{"as_of":"2026-08-10T12:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:23501da52a9597c1f8308ea424078e8877c648604f2d8ed81173b32524af52d1","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:53:50.298671Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T08:57:15.573913Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.04642","snapshot_observed_at":"2026-08-04T08:57:15.573913Z","title":"Robotron-sim: Improving real-world driving via simulated hard-case.arXiv preprint arXiv:2508.04642,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.18313","last_updated":"2026-06-29T02:20:45Z","snapshot_observed_at":"2026-08-06T20:20:34.094359Z","submitted_at":"2025-10-21T05:49:01Z","title":"OmniNWM: Omniscient Driving Navigation World Models","version":6},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-04T08:57:15.573913Z"},"links":{"cited_paper":"/paper/2508.04642","citing_paper":"/paper/2510.18313"},"observation_digest":"sha256:4bd83578516a1d43305c4f2867e683b4a8a4eea882fb6c4f2ef22ef7b27c765c","observation_id":"42883488-ebd0-4d8d-b93b-bd70511f5131","resolution":{"observed_at":"2026-08-04T08:57:15.573913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.04642/citation-record","integrity":"/paper/2508.04642/integrity","json":"/paper/2508.04642/citation-record.json","paper":"/paper/2508.04642"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:59.093634Z","title":"Planning-oriented au- tonomous driving,","venue":null,"work_id":"d7d31110-2896-41c4-ac9d-d8924c9851c3","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.166589Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:e0759a4dab238b283968ad219ca72edceb222d00e6877b0e2b2f21f3c697e3c8","observation_id":"4c1cdc81-27de-4493-b2b5-04650ec04480","resolution":{"observed_at":"2026-08-05T23:53:59.248469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10430","last_updated":"2023-10-22T02:45:33Z","snapshot_observed_at":"2026-07-06T15:28:49.253125Z","submitted_at":"2023-05-17T17:59:11Z","title":"Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10430","snapshot_observed_at":"2026-08-05T23:53:44.251428Z","title":"Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.251428Z"},"links":{"cited_paper":"/paper/2305.10430","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:3ce0ba853dd852155138a3f434aded0a739e1866cba44dd2bb23aed2a3943881","observation_id":"c3e4872d-09c7-48fb-9dfb-9fd480606a35","resolution":{"observed_at":"2026-08-05T23:53:44.251428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:58.640298Z","title":"Is ego status all you need for open-loop end-to-end au- tonomous driving?","venue":null,"work_id":"0074bcd8-83e9-4845-8d49-6d38701d9682","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.356862Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:db471394e68f566902bac344caac8c24d4b32f1bc1efa99a95524c1cabfd3104","observation_id":"ced822f1-deb0-4a35-9fe0-5dfc4c41d92c","resolution":{"observed_at":"2026-08-05T23:53:58.839800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23262","last_updated":"2025-09-23T04:19:59Z","snapshot_observed_at":"2026-08-05T06:31:46.069300Z","submitted_at":"2024-10-30T17:46:31Z","title":"EMMA: End-to-End Multimodal Model for Autonomous Driving","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.23262","snapshot_observed_at":"2026-08-05T23:53:44.445075Z","title":"Emma: End- to-end multimodal model for autonomous driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.445075Z"},"links":{"cited_paper":"/paper/2410.23262","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:7f893f0496c258df0103511fa7ab9be264003e08fcf687debff39c48d5021dac","observation_id":"e9f6ab17-4453-40cc-9847-fa747a5803cc","resolution":{"observed_at":"2026-08-05T23:53:44.445075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:58.540876Z","title":"Visual instruction tuning,","venue":null,"work_id":"6cc0f878-c45e-4611-b804-a3f25d2c697d","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.519433Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:4ad79b181b70dd5fe583c95c2d1c80a32261fc09e7fd25bf7990865a642bb668","observation_id":"5f29f085-1695-4681-a35a-5484ca269ce9","resolution":{"observed_at":"2026-08-05T23:53:58.611283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:58.276926Z","title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023,","venue":null,"work_id":"8df55f8c-f1ff-4df7-bf21-00cea5bf762d","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.580609Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:9f8dd456a2400cb1d7df4b73708514de18b6186fde28d76f10952e47437eddd1","observation_id":"87647c5d-e949-48da-9307-0dfb9e9b4099","resolution":{"observed_at":"2026-08-05T23:53:58.436500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:58.025983Z","title":"Merlin: