{"as_of":"2026-08-20T04:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0b15ea8c82b7cc467d4059541779056afbdd739cd3cd0627f6a6da267b6c8777","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T05:06:16.554760Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.18086/citation-record","integrity":"/paper/2412.18086/integrity","json":"/paper/2412.18086/citation-record.json","paper":"/paper/2412.18086"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:15.469246Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.469246Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:bb46b1e51116053c8858d0ea34e1453c14deb2a073e6b3989e652382a832e9fe","observation_id":"8cb3ab6a-fca9-41d8-954b-87ec79bc2c82","resolution":{"observed_at":"2026-08-11T05:06:15.469246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:15.504751Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.504751Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:6ccb47d37e302e797d387d58e81d6531011cb4584571dfce8f94cbc4704640fc","observation_id":"ab6f8648-59c0-45c6-87dc-f70c696d4bab","resolution":{"observed_at":"2026-08-11T05:06:15.504751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T05:06:15.554758Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.554758Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:fa70143d00cd1002e339de81ec2be63dd64a3c9008b784f49b1a50f9de871e4e","observation_id":"67a99219-1fb5-4334-a9c2-ab48c8fea5b8","resolution":{"observed_at":"2026-08-11T05:06:15.554758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.249369Z","title":null,"venue":null,"work_id":"349a173d-2e8e-40c7-b4b2-eb8321602865","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.604760Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:a2c2cca9cd47e3bed7e7b277a059752edb6a07390e78c2dfbad30656117a8aed","observation_id":"9f786546-1fab-4890-afc2-30cf8d7391dd","resolution":{"observed_at":"2026-08-11T05:06:18.254285Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.229995Z","title":null,"venue":null,"work_id":"a5815173-1f36-4e6f-a08c-8be8eed378ea","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.664755Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:70811f66f408694ac7f3e492db037bfd97eef36cff9340293131df7e5168d531","observation_id":"6280a0d2-369a-4d56-b141-698158849eae","resolution":{"observed_at":"2026-08-11T05:06:18.235963Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.211961Z","title":null,"venue":null,"work_id":"0d5b249f-0f96-458d-a119-039c11f3afcd","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.671658Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:3786f55dfc43bfe570031af5deb2c905a8cf924093f30fc4b31ca89e11c5693d","observation_id":"efc033c2-175f-45d4-8764-28eb4edcb248","resolution":{"observed_at":"2026-08-11T05:06:18.216322Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.09984","last_updated":"2020-09-21T16:08:47Z","snapshot_observed_at":"2026-08-19T21:28:22.981038Z","submitted_at":"2020-09-21T16:08:47Z","title":"TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval","version":1},"cited_work":{"arxiv_id":"2009.09984","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.09984","snapshot_observed_at":"2026-08-11T05:06:16.974738Z","title":"TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval","venue":"cs.CV","work_id":"0ff19533-d64d-4c63-8284-8519048388d8","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.677624Z"},"links":{"cited_paper":"/paper/2009.09984","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:9321fc1b49c818e86fd0856ef1e67c50096a51929b07ee9392055b2de5b71009","observation_id":"dda5670a-590d-49dd-a3fd-cb65a3fe82e1","resolution":{"observed_at":"2026-08-11T05:06:16.987714Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.196880Z","title":null,"venue":null,"work_id":"a1c499e0-0f15-4205-ad1b-cf849c066d20","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.696088Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:4fd28273bf944c0dee02c64d88cdd6660e9cd8f09eede3d6eac1057ba78dcf85","observation_id":"603ca4ad-3bd8-48de-8b80-3d63a74efc0a","resolution":{"observed_at":"2026-08-11T05:06:18.202496Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.181834Z","title":null,"venue":null,"work_id":"0adf3da0-75f5-4225-8fbb-2bc085dd196e","year