{"as_of":"2026-08-14T12:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8dbdf5c2a80f2aad5d611a398ae00d0310966e3dde7c25e667913a3613114c27","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T09:20:33.263335Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2606.29097/citation-record","integrity":"/paper/2606.29097/integrity","json":"/paper/2606.29097/citation-record.json","paper":"/paper/2606.29097"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T21:06:35.671247Z","title":"Testing autonomous cars for feature interaction failures using many-objective search","venue":null,"work_id":"ea455bef-a121-4cf9-bd18-47daa8cb8b70","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:62bc23686622c29be67994aad1237e76dc808119f39e473e3c062a2d1def3a65","observation_id":"4f9ecc9c-c2be-4f1f-b9e2-d831ef94d61e","resolution":{"observed_at":"2026-07-09T21:06:35.672399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.629412Z","title":"Generating adversarial driving scenarios in high- fidelity simulators","venue":null,"work_id":"49b2942f-e6e2-48c0-83d0-3ae6bbe6a68a","year":2019},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:2623e561bbc5a7a0a218395321aa38ebc80bb49fce26bc5bbf1c9a5ffbb05800","observation_id":"a97841ba-4c4e-401d-a8a4-fc0ba8286f19","resolution":{"observed_at":"2026-07-09T21:06:35.630545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.664443Z","title":"Generating traffic scenarios via in- context learning to learn better motion planner","venue":null,"work_id":"41bf5967-5edb-49b5-b199-acef95a46779","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:5aa5ba5158871735e10c8005aa944a63c6a43b72b2b98168ccf9352d502b4be3","observation_id":"0411c624-9b39-4bc4-8440-c5776645c851","resolution":{"observed_at":"2026-07-09T21:06:35.665599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.602453Z","title":"System card: Claude opus 4 & claude sonnet 4","venue":null,"work_id":"04c4eddb-3d02-4633-ac62-00e78394bcdb","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:ab82adc97b8d49a190cc1795183a70ebb28b8994f7595383dfd97d9487de5c1f","observation_id":"dbc277ce-fd6b-48aa-a976-f3abf8ef1af0","resolution":{"observed_at":"2026-07-09T21:06:35.603691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.642589Z","title":"Ontology based scene creation for the development of automated ve- hicles","venue":null,"work_id":"1e59c1ed-f51a-4aa1-8413-27f0ef868bd4","year":2018},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:f03c04c8c74cae67a77f85b0dd8697305157be6024be0b1a05cdce6ce0e40069","observation_id":"28d70537-d145-4286-8605-f3acfa7592fa","resolution":{"observed_at":"2026-07-09T21:06:35.643705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.676459Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020","venue":null,"work_id":"7c3fcdab-6938-4254-acad-fbea7ab71582","year":1901},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:41716aac7b3ded555833d4299350a01dfb80bb84deca2038b1ac657a14f50032","observation_id":"251c2a7b-9a06-4046-93a4-bf49a8d6a48b","resolution":{"observed_at":"2026-07-09T21:06:35.677745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.644196Z","title":"Behavexplor: Behavior diversity guided testing for autonomous driving systems","venue":null,"work_id":"bdf71c85-a420-4c3e-a508-b3198c6e121d","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:a5682e5d8d21e8ed1ad69a3a5ba81a2e28979c84a7c98f8f292752fcf90f0bbb","observation_id":"4550c19f-aecd-4178-a79a-0b0aef6a0baa","resolution":{"observed_at":"2026-07-09T21:06:35.645254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.611281Z","title":"Sledge: Synthesizing driving environments with generative models and rule-based traffic","venue":null,"work_id":"f6140dba-ed30-435e-ba1b-72ff70e59583","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:b149408dd1a6aded2df8c5eae25d5394e6dd3d27cd80127b459451711d2bc6a8","observation_id":"35d5ed99-bce4-45a8-8261-b6e7d2119016","resolution":{"observed_at":"2026-07-09T21:06:35.612382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.624476Z","title":"Deepseek-v3 