Empowering mul- timodal llms with foresight minds,","venue":null,"work_id":"a324374c-f6e8-4f60-8e40-1c0a3d457d99","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.678531Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:861e88203f4c7a253f38936bf19cc6eb1ec3b9675b62c08d3130a286c45e1803","observation_id":"7d933895-8da9-42e4-9716-7004763d702d","resolution":{"observed_at":"2026-08-05T23:53:58.150099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:57.719437Z","title":"Think2drive: Efficient reinforcement learning by thinking with latent world model for autonomous driving (in carla-v2),","venue":null,"work_id":"48addb0a-8f1a-4d23-9a4b-1da9e91df28b","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.768369Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:2a2ebc594e8df12faa244a4b95698b7a899f6b3c8f09bfb4e4dc9078af5a3fff","observation_id":"bd2a675b-6f7d-451d-929b-2794b41746fb","resolution":{"observed_at":"2026-08-05T23:53:57.868905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:57.375375Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":"31e7489b-208a-47a7-845d-596e71b326ab","year":2020},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:44.899805Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:035ff0a6426f14eb2d1b71949d47cfd16e27a7ce5738d5494a99a8e241387e2f","observation_id":"b4cd62eb-68d1-4bfd-b57b-22ba6b55a59a","resolution":{"observed_at":"2026-08-05T23:53:57.562127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:57.089783Z","title":"Carla: An open urban driving simulator,","venue":null,"work_id":"553c9c14-0334-4823-965d-328a19c5c0f0","year":2017},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.004491Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:098e6bb47c7dc9a12f4aa20045436a06fb1d708647f4bf72e9d369bc665e89fb","observation_id":"c1b3f1a3-3275-47fc-8d0e-ec2f9d91f36f","resolution":{"observed_at":"2026-08-05T23:53:57.211540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:56.775067Z","title":"Airsim: High- fidelity visual and physical simulation for autonomous ve- hicles,","venue":null,"work_id":"289aab26-b1d0-486c-b1c3-19cc3495dead","year":2018},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.140276Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:adf6d1ac08310c768af17886baba38b28a8406765ddf7d19912bc4ca713e1c33","observation_id":"5d650794-b1ea-4a93-9487-b7be01e5ee0a","resolution":{"observed_at":"2026-08-05T23:53:56.899146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:56.447626Z","title":"Making large language models better plan- ners with reasoning-decision alignment,","venue":null,"work_id":"2b9bcbc2-6494-42f5-95d6-60bc49bded4c","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.215671Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:12a02eff448a452672ea0201de903204438a6d294d2272a63404bbc82e34d7e9","observation_id":"0fe0ba79-0c34-40df-a3ec-7a6e5e5feaa6","resolution":{"observed_at":"2026-08-05T23:53:56.623308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22313","last_updated":"2024-10-29T17:53:56Z","snapshot_observed_at":"2026-07-31T01:16:26.372370Z","submitted_at":"2024-10-29T17:53:56Z","title":"Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22313","snapshot_observed_at":"2026-08-05T23:53:45.292650Z","title":"Senna: Bridging large vision-language models and end-to-end autonomous driv- ing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.292650Z"},"links":{"cited_paper":"/paper/2410.22313","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:a9d4fc31217e0e76a4e5b4235988bd33d42e4ffc6eb87005b18ff49f9befe5c3","observation_id":"1cea3738-c250-4693-9ea0-2c3376da046a","resolution":{"observed_at":"2026-08-05T23:53:45.292650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03326","last_updated":"2024-10-26T16:35:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:59:44Z","title":"LLaVA-OneVision: Easy Visual Task Transfer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03326","snapshot_observed_at":"2026-08-05T23:53:45.418885Z","title":"Llava-onevision: Easy visual task transfer,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.418885Z"},"links":{"cited_paper":"/paper/2408.03326","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:88ed2ddc47b517eefa743f2974a35d60a47ba61ecfe6fd25dd4af3d32beb1777","observation_id":"bce6a1bc-8038-4597-ad2f-b91a728597ec","resolution":{"observed_at":"2026-08-05T23:53:45.418885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10595","last_updated":"2024-12-06T01:54:59Z","snapshot_observed_at":"2026-08-09T19:39:22.676077Z","submitted_at":"2024-04-16T14:20:55Z","title":"Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10595","snapshot_observed_at":"2026-08-05T23:53:45.537163Z","title":"Automated evaluation of large vision-language models on self-driving corner cases,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.537163Z"},"links":{"cited_paper":"/paper/2404.10595","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:a38248d628987c5d005dc32984891194bb0bf7a0d75d9533b8eb5194bda7f5f8","observation_id":"90c76992-5bf4-4887-b4b3-6d43bd02fd41","resolution":{"observed_at":"2026-08-05T23:53:45.537163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:56.093471Z","title":"Lingoqa: Video question answering for autonomous driving,","venue":null,"work_id":"ee8eea02-973c-4162-a019-6ea7db53f3bf","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.626586Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:98f70f68669618b6093420759dfac80e45cf1c345d04afd5903ebf08c0747f67","observation_id":"292da29f-7a89-43e1-a3b1-34ebbdf707f2","resolution":{"observed_at":"2026-08-05T23:53:56.245424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:55.741741Z","title":"Holistic autonomous driving understanding by bird’s-eye- view injected multi-modal large models,","venue":null,"work_id":"876f6ebc-6dc7-4a8d-9f22-d6a18f85429b","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.695465Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:ad2d64cfb687cbe6c7686f6d62cb6d6f58a1466f080afcd607677225014eb956","observation_id":"494b5508-fe05-42b7-b899-4f6da59e0612","resolution":{"observed_at":"2026-08-05T23:53:55.933532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12289","last_updated":"2024-06-25T17:55:35Z","snapshot_observed_at":"2026-08-10T03:38:23.239399Z","submitted_at":"2024-02-19T17:04:04Z","title":"DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12289","snapshot_observed_at":"2026-08-05T23:53:45.786781Z","title":"Drivevlm: The convergence of autonomous driving and large vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.786781Z"},"links":{"cited_paper":"/paper/2402.12289","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:8d16fa220b2d5be88315a80a7eb135991f3fcd48b3b4856fc90d589b52816552","observation_id":"2f536079-ee31-47b8-996d-0d9c331b99bb","resolution":{"observed_at":"2026-08-05T23:53:45.786781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T23:53:45.885509Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.885509Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:e7765e153e65b9fbbd28ec55501167bafa063cf76dcb7f61a5ba9c519125a866","observation_id":"4819e3fb-d6a5-42ce-ad06-aca740220228","resolution":{"observed_at":"2026-08-05T23:53:45.885509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16292","last_updated":"2024-02-22T03:24:26Z","snapshot_observed_at":"2026-07-06T16:24:49.716011Z","submitted_at":"2023-09-28T09:41:35Z","title":"DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16292","snapshot_observed_at":"2026-08-05T23:53:45.998341Z","title":"Dilu: A knowledge-driven approach to autonomous driving with large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:45.998341Z"},"links":{"cited_paper":"/paper/2309.16292","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:9947c1b06c00ba9b3e9f30847e7e262efcf6d23993100493aac184a6c38f1b63","observation_id":"867393f6-fc0a-4c86-beed-f3381304e50d","resolution":{"observed_at":"2026-08-05T23:53:45.998341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:55.446862Z","title":"Drivegpt4: Interpretable end-to-end au- tonomous driving via large language