":2021},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.719638Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:320691fe342b7ce8b3b8ad94330049d37ab518a26a9d4d8171358cd6a2457e52","observation_id":"041dc67e-9b34-427d-82f4-8f2ebfa9f10d","resolution":{"observed_at":"2026-08-11T05:06:18.187692Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.144620Z","title":"H.; Vora, S.; Liong, V","venue":null,"work_id":"225c2af2-7166-46d6-8b6a-45483971b49c","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.756126Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:2ee3c45b85e156e107c9c6635c9a48435f0075c2e8142b68f618a9122b214ed9","observation_id":"0c7e3901-6d36-4985-9b36-29de59270189","resolution":{"observed_at":"2026-08-11T05:06:18.157490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.103720Z","title":null,"venue":null,"work_id":"39bb9c43-2da9-4e38-96c0-da16ca1bcfc9","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.770049Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:98f82d3cbcbd4ea9a97762d5dada7af33fba6085d38933c378ba79e94ecafcd5","observation_id":"82262fbf-c3d9-41a8-827d-801dbf27ff7a","resolution":{"observed_at":"2026-08-11T05:06:18.112036Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.070130Z","title":null,"venue":null,"work_id":"82336883-ee24-462b-a728-dab71266f215","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.819457Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:d54a3906f8b3686e6ef346d057b050956e9f73427f953627642e174d6edfd29b","observation_id":"01eb3003-48b9-4c79-b8dd-50018a993206","resolution":{"observed_at":"2026-08-11T05:06:18.081337Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10559","last_updated":"2023-05-15T11:45:12Z","snapshot_observed_at":"2026-08-19T03:27:40.295531Z","submitted_at":"2022-12-20T18:58:48Z","title":"Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10559","snapshot_observed_at":"2026-08-11T05:06:15.854754Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.854754Z"},"links":{"cited_paper":"/paper/2212.10559","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:81ccfe4ef8cfc03b12504c498a47240935bfcc54f425ea23348543b3d9ec09e7","observation_id":"5aaac2cf-0b3b-46fe-bcd8-cc2e69e61707","resolution":{"observed_at":"2026-08-11T05:06:15.854754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06018","last_updated":"2025-05-15T22:10:56Z","snapshot_observed_at":"2026-08-16T15:34:13.172421Z","submitted_at":"2023-05-10T10:04:08Z","title":"TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06018","snapshot_observed_at":"2026-08-11T05:06:15.874829Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.874829Z"},"links":{"cited_paper":"/paper/2305.06018","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:0a09a87e4b4ba8247f164a1d824eb48acf2dbbdcbe2aad253127a5e220174eff","observation_id":"d0eb02f2-40d0-46d9-a264-994ec23e4b60","resolution":{"observed_at":"2026-08-11T05:06:15.874829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.039712Z","title":null,"venue":null,"work_id":"90dc848b-2ad6-4da4-959a-971ef43c2b03","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.894780Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:04a802c5132f6d2f297100c487b794a04ee21e218a2edb8e9df994751799d1f1","observation_id":"1a6c60a1-f374-43de-97b0-ebfa145c8d1a","resolution":{"observed_at":"2026-08-11T05:06:18.052446Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:15.917192Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.917192Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:1298ab02e03f7bbe29ba942be3fbf7421dd8018b25f3a1cae4dbc5688cd2e612","observation_id":"4d94e31b-4a3c-4f5a-9ec3-dfaea5c7b8ba","resolution":{"observed_at":"2026-08-11T05:06:15.917192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:18.003665Z","title":null,"venue":null,"work_id":"eeed0a6c-72ab-4b37-bebc-53fa558aa5b2","year":2021},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.944750Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:20684ee7a018e525bda5282acd9f9c8fda83dce226cdf57e23ca5aa8f6822b72","observation_id":"2eccfa8d-c5dd-4cb6-9cdf-46b6fce1d65e","resolution":{"observed_at":"2026-08-11T05:06:18.008041Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.990375Z","title":null,"venue":null,"work_id