technical report, 2024","venue":null,"work_id":"455ae970-7da6-482b-95df-d13502bd978c","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:6107a4709c10abe5b276d4c1a3e8943d00dc43463ddf0b86032a28dbc04d5dc1","observation_id":"10079056-b36b-43f4-9d03-9bec5aac9e22","resolution":{"observed_at":"2026-07-09T21:06:35.625484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.672917Z","title":"TARGET: traffic rule-based test generation for autonomous driving via validated llm-guided knowledge extraction.IEEE Trans","venue":null,"work_id":"29f45d06-149e-40c8-9ab7-c73e0ce8181d","year":1950},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:7d39f19b15b0e71a6a0728bd2f21db1254e0923cd583d134a4c9cce725c14a8e","observation_id":"edc8fff0-16d2-48e0-a4f2-f940d683959a","resolution":{"observed_at":"2026-07-09T21:06:35.674072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.683056Z","title":"Meta-sim2: Unsupervised learning of scene structure for synthetic data generation","venue":null,"work_id":"9c62e307-9749-499b-af4a-92d398937759","year":2020},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:e6944c36381871fba74906f1f9e947354ca80b21a88035e86cdec427d14853bc","observation_id":"e29a0432-c7e1-42c7-be35-42d762fcb7a6","resolution":{"observed_at":"2026-07-09T21:06:35.684209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.622817Z","title":"Learning to collide: An adaptive safety-critical scenarios gen- erating method","venue":null,"work_id":"2ce803af-7f8a-4933-8c9d-87c40caca60d","year":2020},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:ae43e17c50e6954ea9c7451431540ac8cb92c8be6a11e7436f22d1b306e81ba5","observation_id":"4dc1a23b-ecdb-487c-8c95-492ffe058e18","resolution":{"observed_at":"2026-07-09T21:06:35.623951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.612927Z","title":"Cmts: A condi- tional multiple trajectory synthesizer for generating safety- critical driving scenarios","venue":null,"work_id":"aacc71a3-17a2-4ca7-9e04-c92933062117","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:640b34700d5193e7ec5cbf7e38f84b59bf77482d5927a632a4140c6ec758baca","observation_id":"2a642654-2ec6-49dd-a224-f9ca7d7b6a87","resolution":{"observed_at":"2026-07-09T21:06:35.614044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.639446Z","title":"CARLA: An open urban driving simulator","venue":null,"work_id":"4a355df6-2fcb-479f-b462-804a4a6f5498","year":2017},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:e1898cba53b85cd95913bbb04fddfd478c32f6eea567ba83dfdd27f1f8eb69b7","observation_id":"f46c4bf0-386c-48ea-a6c6-c186d113ef3c","resolution":{"observed_at":"2026-07-09T21:06:35.640491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03709","last_updated":"2024-10-02T22:58:42Z","snapshot_observed_at":"2026-08-13T00:15:02.757737Z","submitted_at":"2024-05-03T23:06:31Z","title":"ScenicNL: Generating Probabilistic Scenario Programs from Natural Language","version":3},"cited_work":{"arxiv_id":"2405.03709","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.03709","snapshot_observed_at":"2026-06-30T09:24:32.403553Z","title":"Scenicnl: generating probabilis- tic scenario programs from natural language.arXiv preprint arXiv:2405.03709, 2024","venue":null,"work_id":"9a47e2ec-7b6b-45aa-9884-b71375c10ee6","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/2405.03709","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:668bbc7500394a20c8520d048e0a869ff207bde3a8648904e335b7834dad1e86","observation_id":"e3db6655-70a0-450a-8618-e6751619f2e4","resolution":{"observed_at":"2026-06-30T09:24:32.405142Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.688235Z","title":"Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L","venue":null,"work_id":"3a6bc0df-0df4-4f0a-9082-f1fb00643c76","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:b0a8745997183e6f86a7952da869483fa46adad35d064d3c9fd1281c5f671d47","observation_id":"fc491421-4f2b-4ffd-b2f5-a600d5407be8","resolution":{"observed_at":"2026-07-09T21:06:35.689335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.593724Z","title":"Addressing