model,","venue":null,"work_id":"73868692-84ee-49bc-8b3d-35db6eb1832d","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.101313Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:59876e7188bf58fdc680fc52c7e4956f54eb214f0ec05a21f7ed57c0b951108d","observation_id":"fcf468f2-5788-45e6-b40d-c93423f75316","resolution":{"observed_at":"2026-08-05T23:53:55.572259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:55.168862Z","title":"Making large language models better plan- ners with reasoning-decision alignment,","venue":null,"work_id":"df29165b-a136-4157-a9b2-79f7e26cd58c","year":2025},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.178666Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:d6afd4f21a5775ffd87ffd63116117b514a45d10ec8ba0542ad57b10f0825c44","observation_id":"82eb03e6-ebab-43fd-a647-cf03b03f6407","resolution":{"observed_at":"2026-08-05T23:53:55.311600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:54.908766Z","title":"Drive as you speak: Enabling human-like interaction with large lan- guage models in autonomous vehicles,","venue":null,"work_id":"84f819c7-7f9e-4692-9454-c402fe4975a2","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.289714Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:e0bb23a584528562020c8a454f3aa8210893601ebd27642a7ca34f09c56961a4","observation_id":"34b840db-d822-4cfb-b81c-782b13c8147d","resolution":{"observed_at":"2026-08-05T23:53:55.046626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:54.659501Z","title":"Lmdrive: Closed-loop end-to-end driving with large language models,","venue":null,"work_id":"9cb0af86-f3b0-4d61-9575-542c6b91d777","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.388058Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:a6200edbb84d6906f9c3798f02a6fdd9372b09733f1aa465cfd9db77f7214707","observation_id":"a7b947dd-d6a9-4424-bdc5-866108c1fb6c","resolution":{"observed_at":"2026-08-05T23:53:54.772729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01533","last_updated":"2025-04-16T15:12:21Z","snapshot_observed_at":"2026-08-07T03:24:13.032388Z","submitted_at":"2024-05-02T17:59:24Z","title":"OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01533","snapshot_observed_at":"2026-08-05T23:53:46.525390Z","title":"Omnidrive: A holistic llm- agent framework for autonomous driving with 3d perception, reasoning and planning,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.525390Z"},"links":{"cited_paper":"/paper/2405.01533","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:7f1ba162b887440c9531d75119d2320159f4517d97ca0dfc7a0db30a8133d7a6","observation_id":"4d5d7f2f-0c08-4c91-b5b8-d1dcc5433595","resolution":{"observed_at":"2026-08-05T23:53:46.525390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:54.343034Z","title":"Visual instruction tuning,","venue":null,"work_id":"fe1306b6-6f53-4c32-82c1-ccd7ef3a1a96","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.607425Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:185c25b3eef1ef4afebbb45ced3e3f053119fa1612aba92987d1719226c29aff","observation_id":"344d48c3-61e6-4310-8d81-938fb911dccb","resolution":{"observed_at":"2026-08-05T23:53:54.471860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:54.061401Z","title":"End-to-end autonomous driving: Challenges and frontiers,","venue":null,"work_id":"8907ea98-06a8-40cd-b719-66fd70cf1ac2","year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.842795Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:8c1d75883c16a66fa6ce11d020c5f03eee4eda07226e69c03f3b01037c8ed631","observation_id":"5d5f48f5-5d35-406e-9323-4f7d7ad12f4b","resolution":{"observed_at":"2026-08-05T23:53:54.204256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.13501","last_updated":"2024-04-21T01:49:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-04-21T01:49:46Z","title":"A Survey on the Memory Mechanism of Large Language Model based Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.13501","snapshot_observed_at":"2026-08-05T23:53:46.933179Z","title":"A survey on the memory mecha- nism of large language model based agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:46.933179Z"},"links":{"cited_paper":"/paper/2404.13501","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:ce6596d81321aaca3d119f3ca7294dd5239576137f2a3e9f8ee1700cf55ae47a","observation_id":"a027ce57-120f-4235-84cc-de564df00ea1","resolution":{"observed_at":"2026-08-05T23:53:46.933179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11790","last_updated":"2022-06-16T09:15:52Z","snapshot_observed_at":"2026-07-06T12:21:28.059661Z","submitted_at":"2021-12-22T10:48:06Z","title":"BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11790","snapshot_observed_at":"2026-08-05T23:53:47.055162Z","title":"Bevdet: High-performance multi-camera 3d object detection in bird- eye-view,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.055162Z"},"links":{"cited_paper":"/paper/2112.11790","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:f73c2ef9102fb58dbd8d167a54c8e28732e0cb8f285dff4fd1230230bac5a7f9","observation_id":"88cad292-3c52-48c7-88d1-db4317f7fcbd","resolution":{"observed_at":"2026-08-05T23:53:47.055162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:53.751381Z","title":"Bevfusion: A simple and robust lidar-camera fusion framework,","venue":null,"work_id":"b50fdff7-9031-4011-a930-cfeda6e69ffd","year":2022},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.225647Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:e54cedaa58f33702ceb73a52edb5546537ea73f48e92514d3319d1fad6132f10","observation_id":"67465283-c802-413e-a93c-081fbd044f29","resolution":{"observed_at":"2026-08-05T23:53:53.891873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:53.425456Z","title":"Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,","venue":null,"work_id":"41496064-4675-4e9a-8189-af8942b80c5a","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.302704Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:f8f7cf089d4f4e712ec126a725660c7512dcba58d3532af13ad8838de5fc0dc7","observation_id":"cb797b2c-23c1-4171-82a6-3f970baad93b","resolution":{"observed_at":"2026-08-05T23:53:53.564910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:53.117166Z","title":"Vip3d: End-to-end visual trajectory prediction via 3d agent queries,","venue":null,"work_id":"b6efef23-59c8-478b-92d4-84e3071fff91","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.490386Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:77c90f7b85dd3026404177a58e51db8801480926c198dc1fbc21441dfb4bb78b","observation_id":"9dceddf8-d034-4899-ae11-a6d11c3946da","resolution":{"observed_at":"2026-08-05T23:53:53.282348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:52.829609Z","title":"Vectornet: Encoding hd maps and agent dy- namics from vectorized representation,","venue":null,"work_id":"d38934f1-ca4c-45e9-9abf-56288c0b63b8","year":2020},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.623102Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:b9399047fab68c709f59d73dd999bb6402a59c2a41f5c353c529fb204fddabc2","observation_id":"70a45760-9b3a-4aa9-b8be-8aa4892bbe45","resolution":{"observed_at":"2026-08-05T23:53:52.946298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:52.626669Z","title":"Path-aware graph attention for hd maps in motion prediction,","venue":null,"work_id":"ff2208d3-0ebf-445b-a647-cea240abb936","year":2022},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.713372Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:1caf23a439fcee2b30789e840d86cf6ea768364f0633e2f82c2761e2fbb29bf0","observation_id":"726614ef-24fe-4760-a18d-17556c30dcf1","resolution":{"observed_at":"2026-08-05T23:53:52.717253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:52.426000Z","title":"Urban driver: Learning to drive from real-world demonstrations using policy gradients,","venue":null,"work_id":"9d006b77-c486-451f-af52-d3ddea4d5609","year":2022},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:47.878592Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:94f88bac1318f35411cd91187ef0b4944947b186cb659dc7a09ea0742c6d3963","observation_id":"74b53e03-d69c-4ad1-80eb-e092cdb0d689","resolution":{"observed_at":"2026-08-05T23:53:52.520965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:52.265948Z","title":"Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,","venue":null,"work_id":"9c2be678-498a-4b2f-a71e-43ade9ed95aa","year":2020},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.092138Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:91106e08734f0251d7f3a917791d20162cc252862942d8f1b786f79dfcec2373","observation_id":"38411c33-b4fa-4fc8-9410-4c81fb7a3625","resolution":{"observed_at":"2026-08-05T23:53:52.348506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:52.141851Z","title":"Cola-hrl: Continuous-lattice hier- archical reinforcement learning for autonomous driving,","venue":null,"work_id":"c7987058-4641-4e8f-be88-a0093679ce70","year":2022},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.282822Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:bd645ebd4f9737c1b2bce63b414bd25c57855b7dfc7d070ccaa99ca86ce56db7","observation_id":"e0fd823c-39f4-419f-bac1-4b2300a0347e","resolution":{"observed_at":"2026-08-05T23:53:52.191730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01043","last_updated":"2024-08-12T11:53:28Z","snapshot_observed_at":"2026-08-10T06:32:51.712930Z","submitted_at":"2023-11-02T07:23:33Z","title":"LLM4Drive: A Survey of Large Language Models for Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01043","snapshot_observed_at":"2026-08-05T23:53:48.447263Z","title":"Llm4drive: A survey of large language models for autonomous driving,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.447263Z"},"links":{"cited_paper":"/paper/2311.01043","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:5f08ad238c3a5ee0c96e2f1dd78b623fe364347fa9d56e5b7e55e22f1e26b1a3","observation_id":"f69a4da0-bba4-4e19-8403-c3556fbe9f7e","resolution":{"observed_at":"2026-08-05T23:53:48.447263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:51.967110Z","title":"Language models are few-shot learners,","venue":null,"work_id":"e24f2546-55e9-401a-8b50-3ac4d99d7a02","year":1901},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.584638Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:9662ab8271fb15828112854b23ab5788a2779c2b1920418d903d905313619b64","observation_id":"9299c620-fd67-4f25-835d-8086da1a0d6c","resolution":{"observed_at":"2026-08-05T23:53:52.044596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.01006","last_updated":"2023-03-15T00:45:46Z","snapshot_observed_at":"2026-08-03T14:55:02.216927Z","submitted_at":"2023-01-03T08:52:49Z","title":"Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.01006","snapshot_observed_at":"2026-08-05T23:53:48.817246Z","title":"Policy pre-training for autonomous driving via self-supervised ge- ometric modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.817246Z"},"links":{"cited_paper":"/paper/2301.01006","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:d1ad64a583c8e1a4bd8394fa9d8943abcfb2c292d1c6b45e05f0642572b5ec1b","observation_id":"37082156-d4c0-49b7-8267-d1e511c76af4","resolution":{"observed_at":"2026-08-05T23:53:48.817246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03877","last_updated":"2024-11-27T09:31:04Z","snapshot_observed_at":"2026-08-10T01:39:25.573271Z","submitted_at":"2024-06-06T09:12:30Z","title":"Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03877","snapshot_observed_at":"2026-08-05T23:53:48.972778Z","title":"Bench2drive: Towards multi-ability benchmarking of closed-loop end-to- end autonomous driving,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:48.972778Z"},"links":{"cited_paper":"/paper/2406.03877","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:6266beaefce417a0a1ddd7d24f68fda5efb0a56543cdd33083ee20194e136a67","observation_id":"5f6f3fb6-c938-4dcf-afd3-63efdd5869d9","resolution":{"observed_at":"2026-08-05T23:53:48.972778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11581","last_updated":"2025-03-23T13:00:03Z","snapshot_observed_at":"2026-08-02T06:52:32.786538Z","submitted_at":"2024-11-18T13:57:35Z","title":"OASIS: Open Agent Social Interaction Simulations with One Million Agents","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.11581","snapshot_observed_at":"2026-08-05T23:53:49.158851Z","title":"Oasis: Open agent social interaction simulations with one million agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:49.158851Z"},"links":{"cited_paper":"/paper/2411.11581","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:8a1d7ccbceb70e67bd3a04b42c5971c359f118ed710311ec62e73707d7784e35","observation_id":"b211b623-e206-4a07-8524-61c948e0670f","resolution":{"observed_at":"2026-08-05T23:53:49.158851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:51.685036Z","title":"Scalability in perception for autonomous driving: Waymo open