":"95802e5b-2bf6-49b9-81df-d9f652691e65","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:15.984836Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:48bf7177e733faa471510886763327f6b3e2ed2b0cc31304acc6c880a22a61b5","observation_id":"e8643bbe-9073-4325-93b2-a3a9b1a63c9d","resolution":{"observed_at":"2026-08-11T05:06:17.994135Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.976997Z","title":"J.; Dreossi, T.; Ghosh, S.; Yue, X.; Sangiovanni-Vincentelli, A","venue":null,"work_id":"05d8b21c-162a-425a-9ed2-322067a3e195","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.024756Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:0e5cc9fd4b5358afc180d72aae41486af310b211b4f4e45a1b47bf6a2ce64fd7","observation_id":"26dd2979-a1c4-4fa9-949f-e7e090b0faa2","resolution":{"observed_at":"2026-08-11T05:06:17.982258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.966027Z","title":null,"venue":null,"work_id":"a50b6a07-e417-44de-886d-32dd75fdc7e5","year":2013},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.041921Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:006e0138ca2b97553702f03a8613284d53148ef320ba014003ce82dc5c5252da","observation_id":"a22d1f57-c758-472e-a18a-4dd219a05a02","resolution":{"observed_at":"2026-08-11T05:06:17.969713Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.943824Z","title":null,"venue":null,"work_id":"40796863-8536-4dd3-9f98-0ded4d93807a","year":2004},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.051755Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:36a4409f7a76aff7dfc6aa58e43f274ce824fd2254a17cb7da451a767fbcca44","observation_id":"6e05b2cf-b383-4a84-8c58-0c0dcd765ec8","resolution":{"observed_at":"2026-08-11T05:06:17.948775Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09784","last_updated":"2023-11-16T11:03:13Z","snapshot_observed_at":"2026-08-18T02:05:43.128211Z","submitted_at":"2023-11-16T11:03:13Z","title":"Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09784","snapshot_observed_at":"2026-08-11T05:06:16.057427Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.057427Z"},"links":{"cited_paper":"/paper/2311.09784","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:7d4eb6a5b35ec83e0489d14640d5b8aed7222d5a582f426a5443d05d1ce96e7d","observation_id":"45dd6860-7014-42a6-8d76-27422f208e0f","resolution":{"observed_at":"2026-08-11T05:06:16.057427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.923010Z","title":null,"venue":null,"work_id":"fc69c851-f779-4a22-a67d-2065945e79c8","year":2016},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.062978Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:6d7aa66d51310e6a2dac41be7406397dea8b45c936201a89e5f152b411990554","observation_id":"72eed1a7-734f-4f6a-a317-ded2fe211312","resolution":{"observed_at":"2026-08-11T05:06:17.927161Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.907579Z","title":null,"venue":null,"work_id":"5bc77976-66b8-40c6-84ca-ac721822bf02","year":2018},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.072480Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:ac3741d154debb3ef261ddbe3d3c77e030bfe7a3524949bb04a433bc4ed1192e","observation_id":"ba03d4c6-f78f-45fe-a6fd-6f91e34af94e","resolution":{"observed_at":"2026-08-11T05:06:17.912688Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.893461Z","title":"E.; Schiegg, F.; and Z \\\"o llner, J","venue":null,"work_id":"c44a2440-082b-4fe5-8345-80e38947ce65","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.080160Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:4e4050c3cf49923cfcfac671efd74c63fac711a1cf7328c9a0a51717cc3a4ad4","observation_id":"c0ce6a87-de51-482f-b198-feaa0836cdab","resolution":{"observed_at":"2026-08-11T05:06:17.898351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.874767Z","title":null,"venue":null,"work_id":"86fc7c3d-bd16-43e5-a272-a25badebe5cb","year":2016},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.086165Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:21f39306c1d0ebb615500988633bf5cf8da70666ee2ef869359520ddab60e9ec","observation_id":"2f7c97d9-9d3e-4402-b028-8c9216d43c23","resolution":{"observed_at":"2026-08-11T05:06:17.878783Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.854310Z","title":null,"venue":null,"work_id":"b565b5d2-160f-4e6e-b527-46284c1434cc","year":2024},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.093436Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:17282a7253eb753168fd4bd1b803c4e82b5e1698d3dd755a856ab3146e74bce2","observation_id":"e02f7ad0-a5e6-4bdb-83bb-73ec5f575226","resolution":{"observed_at":"2026-08-11T05:06:17.861611Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.829702Z","title":"G.; Alexiadis, V.; and