function approximation error in actor-critic methods","venue":null,"work_id":"3a5f5e2a-92e8-4a9d-86d5-5221478de325","year":2018},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:143b8d992befbfef36e1963002e34ea0df73c5928884178413643747d5341899","observation_id":"419045ac-8003-48f8-8fc8-72747e53a865","resolution":{"observed_at":"2026-07-09T21:06:35.594875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.661112Z","title":"Generating effective test cases for self-driving cars from police reports","venue":null,"work_id":"bda4f47a-4870-4ed7-956b-8f0c910012cd","year":2019},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:9f3f39dc6db872671ddda2509097b48b5c6af0e6635268807eb517f7b0da9454","observation_id":"bbd033e8-46c4-44e0-a929-721d4a24991b","resolution":{"observed_at":"2026-07-09T21:06:35.662219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.598997Z","title":"Sovar: Build generalizable scenarios from accident reports for autonomous driving testing","venue":null,"work_id":"216f9d71-5bae-4933-a7ea-df2bb449e3c9","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:238e9d877d8fb611606e3fe0dd0b429627373d61b6fc618a2ca508d796986c63","observation_id":"922c820f-762f-497b-bf1a-582fa7768c9a","resolution":{"observed_at":"2026-07-09T21:06:35.600167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.654512Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","venue":null,"work_id":"610b0231-f650-4dbb-8f46-e500efb55462","year":2018},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:a09608aeab354c7a1a68e0a23fa3c406a98de1635c75de92c7f57c064876f6db","observation_id":"b4296619-827d-4547-b9d0-8af23ac6f41f","resolution":{"observed_at":"2026-07-09T21:06:35.655658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.590401Z","title":"Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli","venue":null,"work_id":"f158ad1f-ce2b-47d3-bd69-ac474519d927","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:bc987a80a268393220b048570afb87f06046235bb34c4fd4881da421b1bc7a0c","observation_id":"d609f7fc-2379-4c94-acb0-7d44fb7deb7a","resolution":{"observed_at":"2026-07-09T21:06:35.591464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.617769Z","title":"Meta-sim: Learning to generate synthetic datasets","venue":null,"work_id":"8eec7bc9-e921-40f8-b35a-3ea7128db28e","year":2019},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:e613648c04c7a7bbcbb17b93efb773f848384b92e9a729033d54d9ed3cc2376f","observation_id":"966ddd5b-cd57-42a6-b60f-db626a1df38b","resolution":{"observed_at":"2026-07-09T21:06:35.618853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.636056Z","title":"Drivefuzz: Discovering autonomous driving bugs through driving quality- guided fuzzing","venue":null,"work_id":"cc8af924-69ad-4e30-8d18-11c5a544ec92","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:046eebb869a82e772bf40f47be322c64c10cbe64e8ef06ef0440c82d91c263f6","observation_id":"7d85a595-6094-45cb-be19-a1e024a53db6","resolution":{"observed_at":"2026-07-09T21:06:35.637200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.606024Z","title":null,"venue":null,"work_id":"89903dd6-248f-4af8-923a-bc9b27dd33c7","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:6562e2191931575714c440797694b658cdc4b8695d42dfb86d0edb57e932bc67","observation_id":"73d8ac4a-dc76-48dc-8517-e72493a72b2c","resolution":{"observed_at":"2026-07-09T21:06:35.607101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.607893Z","title":"Scenario factory: Creating safety-critical traffic scenarios for automated vehicles","venue":null,"work_id":"cc4cb075-efbc-4650-9993-0983ce9cc0b4","year":2020},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:44e5bc918d6dd5f5cd836d6412b76c8df2307136a0c178dd81d8b937442326c5","observation_id":"6e825bb0-b42a-48e3-b3ad-b04b0e0ee2b2","resolution":{"observed_at":"2026-07-09T21:06:35.609089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.614557Z","title":"Kochenderfer, Ole