dataset,","venue":null,"work_id":"d150ecb6-e8c7-4b93-8f81-c6e612085fed","year":2020},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:49.401287Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:96ffcc02e4b2d2f518f231f3afca5bdf55ac9cc3ea4bc4bffc8a72f79cb9f341","observation_id":"37c3a982-e2da-45dc-9610-51063a106874","resolution":{"observed_at":"2026-08-05T23:53:51.830385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08142","last_updated":"2021-08-09T16:51:06Z","snapshot_observed_at":"2026-08-10T06:27:31.351987Z","submitted_at":"2021-07-16T23:20:26Z","title":"Autonomy 2.0: Why is self-driving always 5 years away?","version":3},"cited_work":{"arxiv_id":"2107.08142","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.08142","snapshot_observed_at":"2026-08-05T23:53:50.974153Z","title":"Autonomy 2.0: Why is self-driving always 5 years away?","venue":"cs.RO","work_id":"ec2ee3f9-e1c6-4db4-90ba-97ab4f9b7fec","year":2021},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:49.558379Z"},"links":{"cited_paper":"/paper/2107.08142","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:ed0d89a0611e121989d46739cda31f94eb316c3c7f5ba8e2c2095879038fa478","observation_id":"3532a42c-d570-4e04-9820-7c5df52a2464","resolution":{"observed_at":"2026-08-05T23:53:51.163976Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07689","last_updated":"2025-08-07T06:38:32Z","snapshot_observed_at":"2026-07-06T20:04:48.422043Z","submitted_at":"2024-12-10T17:27:32Z","title":"RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07689","snapshot_observed_at":"2026-08-05T23:53:49.792907Z","title":"Drivemm: All-in-one large mul- timodal model for autonomous driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:49.792907Z"},"links":{"cited_paper":"/paper/2412.07689","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:e0ec7514f06d7eee84a12c944ecbfa229b45d5009ad90dda78b760fdd2de5e8e","observation_id":"0881fcfb-0257-4341-8aad-59062318f83f","resolution":{"observed_at":"2026-08-05T23:53:49.792907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:49.998116Z","title":"Robomm: All-in-one multimodal large model for robotic manipulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:49.998116Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:39eb3e8ec47e15f954a75a57fb43f08e8e5ae2250c2ad7102ba9380e2021990d","observation_id":"d8c4f036-59b1-4a82-9cf2-43815e21976b","resolution":{"observed_at":"2026-08-05T23:53:49.998116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18525","last_updated":"2025-08-08T11:19:25Z","snapshot_observed_at":"2026-08-09T13:14:16.846117Z","submitted_at":"2025-03-24T10:29:47Z","title":"RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction","version":4},"cited_work":{"arxiv_id":"2503.18525","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.18525","snapshot_observed_at":"2026-08-05T23:53:50.500548Z","title":"RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction","venue":"cs.RO","work_id":"d8af92d1-f2f6-495e-a063-b623a9cffa91","year":2025},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:50.148533Z"},"links":{"cited_paper":"/paper/2503.18525","citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:1fbddfd157678643a0102ae61887db61407eda913fe195f1379f63c7d92fbb64","observation_id":"90ee8e54-264e-4193-b5a1-37eecc5ea06d","resolution":{"observed_at":"2026-08-05T23:53:50.696946Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:53:51.472688Z","title":"Vad: Vector- ized scene representation for efficient autonomous driving,","venue":null,"work_id":"6ffb2352-b390-405b-bdc4-9ae1f6e05650","year":2023},"citing_paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T23:53:50.298671Z"},"links":{"citing_paper":"/paper/2508.04642"},"observation_digest":"sha256:3f23d1628a7c550d5eccc7e7985a92060a56273d8d385001c5e0fa2b30291215","observation_id":"c2052511-165a-4ab1-9ea3-3c8d3561e27b","resolution":{"observed_at":"2026-08-05T23:53:51.556756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.04642","last_updated":"2025-08-06T17:07:25Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T06:46:34.921668Z","submitted_at":"2025-08-06T17:07:25Z","title":"RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":29},"total_outbound_references":48},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2508.04642."}