{"as_of":"2026-08-08T19:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd507c0087158b167724e3ce57f83922aed9d16548e12431a45934696fdfe99c","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T14:37:07.072292Z","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":"2026-05-22T00:20:50.463722Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":"2312.07488","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2312.07488","venue":null,"work_id":"4afad2eb-fcec-48fe-8841-9c0f5355c863","year":2023},"citing_paper":{"arxiv_id":"2401.05459","last_updated":"2024-05-08T06:16:23Z","snapshot_observed_at":"2026-08-02T13:57:57.119489Z","submitted_at":"2024-01-10T09:25:45Z","title":"Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security","version":2},"reference_index":152,"source":"pdf_text","source_observed_at":"2026-05-17T00:57:26.303195Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2401.05459"},"observation_digest":"sha256:a26d2b5290f7c6402c5b34a1b0e8cb8b0acc33274b3f0954ccdf8e7dc46766a7","observation_id":"44e3eb50-33c1-40c1-b1d3-947599904a18","resolution":{"observed_at":"2026-05-17T00:57:26.673014Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-08-07T14:37:07.072292Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18334","last_updated":"2026-07-26T23:35:41Z","snapshot_observed_at":"2026-08-07T14:30:46.454257Z","submitted_at":"2025-05-23T19:40:09Z","title":"CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:37:07.072292Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2505.18334"},"observation_digest":"sha256:087d11abb1b920c8ef65ce73692934b422c701ceb24b44ac05a2d19592b9a86b","observation_id":"0bc78738-a895-492a-8a9c-cba99b8b141d","resolution":{"observed_at":"2026-08-07T14:37:07.072292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":"2312.07488","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2312.07488","venue":null,"work_id":"4afad2eb-fcec-48fe-8841-9c0f5355c863","year":2023},"citing_paper":{"arxiv_id":"2506.05442","last_updated":"2026-05-16T07:36:33Z","snapshot_observed_at":"2026-08-04T06:43:04.817496Z","submitted_at":"2025-06-05T12:59:35Z","title":"Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-22T00:16:35.823270Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2506.05442"},"observation_digest":"sha256:5c90150363431fafae50a707a0adf42471e0e301bd8d0f5b5c5b60800a65d8b9","observation_id":"d8682bfc-9686-4646-a071-af17c38d800f","resolution":{"observed_at":"2026-05-22T00:20:50.466740Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-08-05T11:32:33.037882Z","title":"Lmdrive: Closed-loop end- to-end driving with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.02659","last_updated":"2025-09-02T17:52:29Z","snapshot_observed_at":"2026-08-07T21:39:22.286238Z","submitted_at":"2025-09-02T17:52:29Z","title":"2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:32:33.037882Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2509.02659"},"observation_digest":"sha256:eda5e52142b0d51791e7eca3c7a315fa4c5bc1cf9400effaba5f399d3cc361ce","observation_id":"7c8d7078-2c7b-4cad-a178-ced6218b842c","resolution":{"observed_at":"2026-08-05T11:32:33.037882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":"2312.07488","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2312.07488","venue":null,"work_id":"4afad2eb-fcec-48fe-8841-9c0f5355c863","year":2023},"citing_paper":{"arxiv_id":"2510.18034","last_updated":"2026-05-20T12:36:58Z","snapshot_observed_at":"2026-08-08T10:38:41.750225Z","submitted_at":"2025-10-20T19:14:29Z","title":"Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T05:47:39.489674Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2510.18034"},"observation_digest":"sha256:7b369644cef2b30134be466fdbbac58543f8fdf45e25fb79f475ad93a71f396a","observation_id":"d49688d1-aedf-4c03-b07a-b342b08a9024","resolution":{"observed_at":"2026-05-18T05:50:57.245731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":"2312.07488","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2312.07488","venue":null,"work_id":"4afad2eb-fcec-48fe-8841-9c0f5355c863","year":2023},"citing_paper":{"arxiv_id":"2510.18034","last_updated":"2026-05-20T12:36:58Z","snapshot_observed_at":"2026-08-08T10:38:41.750225Z","submitted_at":"2025-10-20T19:14:29Z","title":"Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T20:47:38.789375Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2510.18034"},"observation_digest":"sha256:417574de9ea5056f7d5a3beba85cbd35381418bac11fc6a9b3e94e5300bd413b","observation_id":"b28847db-6794-4779-abf2-11b7ab06677d","resolution":{"observed_at":"2026-05-21T20:50:36.617721Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07488","snapshot_observed_at":"2026-08-01T22:49:35.391193Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), http://arxiv.org/abs/2312.074882, 4, 5","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15621","last_updated":"2026-07-17T04:40:02Z","snapshot_observed_at":"2026-08-01T22:49:32.979717Z","submitted_at":"2026-07-17T04:40:02Z","title":"Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T22:49:35.391193Z"},"links":{"cited_paper":"/paper/2312.07488","citing_paper":"/paper/2607.15621"},"observation_digest":"sha256:42350de64d9c86eb0cbe9744966d4127df4dbc9bcd00e60119f893d9e92d9b15","observation_id":"98fea146-e806-48a9-990e-080a4856fe87","resolution":{"observed_at":"2026-08-01T22:49:35.391193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2312.07488/citation-record","integrity":"/paper/2312.07488/integrity","json":"/paper/2312.07488/citation-record.json","paper":"/paper/2312.07488"},"outbound":[],"paper":{"arxiv_id":"2312.07488","last_updated":"2023-12-21T05:37:58Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T06:55:33.879623Z","submitted_at":"2023-12-12T18:24:15Z","title":"LMDrive: Closed-Loop End-to-End Driving with Large Language 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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2312.07488."}