J","venue":null,"work_id":"fa4bbd70-55ae-4ea5-9190-f8bd15bba60d","year":2015},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:f1e00b714cb52a720b2a10fd7a740828b6ace9c27fdbea5cadc7888044422ecc","observation_id":"9cf0a097-f29e-4aae-a14a-1406b2f60b50","resolution":{"observed_at":"2026-07-09T21:06:35.615674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.667895Z","title":"Av-fuzzer: Finding safety violations in autonomous driving systems","venue":null,"work_id":"b78f903c-dad2-453f-ac60-d7b90a0a7ffd","year":2020},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:f5afbd4cebe7f4fa0e994a8b3781802e7ab6187321301da1e6990bf586cfb855","observation_id":"060a8660-893e-46e8-96ea-1e5aadcfd64d","resolution":{"observed_at":"2026-07-09T21:06:35.669016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.634399Z","title":"Flame: Factuality- aware alignment for large language models.Advances in Neural Information Processing Systems, 37:115588–115614,","venue":null,"work_id":"6027578b-219b-4834-9fe8-e5c3caa3e1e8","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:8bc9c386a6cdb1f3d7ab6d67ae179cf13b242110f3736a28057b0dfe46867d45","observation_id":"42b3f876-2f43-4112-a497-325dd6f00ad5","resolution":{"observed_at":"2026-07-09T21:06:35.635552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.588642Z","title":"Targeting requirements violations of autonomous driving systems by dynamic evolutionary search","venue":null,"work_id":"18608256-f2e8-4695-9261-f21746aab61e","year":2021},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:ee0f08b99adb9e86c9f4b212f5608cfd9480f21595ede99ea42491c7deda0627","observation_id":"2c170a88-f2df-487e-9947-d5fd46535b55","resolution":{"observed_at":"2026-07-09T21:06:35.589807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":"1802.03426","doi":"10.1037/adb0001138","metadata_source":"pith","pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","venue":"stat.ML","work_id":"54c15172-6304-4008-a3b6-c4cc0803c054","year":2018},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:0222c6dfbc3d3c4ef05724910d6937ad74d333516b531e7ea062bc9bf8bc5772","observation_id":"f61c970b-402c-4b74-8940-34f4bbaf5f5e","resolution":{"observed_at":"2026-06-30T09:24:32.407270Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-05-25T15:24:07.856203+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T15:24:07.856203+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.619564Z","title":"Llama-3.2-3B-Instruct","venue":null,"work_id":"13299085-710c-4bea-9970-e87c800c273d","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:5fb3b5c98b7c1e3f9785e78e82dcbd9573d575cadd8b79c7fd12b5db3a0998d2","observation_id":"1d12d608-4f6b-468c-96a1-9d41877250b0","resolution":{"observed_at":"2026-07-09T21:06:35.620620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.637737Z","title":"Standing general order on crash reporting","venue":null,"work_id":"c0fc137a-1b19-41ab-a0e8-0445f092081b","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:72fd46c8c5f98b5b7ed86743b13b8b655291c29f173d7518b44041bbaa831dd1","observation_id":"641c8bc0-5110-4973-8ee0-a462b9effbf3","resolution":{"observed_at":"2026-07-09T21:06:35.638911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":"2410.21276","doi":"10.1177/15248380231178756","metadata_source":"pith","pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4o System Card","venue":"cs.CL","work_id":"f37bf1c7-4964-4e56-9762-d20da8d9009f","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:ccc086015b5dce5490222f4f0381da0b82f1484ff1247125cbc20d534ab1b560","observation_id":"001a0602-5a52-423b-944e-53f7a22380bf","resolution":{"observed_at":"2026-06-30T09:24:32.402798Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.609650Z","title":"GPT-4.1 nano","venue":null,"work_id":"d3b5d4b8-d985-4b79-83f3-773d5f9e640a","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:31aa9dd5faf4e177f37b4404c577e1f6da50a4de467d38e83590edeb0d130950","observation_id":"c2654619-3b3f-4c28-a1c7-3f76736f151d","resolution":{"observed_at":"2026-07-09T21:06:35.610710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.616217Z","title":"GPT-5 