"observation_digest":"sha256:7ad1a9c0b8216f78a9013962d3a9550023aa5bd09c4a0b8e01af9683870e35e0","observation_id":"149f5536-42ec-4d19-9965-3ea8ab966e7b","resolution":{"observed_at":"2026-08-14T04:15:47.263691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.12589/citation-record","integrity":"/paper/2505.12589/integrity","json":"/paper/2505.12589/citation-record.json","paper":"/paper/2505.12589"},"outbound":[],"paper":{"arxiv_id":"2505.12589","last_updated":"2025-05-19T00:57:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T15:43:34.450000Z","submitted_at":"2025-05-19T00:57:04Z","title":"SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large 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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2505.12589."}