Zhang PE, L","venue":null,"work_id":"916e25e7-916e-4334-bfb5-2dc8f51bf3c4","year":2007},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.099992Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:a96fe29750e0b1b7a0b95f5d47d318f17af976c2e1a0b3cba52b080e63beaae3","observation_id":"91c1dc26-12f8-4f42-88e7-07e854cefb67","resolution":{"observed_at":"2026-08-11T05:06:17.838159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.770804Z","title":null,"venue":null,"work_id":"65b7ebb7-56f5-49bb-beef-804a9b394469","year":2018},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.104629Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:36d8efd7b45cb62e1757c06c7a680a7a9eccde0c4fb68cc7eede5dcbe5a37691","observation_id":"1dc7ccd7-c1db-4c09-a19e-e37734f797ac","resolution":{"observed_at":"2026-08-11T05:06:17.786113Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.716371Z","title":null,"venue":null,"work_id":"fe3b97f3-6617-48d4-b116-9dc4b1486902","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.109552Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:647e4635684361ebc2536aa1bc897dfc8ba2ba68247d46ed0a670b1c6a58e20c","observation_id":"55d44aaf-556d-405f-9586-1db26fd67c7d","resolution":{"observed_at":"2026-08-11T05:06:17.722138Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.691646Z","title":null,"venue":null,"work_id":"7d2e8de8-02e2-4ada-ab89-b1bdea33e0bc","year":2007},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.113216Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:3611c4f7302b581ebb788f695f0d58bf9ce4d1538daf8b4f6b0166224ee641d7","observation_id":"7de94dcb-850b-4b42-91ab-e62bdc1ab491","resolution":{"observed_at":"2026-08-11T05:06:17.697836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.673962Z","title":null,"venue":null,"work_id":"4713b859-ba4a-4803-b556-0162fa05fe26","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.122337Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:02fe072003885716902f3590618896286d485d3eb8a3f30a25ac9da444bb5c41","observation_id":"3bf88f70-47b8-4c8f-8ec4-191abb57cda6","resolution":{"observed_at":"2026-08-11T05:06:17.680074Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.652809Z","title":"M.; Feng, L.; Liu, Z.; Duan, C.; Mo, W.; and Zhou, B","venue":null,"work_id":"c5c4e0af-745b-4bc2-8a82-114a596a4f10","year":2024},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.129642Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:4b683582f1095f06c211da950cafa713f67c22cccbe3397a08368405a62b2c76","observation_id":"f2d49de0-84cc-449c-ad73-dc12b9c0a1ea","resolution":{"observed_at":"2026-08-11T05:06:17.661145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.637651Z","title":null,"venue":null,"work_id":"424341eb-c16a-4f35-b44f-184e2248b653","year":2024},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.134063Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:237188714dd24e5932c1815609e7ed3c97ab034506d132074fef6d24f0648846","observation_id":"71187d2e-af30-4092-8930-19fcf9e50d38","resolution":{"observed_at":"2026-08-11T05:06:17.643227Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.618940Z","title":null,"venue":null,"work_id":"bce46e26-689e-42c7-9dfc-f358628840de","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.139656Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:19d077008f9fd6fd891987ace3d0ab0e2bfe16b7c8afbf6d8eea7b50869a3e4d","observation_id":"831938bb-b875-414a-9cc4-9411f70d2789","resolution":{"observed_at":"2026-08-11T05:06:17.626415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.598329Z","title":null,"venue":null,"work_id":"2fc07a9f-2492-43b1-a19d-772e401a8620","year":2019},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.146080Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:9b45d2983d7636856ecb8c75943