system card","venue":null,"work_id":"0f7bcd57-7f2e-4a03-8528-bbbc1f59daaf","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:d80256ccad380befd562d2306d98450b2d2d4f05ba3eef9059d7c100d3e5dcbf","observation_id":"149509bd-483b-438e-b9ac-4291081441f7","resolution":{"observed_at":"2026-07-09T21:06:35.617227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.647476Z","title":"Scenario diffusion: Controllable driving scenario gen- eration with diffusion","venue":null,"work_id":"b4e47bd3-c1a1-4d33-a031-35bdd236e889","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:3be78541e1edc5f30880b1ff9308b6cb1760b47ad0e2d183920c41465944e501","observation_id":"2e131837-d37c-40d1-bf71-ff26c6e9c722","resolution":{"observed_at":"2026-07-09T21:06:35.648617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.679936Z","title":"Qwen3-32b-fp8","venue":null,"work_id":"9afc0077-fed9-476f-a377-fe881042fcdc","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:85d7200ad45a2598e64cfe8e03c7cf5739e4b2e6bfb4f7954e95b8989f07834e","observation_id":"ec389886-dbde-48e5-bbda-858696b4ad1c","resolution":{"observed_at":"2026-07-09T21:06:35.680930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.600719Z","title":"Sentence-bert: Sentence embeddings using siamese bert-networks","venue":null,"work_id":"a8df9be7-f35b-415a-bb87-ef1a9b54bc00","year":2019},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:6bf671b4d15e5ac759ff8e317dcf8a698fd2927a542a6fe87e69e1603013828f","observation_id":"ab8b3379-1ef7-413d-8e9c-e9b5c47ee983","resolution":{"observed_at":"2026-07-09T21:06:35.601920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.689899Z","title":"Generating useful accident-prone driving scenarios via a learned traffic prior","venue":null,"work_id":"4de20e9f-41d2-40d1-bcc3-487c9ab9dc65","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:2c14e415873499089aae206e30288a29c1461e0aa864e8bb295021bfa9545880","observation_id":"715e13f3-f40f-4e97-bc46-8e97caf83dca","resolution":{"observed_at":"2026-07-09T21:06:35.691010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.666218Z","title":"Automated scenario generation for regression testing of autonomous vehicles","venue":null,"work_id":"831f28ec-e6a3-4c4a-bea1-1a76be92759e","year":2017},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:2904a80cc9dc94b17a5ca21dd2820f33b8d47e6feac1cdce17c9bb0a3b24bcef","observation_id":"bc59c1d9-b289-40d3-b46f-10b8b9d5865d","resolution":{"observed_at":"2026-07-09T21:06:35.667355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.631094Z","title":"Scanlon, Kristofer D","venue":null,"work_id":"3d648f09-557b-414f-98a5-996c2d51c2a4","year":2021},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:a460cf63af91e53d5d9f1d026844049f850d4e586beed313fdc4035b635b254b","observation_id":"12903fe4-7883-492f-990d-bee7ce54c31a","resolution":{"observed_at":"2026-07-09T21:06:35.632164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:d6270a733b21dd549ff21fd1db1f3e1840a678e4f7407edfc20b37be7c735edb","observation_id":"32ceffb8-b932-4a7f-9e3d-061532ce48be","resolution":{"observed_at":"2026-06-30T09:24:32.410687Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.651057Z","title":"Role- play with large language models, 2023","venue":null,"work_id":"709923c4-e08e-42f6-ae52-ca2257db066d","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:97b583272c488091e906e14ca1ca708c66e6d72ee353433c65d4aa84c0a4cffd","observation_id":"257b68c7-dc33-468d-8f4e-eaef8f7cff91","resolution":{"observed_at":"2026-07-09T21:06:35.652210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.645797Z","title":"Talk2traffic: Interactive and editable traffic scenario generation for autonomous driving with multimodal large lan- guage