{"as_of":"2026-08-09T19:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91c1f9a8d0a63a73950423d7ff8d177fa642580d3e91883088758150e4a84450","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-06T05:57:51.265746Z","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":"2026-07-01T20:56:14.131179Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.13156","last_updated":"2023-12-29T02:14:15Z","snapshot_observed_at":"2026-07-06T17:06:00.378648Z","submitted_at":"2023-12-20T16:19:47Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13156","snapshot_observed_at":"2026-08-06T05:57:51.265746Z","title":"Accidentgpt: Accident analysis and prevention from v2x environmental perception with multi-modal large model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.01057","last_updated":"2025-08-12T12:29:02Z","snapshot_observed_at":"2026-08-06T17:28:47.995101Z","submitted_at":"2025-08-01T20:16:04Z","title":"Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T05:57:51.265746Z"},"links":{"cited_paper":"/paper/2312.13156","citing_paper":"/paper/2508.01057"},"observation_digest":"sha256:d69da5517ddff9dee98a47cd184e60fa0f178be06738f86cb889374468d6a9cc","observation_id":"4eb50c36-18fc-4393-a333-5cf8570cfb2c","resolution":{"observed_at":"2026-08-06T05:57:51.265746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13156","last_updated":"2023-12-29T02:14:15Z","snapshot_observed_at":"2026-07-06T17:06:00.378648Z","submitted_at":"2023-12-20T16:19:47Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13156","snapshot_observed_at":"2026-07-13T21:38:37.329665Z","title":"Accidentgpt: Accident analysis and prevention from v2x environmental perception with multi-modal large model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.20182","last_updated":"2026-07-07T04:36:14Z","snapshot_observed_at":"2026-08-05T23:05:45.930711Z","submitted_at":"2026-03-20T17:57:26Z","title":"IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T21:38:37.329665Z"},"links":{"cited_paper":"/paper/2312.13156","citing_paper":"/paper/2603.20182"},"observation_digest":"sha256:116c44d118cffd24251c8e174852ce3f31349077530474d0f990fdfb2adda564","observation_id":"c166c9c2-534d-489e-8934-cfe69f4d92f2","resolution":{"observed_at":"2026-07-13T21:38:37.329665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13156","last_updated":"2023-12-29T02:14:15Z","snapshot_observed_at":"2026-07-06T17:06:00.378648Z","submitted_at":"2023-12-20T16:19:47Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","version":3},"cited_work":{"arxiv_id":"2312.13156","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.13156","snapshot_observed_at":"2026-07-01T20:56:14.131179Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model,","venue":null,"work_id":"b347fa78-45e7-411d-acc7-285a507bd3e1","year":2023},"citing_paper":{"arxiv_id":"2606.00991","last_updated":"2026-05-31T04:15:30Z","snapshot_observed_at":"2026-08-01T23:28:55.777430Z","submitted_at":"2026-05-31T04:15:30Z","title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-28T17:35:21.127740Z"},"links":{"cited_paper":"/paper/2312.13156","citing_paper":"/paper/2606.00991"},"observation_digest":"sha256:a6614769b6681cded8482e907f607e9a4cdb3a0a8bcb5c763d40b123dbfa389c","observation_id":"ab256124-6f5b-44ec-9605-b705e043657a","resolution":{"observed_at":"2026-07-01T20:56:14.132630Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13156","last_updated":"2023-12-29T02:14:15Z","snapshot_observed_at":"2026-07-06T17:06:00.378648Z","submitted_at":"2023-12-20T16:19:47Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13156","snapshot_observed_at":"2026-08-01T03:34:38.636028Z","title":"AccidentGPT: Accident analysis and prevention from v2x environmental perception with multi-modal large model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23132","last_updated":"2026-07-25T10:25:57Z","snapshot_observed_at":"2026-08-08T02:44:14.107270Z","submitted_at":"2026-07-25T10:25:57Z","title":"DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T03:34:38.636028Z"},"links":{"cited_paper":"/paper/2312.13156","citing_paper":"/paper/2607.23132"},"observation_digest":"sha256:66ed326e2e4f2953c187329dfa326b2a73d391402e4a3140bbad832b49eccbbd","observation_id":"5626f921-d042-4b0b-8a7e-57b758d2901c","resolution":{"observed_at":"2026-08-01T03:34:38.636028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2312.13156/citation-record","integrity":"/paper/2312.13156/integrity","json":"/paper/2312.13156/citation-record.json","paper":"/paper/2312.13156"},"outbound":[],"paper":{"arxiv_id":"2312.13156","last_updated":"2023-12-29T02:14:15Z","latest_version":3,"primary_category":"cs.CE","snapshot_observed_at":"2026-07-06T17:06:00.378648Z","submitted_at":"2023-12-20T16:19:47Z","title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2312.13156."}