{"as_of":"2026-08-09T11:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e49d7ae22b52b7f2cdeedcdc883e8e84837eed84a7fcd97aab6fe6f305fd465e","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T11:23:12.643347Z","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":"2026-07-01T23:06:20.622704Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-08-07T11:23:12.643347Z","title":"Gpt-4v as traffic assistant: an in-depth look at vision language model on complex traffic events,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02615","last_updated":"2025-06-03T08:32:43Z","snapshot_observed_at":"2026-08-07T11:17:56.277660Z","submitted_at":"2025-06-03T08:32:43Z","title":"Hierarchical Question-Answering for Driving Scene Understanding Using Vision-Language Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:23:12.643347Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2506.02615"},"observation_digest":"sha256:07a28df3aa1b5d9b2ba28d70f84cbc78518807e62a4a61e1e2614d5ecba79f50","observation_id":"ebd7634f-7846-439d-a3ec-c488704bf2aa","resolution":{"observed_at":"2026-08-07T11:23:12.643347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-08-06T16:45:57.309457Z","title":"and Knoll, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12755","last_updated":"2025-07-17T03:16:28Z","snapshot_observed_at":"2026-08-08T00:30:58.494371Z","submitted_at":"2025-07-17T03:16:28Z","title":"Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T16:45:57.309457Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2507.12755"},"observation_digest":"sha256:c632f1c216e6e24cc27066480eab6ac8d61c903c4a2e05bcb2cc6de0078aa366","observation_id":"6a7a6b35-3bb4-46be-88c1-e6c1805ffcfc","resolution":{"observed_at":"2026-08-06T16:45:57.309457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":"2402.02205","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-07-01T23:06:20.622704Z","title":"Gpt-4v as traffic assistant: an in-depth look at vision language model on complex traffic events","venue":null,"work_id":"d9849b22-40a9-48c7-9c6f-df7e7e30ebd8","year":2024},"citing_paper":{"arxiv_id":"2509.00789","last_updated":"2026-04-19T12:44:09Z","snapshot_observed_at":"2026-08-07T16:49:08.273704Z","submitted_at":"2025-08-31T10:34:44Z","title":"CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-18T20:10:43.416488Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2509.00789"},"observation_digest":"sha256:81682c226a94b37811d0aa0100e023ad41aa7c83f6f40439b1cf51b5162de0b3","observation_id":"f4349460-cc18-4dff-b172-82e539a25659","resolution":{"observed_at":"2026-05-18T20:11:50.835805Z","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":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":"2402.02205","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-07-01T23:06:20.622704Z","title":"Gpt-4v as traffic assistant: an in-depth look at vision language model on complex traffic events","venue":null,"work_id":"d9849b22-40a9-48c7-9c6f-df7e7e30ebd8","year":2024},"citing_paper":{"arxiv_id":"2605.12542","last_updated":"2026-06-11T05:51:05Z","snapshot_observed_at":"2026-07-06T23:24:13.851504Z","submitted_at":"2026-05-09T08:34:30Z","title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","version":1},"reference_index":283,"source":"pdf_text","source_observed_at":"2026-05-14T22:07:40.242567Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2605.12542"},"observation_digest":"sha256:86bca909ff970ae35215ba209ed8f6d9c7a92a94b16f2d3cf4ed257640f75ab1","observation_id":"0a76d70e-1e88-44f0-9a57-882509bae818","resolution":{"observed_at":"2026-05-14T22:08:03.313292Z","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":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":"2402.02205","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-07-01T23:06:20.622704Z","title":"Gpt-4v as traffic assistant: an in-depth look at vision language model on complex traffic events","venue":null,"work_id":"d9849b22-40a9-48c7-9c6f-df7e7e30ebd8","year":2024},"citing_paper":{"arxiv_id":"2605.12542","last_updated":"2026-06-11T05:51:05Z","snapshot_observed_at":"2026-07-06T23:24:13.851504Z","submitted_at":"2026-05-09T08:34:30Z","title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","version":2},"reference_index":283,"source":"pdf_text","source_observed_at":"2026-06-30T23:07:21.558834Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2605.12542"},"observation_digest":"sha256:41e51558671880ce2d4db4890148fe05a704b7e41688b26d1d890dd93785ff8f","observation_id":"2cc11104-8153-4642-b348-1d97b75900d6","resolution":{"observed_at":"2026-07-01T13:35:46.242792Z","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":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":"2402.02205","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-07-01T23:06:20.622704Z","title":"Gpt-4v as traffic assistant: an in-depth look at vision language model on complex traffic events","venue":null,"work_id":"d9849b22-40a9-48c7-9c6f-df7e7e30ebd8","year":2024},"citing_paper":{"arxiv_id":"2606.01737","last_updated":"2026-06-01T06:01:04Z","snapshot_observed_at":"2026-07-06T23:42:16.661228Z","submitted_at":"2026-06-01T06:01:04Z","title":"TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T14:39:27.242094Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2606.01737"},"observation_digest":"sha256:f2fbeabe07d08eb980427670e78dd174b8bed155b56df74d691f8264fdfb0675","observation_id":"dbaf0a08-b554-492b-8955-5ce3304b8b4b","resolution":{"observed_at":"2026-07-01T23:06:20.624896Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2402.02205","last_updated":"2024-02-07T13:09:15Z","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02205","snapshot_observed_at":"2026-08-01T03:34:38.573004Z","title":"GPT-4V as Traffic Assistant: An in- depth look at vision language model on complex traffic events,","venue":null,"work_id":null,"year":2024},"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":11,"source":"pdf_text","source_observed_at":"2026-08-01T03:34:38.573004Z"},"links":{"cited_paper":"/paper/2402.02205","citing_paper":"/paper/2607.23132"},"observation_digest":"sha256:955bc3fe8776e2f2be367354688374378ff442ac30bc2a84357dd5d9d34dc474","observation_id":"4bdf6b72-ccce-43af-a88c-b1f8609bb29e","resolution":{"observed_at":"2026-08-01T03:34:38.573004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02205/citation-record","integrity":"/paper/2402.02205/integrity","json":"/paper/2402.02205/citation-record.json","paper":"/paper/2402.02205"},"outbound":[],"paper":{"arxiv_id":"2402.02205","last_updated":"2024-02-07T13:09:15Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T07:39:39.887541Z","submitted_at":"2024-02-03T16:38:25Z","title":"GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events"},"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 7 inbound Pith citation observations for arXiv:2402.02205."}