e3dfd7183f16de162d497bccf4539641b96e9","observation_id":"09774412-34e4-429e-95ce-5a764c9c2234","resolution":{"observed_at":"2026-08-11T05:06:17.605134Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-11T05:06:16.187281Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.187281Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:78787eb53f2b6fb55f7a2c1a30094c9ac3dd35981de45dca73aab0844518062e","observation_id":"1254a778-22cd-4ef7-9b1d-0e6365044a80","resolution":{"observed_at":"2026-08-11T05:06:16.187281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12837","last_updated":"2022-10-20T14:04:10Z","snapshot_observed_at":"2026-08-17T18:18:26.304439Z","submitted_at":"2022-02-25T17:25:19Z","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.12837","snapshot_observed_at":"2026-08-11T05:06:16.197154Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.197154Z"},"links":{"cited_paper":"/paper/2202.12837","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:b5d4447d3b9c95eb82d36dc5085add356f08d3cc612b382d4715ba389048eba2","observation_id":"3aea2830-c810-4887-8341-e49c6feb874f","resolution":{"observed_at":"2026-08-11T05:06:16.197154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.556833Z","title":"T.; ; et al","venue":null,"work_id":"e84385fc-0f22-4320-a6d1-5cba3a1c1ef8","year":2011},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.234758Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:12c1447498386237036c287b14138a7672f0514300e2b8e1f6f8645700195246","observation_id":"b7bdf07c-3f8f-440f-b05a-57cdacaacafc","resolution":{"observed_at":"2026-08-11T05:06:17.561157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.534979Z","title":null,"venue":null,"work_id":"cf9aeb3d-1fdf-43b8-8f5b-0dac07a30326","year":2009},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.268414Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:5aa01640d752a2b0dac078958ea0d63c0efd89c661e8946287163c30354020df","observation_id":"f9bcac21-58be-4b96-9e42-6451f86eb6b4","resolution":{"observed_at":"2026-08-11T05:06:17.544699Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.493648Z","title":null,"venue":null,"work_id":"26f23c49-dd8f-4b13-9af2-45effa371626","year":2018},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.294848Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:1455e0aa27df2a7f0f859255848ff73b415dfa4ed76392101d157de5d2d5cf97","observation_id":"a9fbc72a-927e-4e8a-af5d-a6f4b98d9ea6","resolution":{"observed_at":"2026-08-11T05:06:17.502754Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.403199Z","title":"C.; Mirje, M.; Bikkannavar, K","venue":null,"work_id":"c5e9176c-4ee4-42d9-86ae-3a896bc5a4e8","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.320738Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:6cae0cec0be8dd07e74cb3fb0c2fc71175f885e917c94546afe1d1d035150ac3","observation_id":"0debe625-62cc-4df7-acb5-f53d8eb22d07","resolution":{"observed_at":"2026-08-11T05:06:17.424767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.369898Z","title":null,"venue":null,"work_id":"42b0a89c-7aa3-4157-af8d-5a5d5ae411ae","year":2021},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.340696Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:3cc745b0f62f0f6afe8e1e0bf8858caae730392e5819d0e74e0263a92e32d0fa","observation_id":"326b3f3c-4f5b-4c18-8116-cc0516b2177c","resolution":{"observed_at":"2026-08-11T05:06:17.382775Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.294768Z","title":null,"venue":null,"work_id":"55663bfc-bbee-4688-8761-42c31547df8d","year":2024},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.362239Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:123ee9f1b9fe0326c0d917bcb9ce64b1fa140addc7f3bb9d3ddf0315eb62b90b","observation_id":"ca36c992-c4ce-4959-92bf-408f00ead9b7","resolution":{"observed_at":"2026-08-11T05:06:17.314769Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.224754Z","title":null,"venue":null,"work_id":"6ef43bdc-4643-4000-8d70-67793be7212a","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.382255Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:02ebb70bce1e4d30d2b01955ac11fbdade7ff57aebeb9ee6c13034373156f029","observation_id":"b2880603-b679-408f-9f5c-5d33ee98960e","resolution":{"observed_at":"2026-08-11T05:06:17.237685Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01247","last_updated":"2022-04-21T16:03:20Z","snapshot_observed_at":"2026-08-16T17:59:08.214678Z","submitted_at":"2021-09-02T23:46:36Z","title":"Do Prompt-Based Models Really Understand the Meaning of their Prompts?