model","venue":null,"work_id":"2cb20c98-f06c-490e-b51f-cc9d57b35c32","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:1b5250d62155cecefe056d7331fef6ba8abbd3138dfb227314d96f702776f2e1","observation_id":"aeacb0e6-f946-41f9-9c9c-c4926b0e8fbe","resolution":{"observed_at":"2026-07-09T21:06:35.646950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.597321Z","title":"Lawbreaker: An approach for specifying traffic laws and fuzzing autonomous vehicles","venue":null,"work_id":"6a01c0ec-f634-4bed-a0f4-605a7fccf4fe","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:1e75a5514346ffc97ec76f7decdc1a54a9cfe2237926b6c4e14ac67bf2277d74","observation_id":"4a285737-aea7-4734-bd7d-af0fb48ddffb","resolution":{"observed_at":"2026-07-09T21:06:35.598461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.681446Z","title":"Trafficsim: Learning to simulate realistic multi- agent behaviors","venue":null,"work_id":"185bd4af-c133-46b2-a481-c39b7f75a408","year":2021},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:0a4ae04919cfe0baa2a33e8ca5b9880390b5ab892c4eb63f95702ead3c35ff60","observation_id":"ba0fa7f0-df13-4369-9b2c-aef77351df41","resolution":{"observed_at":"2026-07-09T21:06:35.682530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.641022Z","title":"Language conditioned traffic gen- eration","venue":null,"work_id":"4020e2bd-2cc5-4d71-8126-4e76b8ac6f77","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:3eb99d60cbb7cd79ef912e1308a80b21fc6d0346d4965f7ad559e9ba384294a4","observation_id":"bd2bce6c-94e9-43d9-a05b-120b1b16db48","resolution":{"observed_at":"2026-07-09T21:06:35.642073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.604222Z","title":"Legend: A top-down approach to scenario generation of autonomous driving systems as- sisted by large language models","venue":null,"work_id":"d2fd29e8-4100-429c-8af5-3a223cd9c0ba","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:57694a87a0026c21cb4b8bb926e8493ab491a5ebb5465c8da1085fc136861ae1","observation_id":"0d34e2c5-e264-4421-b150-b802f1fab79a","resolution":{"observed_at":"2026-07-09T21:06:35.605439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.686575Z","title":"Carla scenario run- ner","venue":null,"work_id":"2d352fe2-2b02-4e8c-9a9e-2b9c43c43dc3","year":2019},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:2273714b06f2048689fc3a047d721e91f1c01e9a3a632d2c6a28b3f81457f8cc","observation_id":"c287f1cb-29c6-40e4-8607-f1cb3fb8dc9a","resolution":{"observed_at":"2026-07-09T21:06:35.687675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.656196Z","title":"sentence-transformers/all- mpnet-base-v2","venue":null,"work_id":"54c25425-dd7e-443c-9e6b-b693d71c2600","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:4df74359acdd8b48918e24f1289a9d44c55825b509768445d8ee0bf511d34333","observation_id":"fcb44294-daf5-4d6a-b3b7-747fd3a82547","resolution":{"observed_at":"2026-07-09T21:06:35.657319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.657835Z","title":null,"venue":null,"work_id":"4205767d-0ea4-4b60-92d2-7b5447d45be9","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:afb0bb043eea56744622020743c51b10350bed08f4d73040e807a02d4fb4db37","observation_id":"6e459c23-f96f-45bd-bccd-8499b922df92","resolution":{"observed_at":"2026-07-09T21:06:35.658864Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.592005Z","title":"Generating critical test scenarios for autonomous driving systems via influential be- havior patterns","venue":null,"work_id":"bd755075-1110-4ccb-abc9-864c460e5531","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:94ddee5fd6f62231de8508bed564d590ce1fd7ed97c5f4ae978c89c33d68a99a","observation_id":"414e49ad-b1bd-4904-931b-37adfee7dc2a","resolution":{"observed_at":"2026-07-09T21:06:35.593158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.674575Z","title":"Multi- modal traffic scenario generation for autonomous driving system testing.Proc","venue":null,"work_id":"3c301933-8ae0-4705-92fb-9a7c3baf81c4","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:b8a853fdfa88a4afa3985f8e7f0155c555e8a9a44e1c01be4ee2728a7cf5df90","observation_id":"3466dae2-acb3-479c-9dcb-37adf99e3159","resolution":{"observed_at":"2026-07-09T21:06:35.675792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.649324Z","title":"Automated