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01247","snapshot_observed_at":"2026-08-11T05:06:16.415532Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.415532Z"},"links":{"cited_paper":"/paper/2109.01247","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:61997fde92a405828cf5ad5a2c8c8517f4097bc49c2a9e892482406197d8adfd","observation_id":"802ecce5-3aa2-4890-b4e2-0f59d0adc73e","resolution":{"observed_at":"2026-08-11T05:06:16.415532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00493","last_updated":"2023-01-02T00:36:22Z","snapshot_observed_at":"2026-08-12T21:55:36.747659Z","submitted_at":"2023-01-02T00:36:22Z","title":"Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00493","snapshot_observed_at":"2026-08-11T05:06:16.425482Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.425482Z"},"links":{"cited_paper":"/paper/2301.00493","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:451c00a4886c70939bc93bfdcc38edccaa18dd68af2a0a4492c2559f0924c617","observation_id":"a7689427-e8f4-4cac-b1b7-15978dfba9d3","resolution":{"observed_at":"2026-08-11T05:06:16.425482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.157958Z","title":null,"venue":null,"work_id":"4476d91c-1efb-4a2b-a8c2-e93f9ebda3fa","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.454751Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:c712858fddbeffd640fd6a02e86d318e0e92ba04b8a74d14a11ee508d51765f9","observation_id":"86f1de92-1bbb-4fa5-ab28-4b985a626417","resolution":{"observed_at":"2026-08-11T05:06:17.175624Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.140504Z","title":null,"venue":null,"work_id":"3e806547-cd7e-4a25-af21-eb9fd484b521","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.472994Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:bd7b4b8b2b6b1c458617f40ba243b3bf2ac2a836cd42ef26fa5b763e454998df","observation_id":"ba4f96f6-d447-4075-9e77-926e127ffe13","resolution":{"observed_at":"2026-08-11T05:06:17.146930Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.084843Z","title":"J.; Luo, X.; and Wang, M","venue":null,"work_id":"a4559118-fdc7-4fbf-b7d9-b4dfbc2c0d44","year":2020},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.504753Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:51fa507d3aa5e899258b57630cea0f1df0ebc4048ebe1b858fcc1f6b6e03c84f","observation_id":"c4662c06-bdd9-4aea-bbec-0cb070264f64","resolution":{"observed_at":"2026-08-11T05:06:17.106642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:06:17.027912Z","title":null,"venue":null,"work_id":"c582e866-92b9-4103-92e3-22d7ab0920ba","year":2023},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.515942Z"},"links":{"citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:f7a9358c425713b585a2832ea1534d1b975a45166a93fcd14b578c5e0553c3d2","observation_id":"7d089bbc-f8b7-4c3e-baec-53503094270a","resolution":{"observed_at":"2026-08-11T05:06:17.032376Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03493","last_updated":"2022-10-07T12:28:21Z","snapshot_observed_at":"2026-07-06T14:01:50.333970Z","submitted_at":"2022-10-07T12:28:21Z","title":"Automatic Chain of Thought Prompting in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-11T05:06:16.554760Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T05:06:16.554760Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2412.18086"},"observation_digest":"sha256:ea35015a61f41f3bbef8bce5794d14099d551a0a75152d07c33a3eb6b094f6a6","observation_id":"1602ceae-c879-4047-ad33-da2cc42e697a","resolution":{"observed_at":"2026-08-11T05:06:16.554760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.18086","last_updated":"2025-05-01T02:31:17Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-19T21:27:59.483878Z","submitted_at":"2024-12-24T01:52:19Z","title":"Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":43,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":52},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2412.18086."}