generation of virtual driving scenarios from test drive data","venue":null,"work_id":"1955b96e-d803-40bb-9e88-7cfe76472048","year":2015},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:f134a30a63e31b5576849fa5c425533431ec9da71e41eec648baeae92e94d083","observation_id":"949699a7-27a2-470c-9c4c-3cbf34fb63dd","resolution":{"observed_at":"2026-07-09T21:06:35.650459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.669547Z","title":"Advsim: Generating safety-critical scenarios for self-driving vehicles","venue":null,"work_id":"103db53b-e223-4f44-a46e-a40cab224d4e","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:df0bd98dc3f82b07f0603b306d69f8d7b957ff198b58a877a3ce4e7423b0d2a3","observation_id":"4fd7e9a9-1b9f-4a98-9b40-85757e1a8a47","resolution":{"observed_at":"2026-07-09T21:06:35.670682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.659447Z","title":"Self-instruct: Aligning language models with self-generated instructions","venue":null,"work_id":"91c4ee3a-4117-4579-90ad-ccbc8f5fa237","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:c786ba8d5dae514cde9b0e56252dccaea372768471cd1d5f97b4c39f381c1917","observation_id":"481e1974-c88e-42d8-874c-c803cd606cf1","resolution":{"observed_at":"2026-07-09T21:06:35.660594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.684795Z","title":"Adversarial prefer- ence learning for robust LLM alignment","venue":null,"work_id":"d423f8cc-f8f8-4b7c-b5df-4a250ed72d93","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:545ab733f95a80404a59439a471538745a2ed33e8ea66e3772753302a57685c6","observation_id":"877a844b-95ee-4777-9883-dfb205be656e","resolution":{"observed_at":"2026-07-09T21:06:35.685971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05875","last_updated":"2024-04-08T21:15:36Z","snapshot_observed_at":"2026-08-13T14:42:51.476432Z","submitted_at":"2024-04-08T21:15:36Z","title":"CodecLM: Aligning Language Models with Tailored Synthetic Data","version":1},"cited_work":{"arxiv_id":"2404.05875","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05875","snapshot_observed_at":"2026-06-30T09:24:32.394844Z","title":"Codeclm: Aligning language models with tailored synthetic data","venue":null,"work_id":"e694eaa5-9f3b-45e6-866f-0205c722719c","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/2404.05875","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:6306560eed235749112a0107a7278bd3b9ab7c17c4c2166493180d492dc4b415","observation_id":"75db12aa-472a-4a8f-a74d-28d604286e60","resolution":{"observed_at":"2026-06-30T09:24:32.396708Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.595490Z","title":"Chain-of- thought prompting elicits reasoning in large language mod- els.Advances in Neural Information Processing Systems, 35: 24824–24837, 2022","venue":null,"work_id":"ba6e5821-54cd-4bc0-8a8d-0a4e695a60c4","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:a4b3a48724b0a631c95abb8a42fff11f0b4e41cb92570dd92ed742e50fcc5fc2","observation_id":"b0d48a7d-b282-4865-91b8-3c9dcf77f44e","resolution":{"observed_at":"2026-07-09T21:06:35.596724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.586726Z","title":"Selfcodealign: Self-alignment for code generation.Advances in Neural In- formation Processing Systems, 37:62787–62874, 2024","venue":null,"work_id":"f16d138f-4178-4005-a56c-42f217c5dce5","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:ea7bb84afe4344148ad2e010cf2bc1dae2bdd0a2c09b2e9d82525ddf1a12a608","observation_id":"f8539935-d235-49f3-8270-c01c1b1d9e6f","resolution":{"observed_at":"2026-07-09T21:06:35.587938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.621119Z","title":"Safebench: a benchmarking platform for safety evaluation of autonomous vehicles","venue":null,"work_id":"9ef7f666-53e9-4501-b8d0-9d0f12e807f2","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:8a586d813113dc381ed6505d3c2845cffc1494c52d44bd106f46b3ad9b843edc","observation_id":"756dfdcb-23a2-4140-ad19-9153a58e697a","resolution":{"observed_at":"2026-07-09T21:06:35.622281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.632680Z","title":"Wizardlm: Empowering large pre-trained language models to follow complex instructions","venue":null,"work_id":"4016eea6-be0f-40a2-b93e-125cd788b585","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:083e8871195a817d3da9f6f5d6a49ec83956c010eaa8c5c24ee125e83014d7ce","observation_id":"b124b88f-3d7c-48ef-a445-6a38c3500fa9","resolution":{"observed_at":"2026-07-09T21:06:35.633865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:33e3c2af2db5f444c0bc6311141ccf74728618c10c86287c85569194633f736d","observation_id":"17ccb924-be64-4463-af66-ee0b25d82fd7","resolution":{"observed_at":"2026-06-30T09:24:32.407567Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.652767Z","title":"Surfelgan: Synthesizing realistic sensor data for autonomous driving","venue":null,"work_id":"d08ca182-5fef-4353-b0cb-53562bbd6eb4","year":null},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:8789bf49111f31d04ec2193d2fe751db6aea5f3e29b270edd66d49ad8081cc5d","observation_id":"e43a3310-7993-41c1-a1be-459f2ec4b6ff","resolution":{"observed_at":"2026-07-09T21:06:35.653948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.678278Z","title":"Youtube.https://www.youtube.com, 2025","venue":null,"work_id":"63a20ffb-d7cc-42d4-b1f9-3979a61d2daf","year":2025},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:63f072dd8a08c18815c8263acfcd4db54ef140c82d25f855979317c72ee21fce","observation_id":"f9d2e5ff-bbac-4a16-bd6e-07b3c438cd76","resolution":{"observed_at":"2026-07-09T21:06:35.679295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.627741Z","title":"Self- rewarding language models","venue":null,"work_id":"ecad57aa-cea0-4909-a416-b656ba8d686c","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:fc6aca804a23441caf3be931afe79b734531b9eea810db737b8c95b6bfc34439","observation_id":"c73722e3-9cbc-4054-a538-5d478d8c3ba2","resolution":{"observed_at":"2026-07-09T21:06:35.628847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.626072Z","title":"Chatscene: Knowledge- enabled safety-critical scenario generation for autonomous vehicles","venue":null,"work_id":"f5b007d3-b75b-4ea2-83ff-0ddb51858096","year":2024},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:2b6ff924e1de6487c6ffff9bee48ffc2ceda6262170525e57143bcc0034c97b4","observation_id":"27605bf1-0032-4bf0-a1e7-2c2b07897a1d","resolution":{"observed_at":"2026-07-09T21:06:35.627213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-09T21:06:35.662732Z","title":"Cat: Closed-loop adversarial training for safe end-to-end driving","venue":null,"work_id":"7646f109-1f07-4834-b01f-6d9833da1690","year":2023},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:93912e202ede1e5dc8fb76119de7500d9f188eb4dd3292e49cf58c06b11cfa1e","observation_id":"2993034d-d2cf-41a2-af74-26e24cf9e090","resolution":{"observed_at":"2026-07-09T21:06:35.663921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"4690.1830","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T09:24:32.398060Z","title":"This is a one-way road","venue":null,"work_id":"8c49727d-fcf0-4810-b51f-687bae379903","year":2022},"citing_paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-30T09:20:33.263335Z"},"links":{"citing_paper":"/paper/2606.29097"},"observation_digest":"sha256:39b354a574afec21df00794d4815af6ef99a4660c8d541ac29909581bef99bc1","observation_id":"2211bfeb-8f14-4eaf-bb43-921e4c0ba0e9","resolution":{"observed_at":"2026-06-30T09:24:32.399654Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.29097","last_updated":"2026-06-27T21:48:22Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T21:48:22Z","title":"TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":1,"verified_exact":7,"verified_fuzzy":60},"total_outbound_references":69},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2606.29097."}